Welcome to the ZenML SDK Docs

Actions

Actions allow configuring a given action for later execution.

Alerter

Alerters allow you to send alerts from within your pipeline.

This is useful to immediately get notified when failures happen, and also for general monitoring / reporting.

BaseAlerter

Bases: StackComponent, ABC

Base class for all ZenML alerters.

Source code in src/zenml/alerter/base_alerter.py

class BaseAlerter(StackComponent, ABC):
    """Base class for all ZenML alerters."""

    @property
    def config(self) -> BaseAlerterConfig:
        """Returns the `BaseAlerterConfig` config.

        Returns:
            The configuration.
        """
        return cast(BaseAlerterConfig, self._config)

    def post(
        self, message: str, params: Optional[BaseAlerterStepParameters] = None
    ) -> bool:
        """Post a message to a chat service.

        Args:
            message: Message to be posted.
            params: Optional parameters of this function.

        Returns:
            bool: True if operation succeeded, else False.
        """
        return True

    def ask(
        self, question: str, params: Optional[BaseAlerterStepParameters] = None
    ) -> bool:
        """Post a message to a chat service and wait for approval.

        This can be useful to easily get a human in the loop, e.g., when
        deploying models.

        Args:
            question: Question to ask (message to be posted).
            params: Optional parameters of this function.

        Returns:
            bool: True if operation succeeded and was approved, else False.
        """
        return True

config property

Returns the BaseAlerterConfig config.

Returns:

Type	Description
BaseAlerterConfig	The configuration.

ask(question, params=None)

Post a message to a chat service and wait for approval.

This can be useful to easily get a human in the loop, e.g., when deploying models.

Parameters:

Name	Type	Description	Default
question	str	Question to ask (message to be posted).	required
params	Optional[BaseAlerterStepParameters]	Optional parameters of this function.	None

Returns:

Name	Type	Description
bool	bool	True if operation succeeded and was approved, else False.

Source code in src/zenml/alerter/base_alerter.py

def ask(
    self, question: str, params: Optional[BaseAlerterStepParameters] = None
) -> bool:
    """Post a message to a chat service and wait for approval.

    This can be useful to easily get a human in the loop, e.g., when
    deploying models.

    Args:
        question: Question to ask (message to be posted).
        params: Optional parameters of this function.

    Returns:
        bool: True if operation succeeded and was approved, else False.
    """
    return True

post(message, params=None)

Post a message to a chat service.

Parameters:

Name	Type	Description	Default
message	str	Message to be posted.	required
params	Optional[BaseAlerterStepParameters]	Optional parameters of this function.	None

Returns:

Name	Type	Description
bool	bool	True if operation succeeded, else False.

Source code in src/zenml/alerter/base_alerter.py

def post(
    self, message: str, params: Optional[BaseAlerterStepParameters] = None
) -> bool:
    """Post a message to a chat service.

    Args:
        message: Message to be posted.
        params: Optional parameters of this function.

    Returns:
        bool: True if operation succeeded, else False.
    """
    return True

BaseAlerterConfig

Bases: StackComponentConfig

Base config for alerters.

Source code in src/zenml/alerter/base_alerter.py

class BaseAlerterConfig(StackComponentConfig):
    """Base config for alerters."""

BaseAlerterFlavor

Bases: Flavor, ABC

Base class for all ZenML alerter flavors.

Source code in src/zenml/alerter/base_alerter.py

class BaseAlerterFlavor(Flavor, ABC):
    """Base class for all ZenML alerter flavors."""

    @property
    def type(self) -> StackComponentType:
        """Returns the flavor type.

        Returns:
            The flavor type.
        """
        return StackComponentType.ALERTER

    @property
    def config_class(self) -> Type[BaseAlerterConfig]:
        """Returns BaseAlerterConfig class.

        Returns:
            The BaseAlerterConfig class.
        """
        return BaseAlerterConfig

    @property
    def implementation_class(self) -> Type[BaseAlerter]:
        """Implementation class.

        Returns:
            The implementation class.
        """
        return BaseAlerter

config_class property

Returns BaseAlerterConfig class.

Returns:

Type	Description
Type[BaseAlerterConfig]	The BaseAlerterConfig class.

implementation_class property

Implementation class.

Returns:

Type	Description
Type[BaseAlerter]	The implementation class.

type property

Returns the flavor type.

Returns:

Type	Description
StackComponentType	The flavor type.

BaseAlerterStepParameters

Bases: BaseModel

Step parameters definition for all alerters.

Source code in src/zenml/alerter/base_alerter.py

class BaseAlerterStepParameters(BaseModel):
    """Step parameters definition for all alerters."""

Analytics

The 'analytics' module of ZenML.

alias(user_id, previous_id)

Alias user IDs.

Parameters:

Name	Type	Description	Default
user_id	UUID	The user ID.	required
previous_id	UUID	Previous ID for the alias.	required

Returns:

Type	Description
bool	True if event is sent successfully, False is not.

Source code in src/zenml/analytics/__init__.py

def alias(user_id: UUID, previous_id: UUID) -> bool:  # type: ignore[return]
    """Alias user IDs.

    Args:
        user_id: The user ID.
        previous_id: Previous ID for the alias.

    Returns:
        True if event is sent successfully, False is not.
    """
    from zenml.analytics.context import AnalyticsContext

    with AnalyticsContext() as analytics:
        return analytics.alias(user_id=user_id, previous_id=previous_id)

group(group_id, group_metadata=None)

Attach metadata to a segment group.

Parameters:

Name	Type	Description	Default
group_id	UUID	ID of the group.	required
group_metadata	Optional[Dict[str, Any]]	Metadata to attach to the group.	None

Returns:

Type	Description
bool	True if event is sent successfully, False if not.

Source code in src/zenml/analytics/__init__.py

def group(  # type: ignore[return]
    group_id: UUID,
    group_metadata: Optional[Dict[str, Any]] = None,
) -> bool:
    """Attach metadata to a segment group.

    Args:
        group_id: ID of the group.
        group_metadata: Metadata to attach to the group.

    Returns:
        True if event is sent successfully, False if not.
    """
    from zenml.analytics.context import AnalyticsContext

    with AnalyticsContext() as analytics:
        return analytics.group(group_id=group_id, traits=group_metadata)

identify(metadata=None)

Attach metadata to user directly.

Parameters:

Name	Type	Description	Default
metadata	Optional[Dict[str, Any]]	Dict of metadata to attach to the user.	None

Returns:

Type	Description
bool	True if event is sent successfully, False is not.

Source code in src/zenml/analytics/__init__.py

def identify(  # type: ignore[return]
    metadata: Optional[Dict[str, Any]] = None
) -> bool:
    """Attach metadata to user directly.

    Args:
        metadata: Dict of metadata to attach to the user.

    Returns:
        True if event is sent successfully, False is not.
    """
    from zenml.analytics.context import AnalyticsContext

    if metadata is None:
        return False

    with AnalyticsContext() as analytics:
        return analytics.identify(traits=metadata)

track(event, metadata=None)

Track segment event if user opted-in.

Parameters:

Name	Type	Description	Default
event	AnalyticsEvent	Name of event to track in segment.	required
metadata	Optional[Dict[str, Any]]	Dict of metadata to track.	None

Returns:

Type	Description
bool	True if event is sent successfully, False if not.

Source code in src/zenml/analytics/__init__.py

def track(  # type: ignore[return]
    event: "AnalyticsEvent",
    metadata: Optional[Dict[str, Any]] = None,
) -> bool:
    """Track segment event if user opted-in.

    Args:
        event: Name of event to track in segment.
        metadata: Dict of metadata to track.

    Returns:
        True if event is sent successfully, False if not.
    """
    from zenml.analytics.context import AnalyticsContext

    if metadata is None:
        metadata = {}

    metadata.setdefault("event_success", True)

    with AnalyticsContext() as analytics:
        return analytics.track(event=event, properties=metadata)

Annotators

Initialization of the ZenML annotator stack component.

BaseAnnotator

Bases: StackComponent, ABC

Base class for all ZenML annotators.

Source code in src/zenml/annotators/base_annotator.py

class BaseAnnotator(StackComponent, ABC):
    """Base class for all ZenML annotators."""

    @property
    def config(self) -> BaseAnnotatorConfig:
        """Returns the `BaseAnnotatorConfig` config.

        Returns:
            The configuration.
        """
        return cast(BaseAnnotatorConfig, self._config)

    @abstractmethod
    def get_url(self) -> str:
        """Gets the URL of the annotation interface.

        Returns:
            The URL of the annotation interface.
        """

    @abstractmethod
    def get_url_for_dataset(self, dataset_name: str) -> str:
        """Gets the URL of the annotation interface for a specific dataset.

        Args:
            dataset_name: name of the dataset.

        Returns:
            The URL of the dataset annotation interface.
        """

    @abstractmethod
    def get_datasets(self) -> List[Any]:
        """Gets the datasets currently available for annotation.

        Returns:
            The datasets currently available for annotation.
        """

    @abstractmethod
    def get_dataset_names(self) -> List[str]:
        """Gets the names of the datasets currently available for annotation.

        Returns:
            The names of the datasets currently available for annotation.
        """

    @abstractmethod
    def get_dataset_stats(self, dataset_name: str) -> Tuple[int, int]:
        """Gets the statistics of a dataset.

        Args:
            dataset_name: name of the dataset.

        Returns:
            A tuple containing (labeled_task_count, unlabeled_task_count) for
                the dataset.
        """

    @abstractmethod
    def launch(self, **kwargs: Any) -> None:
        """Launches the annotation interface.

        Args:
            **kwargs: Additional keyword arguments to pass to the
                annotation client.
        """

    @abstractmethod
    def add_dataset(self, **kwargs: Any) -> Any:
        """Registers a dataset for annotation.

        Args:
            **kwargs: keyword arguments.

        Returns:
            The dataset or confirmation object on adding the dataset.
        """

    @abstractmethod
    def get_dataset(self, **kwargs: Any) -> Any:
        """Gets the dataset with the given name.

        Args:
            **kwargs: keyword arguments.

        Returns:
            The dataset with the given name.
        """

    @abstractmethod
    def delete_dataset(self, **kwargs: Any) -> None:
        """Deletes a dataset.

        Args:
            **kwargs: keyword arguments.
        """

    @abstractmethod
    def get_labeled_data(self, **kwargs: Any) -> Any:
        """Gets the labeled data for the given dataset.

        Args:
            **kwargs: keyword arguments.

        Returns:
            The labeled data for the given dataset.
        """

    @abstractmethod
    def get_unlabeled_data(self, **kwargs: str) -> Any:
        """Gets the unlabeled data for the given dataset.

        Args:
            **kwargs: Additional keyword arguments to pass to the Label Studio client.

        Returns:
            The unlabeled data for the given dataset.
        """

config property

Returns the BaseAnnotatorConfig config.

Returns:

Type	Description
BaseAnnotatorConfig	The configuration.

add_dataset(**kwargs) abstractmethod

Registers a dataset for annotation.

Parameters:

Name	Type	Description	Default
**kwargs	Any	keyword arguments.	{}

Returns:

Type	Description
Any	The dataset or confirmation object on adding the dataset.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def add_dataset(self, **kwargs: Any) -> Any:
    """Registers a dataset for annotation.

    Args:
        **kwargs: keyword arguments.

    Returns:
        The dataset or confirmation object on adding the dataset.
    """

delete_dataset(**kwargs) abstractmethod

Deletes a dataset.

Parameters:

Name	Type	Description	Default
**kwargs	Any	keyword arguments.	{}

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def delete_dataset(self, **kwargs: Any) -> None:
    """Deletes a dataset.

    Args:
        **kwargs: keyword arguments.
    """

get_dataset(**kwargs) abstractmethod

Gets the dataset with the given name.

Parameters:

Name	Type	Description	Default
**kwargs	Any	keyword arguments.	{}

Returns:

Type	Description
Any	The dataset with the given name.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_dataset(self, **kwargs: Any) -> Any:
    """Gets the dataset with the given name.

    Args:
        **kwargs: keyword arguments.

    Returns:
        The dataset with the given name.
    """

get_dataset_names() abstractmethod

Gets the names of the datasets currently available for annotation.

Returns:

Type	Description
List[str]	The names of the datasets currently available for annotation.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_dataset_names(self) -> List[str]:
    """Gets the names of the datasets currently available for annotation.

    Returns:
        The names of the datasets currently available for annotation.
    """

get_dataset_stats(dataset_name) abstractmethod

Gets the statistics of a dataset.

Parameters:

Name	Type	Description	Default
dataset_name	str	name of the dataset.	required

Returns:

Type	Description
Tuple[int, int]	A tuple containing (labeled_task_count, unlabeled_task_count) for the dataset.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_dataset_stats(self, dataset_name: str) -> Tuple[int, int]:
    """Gets the statistics of a dataset.

    Args:
        dataset_name: name of the dataset.

    Returns:
        A tuple containing (labeled_task_count, unlabeled_task_count) for
            the dataset.
    """

get_datasets() abstractmethod

Gets the datasets currently available for annotation.

Returns:

Type	Description
List[Any]	The datasets currently available for annotation.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_datasets(self) -> List[Any]:
    """Gets the datasets currently available for annotation.

    Returns:
        The datasets currently available for annotation.
    """

get_labeled_data(**kwargs) abstractmethod

Gets the labeled data for the given dataset.

Parameters:

Name	Type	Description	Default
**kwargs	Any	keyword arguments.	{}

Returns:

Type	Description
Any	The labeled data for the given dataset.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_labeled_data(self, **kwargs: Any) -> Any:
    """Gets the labeled data for the given dataset.

    Args:
        **kwargs: keyword arguments.

    Returns:
        The labeled data for the given dataset.
    """

get_unlabeled_data(**kwargs) abstractmethod

Gets the unlabeled data for the given dataset.

Parameters:

Name	Type	Description	Default
**kwargs	str	Additional keyword arguments to pass to the Label Studio client.	{}

Returns:

Type	Description
Any	The unlabeled data for the given dataset.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_unlabeled_data(self, **kwargs: str) -> Any:
    """Gets the unlabeled data for the given dataset.

    Args:
        **kwargs: Additional keyword arguments to pass to the Label Studio client.

    Returns:
        The unlabeled data for the given dataset.
    """

get_url() abstractmethod

Gets the URL of the annotation interface.

Returns:

Type	Description
str	The URL of the annotation interface.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_url(self) -> str:
    """Gets the URL of the annotation interface.

    Returns:
        The URL of the annotation interface.
    """

get_url_for_dataset(dataset_name) abstractmethod

Gets the URL of the annotation interface for a specific dataset.

Parameters:

Name	Type	Description	Default
dataset_name	str	name of the dataset.	required

Returns:

Type	Description
str	The URL of the dataset annotation interface.

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def get_url_for_dataset(self, dataset_name: str) -> str:
    """Gets the URL of the annotation interface for a specific dataset.

    Args:
        dataset_name: name of the dataset.

    Returns:
        The URL of the dataset annotation interface.
    """

launch(**kwargs) abstractmethod

Launches the annotation interface.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Additional keyword arguments to pass to the annotation client.	{}

Source code in src/zenml/annotators/base_annotator.py

@abstractmethod
def launch(self, **kwargs: Any) -> None:
    """Launches the annotation interface.

    Args:
        **kwargs: Additional keyword arguments to pass to the
            annotation client.
    """

Artifact Stores

ZenML's artifact-store stores artifacts in a file system.

In ZenML, the inputs and outputs which go through any step is treated as an artifact and as its name suggests, an ArtifactStore is a place where these artifacts get stored.

Out of the box, ZenML comes with the BaseArtifactStore and LocalArtifactStore implementations. While the BaseArtifactStore establishes an interface for people who want to extend it to their needs, the LocalArtifactStore is a simple implementation for a local setup.

Moreover, additional artifact stores can be found in specific integrations modules, such as the GCPArtifactStore in the gcp integration and the AzureArtifactStore in the azure integration.

BaseArtifactStore

Bases: StackComponent

Base class for all ZenML artifact stores.

Source code in src/zenml/artifact_stores/base_artifact_store.py

class BaseArtifactStore(StackComponent):
    """Base class for all ZenML artifact stores."""

    @property
    def config(self) -> BaseArtifactStoreConfig:
        """Returns the `BaseArtifactStoreConfig` config.

        Returns:
            The configuration.
        """
        return cast(BaseArtifactStoreConfig, self._config)

    @property
    def path(self) -> str:
        """The path to the artifact store.

        Returns:
            The path.
        """
        return self.config.path

    @property
    def custom_cache_key(self) -> Optional[bytes]:
        """Custom cache key.

        Any artifact store can override this property in case they need
        additional control over the caching behavior.

        Returns:
            Custom cache key.
        """
        return None

    # --- User interface ---
    @abstractmethod
    def open(self, path: PathType, mode: str = "r") -> Any:
        """Open a file at the given path.

        Args:
            path: The path of the file to open.
            mode: The mode to open the file.

        Returns:
            The file object.
        """

    @abstractmethod
    def copyfile(
        self, src: PathType, dst: PathType, overwrite: bool = False
    ) -> None:
        """Copy a file from the source to the destination.

        Args:
            src: The source path.
            dst: The destination path.
            overwrite: Whether to overwrite the destination file if it exists.
        """

    @abstractmethod
    def exists(self, path: PathType) -> bool:
        """Checks if a path exists.

        Args:
            path: The path to check.

        Returns:
            `True` if the path exists.
        """

    @abstractmethod
    def glob(self, pattern: PathType) -> List[PathType]:
        """Gets the paths that match a glob pattern.

        Args:
            pattern: The glob pattern.

        Returns:
            The list of paths that match the pattern.
        """

    @abstractmethod
    def isdir(self, path: PathType) -> bool:
        """Returns whether the given path points to a directory.

        Args:
            path: The path to check.

        Returns:
            `True` if the path points to a directory.
        """

    @abstractmethod
    def listdir(self, path: PathType) -> List[PathType]:
        """Returns a list of files under a given directory in the filesystem.

        Args:
            path: The path to list.

        Returns:
            The list of files under the given path.
        """

    @abstractmethod
    def makedirs(self, path: PathType) -> None:
        """Make a directory at the given path, recursively creating parents.

        Args:
            path: The path to create.
        """

    @abstractmethod
    def mkdir(self, path: PathType) -> None:
        """Make a directory at the given path; parent directory must exist.

        Args:
            path: The path to create.
        """

    @abstractmethod
    def remove(self, path: PathType) -> None:
        """Remove the file at the given path. Dangerous operation.

        Args:
            path: The path to remove.
        """

    @abstractmethod
    def rename(
        self, src: PathType, dst: PathType, overwrite: bool = False
    ) -> None:
        """Rename source file to destination file.

        Args:
            src: The source path.
            dst: The destination path.
            overwrite: Whether to overwrite the destination file if it exists.
        """

    @abstractmethod
    def rmtree(self, path: PathType) -> None:
        """Deletes dir recursively. Dangerous operation.

        Args:
            path: The path to delete.
        """

    @abstractmethod
    def stat(self, path: PathType) -> Any:
        """Return the stat descriptor for a given file path.

        Args:
            path: The path to check.

        Returns:
            The stat descriptor.
        """

    @abstractmethod
    def size(self, path: PathType) -> Optional[int]:
        """Get the size of a file in bytes.

        Args:
            path: The path to the file.

        Returns:
            The size of the file in bytes or `None` if the artifact store
            does not implement the `size` method.
        """
        logger.warning(
            "Cannot get size of file '%s' since the artifact store %s does not "
            "implement the `size` method.",
            path,
            self.__class__.__name__,
        )
        return None

    @abstractmethod
    def walk(
        self,
        top: PathType,
        topdown: bool = True,
        onerror: Optional[Callable[..., None]] = None,
    ) -> Iterable[Tuple[PathType, List[PathType], List[PathType]]]:
        """Return an iterator that walks the contents of the given directory.

        Args:
            top: The path to walk.
            topdown: Whether to walk the top-down or bottom-up.
            onerror: The error handler.

        Returns:
            The iterator that walks the contents of the given directory.
        """

    # --- Internal interface ---
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        """Initiate the Pydantic object and register the corresponding filesystem.

        Args:
            *args: The positional arguments to pass to the Pydantic object.
            **kwargs: The keyword arguments to pass to the Pydantic object.
        """
        super(BaseArtifactStore, self).__init__(*args, **kwargs)

        # If running in a ZenML server environment, we don't register
        # the filesystems. We always use the artifact stores directly.
        if ENV_ZENML_SERVER not in os.environ:
            self._register()

    def _register(self) -> None:
        """Create and register a filesystem within the filesystem registry."""
        from zenml.io.filesystem import BaseFilesystem
        from zenml.io.filesystem_registry import default_filesystem_registry
        from zenml.io.local_filesystem import LocalFilesystem

        overloads: Dict[str, Any] = {
            "SUPPORTED_SCHEMES": self.config.SUPPORTED_SCHEMES,
        }
        for abc_method in inspect.getmembers(BaseArtifactStore):
            if getattr(abc_method[1], "__isabstractmethod__", False):
                sanitized_method = _sanitize_paths(
                    getattr(self, abc_method[0]), self.path
                )
                # prepare overloads for filesystem methods
                overloads[abc_method[0]] = staticmethod(sanitized_method)

                # decorate artifact store methods
                setattr(
                    self,
                    abc_method[0],
                    sanitized_method,
                )

        # Local filesystem is always registered, no point in doing it again.
        if isinstance(self, LocalFilesystem):
            return

        filesystem_class = type(
            self.__class__.__name__, (BaseFilesystem,), overloads
        )

        default_filesystem_registry.register(filesystem_class)

    def _remove_previous_file_versions(self, path: PathType) -> None:
        """Remove all file versions but the latest in the given path.

        Method is useful for logs stored in versioned file systems
        like AWS S3.

        Args:
            path: The path to the file.
        """
        return

config property

Returns the BaseArtifactStoreConfig config.

Returns:

Type	Description
BaseArtifactStoreConfig	The configuration.

custom_cache_key property

Custom cache key.

Any artifact store can override this property in case they need additional control over the caching behavior.

Returns:

Type	Description
Optional[bytes]	Custom cache key.

path property

The path to the artifact store.

Returns:

Type	Description
str	The path.

__init__(*args, **kwargs)

Initiate the Pydantic object and register the corresponding filesystem.

Parameters:

Name	Type	Description	Default
*args	Any	The positional arguments to pass to the Pydantic object.	()
**kwargs	Any	The keyword arguments to pass to the Pydantic object.	{}

Source code in src/zenml/artifact_stores/base_artifact_store.py

def __init__(self, *args: Any, **kwargs: Any) -> None:
    """Initiate the Pydantic object and register the corresponding filesystem.

    Args:
        *args: The positional arguments to pass to the Pydantic object.
        **kwargs: The keyword arguments to pass to the Pydantic object.
    """
    super(BaseArtifactStore, self).__init__(*args, **kwargs)

    # If running in a ZenML server environment, we don't register
    # the filesystems. We always use the artifact stores directly.
    if ENV_ZENML_SERVER not in os.environ:
        self._register()

copyfile(src, dst, overwrite=False) abstractmethod

Copy a file from the source to the destination.

Parameters:

Name	Type	Description	Default
src	PathType	The source path.	required
dst	PathType	The destination path.	required
overwrite	bool	Whether to overwrite the destination file if it exists.	False

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def copyfile(
    self, src: PathType, dst: PathType, overwrite: bool = False
) -> None:
    """Copy a file from the source to the destination.

    Args:
        src: The source path.
        dst: The destination path.
        overwrite: Whether to overwrite the destination file if it exists.
    """

exists(path) abstractmethod

Checks if a path exists.

Parameters:

Name	Type	Description	Default
path	PathType	The path to check.	required

Returns:

Type	Description
bool	True if the path exists.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def exists(self, path: PathType) -> bool:
    """Checks if a path exists.

    Args:
        path: The path to check.

    Returns:
        `True` if the path exists.
    """

glob(pattern) abstractmethod

Gets the paths that match a glob pattern.

Parameters:

Name	Type	Description	Default
pattern	PathType	The glob pattern.	required

Returns:

Type	Description
List[PathType]	The list of paths that match the pattern.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def glob(self, pattern: PathType) -> List[PathType]:
    """Gets the paths that match a glob pattern.

    Args:
        pattern: The glob pattern.

    Returns:
        The list of paths that match the pattern.
    """

isdir(path) abstractmethod

Returns whether the given path points to a directory.

Parameters:

Name	Type	Description	Default
path	PathType	The path to check.	required

Returns:

Type	Description
bool	True if the path points to a directory.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def isdir(self, path: PathType) -> bool:
    """Returns whether the given path points to a directory.

    Args:
        path: The path to check.

    Returns:
        `True` if the path points to a directory.
    """

listdir(path) abstractmethod

Returns a list of files under a given directory in the filesystem.

Parameters:

Name	Type	Description	Default
path	PathType	The path to list.	required

Returns:

Type	Description
List[PathType]	The list of files under the given path.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def listdir(self, path: PathType) -> List[PathType]:
    """Returns a list of files under a given directory in the filesystem.

    Args:
        path: The path to list.

    Returns:
        The list of files under the given path.
    """

makedirs(path) abstractmethod

Make a directory at the given path, recursively creating parents.

Parameters:

Name	Type	Description	Default
path	PathType	The path to create.	required

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def makedirs(self, path: PathType) -> None:
    """Make a directory at the given path, recursively creating parents.

    Args:
        path: The path to create.
    """

mkdir(path) abstractmethod

Make a directory at the given path; parent directory must exist.

Parameters:

Name	Type	Description	Default
path	PathType	The path to create.	required

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def mkdir(self, path: PathType) -> None:
    """Make a directory at the given path; parent directory must exist.

    Args:
        path: The path to create.
    """

open(path, mode='r') abstractmethod

Open a file at the given path.

Parameters:

Name	Type	Description	Default
path	PathType	The path of the file to open.	required
mode	str	The mode to open the file.	'r'

Returns:

Type	Description
Any	The file object.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def open(self, path: PathType, mode: str = "r") -> Any:
    """Open a file at the given path.

    Args:
        path: The path of the file to open.
        mode: The mode to open the file.

    Returns:
        The file object.
    """

remove(path) abstractmethod

Remove the file at the given path. Dangerous operation.

Parameters:

Name	Type	Description	Default
path	PathType	The path to remove.	required

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def remove(self, path: PathType) -> None:
    """Remove the file at the given path. Dangerous operation.

    Args:
        path: The path to remove.
    """

rename(src, dst, overwrite=False) abstractmethod

Rename source file to destination file.

Parameters:

Name	Type	Description	Default
src	PathType	The source path.	required
dst	PathType	The destination path.	required
overwrite	bool	Whether to overwrite the destination file if it exists.	False

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def rename(
    self, src: PathType, dst: PathType, overwrite: bool = False
) -> None:
    """Rename source file to destination file.

    Args:
        src: The source path.
        dst: The destination path.
        overwrite: Whether to overwrite the destination file if it exists.
    """

rmtree(path) abstractmethod

Deletes dir recursively. Dangerous operation.

Parameters:

Name	Type	Description	Default
path	PathType	The path to delete.	required

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def rmtree(self, path: PathType) -> None:
    """Deletes dir recursively. Dangerous operation.

    Args:
        path: The path to delete.
    """

size(path) abstractmethod

Get the size of a file in bytes.

Parameters:

Name	Type	Description	Default
path	PathType	The path to the file.	required

Returns:

Type	Description
Optional[int]	The size of the file in bytes or None if the artifact store
Optional[int]	does not implement the size method.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def size(self, path: PathType) -> Optional[int]:
    """Get the size of a file in bytes.

    Args:
        path: The path to the file.

    Returns:
        The size of the file in bytes or `None` if the artifact store
        does not implement the `size` method.
    """
    logger.warning(
        "Cannot get size of file '%s' since the artifact store %s does not "
        "implement the `size` method.",
        path,
        self.__class__.__name__,
    )
    return None

stat(path) abstractmethod

Return the stat descriptor for a given file path.

Parameters:

Name	Type	Description	Default
path	PathType	The path to check.	required

Returns:

Type	Description
Any	The stat descriptor.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def stat(self, path: PathType) -> Any:
    """Return the stat descriptor for a given file path.

    Args:
        path: The path to check.

    Returns:
        The stat descriptor.
    """

walk(top, topdown=True, onerror=None) abstractmethod

Return an iterator that walks the contents of the given directory.

Parameters:

Name	Type	Description	Default
top	PathType	The path to walk.	required
topdown	bool	Whether to walk the top-down or bottom-up.	True
onerror	Optional[Callable[..., None]]	The error handler.	None

Returns:

Type	Description
Iterable[Tuple[PathType, List[PathType], List[PathType]]]	The iterator that walks the contents of the given directory.

Source code in src/zenml/artifact_stores/base_artifact_store.py

@abstractmethod
def walk(
    self,
    top: PathType,
    topdown: bool = True,
    onerror: Optional[Callable[..., None]] = None,
) -> Iterable[Tuple[PathType, List[PathType], List[PathType]]]:
    """Return an iterator that walks the contents of the given directory.

    Args:
        top: The path to walk.
        topdown: Whether to walk the top-down or bottom-up.
        onerror: The error handler.

    Returns:
        The iterator that walks the contents of the given directory.
    """

BaseArtifactStoreConfig

Bases: StackComponentConfig

Config class for BaseArtifactStore.

Source code in src/zenml/artifact_stores/base_artifact_store.py

class BaseArtifactStoreConfig(StackComponentConfig):
    """Config class for `BaseArtifactStore`."""

    path: str

    SUPPORTED_SCHEMES: ClassVar[Set[str]]
    IS_IMMUTABLE_FILESYSTEM: ClassVar[bool] = False

    @model_validator(mode="before")
    @classmethod
    @before_validator_handler
    def _ensure_artifact_store(cls, data: Dict[str, Any]) -> Dict[str, Any]:
        """Validator function for the Artifact Stores.

        Checks whether supported schemes are defined and the given path is
        supported.

        Args:
            data: the input data to construct the artifact store.

        Returns:
            The validated values.

        Raises:
            ArtifactStoreInterfaceError: If the scheme is not supported.
        """
        try:
            getattr(cls, "SUPPORTED_SCHEMES")
        except AttributeError:
            raise ArtifactStoreInterfaceError(
                textwrap.dedent(
                    """
                    When you are working with any classes which subclass from
                    zenml.artifact_store.BaseArtifactStore please make sure
                    that your class has a ClassVar named `SUPPORTED_SCHEMES`
                    which should hold a set of supported file schemes such
                    as {"s3://"} or {"gcs://"}.

                    Example:

                    class MyArtifactStoreConfig(BaseArtifactStoreConfig):
                        ...
                        # Class Variables
                        SUPPORTED_SCHEMES: ClassVar[Set[str]] = {"s3://"}
                        ...
                    """
                )
            )

        if "path" in data:
            data["path"] = data["path"].strip("'\"`")
            if not any(
                data["path"].startswith(i) for i in cls.SUPPORTED_SCHEMES
            ):
                raise ArtifactStoreInterfaceError(
                    f"The path: '{data['path']}' you defined for your "
                    f"artifact store is not supported by the implementation of "
                    f"{cls.schema()['title']}, because it does not start with "
                    f"one of its supported schemes: {cls.SUPPORTED_SCHEMES}."
                )

        return data

BaseArtifactStoreFlavor

Bases: Flavor

Base class for artifact store flavors.

Source code in src/zenml/artifact_stores/base_artifact_store.py

class BaseArtifactStoreFlavor(Flavor):
    """Base class for artifact store flavors."""

    @property
    def type(self) -> StackComponentType:
        """Returns the flavor type.

        Returns:
            The flavor type.
        """
        return StackComponentType.ARTIFACT_STORE

    @property
    def config_class(self) -> Type[StackComponentConfig]:
        """Config class for this flavor.

        Returns:
            The config class.
        """
        return BaseArtifactStoreConfig

    @property
    @abstractmethod
    def implementation_class(self) -> Type["BaseArtifactStore"]:
        """Implementation class.

        Returns:
            The implementation class.
        """

config_class property

Config class for this flavor.

Returns:

Type	Description
Type[StackComponentConfig]	The config class.

implementation_class abstractmethod property

Implementation class.

Returns:

Type	Description
Type[BaseArtifactStore]	The implementation class.

type property

Returns the flavor type.

Returns:

Type	Description
StackComponentType	The flavor type.

LocalArtifactStore

Bases: LocalFilesystem, BaseArtifactStore

Artifact Store for local artifacts.

All methods are inherited from the default LocalFilesystem.

Source code in src/zenml/artifact_stores/local_artifact_store.py

class LocalArtifactStore(LocalFilesystem, BaseArtifactStore):
    """Artifact Store for local artifacts.

    All methods are inherited from the default `LocalFilesystem`.
    """

    _path: Optional[str] = None

    @staticmethod
    def get_default_local_path(id_: "UUID") -> str:
        """Returns the default local path for a local artifact store.

        Args:
            id_: The id of the local artifact store.

        Returns:
            str: The default local path.
        """
        return os.path.join(
            GlobalConfiguration().local_stores_path,
            str(id_),
        )

    @property
    def path(self) -> str:
        """Returns the path to the local artifact store.

        If the user has not defined a path in the config, this will create a
        sub-folder in the global config directory.

        Returns:
            The path to the local artifact store.
        """
        if self._path:
            return self._path

        if self.config.path:
            self._path = self.config.path
        else:
            self._path = self.get_default_local_path(self.id)
        io_utils.create_dir_recursive_if_not_exists(self._path)
        return self._path

    @property
    def local_path(self) -> Optional[str]:
        """Returns the local path of the artifact store.

        Returns:
            The local path of the artifact store.
        """
        return self.path

    @property
    def custom_cache_key(self) -> Optional[bytes]:
        """Custom cache key.

        The client ID is returned here to invalidate caching when using the same
        local artifact store on multiple client machines.

        Returns:
            Custom cache key.
        """
        return GlobalConfiguration().user_id.bytes

custom_cache_key property

Custom cache key.

The client ID is returned here to invalidate caching when using the same local artifact store on multiple client machines.

Returns:

Type	Description
Optional[bytes]	Custom cache key.

local_path property

Returns the local path of the artifact store.

Returns:

Type	Description
Optional[str]	The local path of the artifact store.

path property

Returns the path to the local artifact store.

If the user has not defined a path in the config, this will create a sub-folder in the global config directory.

Returns:

Type	Description
str	The path to the local artifact store.

get_default_local_path(id_) staticmethod

Returns the default local path for a local artifact store.

Parameters:

Name	Type	Description	Default
id_	UUID	The id of the local artifact store.	required

Returns:

Name	Type	Description
str	str	The default local path.

Source code in src/zenml/artifact_stores/local_artifact_store.py

@staticmethod
def get_default_local_path(id_: "UUID") -> str:
    """Returns the default local path for a local artifact store.

    Args:
        id_: The id of the local artifact store.

    Returns:
        str: The default local path.
    """
    return os.path.join(
        GlobalConfiguration().local_stores_path,
        str(id_),
    )

LocalArtifactStoreConfig

Bases: BaseArtifactStoreConfig

Config class for the local artifact store.

Attributes:

Name	Type	Description
path	str	The path to the local artifact store.

Source code in src/zenml/artifact_stores/local_artifact_store.py

class LocalArtifactStoreConfig(BaseArtifactStoreConfig):
    """Config class for the local artifact store.

    Attributes:
        path: The path to the local artifact store.
    """

    SUPPORTED_SCHEMES: ClassVar[Set[str]] = {""}

    path: str = ""

    @field_validator("path")
    @classmethod
    def ensure_path_local(cls, path: str) -> str:
        """Pydantic validator which ensures that the given path is a local path.

        Args:
            path: The path to validate.

        Returns:
            str: The validated (local) path.

        Raises:
            ArtifactStoreInterfaceError: If the given path is not a local path.
        """
        remote_prefixes = ["gs://", "hdfs://", "s3://", "az://", "abfs://"]
        if any(path.startswith(prefix) for prefix in remote_prefixes):
            raise ArtifactStoreInterfaceError(
                f"The path '{path}' you defined for your local artifact store "
                f"starts with a remote prefix."
            )
        return path

    @property
    def is_local(self) -> bool:
        """Checks if this stack component is running locally.

        Returns:
            True if this config is for a local component, False otherwise.
        """
        return True

is_local property

Checks if this stack component is running locally.

Returns:

Type	Description
bool	True if this config is for a local component, False otherwise.

ensure_path_local(path) classmethod

Pydantic validator which ensures that the given path is a local path.

Parameters:

Name	Type	Description	Default
path	str	The path to validate.	required

Returns:

Name	Type	Description
str	str	The validated (local) path.

Raises:

Type	Description
ArtifactStoreInterfaceError	If the given path is not a local path.

Source code in src/zenml/artifact_stores/local_artifact_store.py

@field_validator("path")
@classmethod
def ensure_path_local(cls, path: str) -> str:
    """Pydantic validator which ensures that the given path is a local path.

    Args:
        path: The path to validate.

    Returns:
        str: The validated (local) path.

    Raises:
        ArtifactStoreInterfaceError: If the given path is not a local path.
    """
    remote_prefixes = ["gs://", "hdfs://", "s3://", "az://", "abfs://"]
    if any(path.startswith(prefix) for prefix in remote_prefixes):
        raise ArtifactStoreInterfaceError(
            f"The path '{path}' you defined for your local artifact store "
            f"starts with a remote prefix."
        )
    return path

LocalArtifactStoreFlavor

Bases: BaseArtifactStoreFlavor

Class for the LocalArtifactStoreFlavor.

Source code in src/zenml/artifact_stores/local_artifact_store.py

class LocalArtifactStoreFlavor(BaseArtifactStoreFlavor):
    """Class for the `LocalArtifactStoreFlavor`."""

    @property
    def name(self) -> str:
        """Returns the name of the artifact store flavor.

        Returns:
            str: The name of the artifact store flavor.
        """
        return "local"

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/artifact_store/local.svg"

    @property
    def config_class(self) -> Type[LocalArtifactStoreConfig]:
        """Config class for this flavor.

        Returns:
            The config class.
        """
        return LocalArtifactStoreConfig

    @property
    def implementation_class(self) -> Type[LocalArtifactStore]:
        """Implementation class.

        Returns:
            The implementation class.
        """
        return LocalArtifactStore

config_class property

Config class for this flavor.

Returns:

Type	Description
Type[LocalArtifactStoreConfig]	The config class.

docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

implementation_class property

Implementation class.

Returns:

Type	Description
Type[LocalArtifactStore]	The implementation class.

logo_url property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name property

Returns the name of the artifact store flavor.

Returns:

Name	Type	Description
str	str	The name of the artifact store flavor.

sdk_docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

Artifacts

Client Lazy Loader

Lazy loading functionality for Client methods.

ClientLazyLoader

Bases: BaseModel

Lazy loader for Client methods.

Source code in src/zenml/client_lazy_loader.py

class ClientLazyLoader(BaseModel):
    """Lazy loader for Client methods."""

    method_name: str
    call_chain: List[_CallStep] = []
    exclude_next_call: bool = False

    def __getattr__(self, name: str) -> "ClientLazyLoader":
        """Get attribute not defined in ClientLazyLoader.

        Args:
            name: Name of the attribute to get.

        Returns:
            self
        """
        self_ = ClientLazyLoader(
            method_name=self.method_name, call_chain=self.call_chain.copy()
        )
        # workaround to protect from infinitely looping over in deepcopy called in invocations
        if name != "__deepcopy__":
            self_.call_chain.append(_CallStep(attribute_name=name))
        else:
            self_.exclude_next_call = True
        return self_

    def __call__(self, *args: Any, **kwargs: Any) -> "ClientLazyLoader":
        """Call mocked attribute.

        Args:
            args: Positional arguments.
            kwargs: Keyword arguments.

        Returns:
            self
        """
        # workaround to protect from infinitely looping over in deepcopy called in invocations
        if not self.exclude_next_call:
            self.call_chain.append(
                _CallStep(is_call=True, call_args=args, call_kwargs=kwargs)
            )
        self.exclude_next_call = False
        return self

    def __getitem__(self, item: Any) -> "ClientLazyLoader":
        """Get item from mocked attribute.

        Args:
            item: Item to get.

        Returns:
            self
        """
        self.call_chain.append(_CallStep(selector=item))
        return self

    def evaluate(self) -> Any:
        """Evaluate lazy loaded Client method.

        Returns:
            Evaluated lazy loader chain of calls.
        """
        from zenml.client import Client

        def _iterate_over_lazy_chain(
            self: "ClientLazyLoader", self_: Any, call_chain_: List[_CallStep]
        ) -> Any:
            next_step = call_chain_.pop(0)
            try:
                if next_step.is_call:
                    self_ = self_(
                        *next_step.call_args, **next_step.call_kwargs
                    )
                elif next_step.selector:
                    self_ = self_[next_step.selector]
                elif next_step.attribute_name:
                    self_ = getattr(self_, next_step.attribute_name)
                else:
                    raise ValueError(
                        "Invalid call chain. Reach out to the ZenML team."
                    )
            except Exception as e:
                logger.debug(
                    f"Failed to evaluate lazy load chain `{self.method_name}` "
                    f"+ `{next_step}` + `{self.call_chain}`."
                )
                msg = f"`{self.method_name}("
                if next_step:
                    for arg in next_step.call_args:
                        msg += f"'{arg}',"
                    for k, v in next_step.call_kwargs.items():
                        msg += f"{k}='{v}',"
                    msg = msg[:-1]
                msg += f")` failed during lazy load with error: {e}"
                logger.error(msg)
                raise RuntimeError(msg)
            return self_

        self_ = getattr(Client(), self.method_name)
        call_chain_ = self.call_chain.copy()
        while call_chain_:
            self_ = _iterate_over_lazy_chain(self, self_, call_chain_)
        return self_

__call__(*args, **kwargs)

Call mocked attribute.

Parameters:

Name	Type	Description	Default
args	Any	Positional arguments.	()
kwargs	Any	Keyword arguments.	{}

Returns:

Type	Description
ClientLazyLoader	self

Source code in src/zenml/client_lazy_loader.py

def __call__(self, *args: Any, **kwargs: Any) -> "ClientLazyLoader":
    """Call mocked attribute.

    Args:
        args: Positional arguments.
        kwargs: Keyword arguments.

    Returns:
        self
    """
    # workaround to protect from infinitely looping over in deepcopy called in invocations
    if not self.exclude_next_call:
        self.call_chain.append(
            _CallStep(is_call=True, call_args=args, call_kwargs=kwargs)
        )
    self.exclude_next_call = False
    return self

__getattr__(name)

Get attribute not defined in ClientLazyLoader.

Parameters:

Name	Type	Description	Default
name	str	Name of the attribute to get.	required

Returns:

Type	Description
ClientLazyLoader	self

Source code in src/zenml/client_lazy_loader.py

def __getattr__(self, name: str) -> "ClientLazyLoader":
    """Get attribute not defined in ClientLazyLoader.

    Args:
        name: Name of the attribute to get.

    Returns:
        self
    """
    self_ = ClientLazyLoader(
        method_name=self.method_name, call_chain=self.call_chain.copy()
    )
    # workaround to protect from infinitely looping over in deepcopy called in invocations
    if name != "__deepcopy__":
        self_.call_chain.append(_CallStep(attribute_name=name))
    else:
        self_.exclude_next_call = True
    return self_

__getitem__(item)

Get item from mocked attribute.

Parameters:

Name	Type	Description	Default
item	Any	Item to get.	required

Returns:

Type	Description
ClientLazyLoader	self

Source code in src/zenml/client_lazy_loader.py

def __getitem__(self, item: Any) -> "ClientLazyLoader":
    """Get item from mocked attribute.

    Args:
        item: Item to get.

    Returns:
        self
    """
    self.call_chain.append(_CallStep(selector=item))
    return self

evaluate()

Evaluate lazy loaded Client method.

Returns:

Type	Description
Any	Evaluated lazy loader chain of calls.

Source code in src/zenml/client_lazy_loader.py

def evaluate(self) -> Any:
    """Evaluate lazy loaded Client method.

    Returns:
        Evaluated lazy loader chain of calls.
    """
    from zenml.client import Client

    def _iterate_over_lazy_chain(
        self: "ClientLazyLoader", self_: Any, call_chain_: List[_CallStep]
    ) -> Any:
        next_step = call_chain_.pop(0)
        try:
            if next_step.is_call:
                self_ = self_(
                    *next_step.call_args, **next_step.call_kwargs
                )
            elif next_step.selector:
                self_ = self_[next_step.selector]
            elif next_step.attribute_name:
                self_ = getattr(self_, next_step.attribute_name)
            else:
                raise ValueError(
                    "Invalid call chain. Reach out to the ZenML team."
                )
        except Exception as e:
            logger.debug(
                f"Failed to evaluate lazy load chain `{self.method_name}` "
                f"+ `{next_step}` + `{self.call_chain}`."
            )
            msg = f"`{self.method_name}("
            if next_step:
                for arg in next_step.call_args:
                    msg += f"'{arg}',"
                for k, v in next_step.call_kwargs.items():
                    msg += f"{k}='{v}',"
                msg = msg[:-1]
            msg += f")` failed during lazy load with error: {e}"
            logger.error(msg)
            raise RuntimeError(msg)
        return self_

    self_ = getattr(Client(), self.method_name)
    call_chain_ = self.call_chain.copy()
    while call_chain_:
        self_ = _iterate_over_lazy_chain(self, self_, call_chain_)
    return self_

client_lazy_loader(method_name, *args, **kwargs)

Lazy loader for Client methods helper.

Usage:

def get_something(self, arg1: Any)->SomeResponse:
    if cll:=client_lazy_loader("get_something", arg1):
        return cll # type: ignore[return-value]
    return SomeResponse()

Parameters:

Name	Type	Description	Default
method_name	str	The name of the method to be called.	required
*args	Any	The arguments to be passed to the method.	()
**kwargs	Any	The keyword arguments to be passed to the method.	{}

Returns:

Type	Description
Optional[ClientLazyLoader]	The result of the method call.

Source code in src/zenml/client_lazy_loader.py

def client_lazy_loader(
    method_name: str, *args: Any, **kwargs: Any
) -> Optional[ClientLazyLoader]:
    """Lazy loader for Client methods helper.

    Usage:
    ```
    def get_something(self, arg1: Any)->SomeResponse:
        if cll:=client_lazy_loader("get_something", arg1):
            return cll # type: ignore[return-value]
        return SomeResponse()
    ```

    Args:
        method_name: The name of the method to be called.
        *args: The arguments to be passed to the method.
        **kwargs: The keyword arguments to be passed to the method.

    Returns:
        The result of the method call.
    """
    from zenml import get_pipeline_context

    try:
        get_pipeline_context()
        cll = ClientLazyLoader(
            method_name=method_name,
        )
        return cll(*args, **kwargs)
    except RuntimeError:
        return None

evaluate_all_lazy_load_args_in_client_methods(cls)

Class wrapper to evaluate lazy loader arguments of all methods.

Parameters:

Name	Type	Description	Default
cls	Type[Client]	The class to wrap.	required

Returns:

Type	Description
Type[Client]	Wrapped class.

Source code in src/zenml/client_lazy_loader.py

def evaluate_all_lazy_load_args_in_client_methods(
    cls: Type["Client"],
) -> Type["Client"]:
    """Class wrapper to evaluate lazy loader arguments of all methods.

    Args:
        cls: The class to wrap.

    Returns:
        Wrapped class.
    """
    import inspect

    def _evaluate_args(
        func: Callable[..., Any], is_instance_method: bool
    ) -> Any:
        def _inner(*args: Any, **kwargs: Any) -> Any:
            args_ = list(args)
            if not is_instance_method:
                from zenml.client import Client

                if args and isinstance(args[0], Client):
                    args_ = list(args[1:])

            for i in range(len(args_)):
                if isinstance(args_[i], dict):
                    with contextlib.suppress(ValueError):
                        args_[i] = ClientLazyLoader(**args_[i]).evaluate()
                elif isinstance(args_[i], ClientLazyLoader):
                    args_[i] = args_[i].evaluate()

            for k, v in kwargs.items():
                if isinstance(v, dict):
                    with contextlib.suppress(ValueError):
                        kwargs[k] = ClientLazyLoader(**v).evaluate()

            return func(*args_, **kwargs)

        return _inner

    def _decorate() -> Type["Client"]:
        for name, fn in inspect.getmembers(cls, inspect.isfunction):
            setattr(
                cls,
                name,
                _evaluate_args(fn, "self" in inspect.getfullargspec(fn).args),
            )
        return cls

    return _decorate()

Client

Client implementation.

Client

ZenML client class.

The ZenML client manages configuration options for ZenML stacks as well as their components.

Source code in src/zenml/client.py

@evaluate_all_lazy_load_args_in_client_methods
class Client(metaclass=ClientMetaClass):
    """ZenML client class.

    The ZenML client manages configuration options for ZenML stacks as well
    as their components.
    """

    _active_user: Optional["UserResponse"] = None
    _active_project: Optional["ProjectResponse"] = None
    _active_stack: Optional["StackResponse"] = None

    def __init__(
        self,
        root: Optional[Path] = None,
    ) -> None:
        """Initializes the global client instance.

        Client is a singleton class: only one instance can exist. Calling
        this constructor multiple times will always yield the same instance (see
        the exception below).

        The `root` argument is only meant for internal use and testing purposes.
        User code must never pass them to the constructor.
        When a custom `root` value is passed, an anonymous Client instance
        is created and returned independently of the Client singleton and
        that will have no effect as far as the rest of the ZenML core code is
        concerned.

        Instead of creating a new Client instance to reflect a different
        repository root, to change the active root in the global Client,
        call `Client().activate_root(<new-root>)`.

        Args:
            root: (internal use) custom root directory for the client. If
                no path is given, the repository root is determined using the
                environment variable `ZENML_REPOSITORY_PATH` (if set) and by
                recursively searching in the parent directories of the
                current working directory. Only used to initialize new
                clients internally.
        """
        self._root: Optional[Path] = None
        self._config: Optional[ClientConfiguration] = None

        self._set_active_root(root)

    @classmethod
    def get_instance(cls) -> Optional["Client"]:
        """Return the Client singleton instance.

        Returns:
            The Client singleton instance or None, if the Client hasn't
            been initialized yet.
        """
        return cls._global_client

    @classmethod
    def _reset_instance(cls, client: Optional["Client"] = None) -> None:
        """Reset the Client singleton instance.

        This method is only meant for internal use and testing purposes.

        Args:
            client: The Client instance to set as the global singleton.
                If None, the global Client singleton is reset to an empty
                value.
        """
        cls._global_client = client

    def _set_active_root(self, root: Optional[Path] = None) -> None:
        """Set the supplied path as the repository root.

        If a client configuration is found at the given path or the
        path, it is loaded and used to initialize the client.
        If no client configuration is found, the global configuration is
        used instead to manage the active stack, project etc.

        Args:
            root: The path to set as the active repository root. If not set,
                the repository root is determined using the environment
                variable `ZENML_REPOSITORY_PATH` (if set) and by recursively
                searching in the parent directories of the current working
                directory.
        """
        enable_warnings = handle_bool_env_var(
            ENV_ZENML_ENABLE_REPO_INIT_WARNINGS, False
        )
        self._root = self.find_repository(
            root, enable_warnings=enable_warnings
        )

        if not self._root:
            self._config = None
            if enable_warnings:
                logger.info("Running without an active repository root.")
        else:
            logger.debug("Using repository root %s.", self._root)
            self._config = self._load_config()

        # Sanitize the client configuration to reflect the current
        # settings
        self._sanitize_config()

    def _config_path(self) -> Optional[str]:
        """Path to the client configuration file.

        Returns:
            Path to the client configuration file or None if the client
            root has not been initialized yet.
        """
        if not self.config_directory:
            return None
        return str(self.config_directory / "config.yaml")

    def _sanitize_config(self) -> None:
        """Sanitize and save the client configuration.

        This method is called to ensure that the client configuration
        doesn't contain outdated information, such as an active stack or
        project that no longer exists.
        """
        if not self._config:
            return

        active_project, active_stack = self.zen_store.validate_active_config(
            self._config.active_project_id,
            self._config.active_stack_id,
            config_name="repo",
        )
        self._config.set_active_stack(active_stack)
        if active_project:
            self._config.set_active_project(active_project)

    def _load_config(self) -> Optional[ClientConfiguration]:
        """Loads the client configuration from disk.

        This happens if the client has an active root and the configuration
        file exists. If the configuration file doesn't exist, an empty
        configuration is returned.

        Returns:
            Loaded client configuration or None if the client does not
            have an active root.
        """
        config_path = self._config_path()
        if not config_path:
            return None

        # load the client configuration file if it exists, otherwise use
        # an empty configuration as default
        if fileio.exists(config_path):
            logger.debug(f"Loading client configuration from {config_path}.")
        else:
            logger.debug(
                "No client configuration file found, creating default "
                "configuration."
            )

        return ClientConfiguration(config_file=config_path)

    @staticmethod
    def initialize(
        root: Optional[Path] = None,
    ) -> None:
        """Initializes a new ZenML repository at the given path.

        Args:
            root: The root directory where the repository should be created.
                If None, the current working directory is used.

        Raises:
            InitializationException: If the root directory already contains a
                ZenML repository.
        """
        root = root or Path.cwd()
        logger.debug("Initializing new repository at path %s.", root)
        if Client.is_repository_directory(root):
            raise InitializationException(
                f"Found existing ZenML repository at path '{root}'."
            )

        config_directory = str(root / REPOSITORY_DIRECTORY_NAME)
        io_utils.create_dir_recursive_if_not_exists(config_directory)
        # Initialize the repository configuration at the custom path
        Client(root=root)

    @property
    def uses_local_configuration(self) -> bool:
        """Check if the client is using a local configuration.

        Returns:
            True if the client is using a local configuration,
            False otherwise.
        """
        return self._config is not None

    @staticmethod
    def is_repository_directory(path: Path) -> bool:
        """Checks whether a ZenML client exists at the given path.

        Args:
            path: The path to check.

        Returns:
            True if a ZenML client exists at the given path,
            False otherwise.
        """
        config_dir = path / REPOSITORY_DIRECTORY_NAME
        return fileio.isdir(str(config_dir))

    @staticmethod
    def find_repository(
        path: Optional[Path] = None, enable_warnings: bool = False
    ) -> Optional[Path]:
        """Search for a ZenML repository directory.

        Args:
            path: Optional path to look for the repository. If no path is
                given, this function tries to find the repository using the
                environment variable `ZENML_REPOSITORY_PATH` (if set) and
                recursively searching in the parent directories of the current
                working directory.
            enable_warnings: If `True`, warnings are printed if the repository
                root cannot be found.

        Returns:
            Absolute path to a ZenML repository directory or None if no
            repository directory was found.
        """
        if not path:
            # try to get path from the environment variable
            env_var_path = os.getenv(ENV_ZENML_REPOSITORY_PATH)
            if env_var_path:
                path = Path(env_var_path)

        if path:
            # explicit path via parameter or environment variable, don't search
            # parent directories
            search_parent_directories = False
            warning_message = (
                f"Unable to find ZenML repository at path '{path}'. Make sure "
                f"to create a ZenML repository by calling `zenml init` when "
                f"specifying an explicit repository path in code or via the "
                f"environment variable '{ENV_ZENML_REPOSITORY_PATH}'."
            )
        else:
            # try to find the repository in the parent directories of the
            # current working directory
            path = Path.cwd()
            search_parent_directories = True
            warning_message = (
                f"Unable to find ZenML repository in your current working "
                f"directory ({path}) or any parent directories. If you "
                f"want to use an existing repository which is in a different "
                f"location, set the environment variable "
                f"'{ENV_ZENML_REPOSITORY_PATH}'. If you want to create a new "
                f"repository, run `zenml init`."
            )

        def _find_repository_helper(path_: Path) -> Optional[Path]:
            """Recursively search parent directories for a ZenML repository.

            Args:
                path_: The path to search.

            Returns:
                Absolute path to a ZenML repository directory or None if no
                repository directory was found.
            """
            if Client.is_repository_directory(path_):
                return path_

            if not search_parent_directories or io_utils.is_root(str(path_)):
                return None

            return _find_repository_helper(path_.parent)

        repository_path = _find_repository_helper(path)

        if repository_path:
            return repository_path.resolve()
        if enable_warnings:
            logger.warning(warning_message)
        return None

    @staticmethod
    def is_inside_repository(file_path: str) -> bool:
        """Returns whether a file is inside the active ZenML repository.

        Args:
            file_path: A file path.

        Returns:
            True if the file is inside the active ZenML repository, False
            otherwise.
        """
        if repo_path := Client.find_repository():
            return repo_path in Path(file_path).resolve().parents
        return False

    @property
    def zen_store(self) -> "BaseZenStore":
        """Shortcut to return the global zen store.

        Returns:
            The global zen store.
        """
        return GlobalConfiguration().zen_store

    @property
    def root(self) -> Optional[Path]:
        """The root directory of this client.

        Returns:
            The root directory of this client, or None, if the client
            has not been initialized.
        """
        return self._root

    @property
    def config_directory(self) -> Optional[Path]:
        """The configuration directory of this client.

        Returns:
            The configuration directory of this client, or None, if the
            client doesn't have an active root.
        """
        return self.root / REPOSITORY_DIRECTORY_NAME if self.root else None

    def activate_root(self, root: Optional[Path] = None) -> None:
        """Set the active repository root directory.

        Args:
            root: The path to set as the active repository root. If not set,
                the repository root is determined using the environment
                variable `ZENML_REPOSITORY_PATH` (if set) and by recursively
                searching in the parent directories of the current working
                directory.
        """
        self._set_active_root(root)

    def set_active_project(
        self, project_name_or_id: Union[str, UUID]
    ) -> "ProjectResponse":
        """Set the project for the local client.

        Args:
            project_name_or_id: The name or ID of the project to set active.

        Returns:
            The model of the active project.
        """
        project = self.zen_store.get_project(
            project_name_or_id=project_name_or_id
        )  # raises KeyError
        if self._config:
            self._config.set_active_project(project)
            # Sanitize the client configuration to reflect the current
            # settings
            self._sanitize_config()
        else:
            # set the active project globally only if the client doesn't use
            # a local configuration
            GlobalConfiguration().set_active_project(project)
        return project

    # ----------------------------- Server Settings ----------------------------

    def get_settings(self, hydrate: bool = True) -> ServerSettingsResponse:
        """Get the server settings.

        Args:
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The server settings.
        """
        return self.zen_store.get_server_settings(hydrate=hydrate)

    def update_server_settings(
        self,
        updated_name: Optional[str] = None,
        updated_logo_url: Optional[str] = None,
        updated_enable_analytics: Optional[bool] = None,
        updated_enable_announcements: Optional[bool] = None,
        updated_enable_updates: Optional[bool] = None,
        updated_onboarding_state: Optional[Dict[str, Any]] = None,
    ) -> ServerSettingsResponse:
        """Update the server settings.

        Args:
            updated_name: Updated name for the server.
            updated_logo_url: Updated logo URL for the server.
            updated_enable_analytics: Updated value whether to enable
                analytics for the server.
            updated_enable_announcements: Updated value whether to display
                announcements about ZenML.
            updated_enable_updates: Updated value whether to display updates
                about ZenML.
            updated_onboarding_state: Updated onboarding state for the server.

        Returns:
            The updated server settings.
        """
        update_model = ServerSettingsUpdate(
            server_name=updated_name,
            logo_url=updated_logo_url,
            enable_analytics=updated_enable_analytics,
            display_announcements=updated_enable_announcements,
            display_updates=updated_enable_updates,
            onboarding_state=updated_onboarding_state,
        )
        return self.zen_store.update_server_settings(update_model)

    # ---------------------------------- Users ---------------------------------

    def create_user(
        self,
        name: str,
        password: Optional[str] = None,
        is_admin: bool = False,
    ) -> UserResponse:
        """Create a new user.

        Args:
            name: The name of the user.
            password: The password of the user. If not provided, the user will
                be created with empty password.
            is_admin: Whether the user should be an admin.

        Returns:
            The model of the created user.
        """
        user = UserRequest(
            name=name, password=password or None, is_admin=is_admin
        )
        user.active = (
            password != "" if self.zen_store.type != StoreType.REST else True
        )
        created_user = self.zen_store.create_user(user=user)

        return created_user

    def get_user(
        self,
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> UserResponse:
        """Gets a user.

        Args:
            name_id_or_prefix: The name or ID of the user.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The User
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_user,
            list_method=self.list_users,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )

    def list_users(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        external_user_id: Optional[str] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        full_name: Optional[str] = None,
        email: Optional[str] = None,
        active: Optional[bool] = None,
        email_opted_in: Optional[bool] = None,
        hydrate: bool = False,
    ) -> Page[UserResponse]:
        """List all users.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of stacks to filter by.
            external_user_id: Use the external user id for filtering.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: Use the username for filtering
            full_name: Use the user full name for filtering
            email: Use the user email for filtering
            active: User the user active status for filtering
            email_opted_in: Use the user opt in status for filtering
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The User
        """
        return self.zen_store.list_users(
            UserFilter(
                sort_by=sort_by,
                page=page,
                size=size,
                logical_operator=logical_operator,
                id=id,
                external_user_id=external_user_id,
                created=created,
                updated=updated,
                name=name,
                full_name=full_name,
                email=email,
                active=active,
                email_opted_in=email_opted_in,
            ),
            hydrate=hydrate,
        )

    def update_user(
        self,
        name_id_or_prefix: Union[str, UUID],
        updated_name: Optional[str] = None,
        updated_full_name: Optional[str] = None,
        updated_email: Optional[str] = None,
        updated_email_opt_in: Optional[bool] = None,
        updated_password: Optional[str] = None,
        old_password: Optional[str] = None,
        updated_is_admin: Optional[bool] = None,
        updated_metadata: Optional[Dict[str, Any]] = None,
        updated_default_project_id: Optional[UUID] = None,
        active: Optional[bool] = None,
    ) -> UserResponse:
        """Update a user.

        Args:
            name_id_or_prefix: The name or ID of the user to update.
            updated_name: The new name of the user.
            updated_full_name: The new full name of the user.
            updated_email: The new email of the user.
            updated_email_opt_in: The new email opt-in status of the user.
            updated_password: The new password of the user.
            old_password: The old password of the user. Required for password
                update.
            updated_is_admin: Whether the user should be an admin.
            updated_metadata: The new metadata for the user.
            updated_default_project_id: The new default project ID for the user.
            active: Use to activate or deactivate the user.

        Returns:
            The updated user.

        Raises:
            ValidationError: If the old password is not provided when updating
                the password.
        """
        user = self.get_user(
            name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
        )
        user_update = UserUpdate(name=updated_name or user.name)
        if updated_full_name:
            user_update.full_name = updated_full_name
        if updated_email is not None:
            user_update.email = updated_email
            user_update.email_opted_in = (
                updated_email_opt_in or user.email_opted_in
            )
        if updated_email_opt_in is not None:
            user_update.email_opted_in = updated_email_opt_in
        if updated_password is not None:
            user_update.password = updated_password
            if old_password is None:
                raise ValidationError(
                    "Old password is required to update the password."
                )
            user_update.old_password = old_password
        if updated_is_admin is not None:
            user_update.is_admin = updated_is_admin
        if active is not None:
            user_update.active = active

        if updated_metadata is not None:
            user_update.user_metadata = updated_metadata

        if updated_default_project_id is not None:
            user_update.default_project_id = updated_default_project_id

        return self.zen_store.update_user(
            user_id=user.id, user_update=user_update
        )

    @_fail_for_sql_zen_store
    def deactivate_user(self, name_id_or_prefix: str) -> "UserResponse":
        """Deactivate a user and generate an activation token.

        Args:
            name_id_or_prefix: The name or ID of the user to reset.

        Returns:
            The deactivated user.
        """
        from zenml.zen_stores.rest_zen_store import RestZenStore

        user = self.get_user(name_id_or_prefix, allow_name_prefix_match=False)
        assert isinstance(self.zen_store, RestZenStore)
        return self.zen_store.deactivate_user(user_name_or_id=user.name)

    def delete_user(self, name_id_or_prefix: str) -> None:
        """Delete a user.

        Args:
            name_id_or_prefix: The name or ID of the user to delete.
        """
        user = self.get_user(name_id_or_prefix, allow_name_prefix_match=False)
        self.zen_store.delete_user(user_name_or_id=user.name)

    @property
    def active_user(self) -> "UserResponse":
        """Get the user that is currently in use.

        Returns:
            The active user.
        """
        if self._active_user is None:
            self._active_user = self.zen_store.get_user(include_private=True)
        return self._active_user

    # -------------------------------- Projects ------------------------------

    def create_project(
        self,
        name: str,
        description: str,
        display_name: Optional[str] = None,
    ) -> ProjectResponse:
        """Create a new project.

        Args:
            name: Name of the project.
            description: Description of the project.
            display_name: Display name of the project.

        Returns:
            The created project.
        """
        return self.zen_store.create_project(
            ProjectRequest(
                name=name,
                description=description,
                display_name=display_name or "",
            )
        )

    def get_project(
        self,
        name_id_or_prefix: Optional[Union[UUID, str]],
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> ProjectResponse:
        """Gets a project.

        Args:
            name_id_or_prefix: The name or ID of the project.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The project
        """
        if not name_id_or_prefix:
            return self.active_project
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_project,
            list_method=self.list_projects,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )

    def list_projects(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        display_name: Optional[str] = None,
        hydrate: bool = False,
    ) -> Page[ProjectResponse]:
        """List all projects.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the project ID to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: Use the project name for filtering
            display_name: Use the project display name for filtering
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            Page of projects
        """
        return self.zen_store.list_projects(
            ProjectFilter(
                sort_by=sort_by,
                page=page,
                size=size,
                logical_operator=logical_operator,
                id=id,
                created=created,
                updated=updated,
                name=name,
                display_name=display_name,
            ),
            hydrate=hydrate,
        )

    def update_project(
        self,
        name_id_or_prefix: Optional[Union[UUID, str]],
        new_name: Optional[str] = None,
        new_display_name: Optional[str] = None,
        new_description: Optional[str] = None,
    ) -> ProjectResponse:
        """Update a project.

        Args:
            name_id_or_prefix: Name, ID or prefix of the project to update.
            new_name: New name of the project.
            new_display_name: New display name of the project.
            new_description: New description of the project.

        Returns:
            The updated project.
        """
        project = self.get_project(
            name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
        )
        project_update = ProjectUpdate(
            name=new_name or project.name,
            display_name=new_display_name or project.display_name,
        )
        if new_description:
            project_update.description = new_description
        return self.zen_store.update_project(
            project_id=project.id,
            project_update=project_update,
        )

    def delete_project(self, name_id_or_prefix: str) -> None:
        """Delete a project.

        Args:
            name_id_or_prefix: The name or ID of the project to delete.

        Raises:
            IllegalOperationError: If the project to delete is the active
                project.
        """
        project = self.get_project(
            name_id_or_prefix, allow_name_prefix_match=False
        )
        if self.active_project.id == project.id:
            raise IllegalOperationError(
                f"Project '{name_id_or_prefix}' cannot be deleted since "
                "it is currently active. Please set another project as "
                "active first."
            )
        self.zen_store.delete_project(project_name_or_id=project.id)

    @property
    def active_project(self) -> ProjectResponse:
        """Get the currently active project of the local client.

        If no active project is configured locally for the client, the
        active project in the global configuration is used instead.

        Returns:
            The active project.

        Raises:
            RuntimeError: If the active project is not set.
        """
        if project_id := os.environ.get(ENV_ZENML_ACTIVE_PROJECT_ID):
            if not self._active_project or self._active_project.id != UUID(
                project_id
            ):
                self._active_project = self.get_project(project_id)

            return self._active_project

        from zenml.constants import DEFAULT_PROJECT_NAME

        # If running in a ZenML server environment, the active project is
        # not relevant
        if ENV_ZENML_SERVER in os.environ:
            return self.get_project(DEFAULT_PROJECT_NAME)

        project = (
            self._config.active_project if self._config else None
        ) or GlobalConfiguration().get_active_project()
        if not project:
            raise RuntimeError(
                "No active project is configured. Run "
                "`zenml project set <NAME>` to set the active "
                "project."
            )

        if project.name != DEFAULT_PROJECT_NAME:
            if not self.zen_store.get_store_info().is_pro_server():
                logger.warning(
                    f"You are running with a non-default project "
                    f"'{project.name}'. The ZenML project feature is "
                    "available only in ZenML Pro. Pipelines, pipeline runs and "
                    "artifacts produced in this project will not be "
                    "accessible through the dashboard. Please visit "
                    "https://zenml.io/pro to learn more."
                )
        return project

    # --------------------------------- Stacks ---------------------------------

    def create_stack(
        self,
        name: str,
        components: Mapping[StackComponentType, Union[str, UUID]],
        stack_spec_file: Optional[str] = None,
        labels: Optional[Dict[str, Any]] = None,
    ) -> StackResponse:
        """Registers a stack and its components.

        Args:
            name: The name of the stack to register.
            components: dictionary which maps component types to component names
            stack_spec_file: path to the stack spec file
            labels: The labels of the stack.

        Returns:
            The model of the registered stack.
        """
        stack_components = {}

        for c_type, c_identifier in components.items():
            # Skip non-existent components.
            if not c_identifier:
                continue

            # Get the component.
            component = self.get_stack_component(
                name_id_or_prefix=c_identifier,
                component_type=c_type,
            )
            stack_components[c_type] = [component.id]

        stack = StackRequest(
            name=name,
            components=stack_components,
            stack_spec_path=stack_spec_file,
            labels=labels,
        )

        self._validate_stack_configuration(stack=stack)

        return self.zen_store.create_stack(stack=stack)

    def get_stack(
        self,
        name_id_or_prefix: Optional[Union[UUID, str]] = None,
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> StackResponse:
        """Get a stack by name, ID or prefix.

        If no name, ID or prefix is provided, the active stack is returned.

        Args:
            name_id_or_prefix: The name, ID or prefix of the stack.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The stack.
        """
        if name_id_or_prefix is not None:
            return self._get_entity_by_id_or_name_or_prefix(
                get_method=self.zen_store.get_stack,
                list_method=self.list_stacks,
                name_id_or_prefix=name_id_or_prefix,
                allow_name_prefix_match=allow_name_prefix_match,
                hydrate=hydrate,
            )
        else:
            return self.active_stack_model

    def list_stacks(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        description: Optional[str] = None,
        component_id: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        component: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[StackResponse]:
        """Lists all stacks.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of stacks to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            description: Use the stack description for filtering
            component_id: The id of the component to filter by.
            user: The name/ID of the user to filter by.
            component: The name/ID of the component to filter by.
            name: The name of the stack to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of stacks.
        """
        stack_filter_model = StackFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            component_id=component_id,
            user=user,
            component=component,
            name=name,
            description=description,
            id=id,
            created=created,
            updated=updated,
        )
        return self.zen_store.list_stacks(stack_filter_model, hydrate=hydrate)

    def update_stack(
        self,
        name_id_or_prefix: Optional[Union[UUID, str]] = None,
        name: Optional[str] = None,
        stack_spec_file: Optional[str] = None,
        labels: Optional[Dict[str, Any]] = None,
        description: Optional[str] = None,
        component_updates: Optional[
            Dict[StackComponentType, List[Union[UUID, str]]]
        ] = None,
    ) -> StackResponse:
        """Updates a stack and its components.

        Args:
            name_id_or_prefix: The name, id or prefix of the stack to update.
            name: the new name of the stack.
            stack_spec_file: path to the stack spec file.
            labels: The new labels of the stack component.
            description: the new description of the stack.
            component_updates: dictionary which maps stack component types to
                lists of new stack component names or ids.

        Returns:
            The model of the updated stack.

        Raises:
            EntityExistsError: If the stack name is already taken.
        """
        # First, get the stack
        stack = self.get_stack(
            name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
        )

        # Create the update model
        update_model = StackUpdate(
            stack_spec_path=stack_spec_file,
        )

        if name:
            if self.list_stacks(name=name):
                raise EntityExistsError(
                    "There are already existing stacks with the name "
                    f"'{name}'."
                )

            update_model.name = name

        if description:
            update_model.description = description

        # Get the current components
        if component_updates:
            components_dict = stack.components.copy()

            for component_type, component_id_list in component_updates.items():
                if component_id_list is not None:
                    components_dict[component_type] = [
                        self.get_stack_component(
                            name_id_or_prefix=component_id,
                            component_type=component_type,
                        )
                        for component_id in component_id_list
                    ]

            update_model.components = {
                c_type: [c.id for c in c_list]
                for c_type, c_list in components_dict.items()
            }

        if labels is not None:
            existing_labels = stack.labels or {}
            existing_labels.update(labels)

            existing_labels = {
                k: v for k, v in existing_labels.items() if v is not None
            }
            update_model.labels = existing_labels

        updated_stack = self.zen_store.update_stack(
            stack_id=stack.id,
            stack_update=update_model,
        )
        if updated_stack.id == self.active_stack_model.id:
            if self._config:
                self._config.set_active_stack(updated_stack)
            else:
                GlobalConfiguration().set_active_stack(updated_stack)
        return updated_stack

    def delete_stack(
        self, name_id_or_prefix: Union[str, UUID], recursive: bool = False
    ) -> None:
        """Deregisters a stack.

        Args:
            name_id_or_prefix: The name, id or prefix id of the stack
                to deregister.
            recursive: If `True`, all components of the stack which are not
                associated with any other stack will also be deleted.

        Raises:
            ValueError: If the stack is the currently active stack for this
                client.
        """
        stack = self.get_stack(
            name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
        )

        if stack.id == self.active_stack_model.id:
            raise ValueError(
                f"Unable to deregister active stack '{stack.name}'. Make "
                f"sure to designate a new active stack before deleting this "
                f"one."
            )

        cfg = GlobalConfiguration()
        if stack.id == cfg.active_stack_id:
            raise ValueError(
                f"Unable to deregister '{stack.name}' as it is the active "
                f"stack within your global configuration. Make "
                f"sure to designate a new active stack before deleting this "
                f"one."
            )

        if recursive:
            stack_components_free_for_deletion = []

            # Get all stack components associated with this stack
            for component_type, component_model in stack.components.items():
                # Get stack associated with the stack component

                stacks = self.list_stacks(
                    component_id=component_model[0].id, size=2, page=1
                )

                # Check if the stack component is part of another stack
                if len(stacks) == 1 and stack.id == stacks[0].id:
                    stack_components_free_for_deletion.append(
                        (component_type, component_model)
                    )

            self.delete_stack(stack.id)

            for (
                stack_component_type,
                stack_component_model,
            ) in stack_components_free_for_deletion:
                self.delete_stack_component(
                    stack_component_model[0].name, stack_component_type
                )

            logger.info("Deregistered stack with name '%s'.", stack.name)
            return

        self.zen_store.delete_stack(stack_id=stack.id)
        logger.info("Deregistered stack with name '%s'.", stack.name)

    @property
    def active_stack(self) -> "Stack":
        """The active stack for this client.

        Returns:
            The active stack for this client.
        """
        from zenml.stack.stack import Stack

        return Stack.from_model(self.active_stack_model)

    @property
    def active_stack_model(self) -> StackResponse:
        """The model of the active stack for this client.

        If no active stack is configured locally for the client, the active
        stack in the global configuration is used instead.

        Returns:
            The model of the active stack for this client.

        Raises:
            RuntimeError: If the active stack is not set.
        """
        if env_stack_id := os.environ.get(ENV_ZENML_ACTIVE_STACK_ID):
            if not self._active_stack or self._active_stack.id != UUID(
                env_stack_id
            ):
                self._active_stack = self.get_stack(env_stack_id)

            return self._active_stack

        stack_id: Optional[UUID] = None

        if self._config:
            if self._config._active_stack:
                return self._config._active_stack

            stack_id = self._config.active_stack_id

        if not stack_id:
            # Initialize the zen store so the global config loads the active
            # stack
            _ = GlobalConfiguration().zen_store
            if active_stack := GlobalConfiguration()._active_stack:
                return active_stack

            stack_id = GlobalConfiguration().get_active_stack_id()

        if not stack_id:
            raise RuntimeError(
                "No active stack is configured. Run "
                "`zenml stack set STACK_NAME` to set the active stack."
            )

        return self.get_stack(stack_id)

    def activate_stack(
        self, stack_name_id_or_prefix: Union[str, UUID]
    ) -> None:
        """Sets the stack as active.

        Args:
            stack_name_id_or_prefix: Model of the stack to activate.

        Raises:
            KeyError: If the stack is not registered.
        """
        # Make sure the stack is registered
        try:
            stack = self.get_stack(name_id_or_prefix=stack_name_id_or_prefix)
        except KeyError as e:
            raise KeyError(
                f"Stack '{stack_name_id_or_prefix}' cannot be activated since "
                f"it is not registered yet. Please register it first."
            ) from e

        if self._config:
            self._config.set_active_stack(stack=stack)

        else:
            # set the active stack globally only if the client doesn't use
            # a local configuration
            GlobalConfiguration().set_active_stack(stack=stack)

    def _validate_stack_configuration(self, stack: StackRequest) -> None:
        """Validates the configuration of a stack.

        Args:
            stack: The stack to validate.

        Raises:
            ValidationError: If the stack configuration is invalid.
        """
        local_components: List[str] = []
        remote_components: List[str] = []
        assert stack.components is not None
        for component_type, components in stack.components.items():
            component_flavor: Union[FlavorResponse, str]

            for component in components:
                if isinstance(component, UUID):
                    component_response = self.get_stack_component(
                        name_id_or_prefix=component,
                        component_type=component_type,
                    )
                    component_config = component_response.configuration
                    component_flavor = component_response.flavor
                else:
                    component_config = component.configuration
                    component_flavor = component.flavor

                # Create and validate the configuration
                from zenml.stack.utils import (
                    validate_stack_component_config,
                    warn_if_config_server_mismatch,
                )

                configuration = validate_stack_component_config(
                    configuration_dict=component_config,
                    flavor=component_flavor,
                    component_type=component_type,
                    # Always enforce validation of custom flavors
                    validate_custom_flavors=True,
                )
                # Guaranteed to not be None by setting
                # `validate_custom_flavors=True` above
                assert configuration is not None
                warn_if_config_server_mismatch(configuration)
                flavor_name = (
                    component_flavor.name
                    if isinstance(component_flavor, FlavorResponse)
                    else component_flavor
                )
                if configuration.is_local:
                    local_components.append(
                        f"{component_type.value}: {flavor_name}"
                    )
                elif configuration.is_remote:
                    remote_components.append(
                        f"{component_type.value}: {flavor_name}"
                    )

        if local_components and remote_components:
            logger.warning(
                f"You are configuring a stack that is composed of components "
                f"that are relying on local resources "
                f"({', '.join(local_components)}) as well as "
                f"components that are running remotely "
                f"({', '.join(remote_components)}). This is not recommended as "
                f"it can lead to unexpected behavior, especially if the remote "
                f"components need to access the local resources. Please make "
                f"sure that your stack is configured correctly, or try to use "
                f"component flavors or configurations that do not require "
                f"local resources."
            )

        if not stack.is_valid:
            raise ValidationError(
                "Stack configuration is invalid. A valid"
                "stack must contain an Artifact Store and "
                "an Orchestrator."
            )

    # ----------------------------- Services -----------------------------------

    def create_service(
        self,
        config: "ServiceConfig",
        service_type: ServiceType,
        model_version_id: Optional[UUID] = None,
    ) -> ServiceResponse:
        """Registers a service.

        Args:
            config: The configuration of the service.
            service_type: The type of the service.
            model_version_id: The ID of the model version to associate with the
                service.

        Returns:
            The registered service.
        """
        service_request = ServiceRequest(
            name=config.service_name,
            service_type=service_type,
            config=config.model_dump(),
            project=self.active_project.id,
            model_version_id=model_version_id,
        )
        # Register the service
        return self.zen_store.create_service(service_request)

    def get_service(
        self,
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
        type: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> ServiceResponse:
        """Gets a service.

        Args:
            name_id_or_prefix: The name or ID of the service.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            type: The type of the service.
            project: The project name/ID to filter by.

        Returns:
            The Service
        """

        def type_scoped_list_method(
            hydrate: bool = True,
            **kwargs: Any,
        ) -> Page[ServiceResponse]:
            """Call `zen_store.list_services` with type scoping.

            Args:
                hydrate: Flag deciding whether to hydrate the output model(s)
                    by including metadata fields in the response.
                **kwargs: Keyword arguments to pass to `ServiceFilterModel`.

            Returns:
                The type-scoped list of services.
            """
            service_filter_model = ServiceFilter(**kwargs)
            if type:
                service_filter_model.set_type(type=type)
            return self.zen_store.list_services(
                filter_model=service_filter_model,
                hydrate=hydrate,
            )

        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_service,
            list_method=type_scoped_list_method,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            project=project,
            hydrate=hydrate,
        )

    def list_services(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        type: Optional[str] = None,
        flavor: Optional[str] = None,
        user: Optional[Union[UUID, str]] = None,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = False,
        running: Optional[bool] = None,
        service_name: Optional[str] = None,
        pipeline_name: Optional[str] = None,
        pipeline_run_id: Optional[str] = None,
        pipeline_step_name: Optional[str] = None,
        model_version_id: Optional[Union[str, UUID]] = None,
        config: Optional[Dict[str, Any]] = None,
    ) -> Page[ServiceResponse]:
        """List all services.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of services to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            type: Use the service type for filtering
            flavor: Use the service flavor for filtering
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            running: Use the running status for filtering
            pipeline_name: Use the pipeline name for filtering
            service_name: Use the service name or model name
                for filtering
            pipeline_step_name: Use the pipeline step name for filtering
            model_version_id: Use the model version id for filtering
            config: Use the config for filtering
            pipeline_run_id: Use the pipeline run id for filtering

        Returns:
            The Service response page.
        """
        service_filter_model = ServiceFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            type=type,
            flavor=flavor,
            project=project or self.active_project.id,
            user=user,
            running=running,
            name=service_name,
            pipeline_name=pipeline_name,
            pipeline_step_name=pipeline_step_name,
            model_version_id=model_version_id,
            pipeline_run_id=pipeline_run_id,
            config=dict_to_bytes(config) if config else None,
        )
        return self.zen_store.list_services(
            filter_model=service_filter_model, hydrate=hydrate
        )

    def update_service(
        self,
        id: UUID,
        name: Optional[str] = None,
        service_source: Optional[str] = None,
        admin_state: Optional[ServiceState] = None,
        status: Optional[Dict[str, Any]] = None,
        endpoint: Optional[Dict[str, Any]] = None,
        labels: Optional[Dict[str, str]] = None,
        prediction_url: Optional[str] = None,
        health_check_url: Optional[str] = None,
        model_version_id: Optional[UUID] = None,
    ) -> ServiceResponse:
        """Update a service.

        Args:
            id: The ID of the service to update.
            name: The new name of the service.
            admin_state: The new admin state of the service.
            status: The new status of the service.
            endpoint: The new endpoint of the service.
            service_source: The new service source of the service.
            labels: The new labels of the service.
            prediction_url: The new prediction url of the service.
            health_check_url: The new health check url of the service.
            model_version_id: The new model version id of the service.

        Returns:
            The updated service.
        """
        service_update = ServiceUpdate()
        if name:
            service_update.name = name
        if service_source:
            service_update.service_source = service_source
        if admin_state:
            service_update.admin_state = admin_state
        if status:
            service_update.status = status
        if endpoint:
            service_update.endpoint = endpoint
        if labels:
            service_update.labels = labels
        if prediction_url:
            service_update.prediction_url = prediction_url
        if health_check_url:
            service_update.health_check_url = health_check_url
        if model_version_id:
            service_update.model_version_id = model_version_id
        return self.zen_store.update_service(
            service_id=id, update=service_update
        )

    def delete_service(
        self,
        name_id_or_prefix: UUID,
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete a service.

        Args:
            name_id_or_prefix: The name or ID of the service to delete.
            project: The project name/ID to filter by.
        """
        service = self.get_service(
            name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )
        self.zen_store.delete_service(service_id=service.id)

    # -------------------------------- Components ------------------------------

    def get_stack_component(
        self,
        component_type: StackComponentType,
        name_id_or_prefix: Optional[Union[str, UUID]] = None,
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> ComponentResponse:
        """Fetches a registered stack component.

        If the name_id_or_prefix is provided, it will try to fetch the component
        with the corresponding identifier. If not, it will try to fetch the
        active component of the given type.

        Args:
            component_type: The type of the component to fetch
            name_id_or_prefix: The id of the component to fetch.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The registered stack component.

        Raises:
            KeyError: If no name_id_or_prefix is provided and no such component
                is part of the active stack.
        """
        # If no `name_id_or_prefix` provided, try to get the active component.
        if not name_id_or_prefix:
            components = self.active_stack_model.components.get(
                component_type, None
            )
            if components:
                return components[0]
            raise KeyError(
                "No name_id_or_prefix provided and there is no active "
                f"{component_type} in the current active stack."
            )

        # Else, try to fetch the component with an explicit type filter
        def type_scoped_list_method(
            hydrate: bool = False,
            **kwargs: Any,
        ) -> Page[ComponentResponse]:
            """Call `zen_store.list_stack_components` with type scoping.

            Args:
                hydrate: Flag deciding whether to hydrate the output model(s)
                    by including metadata fields in the response.
                **kwargs: Keyword arguments to pass to `ComponentFilterModel`.

            Returns:
                The type-scoped list of components.
            """
            component_filter_model = ComponentFilter(**kwargs)
            component_filter_model.set_scope_type(
                component_type=component_type
            )
            return self.zen_store.list_stack_components(
                component_filter_model=component_filter_model,
                hydrate=hydrate,
            )

        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_stack_component,
            list_method=type_scoped_list_method,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )

    def list_stack_components(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        name: Optional[str] = None,
        flavor: Optional[str] = None,
        type: Optional[str] = None,
        connector_id: Optional[Union[str, UUID]] = None,
        stack_id: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[ComponentResponse]:
        """Lists all registered stack components.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of component to filter by.
            created: Use to component by time of creation
            updated: Use the last updated date for filtering
            flavor: Use the component flavor for filtering
            type: Use the component type for filtering
            connector_id: The id of the connector to filter by.
            stack_id: The id of the stack to filter by.
            name: The name of the component to filter by.
            user: The ID of name of the user to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of stack components.
        """
        component_filter_model = ComponentFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            connector_id=connector_id,
            stack_id=stack_id,
            name=name,
            flavor=flavor,
            type=type,
            id=id,
            created=created,
            updated=updated,
            user=user,
        )

        return self.zen_store.list_stack_components(
            component_filter_model=component_filter_model, hydrate=hydrate
        )

    def create_stack_component(
        self,
        name: str,
        flavor: str,
        component_type: StackComponentType,
        configuration: Dict[str, str],
        labels: Optional[Dict[str, Any]] = None,
    ) -> "ComponentResponse":
        """Registers a stack component.

        Args:
            name: The name of the stack component.
            flavor: The flavor of the stack component.
            component_type: The type of the stack component.
            configuration: The configuration of the stack component.
            labels: The labels of the stack component.

        Returns:
            The model of the registered component.
        """
        from zenml.stack.utils import (
            validate_stack_component_config,
            warn_if_config_server_mismatch,
        )

        validated_config = validate_stack_component_config(
            configuration_dict=configuration,
            flavor=flavor,
            component_type=component_type,
            # Always enforce validation of custom flavors
            validate_custom_flavors=True,
        )
        # Guaranteed to not be None by setting
        # `validate_custom_flavors=True` above
        assert validated_config is not None
        warn_if_config_server_mismatch(validated_config)

        create_component_model = ComponentRequest(
            name=name,
            type=component_type,
            flavor=flavor,
            configuration=configuration,
            labels=labels,
        )

        # Register the new model
        return self.zen_store.create_stack_component(
            component=create_component_model
        )

    def update_stack_component(
        self,
        name_id_or_prefix: Optional[Union[UUID, str]],
        component_type: StackComponentType,
        name: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        labels: Optional[Dict[str, Any]] = None,
        disconnect: Optional[bool] = None,
        connector_id: Optional[UUID] = None,
        connector_resource_id: Optional[str] = None,
    ) -> ComponentResponse:
        """Updates a stack component.

        Args:
            name_id_or_prefix: The name, id or prefix of the stack component to
                update.
            component_type: The type of the stack component to update.
            name: The new name of the stack component.
            configuration: The new configuration of the stack component.
            labels: The new labels of the stack component.
            disconnect: Whether to disconnect the stack component from its
                service connector.
            connector_id: The new connector id of the stack component.
            connector_resource_id: The new connector resource id of the
                stack component.

        Returns:
            The updated stack component.

        Raises:
            EntityExistsError: If the new name is already taken.
        """
        # Get the existing component model
        component = self.get_stack_component(
            name_id_or_prefix=name_id_or_prefix,
            component_type=component_type,
            allow_name_prefix_match=False,
        )

        update_model = ComponentUpdate()

        if name is not None:
            existing_components = self.list_stack_components(
                name=name,
                type=component_type,
            )
            if existing_components.total > 0:
                raise EntityExistsError(
                    f"There are already existing components with the "
                    f"name '{name}'."
                )
            update_model.name = name

        if configuration is not None:
            existing_configuration = component.configuration
            existing_configuration.update(configuration)
            existing_configuration = {
                k: v
                for k, v in existing_configuration.items()
                if v is not None
            }

            from zenml.stack.utils import (
                validate_stack_component_config,
                warn_if_config_server_mismatch,
            )

            validated_config = validate_stack_component_config(
                configuration_dict=existing_configuration,
                flavor=component.flavor,
                component_type=component.type,
                # Always enforce validation of custom flavors
                validate_custom_flavors=True,
            )
            # Guaranteed to not be None by setting
            # `validate_custom_flavors=True` above
            assert validated_config is not None
            warn_if_config_server_mismatch(validated_config)

            update_model.configuration = existing_configuration

        if labels is not None:
            existing_labels = component.labels or {}
            existing_labels.update(labels)

            existing_labels = {
                k: v for k, v in existing_labels.items() if v is not None
            }
            update_model.labels = existing_labels

        if disconnect:
            update_model.connector = None
            update_model.connector_resource_id = None
        else:
            existing_component = self.get_stack_component(
                name_id_or_prefix=name_id_or_prefix,
                component_type=component_type,
                allow_name_prefix_match=False,
            )
            update_model.connector = connector_id
            update_model.connector_resource_id = connector_resource_id
            if connector_id is None and existing_component.connector:
                update_model.connector = existing_component.connector.id
                update_model.connector_resource_id = (
                    existing_component.connector_resource_id
                )

        # Send the updated component to the ZenStore
        return self.zen_store.update_stack_component(
            component_id=component.id,
            component_update=update_model,
        )

    def delete_stack_component(
        self,
        name_id_or_prefix: Union[str, UUID],
        component_type: StackComponentType,
    ) -> None:
        """Deletes a registered stack component.

        Args:
            name_id_or_prefix: The model of the component to delete.
            component_type: The type of the component to delete.
        """
        component = self.get_stack_component(
            name_id_or_prefix=name_id_or_prefix,
            component_type=component_type,
            allow_name_prefix_match=False,
        )

        self.zen_store.delete_stack_component(component_id=component.id)
        logger.info(
            "Deregistered stack component (type: %s) with name '%s'.",
            component.type,
            component.name,
        )

    # --------------------------------- Flavors --------------------------------

    def create_flavor(
        self,
        source: str,
        component_type: StackComponentType,
    ) -> FlavorResponse:
        """Creates a new flavor.

        Args:
            source: The flavor to create.
            component_type: The type of the flavor.

        Returns:
            The created flavor (in model form).

        Raises:
            ValueError: in case the config_schema of the flavor is too large.
        """
        from zenml.stack.flavor import validate_flavor_source

        flavor = validate_flavor_source(
            source=source, component_type=component_type
        )()

        if len(flavor.config_schema) > TEXT_FIELD_MAX_LENGTH:
            raise ValueError(
                "Json representation of configuration schema"
                "exceeds max length. This could be caused by an"
                "overly long docstring on the flavors "
                "configuration class' docstring."
            )

        flavor_request = flavor.to_model(integration="custom", is_custom=True)
        return self.zen_store.create_flavor(flavor=flavor_request)

    def get_flavor(
        self,
        name_id_or_prefix: str,
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> FlavorResponse:
        """Get a stack component flavor.

        Args:
            name_id_or_prefix: The name, ID or prefix to the id of the flavor
                to get.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The stack component flavor.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_flavor,
            list_method=self.list_flavors,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )

    def list_flavors(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        name: Optional[str] = None,
        type: Optional[str] = None,
        integration: Optional[str] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[FlavorResponse]:
        """Fetches all the flavor models.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of flavors to filter by.
            created: Use to flavors by time of creation
            updated: Use the last updated date for filtering
            user: Filter by user name/ID.
            name: The name of the flavor to filter by.
            type: The type of the flavor to filter by.
            integration: The integration of the flavor to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A list of all the flavor models.
        """
        flavor_filter_model = FlavorFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            user=user,
            name=name,
            type=type,
            integration=integration,
            id=id,
            created=created,
            updated=updated,
        )
        return self.zen_store.list_flavors(
            flavor_filter_model=flavor_filter_model, hydrate=hydrate
        )

    def delete_flavor(self, name_id_or_prefix: str) -> None:
        """Deletes a flavor.

        Args:
            name_id_or_prefix: The name, id or prefix of the id for the
                flavor to delete.
        """
        flavor = self.get_flavor(
            name_id_or_prefix, allow_name_prefix_match=False
        )
        self.zen_store.delete_flavor(flavor_id=flavor.id)

        logger.info(f"Deleted flavor '{flavor.name}' of type '{flavor.type}'.")

    def get_flavors_by_type(
        self, component_type: "StackComponentType"
    ) -> Page[FlavorResponse]:
        """Fetches the list of flavor for a stack component type.

        Args:
            component_type: The type of the component to fetch.

        Returns:
            The list of flavors.
        """
        logger.debug(f"Fetching the flavors of type {component_type}.")

        return self.list_flavors(
            type=component_type,
        )

    def get_flavor_by_name_and_type(
        self, name: str, component_type: "StackComponentType"
    ) -> FlavorResponse:
        """Fetches a registered flavor.

        Args:
            component_type: The type of the component to fetch.
            name: The name of the flavor to fetch.

        Returns:
            The registered flavor.

        Raises:
            KeyError: If no flavor exists for the given type and name.
        """
        logger.debug(
            f"Fetching the flavor of type {component_type} with name {name}."
        )

        if not (
            flavors := self.list_flavors(
                type=component_type, name=name, hydrate=True
            ).items
        ):
            raise KeyError(
                f"No flavor with name '{name}' and type '{component_type}' "
                "exists."
            )
        if len(flavors) > 1:
            raise KeyError(
                f"More than one flavor with name {name} and type "
                f"{component_type} exists."
            )

        return flavors[0]

    # ------------------------------- Pipelines --------------------------------

    def list_pipelines(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        latest_run_status: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        tag: Optional[str] = None,
        tags: Optional[List[str]] = None,
        hydrate: bool = False,
    ) -> Page[PipelineResponse]:
        """List all pipelines.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of pipeline to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: The name of the pipeline to filter by.
            latest_run_status: Filter by the status of the latest run of a
                pipeline.
            project: The project name/ID to filter by.
            user: The name/ID of the user to filter by.
            tag: Tag to filter by.
            tags: Tags to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page with Pipeline fitting the filter description
        """
        pipeline_filter_model = PipelineFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            latest_run_status=latest_run_status,
            project=project or self.active_project.id,
            user=user,
            tag=tag,
            tags=tags,
        )
        return self.zen_store.list_pipelines(
            pipeline_filter_model=pipeline_filter_model,
            hydrate=hydrate,
        )

    def get_pipeline(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> PipelineResponse:
        """Get a pipeline by name, id or prefix.

        Args:
            name_id_or_prefix: The name, ID or ID prefix of the pipeline.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The pipeline.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_pipeline,
            list_method=self.list_pipelines,
            name_id_or_prefix=name_id_or_prefix,
            project=project,
            hydrate=hydrate,
        )

    def delete_pipeline(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete a pipeline.

        Args:
            name_id_or_prefix: The name, ID or ID prefix of the pipeline.
            project: The project name/ID to filter by.
        """
        pipeline = self.get_pipeline(
            name_id_or_prefix=name_id_or_prefix, project=project
        )
        self.zen_store.delete_pipeline(pipeline_id=pipeline.id)

    @_fail_for_sql_zen_store
    def trigger_pipeline(
        self,
        pipeline_name_or_id: Union[str, UUID, None] = None,
        run_configuration: Union[
            PipelineRunConfiguration, Dict[str, Any], None
        ] = None,
        config_path: Optional[str] = None,
        template_id: Optional[UUID] = None,
        stack_name_or_id: Union[str, UUID, None] = None,
        synchronous: bool = False,
        project: Optional[Union[str, UUID]] = None,
    ) -> PipelineRunResponse:
        """Trigger a pipeline from the server.

        Usage examples:
        * Run the latest runnable template for a pipeline:
        ```python
        Client().trigger_pipeline(pipeline_name_or_id=<NAME>)
        ```
        * Run the latest runnable template for a pipeline on a specific stack:
        ```python
        Client().trigger_pipeline(
            pipeline_name_or_id=<NAME>,
            stack_name_or_id=<STACK_NAME_OR_ID>
        )
        ```
        * Run a specific template:
        ```python
        Client().trigger_pipeline(template_id=<ID>)
        ```

        Args:
            pipeline_name_or_id: Name or ID of the pipeline. If this is
                specified, the latest runnable template for this pipeline will
                be used for the run (Runnable here means that the build
                associated with the template is for a remote stack without any
                custom flavor stack components). If not given, a template ID
                that should be run needs to be specified.
            run_configuration: Configuration for the run. Either this or a
                path to a config file can be specified.
            config_path: Path to a YAML configuration file. This file will be
                parsed as a `PipelineRunConfiguration` object. Either this or
                the configuration in code can be specified.
            template_id: ID of the template to run. Either this or a pipeline
                can be specified.
            stack_name_or_id: Name or ID of the stack on which to run the
                pipeline. If not specified, this method will try to find a
                runnable template on any stack.
            synchronous: If `True`, this method will wait until the triggered
                run is finished.
            project: The project name/ID to filter by.

        Raises:
            RuntimeError: If triggering the pipeline failed.

        Returns:
            Model of the pipeline run.
        """
        from zenml.pipelines.run_utils import (
            validate_run_config_is_runnable_from_server,
            validate_stack_is_runnable_from_server,
            wait_for_pipeline_run_to_finish,
        )

        if Counter([template_id, pipeline_name_or_id])[None] != 1:
            raise RuntimeError(
                "You need to specify exactly one of pipeline or template "
                "to trigger."
            )

        if run_configuration and config_path:
            raise RuntimeError(
                "Only config path or runtime configuration can be specified."
            )

        if config_path:
            run_configuration = PipelineRunConfiguration.from_yaml(config_path)

        if isinstance(run_configuration, Dict):
            run_configuration = PipelineRunConfiguration.model_validate(
                run_configuration
            )

        if run_configuration:
            validate_run_config_is_runnable_from_server(run_configuration)

        if template_id:
            if stack_name_or_id:
                logger.warning(
                    "Template ID and stack specified, ignoring the stack and "
                    "using stack associated with the template instead."
                )

            run = self.zen_store.run_template(
                template_id=template_id,
                run_configuration=run_configuration,
            )
        else:
            assert pipeline_name_or_id
            pipeline = self.get_pipeline(name_id_or_prefix=pipeline_name_or_id)

            stack = None
            if stack_name_or_id:
                stack = self.get_stack(
                    stack_name_or_id, allow_name_prefix_match=False
                )
                validate_stack_is_runnable_from_server(
                    zen_store=self.zen_store, stack=stack
                )

            templates = depaginate(
                self.list_run_templates,
                pipeline_id=pipeline.id,
                stack_id=stack.id if stack else None,
                project=project or pipeline.project.id,
            )

            for template in templates:
                if not template.build:
                    continue

                stack = template.build.stack
                if not stack:
                    continue

                try:
                    validate_stack_is_runnable_from_server(
                        zen_store=self.zen_store, stack=stack
                    )
                except ValueError:
                    continue

                run = self.zen_store.run_template(
                    template_id=template.id,
                    run_configuration=run_configuration,
                )
                break
            else:
                raise RuntimeError(
                    "Unable to find a runnable template for the given stack "
                    "and pipeline."
                )

        if synchronous:
            run = wait_for_pipeline_run_to_finish(run_id=run.id)

        return run

    # -------------------------------- Builds ----------------------------------

    def get_build(
        self,
        id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> PipelineBuildResponse:
        """Get a build by id or prefix.

        Args:
            id_or_prefix: The id or id prefix of the build.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The build.

        Raises:
            KeyError: If no build was found for the given id or prefix.
            ZenKeyError: If multiple builds were found that match the given
                id or prefix.
        """
        from zenml.utils.uuid_utils import is_valid_uuid

        # First interpret as full UUID
        if is_valid_uuid(id_or_prefix):
            if not isinstance(id_or_prefix, UUID):
                id_or_prefix = UUID(id_or_prefix, version=4)

            return self.zen_store.get_build(
                id_or_prefix,
                hydrate=hydrate,
            )

        list_kwargs: Dict[str, Any] = dict(
            id=f"startswith:{id_or_prefix}",
            hydrate=hydrate,
        )
        scope = ""
        if project:
            list_kwargs["project"] = project
            scope = f" in project {project}"

        entity = self.list_builds(**list_kwargs)

        # If only a single entity is found, return it.
        if entity.total == 1:
            return entity.items[0]

        # If no entity is found, raise an error.
        if entity.total == 0:
            raise KeyError(
                f"No builds have been found that have either an id or prefix "
                f"that matches the provided string '{id_or_prefix}'{scope}."
            )

        raise ZenKeyError(
            f"{entity.total} builds have been found{scope} that have "
            f"an ID that matches the provided "
            f"string '{id_or_prefix}':\n"
            f"{[entity.items]}.\n"
            f"Please use the id to uniquely identify "
            f"only one of the builds."
        )

    def list_builds(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        pipeline_id: Optional[Union[str, UUID]] = None,
        stack_id: Optional[Union[str, UUID]] = None,
        container_registry_id: Optional[Union[UUID, str]] = None,
        is_local: Optional[bool] = None,
        contains_code: Optional[bool] = None,
        zenml_version: Optional[str] = None,
        python_version: Optional[str] = None,
        checksum: Optional[str] = None,
        stack_checksum: Optional[str] = None,
        duration: Optional[Union[int, str]] = None,
        hydrate: bool = False,
    ) -> Page[PipelineBuildResponse]:
        """List all builds.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of build to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            pipeline_id: The id of the pipeline to filter by.
            stack_id: The id of the stack to filter by.
            container_registry_id: The id of the container registry to
                filter by.
            is_local: Use to filter local builds.
            contains_code: Use to filter builds that contain code.
            zenml_version: The version of ZenML to filter by.
            python_version: The Python version to filter by.
            checksum: The build checksum to filter by.
            stack_checksum: The stack checksum to filter by.
            duration: The duration of the build in seconds to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page with builds fitting the filter description
        """
        build_filter_model = PipelineBuildFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            project=project or self.active_project.id,
            user=user,
            pipeline_id=pipeline_id,
            stack_id=stack_id,
            container_registry_id=container_registry_id,
            is_local=is_local,
            contains_code=contains_code,
            zenml_version=zenml_version,
            python_version=python_version,
            checksum=checksum,
            stack_checksum=stack_checksum,
            duration=duration,
        )
        return self.zen_store.list_builds(
            build_filter_model=build_filter_model,
            hydrate=hydrate,
        )

    def delete_build(
        self, id_or_prefix: str, project: Optional[Union[str, UUID]] = None
    ) -> None:
        """Delete a build.

        Args:
            id_or_prefix: The id or id prefix of the build.
            project: The project name/ID to filter by.
        """
        build = self.get_build(id_or_prefix=id_or_prefix, project=project)
        self.zen_store.delete_build(build_id=build.id)

    # --------------------------------- Event Sources -------------------------

    @_fail_for_sql_zen_store
    def create_event_source(
        self,
        name: str,
        configuration: Dict[str, Any],
        flavor: str,
        event_source_subtype: PluginSubType,
        description: str = "",
    ) -> EventSourceResponse:
        """Registers an event source.

        Args:
            name: The name of the event source to create.
            configuration: Configuration for this event source.
            flavor: The flavor of event source.
            event_source_subtype: The event source subtype.
            description: The description of the event source.

        Returns:
            The model of the registered event source.
        """
        event_source = EventSourceRequest(
            name=name,
            configuration=configuration,
            description=description,
            flavor=flavor,
            plugin_type=PluginType.EVENT_SOURCE,
            plugin_subtype=event_source_subtype,
            project=self.active_project.id,
        )

        return self.zen_store.create_event_source(event_source=event_source)

    @_fail_for_sql_zen_store
    def get_event_source(
        self,
        name_id_or_prefix: Union[UUID, str],
        allow_name_prefix_match: bool = True,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> EventSourceResponse:
        """Get an event source by name, ID or prefix.

        Args:
            name_id_or_prefix: The name, ID or prefix of the stack.
            allow_name_prefix_match: If True, allow matching by name prefix.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The event_source.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_event_source,
            list_method=self.list_event_sources,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            project=project,
            hydrate=hydrate,
        )

    def list_event_sources(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        name: Optional[str] = None,
        flavor: Optional[str] = None,
        event_source_type: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[EventSourceResponse]:
        """Lists all event_sources.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of event_sources to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            name: The name of the event_source to filter by.
            flavor: The flavor of the event_source to filter by.
            event_source_type: The subtype of the event_source to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of event_sources.
        """
        event_source_filter_model = EventSourceFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            project=project or self.active_project.id,
            user=user,
            name=name,
            flavor=flavor,
            plugin_subtype=event_source_type,
            id=id,
            created=created,
            updated=updated,
        )
        return self.zen_store.list_event_sources(
            event_source_filter_model, hydrate=hydrate
        )

    @_fail_for_sql_zen_store
    def update_event_source(
        self,
        name_id_or_prefix: Union[UUID, str],
        name: Optional[str] = None,
        description: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        rotate_secret: Optional[bool] = None,
        is_active: Optional[bool] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> EventSourceResponse:
        """Updates an event_source.

        Args:
            name_id_or_prefix: The name, id or prefix of the event_source to update.
            name: the new name of the event_source.
            description: the new description of the event_source.
            configuration: The event source configuration.
            rotate_secret: Allows rotating of secret, if true, the response will
                contain the new secret value
            is_active: Optional[bool] = Allows for activation/deactivating the
                event source
            project: The project name/ID to filter by.

        Returns:
            The model of the updated event_source.

        Raises:
            EntityExistsError: If the event_source name is already taken.
        """
        # First, get the eve
        event_source = self.get_event_source(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )

        # Create the update model
        update_model = EventSourceUpdate(
            name=name,
            description=description,
            configuration=configuration,
            rotate_secret=rotate_secret,
            is_active=is_active,
        )

        if name:
            if self.list_event_sources(name=name):
                raise EntityExistsError(
                    "There are already existing event_sources with the name "
                    f"'{name}'."
                )

        updated_event_source = self.zen_store.update_event_source(
            event_source_id=event_source.id,
            event_source_update=update_model,
        )
        return updated_event_source

    @_fail_for_sql_zen_store
    def delete_event_source(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Deletes an event_source.

        Args:
            name_id_or_prefix: The name, id or prefix id of the event_source
                to deregister.
            project: The project name/ID to filter by.
        """
        event_source = self.get_event_source(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )

        self.zen_store.delete_event_source(event_source_id=event_source.id)
        logger.info("Deleted event_source with name '%s'.", event_source.name)

    # --------------------------------- Actions -------------------------

    @_fail_for_sql_zen_store
    def create_action(
        self,
        name: str,
        flavor: str,
        action_type: PluginSubType,
        configuration: Dict[str, Any],
        service_account_id: UUID,
        auth_window: Optional[int] = None,
        description: str = "",
    ) -> ActionResponse:
        """Create an action.

        Args:
            name: The name of the action.
            flavor: The flavor of the action,
            action_type: The action subtype.
            configuration: The action configuration.
            service_account_id: The service account that is used to execute the
                action.
            auth_window: The time window in minutes for which the service
                account is authorized to execute the action. Set this to 0 to
                authorize the service account indefinitely (not recommended).
            description: The description of the action.

        Returns:
            The created action
        """
        action = ActionRequest(
            name=name,
            description=description,
            flavor=flavor,
            plugin_subtype=action_type,
            configuration=configuration,
            service_account_id=service_account_id,
            auth_window=auth_window,
            project=self.active_project.id,
        )

        return self.zen_store.create_action(action=action)

    @_fail_for_sql_zen_store
    def get_action(
        self,
        name_id_or_prefix: Union[UUID, str],
        allow_name_prefix_match: bool = True,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> ActionResponse:
        """Get an action by name, ID or prefix.

        Args:
            name_id_or_prefix: The name, ID or prefix of the action.
            allow_name_prefix_match: If True, allow matching by name prefix.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The action.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_action,
            list_method=self.list_actions,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            project=project,
            hydrate=hydrate,
        )

    @_fail_for_sql_zen_store
    def list_actions(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        name: Optional[str] = None,
        flavor: Optional[str] = None,
        action_type: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[ActionResponse]:
        """List actions.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of the action to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            name: The name of the action to filter by.
            flavor: The flavor of the action to filter by.
            action_type: The type of the action to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of actions.
        """
        filter_model = ActionFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            project=project or self.active_project.id,
            user=user,
            name=name,
            id=id,
            flavor=flavor,
            plugin_subtype=action_type,
            created=created,
            updated=updated,
        )
        return self.zen_store.list_actions(filter_model, hydrate=hydrate)

    @_fail_for_sql_zen_store
    def update_action(
        self,
        name_id_or_prefix: Union[UUID, str],
        name: Optional[str] = None,
        description: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        service_account_id: Optional[UUID] = None,
        auth_window: Optional[int] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> ActionResponse:
        """Update an action.

        Args:
            name_id_or_prefix: The name, id or prefix of the action to update.
            name: The new name of the action.
            description: The new description of the action.
            configuration: The new configuration of the action.
            service_account_id: The new service account that is used to execute
                the action.
            auth_window: The new time window in minutes for which the service
                account is authorized to execute the action. Set this to 0 to
                authorize the service account indefinitely (not recommended).
            project: The project name/ID to filter by.

        Returns:
            The updated action.
        """
        action = self.get_action(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )

        update_model = ActionUpdate(
            name=name,
            description=description,
            configuration=configuration,
            service_account_id=service_account_id,
            auth_window=auth_window,
        )

        return self.zen_store.update_action(
            action_id=action.id,
            action_update=update_model,
        )

    @_fail_for_sql_zen_store
    def delete_action(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete an action.

        Args:
            name_id_or_prefix: The name, id or prefix id of the action
                to delete.
            project: The project name/ID to filter by.
        """
        action = self.get_action(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )

        self.zen_store.delete_action(action_id=action.id)
        logger.info("Deleted action with name '%s'.", action.name)

    # --------------------------------- Triggers -------------------------

    @_fail_for_sql_zen_store
    def create_trigger(
        self,
        name: str,
        event_source_id: UUID,
        event_filter: Dict[str, Any],
        action_id: UUID,
        description: str = "",
    ) -> TriggerResponse:
        """Registers a trigger.

        Args:
            name: The name of the trigger to create.
            event_source_id: The id of the event source id
            event_filter: The event filter
            action_id: The ID of the action that should be triggered.
            description: The description of the trigger

        Returns:
            The created trigger.
        """
        trigger = TriggerRequest(
            name=name,
            description=description,
            event_source_id=event_source_id,
            event_filter=event_filter,
            action_id=action_id,
            project=self.active_project.id,
        )

        return self.zen_store.create_trigger(trigger=trigger)

    @_fail_for_sql_zen_store
    def get_trigger(
        self,
        name_id_or_prefix: Union[UUID, str],
        allow_name_prefix_match: bool = True,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> TriggerResponse:
        """Get a trigger by name, ID or prefix.

        Args:
            name_id_or_prefix: The name, ID or prefix of the trigger.
            allow_name_prefix_match: If True, allow matching by name prefix.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The trigger.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_trigger,
            list_method=self.list_triggers,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            project=project,
            hydrate=hydrate,
        )

    @_fail_for_sql_zen_store
    def list_triggers(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        name: Optional[str] = None,
        event_source_id: Optional[UUID] = None,
        action_id: Optional[UUID] = None,
        event_source_flavor: Optional[str] = None,
        event_source_subtype: Optional[str] = None,
        action_flavor: Optional[str] = None,
        action_subtype: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[TriggerResponse]:
        """Lists all triggers.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of triggers to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            name: The name of the trigger to filter by.
            event_source_id: The event source associated with the trigger.
            action_id: The action associated with the trigger.
            event_source_flavor: Flavor of the event source associated with the
                trigger.
            event_source_subtype: Type of the event source associated with the
                trigger.
            action_flavor: Flavor of the action associated with the trigger.
            action_subtype: Type of the action associated with the trigger.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of triggers.
        """
        trigger_filter_model = TriggerFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            project=project or self.active_project.id,
            user=user,
            name=name,
            event_source_id=event_source_id,
            action_id=action_id,
            event_source_flavor=event_source_flavor,
            event_source_subtype=event_source_subtype,
            action_flavor=action_flavor,
            action_subtype=action_subtype,
            id=id,
            created=created,
            updated=updated,
        )
        return self.zen_store.list_triggers(
            trigger_filter_model, hydrate=hydrate
        )

    @_fail_for_sql_zen_store
    def update_trigger(
        self,
        name_id_or_prefix: Union[UUID, str],
        name: Optional[str] = None,
        description: Optional[str] = None,
        event_filter: Optional[Dict[str, Any]] = None,
        is_active: Optional[bool] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> TriggerResponse:
        """Updates a trigger.

        Args:
            name_id_or_prefix: The name, id or prefix of the trigger to update.
            name: the new name of the trigger.
            description: the new description of the trigger.
            event_filter: The event filter configuration.
            is_active: Whether the trigger is active or not.
            project: The project name/ID to filter by.

        Returns:
            The model of the updated trigger.

        Raises:
            EntityExistsError: If the trigger name is already taken.
        """
        # First, get the eve
        trigger = self.get_trigger(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )

        # Create the update model
        update_model = TriggerUpdate(
            name=name,
            description=description,
            event_filter=event_filter,
            is_active=is_active,
        )

        if name:
            if self.list_triggers(name=name):
                raise EntityExistsError(
                    "There are already is an existing trigger with the name "
                    f"'{name}'."
                )

        updated_trigger = self.zen_store.update_trigger(
            trigger_id=trigger.id,
            trigger_update=update_model,
        )
        return updated_trigger

    @_fail_for_sql_zen_store
    def delete_trigger(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Deletes an trigger.

        Args:
            name_id_or_prefix: The name, id or prefix id of the trigger
                to deregister.
            project: The project name/ID to filter by.
        """
        trigger = self.get_trigger(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )

        self.zen_store.delete_trigger(trigger_id=trigger.id)
        logger.info("Deleted trigger with name '%s'.", trigger.name)

    # ------------------------------ Deployments -------------------------------

    def get_deployment(
        self,
        id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> PipelineDeploymentResponse:
        """Get a deployment by id or prefix.

        Args:
            id_or_prefix: The id or id prefix of the deployment.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The deployment.

        Raises:
            KeyError: If no deployment was found for the given id or prefix.
            ZenKeyError: If multiple deployments were found that match the given
                id or prefix.
        """
        from zenml.utils.uuid_utils import is_valid_uuid

        # First interpret as full UUID
        if is_valid_uuid(id_or_prefix):
            id_ = (
                UUID(id_or_prefix)
                if isinstance(id_or_prefix, str)
                else id_or_prefix
            )
            return self.zen_store.get_deployment(id_, hydrate=hydrate)

        list_kwargs: Dict[str, Any] = dict(
            id=f"startswith:{id_or_prefix}",
            hydrate=hydrate,
        )
        scope = ""
        if project:
            list_kwargs["project"] = project
            scope = f" in project {project}"

        entity = self.list_deployments(**list_kwargs)

        # If only a single entity is found, return it.
        if entity.total == 1:
            return entity.items[0]

        # If no entity is found, raise an error.
        if entity.total == 0:
            raise KeyError(
                f"No deployment have been found that have either an id or "
                f"prefix that matches the provided string '{id_or_prefix}'{scope}."
            )

        raise ZenKeyError(
            f"{entity.total} deployments have been found{scope} that have "
            f"an ID that matches the provided "
            f"string '{id_or_prefix}':\n"
            f"{[entity.items]}.\n"
            f"Please use the id to uniquely identify "
            f"only one of the deployments."
        )

    def list_deployments(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        pipeline_id: Optional[Union[str, UUID]] = None,
        stack_id: Optional[Union[str, UUID]] = None,
        build_id: Optional[Union[str, UUID]] = None,
        template_id: Optional[Union[str, UUID]] = None,
        hydrate: bool = False,
    ) -> Page[PipelineDeploymentResponse]:
        """List all deployments.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of build to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            pipeline_id: The id of the pipeline to filter by.
            stack_id: The id of the stack to filter by.
            build_id: The id of the build to filter by.
            template_id: The ID of the template to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page with deployments fitting the filter description
        """
        deployment_filter_model = PipelineDeploymentFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            project=project or self.active_project.id,
            user=user,
            pipeline_id=pipeline_id,
            stack_id=stack_id,
            build_id=build_id,
            template_id=template_id,
        )
        return self.zen_store.list_deployments(
            deployment_filter_model=deployment_filter_model,
            hydrate=hydrate,
        )

    def delete_deployment(
        self,
        id_or_prefix: str,
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete a deployment.

        Args:
            id_or_prefix: The id or id prefix of the deployment.
            project: The project name/ID to filter by.
        """
        deployment = self.get_deployment(
            id_or_prefix=id_or_prefix,
            project=project,
            hydrate=False,
        )
        self.zen_store.delete_deployment(deployment_id=deployment.id)

    # ------------------------------ Run templates -----------------------------

    def create_run_template(
        self,
        name: str,
        deployment_id: UUID,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
    ) -> RunTemplateResponse:
        """Create a run template.

        Args:
            name: The name of the run template.
            deployment_id: ID of the deployment which this template should be
                based off of.
            description: The description of the run template.
            tags: Tags associated with the run template.

        Returns:
            The created run template.
        """
        return self.zen_store.create_run_template(
            template=RunTemplateRequest(
                name=name,
                description=description,
                source_deployment_id=deployment_id,
                tags=tags,
                project=self.active_project.id,
            )
        )

    def get_run_template(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> RunTemplateResponse:
        """Get a run template.

        Args:
            name_id_or_prefix: Name/ID/ID prefix of the template to get.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The run template.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_run_template,
            list_method=self.list_run_templates,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
            hydrate=hydrate,
        )

    def list_run_templates(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        id: Optional[Union[UUID, str]] = None,
        name: Optional[str] = None,
        hidden: Optional[bool] = False,
        tag: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        pipeline_id: Optional[Union[str, UUID]] = None,
        build_id: Optional[Union[str, UUID]] = None,
        stack_id: Optional[Union[str, UUID]] = None,
        code_repository_id: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        pipeline: Optional[Union[UUID, str]] = None,
        stack: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[RunTemplateResponse]:
        """Get a page of run templates.

        Args:
            sort_by: The column to sort by.
            page: The page of items.
            size: The maximum size of all pages.
            logical_operator: Which logical operator to use [and, or].
            created: Filter by the creation date.
            updated: Filter by the last updated date.
            id: Filter by run template ID.
            name: Filter by run template name.
            hidden: Filter by run template hidden status.
            tag: Filter by run template tags.
            project: Filter by project name/ID.
            pipeline_id: Filter by pipeline ID.
            build_id: Filter by build ID.
            stack_id: Filter by stack ID.
            code_repository_id: Filter by code repository ID.
            user: Filter by user name/ID.
            pipeline: Filter by pipeline name/ID.
            stack: Filter by stack name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of run templates.
        """
        filter = RunTemplateFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            created=created,
            updated=updated,
            id=id,
            name=name,
            hidden=hidden,
            tag=tag,
            project=project,
            pipeline_id=pipeline_id,
            build_id=build_id,
            stack_id=stack_id,
            code_repository_id=code_repository_id,
            user=user,
            pipeline=pipeline,
            stack=stack,
        )

        return self.zen_store.list_run_templates(
            template_filter_model=filter, hydrate=hydrate
        )

    def update_run_template(
        self,
        name_id_or_prefix: Union[str, UUID],
        name: Optional[str] = None,
        description: Optional[str] = None,
        hidden: Optional[bool] = None,
        add_tags: Optional[List[str]] = None,
        remove_tags: Optional[List[str]] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> RunTemplateResponse:
        """Update a run template.

        Args:
            name_id_or_prefix: Name/ID/ID prefix of the template to update.
            name: The new name of the run template.
            description: The new description of the run template.
            hidden: The new hidden status of the run template.
            add_tags: Tags to add to the run template.
            remove_tags: Tags to remove from the run template.
            project: The project name/ID to filter by.

        Returns:
            The updated run template.
        """
        if is_valid_uuid(name_id_or_prefix):
            template_id = (
                UUID(name_id_or_prefix)
                if isinstance(name_id_or_prefix, str)
                else name_id_or_prefix
            )
        else:
            template_id = self.get_run_template(
                name_id_or_prefix,
                project=project,
                hydrate=False,
            ).id

        return self.zen_store.update_run_template(
            template_id=template_id,
            template_update=RunTemplateUpdate(
                name=name,
                description=description,
                hidden=hidden,
                add_tags=add_tags,
                remove_tags=remove_tags,
            ),
        )

    def delete_run_template(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete a run template.

        Args:
            name_id_or_prefix: Name/ID/ID prefix of the template to delete.
            project: The project name/ID to filter by.
        """
        if is_valid_uuid(name_id_or_prefix):
            template_id = (
                UUID(name_id_or_prefix)
                if isinstance(name_id_or_prefix, str)
                else name_id_or_prefix
            )
        else:
            template_id = self.get_run_template(
                name_id_or_prefix,
                project=project,
                hydrate=False,
            ).id

        self.zen_store.delete_run_template(template_id=template_id)

    # ------------------------------- Schedules --------------------------------

    def get_schedule(
        self,
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> ScheduleResponse:
        """Get a schedule by name, id or prefix.

        Args:
            name_id_or_prefix: The name, id or prefix of the schedule.
            allow_name_prefix_match: If True, allow matching by name prefix.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The schedule.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_schedule,
            list_method=self.list_schedules,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            project=project,
            hydrate=hydrate,
        )

    def list_schedules(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        pipeline_id: Optional[Union[str, UUID]] = None,
        orchestrator_id: Optional[Union[str, UUID]] = None,
        active: Optional[Union[str, bool]] = None,
        cron_expression: Optional[str] = None,
        start_time: Optional[Union[datetime, str]] = None,
        end_time: Optional[Union[datetime, str]] = None,
        interval_second: Optional[int] = None,
        catchup: Optional[Union[str, bool]] = None,
        hydrate: bool = False,
        run_once_start_time: Optional[Union[datetime, str]] = None,
    ) -> Page[ScheduleResponse]:
        """List schedules.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of stacks to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: The name of the stack to filter by.
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            pipeline_id: The id of the pipeline to filter by.
            orchestrator_id: The id of the orchestrator to filter by.
            active: Use to filter by active status.
            cron_expression: Use to filter by cron expression.
            start_time: Use to filter by start time.
            end_time: Use to filter by end time.
            interval_second: Use to filter by interval second.
            catchup: Use to filter by catchup.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            run_once_start_time: Use to filter by run once start time.

        Returns:
            A list of schedules.
        """
        schedule_filter_model = ScheduleFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            project=project or self.active_project.id,
            user=user,
            pipeline_id=pipeline_id,
            orchestrator_id=orchestrator_id,
            active=active,
            cron_expression=cron_expression,
            start_time=start_time,
            end_time=end_time,
            interval_second=interval_second,
            catchup=catchup,
            run_once_start_time=run_once_start_time,
        )
        return self.zen_store.list_schedules(
            schedule_filter_model=schedule_filter_model,
            hydrate=hydrate,
        )

    def delete_schedule(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete a schedule.

        Args:
            name_id_or_prefix: The name, id or prefix id of the schedule
                to delete.
            project: The project name/ID to filter by.
        """
        schedule = self.get_schedule(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )
        logger.warning(
            f"Deleting schedule '{name_id_or_prefix}'... This will only delete "
            "the reference of the schedule from ZenML. Please make sure to "
            "manually stop/delete this schedule in your orchestrator as well!"
        )
        self.zen_store.delete_schedule(schedule_id=schedule.id)

    # ----------------------------- Pipeline runs ------------------------------

    def get_pipeline_run(
        self,
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> PipelineRunResponse:
        """Gets a pipeline run by name, ID, or prefix.

        Args:
            name_id_or_prefix: Name, ID, or prefix of the pipeline run.
            allow_name_prefix_match: If True, allow matching by name prefix.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The pipeline run.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_run,
            list_method=self.list_pipeline_runs,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            project=project,
            hydrate=hydrate,
        )

    def list_pipeline_runs(
        self,
        sort_by: str = "desc:created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        pipeline_id: Optional[Union[str, UUID]] = None,
        pipeline_name: Optional[str] = None,
        stack_id: Optional[Union[str, UUID]] = None,
        schedule_id: Optional[Union[str, UUID]] = None,
        build_id: Optional[Union[str, UUID]] = None,
        deployment_id: Optional[Union[str, UUID]] = None,
        code_repository_id: Optional[Union[str, UUID]] = None,
        template_id: Optional[Union[str, UUID]] = None,
        model_version_id: Optional[Union[str, UUID]] = None,
        orchestrator_run_id: Optional[str] = None,
        status: Optional[str] = None,
        start_time: Optional[Union[datetime, str]] = None,
        end_time: Optional[Union[datetime, str]] = None,
        unlisted: Optional[bool] = None,
        templatable: Optional[bool] = None,
        tag: Optional[str] = None,
        tags: Optional[List[str]] = None,
        user: Optional[Union[UUID, str]] = None,
        run_metadata: Optional[List[str]] = None,
        pipeline: Optional[Union[UUID, str]] = None,
        code_repository: Optional[Union[UUID, str]] = None,
        model: Optional[Union[UUID, str]] = None,
        stack: Optional[Union[UUID, str]] = None,
        stack_component: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[PipelineRunResponse]:
        """List all pipeline runs.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: The id of the runs to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            project: The project name/ID to filter by.
            pipeline_id: The id of the pipeline to filter by.
            pipeline_name: DEPRECATED. Use `pipeline` instead to filter by
                pipeline name.
            stack_id: The id of the stack to filter by.
            schedule_id: The id of the schedule to filter by.
            build_id: The id of the build to filter by.
            deployment_id: The id of the deployment to filter by.
            code_repository_id: The id of the code repository to filter by.
            template_id: The ID of the template to filter by.
            model_version_id: The ID of the model version to filter by.
            orchestrator_run_id: The run id of the orchestrator to filter by.
            name: The name of the run to filter by.
            status: The status of the pipeline run
            start_time: The start_time for the pipeline run
            end_time: The end_time for the pipeline run
            unlisted: If the runs should be unlisted or not.
            templatable: If the runs should be templatable or not.
            tag: Tag to filter by.
            tags: Tags to filter by.
            user: The name/ID of the user to filter by.
            run_metadata: The run_metadata of the run to filter by.
            pipeline: The name/ID of the pipeline to filter by.
            code_repository: Filter by code repository name/ID.
            model: Filter by model name/ID.
            stack: Filter by stack name/ID.
            stack_component: Filter by stack component name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page with Pipeline Runs fitting the filter description
        """
        runs_filter_model = PipelineRunFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            project=project or self.active_project.id,
            pipeline_id=pipeline_id,
            pipeline_name=pipeline_name,
            schedule_id=schedule_id,
            build_id=build_id,
            deployment_id=deployment_id,
            code_repository_id=code_repository_id,
            template_id=template_id,
            model_version_id=model_version_id,
            orchestrator_run_id=orchestrator_run_id,
            stack_id=stack_id,
            status=status,
            start_time=start_time,
            end_time=end_time,
            tag=tag,
            tags=tags,
            unlisted=unlisted,
            user=user,
            run_metadata=run_metadata,
            pipeline=pipeline,
            code_repository=code_repository,
            stack=stack,
            model=model,
            stack_component=stack_component,
            templatable=templatable,
        )
        return self.zen_store.list_runs(
            runs_filter_model=runs_filter_model,
            hydrate=hydrate,
        )

    def delete_pipeline_run(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Deletes a pipeline run.

        Args:
            name_id_or_prefix: Name, ID, or prefix of the pipeline run.
            project: The project name/ID to filter by.
        """
        run = self.get_pipeline_run(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )
        self.zen_store.delete_run(run_id=run.id)

    # -------------------------------- Step run --------------------------------

    def get_run_step(
        self,
        step_run_id: UUID,
        hydrate: bool = True,
    ) -> StepRunResponse:
        """Get a step run by ID.

        Args:
            step_run_id: The ID of the step run to get.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The step run.
        """
        return self.zen_store.get_run_step(
            step_run_id,
            hydrate=hydrate,
        )

    def list_run_steps(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        cache_key: Optional[str] = None,
        code_hash: Optional[str] = None,
        status: Optional[str] = None,
        start_time: Optional[Union[datetime, str]] = None,
        end_time: Optional[Union[datetime, str]] = None,
        pipeline_run_id: Optional[Union[str, UUID]] = None,
        deployment_id: Optional[Union[str, UUID]] = None,
        original_step_run_id: Optional[Union[str, UUID]] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        model_version_id: Optional[Union[str, UUID]] = None,
        model: Optional[Union[UUID, str]] = None,
        run_metadata: Optional[List[str]] = None,
        hydrate: bool = False,
    ) -> Page[StepRunResponse]:
        """List all pipelines.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of runs to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            start_time: Use to filter by the time when the step started running
            end_time: Use to filter by the time when the step finished running
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            pipeline_run_id: The id of the pipeline run to filter by.
            deployment_id: The id of the deployment to filter by.
            original_step_run_id: The id of the original step run to filter by.
            model_version_id: The ID of the model version to filter by.
            model: Filter by model name/ID.
            name: The name of the step run to filter by.
            cache_key: The cache key of the step run to filter by.
            code_hash: The code hash of the step run to filter by.
            status: The name of the run to filter by.
            run_metadata: Filter by run metadata.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page with Pipeline fitting the filter description
        """
        step_run_filter_model = StepRunFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            cache_key=cache_key,
            code_hash=code_hash,
            pipeline_run_id=pipeline_run_id,
            deployment_id=deployment_id,
            original_step_run_id=original_step_run_id,
            status=status,
            created=created,
            updated=updated,
            start_time=start_time,
            end_time=end_time,
            name=name,
            project=project or self.active_project.id,
            user=user,
            model_version_id=model_version_id,
            model=model,
            run_metadata=run_metadata,
        )
        return self.zen_store.list_run_steps(
            step_run_filter_model=step_run_filter_model,
            hydrate=hydrate,
        )

    # ------------------------------- Artifacts -------------------------------

    def get_artifact(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = False,
    ) -> ArtifactResponse:
        """Get an artifact by name, id or prefix.

        Args:
            name_id_or_prefix: The name, ID or prefix of the artifact to get.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The artifact.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_artifact,
            list_method=self.list_artifacts,
            name_id_or_prefix=name_id_or_prefix,
            project=project,
            hydrate=hydrate,
        )

    def list_artifacts(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        has_custom_name: Optional[bool] = None,
        user: Optional[Union[UUID, str]] = None,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = False,
        tag: Optional[str] = None,
        tags: Optional[List[str]] = None,
    ) -> Page[ArtifactResponse]:
        """Get a list of artifacts.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of artifact to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: The name of the artifact to filter by.
            has_custom_name: Filter artifacts with/without custom names.
            user: Filter by user name or ID.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            tag: Filter artifacts by tag.
            tags: Tags to filter by.

        Returns:
            A list of artifacts.
        """
        artifact_filter_model = ArtifactFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            has_custom_name=has_custom_name,
            tag=tag,
            tags=tags,
            user=user,
            project=project or self.active_project.id,
        )
        return self.zen_store.list_artifacts(
            artifact_filter_model,
            hydrate=hydrate,
        )

    def update_artifact(
        self,
        name_id_or_prefix: Union[str, UUID],
        new_name: Optional[str] = None,
        add_tags: Optional[List[str]] = None,
        remove_tags: Optional[List[str]] = None,
        has_custom_name: Optional[bool] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> ArtifactResponse:
        """Update an artifact.

        Args:
            name_id_or_prefix: The name, ID or prefix of the artifact to update.
            new_name: The new name of the artifact.
            add_tags: Tags to add to the artifact.
            remove_tags: Tags to remove from the artifact.
            has_custom_name: Whether the artifact has a custom name.
            project: The project name/ID to filter by.

        Returns:
            The updated artifact.
        """
        artifact = self.get_artifact(
            name_id_or_prefix=name_id_or_prefix,
            project=project,
        )
        artifact_update = ArtifactUpdate(
            name=new_name,
            add_tags=add_tags,
            remove_tags=remove_tags,
            has_custom_name=has_custom_name,
        )
        return self.zen_store.update_artifact(
            artifact_id=artifact.id, artifact_update=artifact_update
        )

    def delete_artifact(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete an artifact.

        Args:
            name_id_or_prefix: The name, ID or prefix of the artifact to delete.
            project: The project name/ID to filter by.
        """
        artifact = self.get_artifact(
            name_id_or_prefix=name_id_or_prefix,
            project=project,
        )
        self.zen_store.delete_artifact(artifact_id=artifact.id)
        logger.info(f"Deleted artifact '{artifact.name}'.")

    def prune_artifacts(
        self,
        only_versions: bool = True,
        delete_from_artifact_store: bool = False,
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete all unused artifacts and artifact versions.

        Args:
            only_versions: Only delete artifact versions, keeping artifacts
            delete_from_artifact_store: Delete data from artifact metadata
            project: The project name/ID to filter by.
        """
        if delete_from_artifact_store:
            unused_artifact_versions = depaginate(
                self.list_artifact_versions,
                only_unused=True,
                project=project,
            )
            for unused_artifact_version in unused_artifact_versions:
                self._delete_artifact_from_artifact_store(
                    unused_artifact_version
                )

        project = project or self.active_project.id

        self.zen_store.prune_artifact_versions(
            project_name_or_id=project, only_versions=only_versions
        )
        logger.info("All unused artifacts and artifact versions deleted.")

    # --------------------------- Artifact Versions ---------------------------

    def get_artifact_version(
        self,
        name_id_or_prefix: Union[str, UUID],
        version: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> ArtifactVersionResponse:
        """Get an artifact version by ID or artifact name.

        Args:
            name_id_or_prefix: Either the ID of the artifact version or the
                name of the artifact.
            version: The version of the artifact to get. Only used if
                `name_id_or_prefix` is the name of the artifact. If not
                specified, the latest version is returned.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The artifact version.
        """
        from zenml import get_step_context

        if cll := client_lazy_loader(
            method_name="get_artifact_version",
            name_id_or_prefix=name_id_or_prefix,
            version=version,
            project=project,
            hydrate=hydrate,
        ):
            return cll  # type: ignore[return-value]

        artifact = self._get_entity_version_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_artifact_version,
            list_method=self.list_artifact_versions,
            name_id_or_prefix=name_id_or_prefix,
            version=version,
            project=project,
            hydrate=hydrate,
        )
        try:
            step_run = get_step_context().step_run
            client = Client()
            client.zen_store.update_run_step(
                step_run_id=step_run.id,
                step_run_update=StepRunUpdate(
                    loaded_artifact_versions={artifact.name: artifact.id}
                ),
            )
        except RuntimeError:
            pass  # Cannot link to step run if called outside a step
        return artifact

    def list_artifact_versions(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        artifact: Optional[Union[str, UUID]] = None,
        name: Optional[str] = None,
        version: Optional[Union[str, int]] = None,
        version_number: Optional[int] = None,
        artifact_store_id: Optional[Union[str, UUID]] = None,
        type: Optional[ArtifactType] = None,
        data_type: Optional[str] = None,
        uri: Optional[str] = None,
        materializer: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        model_version_id: Optional[Union[str, UUID]] = None,
        only_unused: Optional[bool] = False,
        has_custom_name: Optional[bool] = None,
        user: Optional[Union[UUID, str]] = None,
        model: Optional[Union[UUID, str]] = None,
        pipeline_run: Optional[Union[UUID, str]] = None,
        run_metadata: Optional[List[str]] = None,
        tag: Optional[str] = None,
        tags: Optional[List[str]] = None,
        hydrate: bool = False,
    ) -> Page[ArtifactVersionResponse]:
        """Get a list of artifact versions.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of artifact version to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            artifact: The name or ID of the artifact to filter by.
            name: The name of the artifact to filter by.
            version: The version of the artifact to filter by.
            version_number: The version number of the artifact to filter by.
            artifact_store_id: The id of the artifact store to filter by.
            type: The type of the artifact to filter by.
            data_type: The data type of the artifact to filter by.
            uri: The uri of the artifact to filter by.
            materializer: The materializer of the artifact to filter by.
            project: The project name/ID to filter by.
            model_version_id: Filter by model version ID.
            only_unused: Only return artifact versions that are not used in
                any pipeline runs.
            has_custom_name: Filter artifacts with/without custom names.
            tag: A tag to filter by.
            tags: Tags to filter by.
            user: Filter by user name or ID.
            model: Filter by model name or ID.
            pipeline_run: Filter by pipeline run name or ID.
            run_metadata: Filter by run metadata.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A list of artifact versions.
        """
        if name:
            artifact = name

        artifact_version_filter_model = ArtifactVersionFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            artifact=artifact,
            version=str(version) if version else None,
            version_number=version_number,
            artifact_store_id=artifact_store_id,
            type=type,
            data_type=data_type,
            uri=uri,
            materializer=materializer,
            project=project or self.active_project.id,
            model_version_id=model_version_id,
            only_unused=only_unused,
            has_custom_name=has_custom_name,
            tag=tag,
            tags=tags,
            user=user,
            model=model,
            pipeline_run=pipeline_run,
            run_metadata=run_metadata,
        )
        return self.zen_store.list_artifact_versions(
            artifact_version_filter_model,
            hydrate=hydrate,
        )

    def update_artifact_version(
        self,
        name_id_or_prefix: Union[str, UUID],
        version: Optional[str] = None,
        add_tags: Optional[List[str]] = None,
        remove_tags: Optional[List[str]] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> ArtifactVersionResponse:
        """Update an artifact version.

        Args:
            name_id_or_prefix: The name, ID or prefix of the artifact to update.
            version: The version of the artifact to update. Only used if
                `name_id_or_prefix` is the name of the artifact. If not
                specified, the latest version is updated.
            add_tags: Tags to add to the artifact version.
            remove_tags: Tags to remove from the artifact version.
            project: The project name/ID to filter by.

        Returns:
            The updated artifact version.
        """
        artifact_version = self.get_artifact_version(
            name_id_or_prefix=name_id_or_prefix,
            version=version,
            project=project,
        )
        artifact_version_update = ArtifactVersionUpdate(
            add_tags=add_tags, remove_tags=remove_tags
        )
        return self.zen_store.update_artifact_version(
            artifact_version_id=artifact_version.id,
            artifact_version_update=artifact_version_update,
        )

    def delete_artifact_version(
        self,
        name_id_or_prefix: Union[str, UUID],
        version: Optional[str] = None,
        delete_metadata: bool = True,
        delete_from_artifact_store: bool = False,
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete an artifact version.

        By default, this will delete only the metadata of the artifact from the
        database, not the actual object stored in the artifact store.

        Args:
            name_id_or_prefix: The ID of artifact version or name or prefix of the artifact to
                delete.
            version: The version of the artifact to delete.
            delete_metadata: If True, delete the metadata of the artifact
                version from the database.
            delete_from_artifact_store: If True, delete the artifact object
                    itself from the artifact store.
            project: The project name/ID to filter by.
        """
        artifact_version = self.get_artifact_version(
            name_id_or_prefix=name_id_or_prefix,
            version=version,
            project=project,
        )
        if delete_from_artifact_store:
            self._delete_artifact_from_artifact_store(
                artifact_version=artifact_version
            )
        if delete_metadata:
            self._delete_artifact_version(artifact_version=artifact_version)

    def _delete_artifact_version(
        self, artifact_version: ArtifactVersionResponse
    ) -> None:
        """Delete the metadata of an artifact version from the database.

        Args:
            artifact_version: The artifact version to delete.

        Raises:
            ValueError: If the artifact version is still used in any runs.
        """
        if artifact_version not in depaginate(
            self.list_artifact_versions, only_unused=True
        ):
            raise ValueError(
                "The metadata of artifact versions that are used in runs "
                "cannot be deleted. Please delete all runs that use this "
                "artifact first."
            )
        self.zen_store.delete_artifact_version(artifact_version.id)
        logger.info(
            f"Deleted version '{artifact_version.version}' of artifact "
            f"'{artifact_version.artifact.name}'."
        )

    def _delete_artifact_from_artifact_store(
        self, artifact_version: ArtifactVersionResponse
    ) -> None:
        """Delete an artifact object from the artifact store.

        Args:
            artifact_version: The artifact version to delete.

        Raises:
            Exception: If the artifact store is inaccessible.
        """
        from zenml.artifact_stores.base_artifact_store import BaseArtifactStore
        from zenml.stack.stack_component import StackComponent

        if not artifact_version.artifact_store_id:
            logger.warning(
                f"Artifact '{artifact_version.uri}' does not have an artifact "
                "store associated with it. Skipping deletion from artifact "
                "store."
            )
            return
        try:
            artifact_store_model = self.get_stack_component(
                component_type=StackComponentType.ARTIFACT_STORE,
                name_id_or_prefix=artifact_version.artifact_store_id,
            )
            artifact_store = StackComponent.from_model(artifact_store_model)
            assert isinstance(artifact_store, BaseArtifactStore)
            artifact_store.rmtree(artifact_version.uri)
        except Exception as e:
            logger.error(
                f"Failed to delete artifact '{artifact_version.uri}' from the "
                "artifact store. This might happen if your local client "
                "does not have access to the artifact store or does not "
                "have the required integrations installed. Full error: "
                f"{e}"
            )
            raise e
        else:
            logger.info(
                f"Deleted artifact '{artifact_version.uri}' from the artifact "
                "store."
            )

    # ------------------------------ Run Metadata ------------------------------

    def create_run_metadata(
        self,
        metadata: Dict[str, "MetadataType"],
        resources: List[RunMetadataResource],
        stack_component_id: Optional[UUID] = None,
        publisher_step_id: Optional[UUID] = None,
    ) -> None:
        """Create run metadata.

        Args:
            metadata: The metadata to create as a dictionary of key-value pairs.
            resources: The list of IDs and types of the resources for that the
                metadata was produced.
            stack_component_id: The ID of the stack component that produced
                the metadata.
            publisher_step_id: The ID of the step execution that publishes
                this metadata automatically.
        """
        from zenml.metadata.metadata_types import get_metadata_type

        values: Dict[str, "MetadataType"] = {}
        types: Dict[str, "MetadataTypeEnum"] = {}
        for key, value in metadata.items():
            # Skip metadata that is too large to be stored in the database.
            if len(json.dumps(value)) > TEXT_FIELD_MAX_LENGTH:
                logger.warning(
                    f"Metadata value for key '{key}' is too large to be "
                    "stored in the database. Skipping."
                )
                continue
            # Skip metadata that is not of a supported type.
            try:
                metadata_type = get_metadata_type(value)
            except ValueError as e:
                logger.warning(
                    f"Metadata value for key '{key}' is not of a supported "
                    f"type. Skipping. Full error: {e}"
                )
                continue
            values[key] = value
            types[key] = metadata_type

        run_metadata = RunMetadataRequest(
            project=self.active_project.id,
            resources=resources,
            stack_component_id=stack_component_id,
            publisher_step_id=publisher_step_id,
            values=values,
            types=types,
        )
        self.zen_store.create_run_metadata(run_metadata)

    # -------------------------------- Secrets ---------------------------------

    def create_secret(
        self,
        name: str,
        values: Dict[str, str],
        private: bool = False,
    ) -> SecretResponse:
        """Creates a new secret.

        Args:
            name: The name of the secret.
            values: The values of the secret.
            private: Whether the secret is private. A private secret is only
                accessible to the user who created it.

        Returns:
            The created secret (in model form).

        Raises:
            NotImplementedError: If centralized secrets management is not
                enabled.
        """
        create_secret_request = SecretRequest(
            name=name,
            values=values,
            private=private,
        )
        try:
            return self.zen_store.create_secret(secret=create_secret_request)
        except NotImplementedError:
            raise NotImplementedError(
                "centralized secrets management is not supported or explicitly "
                "disabled in the target ZenML deployment."
            )

    def get_secret(
        self,
        name_id_or_prefix: Union[str, UUID],
        private: Optional[bool] = None,
        allow_partial_name_match: bool = True,
        allow_partial_id_match: bool = True,
        hydrate: bool = True,
    ) -> SecretResponse:
        """Get a secret.

        Get a secret identified by a name, ID or prefix of the name or ID and
        optionally a scope.

        If a private status is not provided, privately scoped secrets will be
        searched for first, followed by publicly scoped secrets. When a name or
        prefix is used instead of a UUID value, each scope is first searched for
        an exact match, then for a ID prefix or name substring match before
        moving on to the next scope.

        Args:
            name_id_or_prefix: The name, ID or prefix to the id of the secret
                to get.
            private: Whether the secret is private. If not set, all secrets will
                be searched for, prioritizing privately scoped secrets.
            allow_partial_name_match: If True, allow partial name matches.
            allow_partial_id_match: If True, allow partial ID matches.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The secret.

        Raises:
            KeyError: If no secret is found.
            ZenKeyError: If multiple secrets are found.
            NotImplementedError: If centralized secrets management is not
                enabled.
        """
        from zenml.utils.uuid_utils import is_valid_uuid

        try:
            # First interpret as full UUID
            if is_valid_uuid(name_id_or_prefix):
                # Fetch by ID; filter by scope if provided
                secret = self.zen_store.get_secret(
                    secret_id=UUID(name_id_or_prefix)
                    if isinstance(name_id_or_prefix, str)
                    else name_id_or_prefix,
                    hydrate=hydrate,
                )
                if private is not None and secret.private != private:
                    raise KeyError(
                        f"No secret found with ID {str(name_id_or_prefix)}"
                    )

                return secret
        except NotImplementedError:
            raise NotImplementedError(
                "centralized secrets management is not supported or explicitly "
                "disabled in the target ZenML deployment."
            )

        # If not a UUID, try to find by name and then by prefix
        assert not isinstance(name_id_or_prefix, UUID)

        # Private statuses to search in order of priority
        search_private_statuses = (
            [False, True] if private is None else [private]
        )

        secrets = self.list_secrets(
            logical_operator=LogicalOperators.OR,
            name=f"contains:{name_id_or_prefix}"
            if allow_partial_name_match
            else f"equals:{name_id_or_prefix}",
            id=f"startswith:{name_id_or_prefix}"
            if allow_partial_id_match
            else None,
            hydrate=hydrate,
        )

        for search_private_status in search_private_statuses:
            partial_matches: List[SecretResponse] = []
            for secret in secrets.items:
                if secret.private != search_private_status:
                    continue
                # Exact match
                if secret.name == name_id_or_prefix:
                    # Need to fetch the secret again to get the secret values
                    return self.zen_store.get_secret(
                        secret_id=secret.id,
                        hydrate=hydrate,
                    )
                # Partial match
                partial_matches.append(secret)

            if len(partial_matches) > 1:
                match_summary = "\n".join(
                    [
                        f"[{secret.id}]: name = {secret.name}"
                        for secret in partial_matches
                    ]
                )
                raise ZenKeyError(
                    f"{len(partial_matches)} secrets have been found that have "
                    f"a name or ID that matches the provided "
                    f"string '{name_id_or_prefix}':\n"
                    f"{match_summary}.\n"
                    f"Please use the id to uniquely identify "
                    f"only one of the secrets."
                )

            # If only a single secret is found, return it
            if len(partial_matches) == 1:
                # Need to fetch the secret again to get the secret values
                return self.zen_store.get_secret(
                    secret_id=partial_matches[0].id,
                    hydrate=hydrate,
                )
        private_status = ""
        if private is not None:
            private_status = "private " if private else "public "
        msg = (
            f"No {private_status}secret found with name, ID or prefix "
            f"'{name_id_or_prefix}'"
        )

        raise KeyError(msg)

    def list_secrets(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        name: Optional[str] = None,
        private: Optional[bool] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[SecretResponse]:
        """Fetches all the secret models.

        The returned secrets do not contain the secret values. To get the
        secret values, use `get_secret` individually for each secret.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of secrets to filter by.
            created: Use to secrets by time of creation
            updated: Use the last updated date for filtering
            name: The name of the secret to filter by.
            private: The private status of the secret to filter by.
            user: Filter by user name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A list of all the secret models without the secret values.

        Raises:
            NotImplementedError: If centralized secrets management is not
                enabled.
        """
        secret_filter_model = SecretFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            user=user,
            name=name,
            private=private,
            id=id,
            created=created,
            updated=updated,
        )
        try:
            return self.zen_store.list_secrets(
                secret_filter_model=secret_filter_model,
                hydrate=hydrate,
            )
        except NotImplementedError:
            raise NotImplementedError(
                "centralized secrets management is not supported or explicitly "
                "disabled in the target ZenML deployment."
            )

    def update_secret(
        self,
        name_id_or_prefix: Union[str, UUID],
        private: Optional[bool] = None,
        new_name: Optional[str] = None,
        update_private: Optional[bool] = None,
        add_or_update_values: Optional[Dict[str, str]] = None,
        remove_values: Optional[List[str]] = None,
    ) -> SecretResponse:
        """Updates a secret.

        Args:
            name_id_or_prefix: The name, id or prefix of the id for the
                secret to update.
            private: The private status of the secret to update.
            new_name: The new name of the secret.
            update_private: New value used to update the private status of the
                secret.
            add_or_update_values: The values to add or update.
            remove_values: The values to remove.

        Returns:
            The updated secret.

        Raises:
            KeyError: If trying to remove a value that doesn't exist.
            ValueError: If a key is provided in both add_or_update_values and
                remove_values.
        """
        secret = self.get_secret(
            name_id_or_prefix=name_id_or_prefix,
            private=private,
            # Don't allow partial name matches, but allow partial ID matches
            allow_partial_name_match=False,
            allow_partial_id_match=True,
            hydrate=True,
        )

        secret_update = SecretUpdate(name=new_name or secret.name)

        if update_private:
            secret_update.private = update_private
        values: Dict[str, Optional[SecretStr]] = {}
        if add_or_update_values:
            values.update(
                {
                    key: SecretStr(value)
                    for key, value in add_or_update_values.items()
                }
            )
        if remove_values:
            for key in remove_values:
                if key not in secret.values:
                    raise KeyError(
                        f"Cannot remove value '{key}' from secret "
                        f"'{secret.name}' because it does not exist."
                    )
                if key in values:
                    raise ValueError(
                        f"Key '{key}' is supplied both in the values to add or "
                        f"update and the values to be removed."
                    )
                values[key] = None
        if values:
            secret_update.values = values

        return Client().zen_store.update_secret(
            secret_id=secret.id, secret_update=secret_update
        )

    def delete_secret(
        self, name_id_or_prefix: str, private: Optional[bool] = None
    ) -> None:
        """Deletes a secret.

        Args:
            name_id_or_prefix: The name or ID of the secret.
            private: The private status of the secret to delete.
        """
        secret = self.get_secret(
            name_id_or_prefix=name_id_or_prefix,
            private=private,
            # Don't allow partial name matches, but allow partial ID matches
            allow_partial_name_match=False,
            allow_partial_id_match=True,
        )

        self.zen_store.delete_secret(secret_id=secret.id)

    def get_secret_by_name_and_private_status(
        self,
        name: str,
        private: Optional[bool] = None,
        hydrate: bool = True,
    ) -> SecretResponse:
        """Fetches a registered secret with a given name and optional private status.

        This is a version of get_secret that restricts the search to a given
        name and an optional private status, without doing any prefix or UUID
        matching.

        If no private status is provided, the search will be done first for
        private secrets, then for public secrets.

        Args:
            name: The name of the secret to get.
            private: The private status of the secret to get.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The registered secret.

        Raises:
            KeyError: If no secret exists for the given name in the given scope.
        """
        logger.debug(
            f"Fetching the secret with name '{name}' and private status "
            f"'{private}'."
        )

        # Private statuses to search in order of priority
        search_private_statuses = (
            [False, True] if private is None else [private]
        )

        for search_private_status in search_private_statuses:
            secrets = self.list_secrets(
                logical_operator=LogicalOperators.AND,
                name=f"equals:{name}",
                private=search_private_status,
                hydrate=hydrate,
            )

            if len(secrets.items) >= 1:
                # Need to fetch the secret again to get the secret values
                return self.zen_store.get_secret(
                    secret_id=secrets.items[0].id, hydrate=hydrate
                )

        private_status = ""
        if private is not None:
            private_status = "private " if private else "public "
        msg = f"No {private_status}secret with name '{name}' was found"

        raise KeyError(msg)

    def list_secrets_by_private_status(
        self,
        private: bool,
        hydrate: bool = False,
    ) -> Page[SecretResponse]:
        """Fetches the list of secrets with a given private status.

        The returned secrets do not contain the secret values. To get the
        secret values, use `get_secret` individually for each secret.

        Args:
            private: The private status of the secrets to search for.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The list of secrets in the given scope without the secret values.
        """
        logger.debug(f"Fetching the secrets with private status '{private}'.")

        return self.list_secrets(private=private, hydrate=hydrate)

    def backup_secrets(
        self,
        ignore_errors: bool = True,
        delete_secrets: bool = False,
    ) -> None:
        """Backs up all secrets to the configured backup secrets store.

        Args:
            ignore_errors: Whether to ignore individual errors during the backup
                process and attempt to backup all secrets.
            delete_secrets: Whether to delete the secrets that have been
                successfully backed up from the primary secrets store. Setting
                this flag effectively moves all secrets from the primary secrets
                store to the backup secrets store.
        """
        self.zen_store.backup_secrets(
            ignore_errors=ignore_errors, delete_secrets=delete_secrets
        )

    def restore_secrets(
        self,
        ignore_errors: bool = False,
        delete_secrets: bool = False,
    ) -> None:
        """Restore all secrets from the configured backup secrets store.

        Args:
            ignore_errors: Whether to ignore individual errors during the
                restore process and attempt to restore all secrets.
            delete_secrets: Whether to delete the secrets that have been
                successfully restored from the backup secrets store. Setting
                this flag effectively moves all secrets from the backup secrets
                store to the primary secrets store.
        """
        self.zen_store.restore_secrets(
            ignore_errors=ignore_errors, delete_secrets=delete_secrets
        )

    # --------------------------- Code repositories ---------------------------

    @staticmethod
    def _validate_code_repository_config(
        source: Source, config: Dict[str, Any]
    ) -> None:
        """Validate a code repository config.

        Args:
            source: The code repository source.
            config: The code repository config.

        Raises:
            RuntimeError: If the provided config is invalid.
        """
        from zenml.code_repositories import BaseCodeRepository

        code_repo_class: Type[BaseCodeRepository] = (
            source_utils.load_and_validate_class(
                source=source, expected_class=BaseCodeRepository
            )
        )
        try:
            code_repo_class.validate_config(config)
        except Exception as e:
            raise RuntimeError(
                "Failed to validate code repository config."
            ) from e

    def create_code_repository(
        self,
        name: str,
        config: Dict[str, Any],
        source: Source,
        description: Optional[str] = None,
        logo_url: Optional[str] = None,
    ) -> CodeRepositoryResponse:
        """Create a new code repository.

        Args:
            name: Name of the code repository.
            config: The configuration for the code repository.
            source: The code repository implementation source.
            description: The code repository description.
            logo_url: URL of a logo (png, jpg or svg) for the code repository.

        Returns:
            The created code repository.
        """
        self._validate_code_repository_config(source=source, config=config)
        repo_request = CodeRepositoryRequest(
            project=self.active_project.id,
            name=name,
            config=config,
            source=source,
            description=description,
            logo_url=logo_url,
        )
        return self.zen_store.create_code_repository(
            code_repository=repo_request
        )

    def get_code_repository(
        self,
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> CodeRepositoryResponse:
        """Get a code repository by name, id or prefix.

        Args:
            name_id_or_prefix: The name, ID or ID prefix of the code repository.
            allow_name_prefix_match: If True, allow matching by name prefix.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The code repository.
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_code_repository,
            list_method=self.list_code_repositories,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
            project=project,
        )

    def list_code_repositories(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        project: Optional[Union[str, UUID]] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[CodeRepositoryResponse]:
        """List all code repositories.

        Args:
            sort_by: The column to sort by.
            page: The page of items.
            size: The maximum size of all pages.
            logical_operator: Which logical operator to use [and, or].
            id: Use the id of the code repository to filter by.
            created: Use to filter by time of creation.
            updated: Use the last updated date for filtering.
            name: The name of the code repository to filter by.
            project: The project name/ID to filter by.
            user: Filter by user name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of code repositories matching the filter description.
        """
        filter_model = CodeRepositoryFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            project=project or self.active_project.id,
            user=user,
        )
        return self.zen_store.list_code_repositories(
            filter_model=filter_model,
            hydrate=hydrate,
        )

    def update_code_repository(
        self,
        name_id_or_prefix: Union[UUID, str],
        name: Optional[str] = None,
        description: Optional[str] = None,
        logo_url: Optional[str] = None,
        config: Optional[Dict[str, Any]] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> CodeRepositoryResponse:
        """Update a code repository.

        Args:
            name_id_or_prefix: Name, ID or prefix of the code repository to
                update.
            name: New name of the code repository.
            description: New description of the code repository.
            logo_url: New logo URL of the code repository.
            config: New configuration options for the code repository. Will
                be used to update the existing configuration values. To remove
                values from the existing configuration, set the value for that
                key to `None`.
            project: The project name/ID to filter by.

        Returns:
            The updated code repository.
        """
        repo = self.get_code_repository(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )
        update = CodeRepositoryUpdate(
            name=name, description=description, logo_url=logo_url
        )
        if config is not None:
            combined_config = repo.config
            combined_config.update(config)
            combined_config = {
                k: v for k, v in combined_config.items() if v is not None
            }

            self._validate_code_repository_config(
                source=repo.source, config=combined_config
            )
            update.config = combined_config

        return self.zen_store.update_code_repository(
            code_repository_id=repo.id, update=update
        )

    def delete_code_repository(
        self,
        name_id_or_prefix: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Delete a code repository.

        Args:
            name_id_or_prefix: The name, ID or prefix of the code repository.
            project: The project name/ID to filter by.
        """
        repo = self.get_code_repository(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
            project=project,
        )
        self.zen_store.delete_code_repository(code_repository_id=repo.id)

    # --------------------------- Service Connectors ---------------------------

    def create_service_connector(
        self,
        name: str,
        connector_type: str,
        resource_type: Optional[str] = None,
        auth_method: Optional[str] = None,
        configuration: Optional[Dict[str, str]] = None,
        resource_id: Optional[str] = None,
        description: str = "",
        expiration_seconds: Optional[int] = None,
        expires_at: Optional[datetime] = None,
        expires_skew_tolerance: Optional[int] = None,
        labels: Optional[Dict[str, str]] = None,
        auto_configure: bool = False,
        verify: bool = True,
        list_resources: bool = True,
        register: bool = True,
    ) -> Tuple[
        Optional[
            Union[
                ServiceConnectorResponse,
                ServiceConnectorRequest,
            ]
        ],
        Optional[ServiceConnectorResourcesModel],
    ]:
        """Create, validate and/or register a service connector.

        Args:
            name: The name of the service connector.
            connector_type: The service connector type.
            auth_method: The authentication method of the service connector.
                May be omitted if auto-configuration is used.
            resource_type: The resource type for the service connector.
            configuration: The configuration of the service connector.
            resource_id: The resource id of the service connector.
            description: The description of the service connector.
            expiration_seconds: The expiration time of the service connector.
            expires_at: The expiration time of the service connector.
            expires_skew_tolerance: The allowed expiration skew for the service
                connector credentials.
            labels: The labels of the service connector.
            auto_configure: Whether to automatically configure the service
                connector from the local environment.
            verify: Whether to verify that the service connector configuration
                and credentials can be used to gain access to the resource.
            list_resources: Whether to also list the resources that the service
                connector can give access to (if verify is True).
            register: Whether to register the service connector or not.

        Returns:
            The model of the registered service connector and the resources
            that the service connector can give access to (if verify is True).

        Raises:
            ValueError: If the arguments are invalid.
            KeyError: If the service connector type is not found.
            NotImplementedError: If auto-configuration is not supported or
                not implemented for the service connector type.
            AuthorizationException: If the connector verification failed due
                to authorization issues.
        """
        from zenml.service_connectors.service_connector_registry import (
            service_connector_registry,
        )

        connector_instance: Optional[ServiceConnector] = None
        connector_resources: Optional[ServiceConnectorResourcesModel] = None

        # Get the service connector type class
        try:
            connector = self.zen_store.get_service_connector_type(
                connector_type=connector_type,
            )
        except KeyError:
            raise KeyError(
                f"Service connector type {connector_type} not found."
                "Please check that you have installed all required "
                "Python packages and ZenML integrations and try again."
            )

        if not resource_type and len(connector.resource_types) == 1:
            resource_type = connector.resource_types[0].resource_type

        # If auto_configure is set, we will try to automatically configure the
        # service connector from the local environment
        if auto_configure:
            if not connector.supports_auto_configuration:
                raise NotImplementedError(
                    f"The {connector.name} service connector type "
                    "does not support auto-configuration."
                )
            if not connector.local:
                raise NotImplementedError(
                    f"The {connector.name} service connector type "
                    "implementation is not available locally. Please "
                    "check that you have installed all required Python "
                    "packages and ZenML integrations and try again, or "
                    "skip auto-configuration."
                )

            assert connector.connector_class is not None

            connector_instance = connector.connector_class.auto_configure(
                resource_type=resource_type,
                auth_method=auth_method,
                resource_id=resource_id,
            )
            assert connector_instance is not None
            connector_request = connector_instance.to_model(
                name=name,
                description=description or "",
                labels=labels,
            )

            if verify:
                # Prefer to verify the connector config server-side if the
                # implementation if available there, because it ensures
                # that the connector can be shared with other users or used
                # from other machines and because some auth methods rely on the
                # server-side authentication environment
                if connector.remote:
                    connector_resources = (
                        self.zen_store.verify_service_connector_config(
                            connector_request,
                            list_resources=list_resources,
                        )
                    )
                else:
                    connector_resources = connector_instance.verify(
                        list_resources=list_resources,
                    )

                if connector_resources.error:
                    # Raise an exception if the connector verification failed
                    raise AuthorizationException(connector_resources.error)

        else:
            if not auth_method:
                if len(connector.auth_methods) == 1:
                    auth_method = connector.auth_methods[0].auth_method
                else:
                    raise ValueError(
                        f"Multiple authentication methods are available for "
                        f"the {connector.name} service connector type. Please "
                        f"specify one of the following: "
                        f"{list(connector.auth_method_dict.keys())}."
                    )

            connector_request = ServiceConnectorRequest(
                name=name,
                connector_type=connector_type,
                description=description,
                auth_method=auth_method,
                expiration_seconds=expiration_seconds,
                expires_at=expires_at,
                expires_skew_tolerance=expires_skew_tolerance,
                labels=labels or {},
            )
            # Validate and configure the resources
            connector_request.validate_and_configure_resources(
                connector_type=connector,
                resource_types=resource_type,
                resource_id=resource_id,
                configuration=configuration,
            )
            if verify:
                # Prefer to verify the connector config server-side if the
                # implementation if available there, because it ensures
                # that the connector can be shared with other users or used
                # from other machines and because some auth methods rely on the
                # server-side authentication environment
                if connector.remote:
                    connector_resources = (
                        self.zen_store.verify_service_connector_config(
                            connector_request,
                            list_resources=list_resources,
                        )
                    )
                else:
                    connector_instance = (
                        service_connector_registry.instantiate_connector(
                            model=connector_request
                        )
                    )
                    connector_resources = connector_instance.verify(
                        list_resources=list_resources,
                    )

                if connector_resources.error:
                    # Raise an exception if the connector verification failed
                    raise AuthorizationException(connector_resources.error)

                # For resource types that don't support multi-instances, it's
                # better to save the default resource ID in the connector, if
                # available. Otherwise, we'll need to instantiate the connector
                # again to get the default resource ID.
                connector_request.resource_id = (
                    connector_request.resource_id
                    or connector_resources.get_default_resource_id()
                )

        if not register:
            return connector_request, connector_resources

        # Register the new model
        connector_response = self.zen_store.create_service_connector(
            service_connector=connector_request
        )

        if connector_resources:
            connector_resources.id = connector_response.id
            connector_resources.name = connector_response.name
            connector_resources.connector_type = (
                connector_response.connector_type
            )

        return connector_response, connector_resources

    def get_service_connector(
        self,
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        load_secrets: bool = False,
        hydrate: bool = True,
    ) -> ServiceConnectorResponse:
        """Fetches a registered service connector.

        Args:
            name_id_or_prefix: The id of the service connector to fetch.
            allow_name_prefix_match: If True, allow matching by name prefix.
            load_secrets: If True, load the secrets for the service connector.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The registered service connector.
        """
        connector = self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_service_connector,
            list_method=self.list_service_connectors,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )

        if load_secrets and connector.secret_id:
            client = Client()
            try:
                secret = client.get_secret(
                    name_id_or_prefix=connector.secret_id,
                    allow_partial_id_match=False,
                    allow_partial_name_match=False,
                )
            except KeyError as err:
                logger.error(
                    "Unable to retrieve secret values associated with "
                    f"service connector '{connector.name}': {err}"
                )
            else:
                # Add secret values to connector configuration
                connector.secrets.update(secret.values)

        return connector

    def list_service_connectors(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[datetime] = None,
        updated: Optional[datetime] = None,
        name: Optional[str] = None,
        connector_type: Optional[str] = None,
        auth_method: Optional[str] = None,
        resource_type: Optional[str] = None,
        resource_id: Optional[str] = None,
        user: Optional[Union[UUID, str]] = None,
        labels: Optional[Dict[str, Optional[str]]] = None,
        secret_id: Optional[Union[str, UUID]] = None,
        hydrate: bool = False,
    ) -> Page[ServiceConnectorResponse]:
        """Lists all registered service connectors.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: The id of the service connector to filter by.
            created: Filter service connectors by time of creation
            updated: Use the last updated date for filtering
            connector_type: Use the service connector type for filtering
            auth_method: Use the service connector auth method for filtering
            resource_type: Filter service connectors by the resource type that
                they can give access to.
            resource_id: Filter service connectors by the resource id that
                they can give access to.
            user: Filter by user name/ID.
            name: The name of the service connector to filter by.
            labels: The labels of the service connector to filter by.
            secret_id: Filter by the id of the secret that is referenced by the
                service connector.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of service connectors.
        """
        connector_filter_model = ServiceConnectorFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            user=user,
            name=name,
            connector_type=connector_type,
            auth_method=auth_method,
            resource_type=resource_type,
            resource_id=resource_id,
            id=id,
            created=created,
            updated=updated,
            labels=labels,
            secret_id=secret_id,
        )
        return self.zen_store.list_service_connectors(
            filter_model=connector_filter_model,
            hydrate=hydrate,
        )

    def update_service_connector(
        self,
        name_id_or_prefix: Union[UUID, str],
        name: Optional[str] = None,
        auth_method: Optional[str] = None,
        resource_type: Optional[str] = None,
        configuration: Optional[Dict[str, str]] = None,
        resource_id: Optional[str] = None,
        description: Optional[str] = None,
        expires_at: Optional[datetime] = None,
        expires_skew_tolerance: Optional[int] = None,
        expiration_seconds: Optional[int] = None,
        labels: Optional[Dict[str, Optional[str]]] = None,
        verify: bool = True,
        list_resources: bool = True,
        update: bool = True,
    ) -> Tuple[
        Optional[
            Union[
                ServiceConnectorResponse,
                ServiceConnectorUpdate,
            ]
        ],
        Optional[ServiceConnectorResourcesModel],
    ]:
        """Validate and/or register an updated service connector.

        If the `resource_type`, `resource_id` and `expiration_seconds`
        parameters are set to their "empty" values (empty string for resource
        type and resource ID, 0 for expiration seconds), the existing values
        will be removed from the service connector. Setting them to None or
        omitting them will not affect the existing values.

        If supplied, the `configuration` parameter is a full replacement of the
        existing configuration rather than a partial update.

        Labels can be updated or removed by setting the label value to None.

        Args:
            name_id_or_prefix: The name, id or prefix of the service connector
                to update.
            name: The new name of the service connector.
            auth_method: The new authentication method of the service connector.
            resource_type: The new resource type for the service connector.
                If set to the empty string, the existing resource type will be
                removed.
            configuration: The new configuration of the service connector. If
                set, this needs to be a full replacement of the existing
                configuration rather than a partial update.
            resource_id: The new resource id of the service connector.
                If set to the empty string, the existing resource ID will be
                removed.
            description: The description of the service connector.
            expires_at: The new UTC expiration time of the service connector.
            expires_skew_tolerance: The allowed expiration skew for the service
                connector credentials.
            expiration_seconds: The expiration time of the service connector.
                If set to 0, the existing expiration time will be removed.
            labels: The service connector to update or remove. If a label value
                is set to None, the label will be removed.
            verify: Whether to verify that the service connector configuration
                and credentials can be used to gain access to the resource.
            list_resources: Whether to also list the resources that the service
                connector can give access to (if verify is True).
            update: Whether to update the service connector or not.

        Returns:
            The model of the registered service connector and the resources
            that the service connector can give access to (if verify is True).

        Raises:
            AuthorizationException: If the service connector verification
                fails due to invalid credentials or insufficient permissions.
        """
        from zenml.service_connectors.service_connector_registry import (
            service_connector_registry,
        )

        connector_model = self.get_service_connector(
            name_id_or_prefix,
            allow_name_prefix_match=False,
            load_secrets=True,
        )

        connector_instance: Optional[ServiceConnector] = None
        connector_resources: Optional[ServiceConnectorResourcesModel] = None

        if isinstance(connector_model.connector_type, str):
            connector = self.get_service_connector_type(
                connector_model.connector_type
            )
        else:
            connector = connector_model.connector_type

        resource_types: Optional[Union[str, List[str]]] = None
        if resource_type == "":
            resource_types = None
        elif resource_type is None:
            resource_types = connector_model.resource_types
        else:
            resource_types = resource_type

        if not resource_type and len(connector.resource_types) == 1:
            resource_types = connector.resource_types[0].resource_type

        if resource_id == "":
            resource_id = None
        elif resource_id is None:
            resource_id = connector_model.resource_id

        if expiration_seconds == 0:
            expiration_seconds = None
        elif expiration_seconds is None:
            expiration_seconds = connector_model.expiration_seconds

        connector_update = ServiceConnectorUpdate(
            name=name or connector_model.name,
            connector_type=connector.connector_type,
            description=description or connector_model.description,
            auth_method=auth_method or connector_model.auth_method,
            expires_at=expires_at,
            expires_skew_tolerance=expires_skew_tolerance,
            expiration_seconds=expiration_seconds,
        )

        # Validate and configure the resources
        if configuration is not None:
            # The supplied configuration is a drop-in replacement for the
            # existing configuration and secrets
            connector_update.validate_and_configure_resources(
                connector_type=connector,
                resource_types=resource_types,
                resource_id=resource_id,
                configuration=configuration,
            )
        else:
            connector_update.validate_and_configure_resources(
                connector_type=connector,
                resource_types=resource_types,
                resource_id=resource_id,
                configuration=connector_model.configuration,
                secrets=connector_model.secrets,
            )

        # Add the labels
        if labels is not None:
            # Apply the new label values, but don't keep any labels that
            # have been set to None in the update
            connector_update.labels = {
                **{
                    label: value
                    for label, value in connector_model.labels.items()
                    if label not in labels
                },
                **{
                    label: value
                    for label, value in labels.items()
                    if value is not None
                },
            }
        else:
            connector_update.labels = connector_model.labels

        if verify:
            # Prefer to verify the connector config server-side if the
            # implementation, if available there, because it ensures
            # that the connector can be shared with other users or used
            # from other machines and because some auth methods rely on the
            # server-side authentication environment

            # Convert the update model to a request model for validation
            connector_request_dict = connector_update.model_dump()
            connector_request = ServiceConnectorRequest.model_validate(
                connector_request_dict
            )

            if connector.remote:
                connector_resources = (
                    self.zen_store.verify_service_connector_config(
                        service_connector=connector_request,
                        list_resources=list_resources,
                    )
                )
            else:
                connector_instance = (
                    service_connector_registry.instantiate_connector(
                        model=connector_request,
                    )
                )
                connector_resources = connector_instance.verify(
                    list_resources=list_resources
                )

            if connector_resources.error:
                raise AuthorizationException(connector_resources.error)

            # For resource types that don't support multi-instances, it's
            # better to save the default resource ID in the connector, if
            # available. Otherwise, we'll need to instantiate the connector
            # again to get the default resource ID.
            connector_update.resource_id = (
                connector_update.resource_id
                or connector_resources.get_default_resource_id()
            )

        if not update:
            return connector_update, connector_resources

        # Update the model
        connector_response = self.zen_store.update_service_connector(
            service_connector_id=connector_model.id,
            update=connector_update,
        )

        if connector_resources:
            connector_resources.id = connector_response.id
            connector_resources.name = connector_response.name
            connector_resources.connector_type = (
                connector_response.connector_type
            )

        return connector_response, connector_resources

    def delete_service_connector(
        self,
        name_id_or_prefix: Union[str, UUID],
    ) -> None:
        """Deletes a registered service connector.

        Args:
            name_id_or_prefix: The ID or name of the service connector to delete.
        """
        service_connector = self.get_service_connector(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
        )

        self.zen_store.delete_service_connector(
            service_connector_id=service_connector.id
        )
        logger.info(
            "Removed service connector (type: %s) with name '%s'.",
            service_connector.type,
            service_connector.name,
        )

    def verify_service_connector(
        self,
        name_id_or_prefix: Union[UUID, str],
        resource_type: Optional[str] = None,
        resource_id: Optional[str] = None,
        list_resources: bool = True,
    ) -> "ServiceConnectorResourcesModel":
        """Verifies if a service connector has access to one or more resources.

        Args:
            name_id_or_prefix: The name, id or prefix of the service connector
                to verify.
            resource_type: The type of the resource for which to verify access.
                If not provided, the resource type from the service connector
                configuration will be used.
            resource_id: The ID of the resource for which to verify access. If
                not provided, the resource ID from the service connector
                configuration will be used.
            list_resources: Whether to list the resources that the service
                connector has access to.

        Returns:
            The list of resources that the service connector has access to,
            scoped to the supplied resource type and ID, if provided.

        Raises:
            AuthorizationException: If the service connector does not have
                access to the resources.
        """
        from zenml.service_connectors.service_connector_registry import (
            service_connector_registry,
        )

        # Get the service connector model
        service_connector = self.get_service_connector(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
        )

        connector_type = self.get_service_connector_type(
            service_connector.type
        )

        # Prefer to verify the connector config server-side if the
        # implementation if available there, because it ensures
        # that the connector can be shared with other users or used
        # from other machines and because some auth methods rely on the
        # server-side authentication environment
        if connector_type.remote:
            connector_resources = self.zen_store.verify_service_connector(
                service_connector_id=service_connector.id,
                resource_type=resource_type,
                resource_id=resource_id,
                list_resources=list_resources,
            )
        else:
            connector_instance = (
                service_connector_registry.instantiate_connector(
                    model=service_connector
                )
            )
            connector_resources = connector_instance.verify(
                resource_type=resource_type,
                resource_id=resource_id,
                list_resources=list_resources,
            )

        if connector_resources.error:
            raise AuthorizationException(connector_resources.error)

        return connector_resources

    def login_service_connector(
        self,
        name_id_or_prefix: Union[UUID, str],
        resource_type: Optional[str] = None,
        resource_id: Optional[str] = None,
        **kwargs: Any,
    ) -> "ServiceConnector":
        """Use a service connector to authenticate a local client/SDK.

        Args:
            name_id_or_prefix: The name, id or prefix of the service connector
                to use.
            resource_type: The type of the resource to connect to. If not
                provided, the resource type from the service connector
                configuration will be used.
            resource_id: The ID of a particular resource instance to configure
                the local client to connect to. If the connector instance is
                already configured with a resource ID that is not the same or
                equivalent to the one requested, a `ValueError` exception is
                raised. May be omitted for connectors and resource types that do
                not support multiple resource instances.
            kwargs: Additional implementation specific keyword arguments to use
                to configure the client.

        Returns:
            The service connector client instance that was used to configure the
            local client.
        """
        connector_client = self.get_service_connector_client(
            name_id_or_prefix=name_id_or_prefix,
            resource_type=resource_type,
            resource_id=resource_id,
            verify=False,
        )

        connector_client.configure_local_client(
            **kwargs,
        )

        return connector_client

    def get_service_connector_client(
        self,
        name_id_or_prefix: Union[UUID, str],
        resource_type: Optional[str] = None,
        resource_id: Optional[str] = None,
        verify: bool = False,
    ) -> "ServiceConnector":
        """Get the client side of a service connector instance to use with a local client.

        Args:
            name_id_or_prefix: The name, id or prefix of the service connector
                to use.
            resource_type: The type of the resource to connect to. If not
                provided, the resource type from the service connector
                configuration will be used.
            resource_id: The ID of a particular resource instance to configure
                the local client to connect to. If the connector instance is
                already configured with a resource ID that is not the same or
                equivalent to the one requested, a `ValueError` exception is
                raised. May be omitted for connectors and resource types that do
                not support multiple resource instances.
            verify: Whether to verify that the service connector configuration
                and credentials can be used to gain access to the resource.

        Returns:
            The client side of the indicated service connector instance that can
            be used to connect to the resource locally.
        """
        from zenml.service_connectors.service_connector_registry import (
            service_connector_registry,
        )

        # Get the service connector model
        service_connector = self.get_service_connector(
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
        )

        connector_type = self.get_service_connector_type(
            service_connector.type
        )

        # Prefer to fetch the connector client from the server if the
        # implementation if available there, because some auth methods rely on
        # the server-side authentication environment
        if connector_type.remote:
            connector_client_model = (
                self.zen_store.get_service_connector_client(
                    service_connector_id=service_connector.id,
                    resource_type=resource_type,
                    resource_id=resource_id,
                )
            )

            connector_client = (
                service_connector_registry.instantiate_connector(
                    model=connector_client_model
                )
            )

            if verify:
                # Verify the connector client on the local machine, because the
                # server-side implementation may not be able to do so
                connector_client.verify()
        else:
            connector_instance = (
                service_connector_registry.instantiate_connector(
                    model=service_connector
                )
            )

            # Fetch the connector client
            connector_client = connector_instance.get_connector_client(
                resource_type=resource_type,
                resource_id=resource_id,
            )

        return connector_client

    def list_service_connector_resources(
        self,
        connector_type: Optional[str] = None,
        resource_type: Optional[str] = None,
        resource_id: Optional[str] = None,
    ) -> List[ServiceConnectorResourcesModel]:
        """List resources that can be accessed by service connectors.

        Args:
            connector_type: The type of service connector to filter by.
            resource_type: The type of resource to filter by.
            resource_id: The ID of a particular resource instance to filter by.

        Returns:
            The matching list of resources that available service
            connectors have access to.
        """
        return self.zen_store.list_service_connector_resources(
            ServiceConnectorFilter(
                connector_type=connector_type,
                resource_type=resource_type,
                resource_id=resource_id,
            )
        )

    def list_service_connector_types(
        self,
        connector_type: Optional[str] = None,
        resource_type: Optional[str] = None,
        auth_method: Optional[str] = None,
    ) -> List[ServiceConnectorTypeModel]:
        """Get a list of service connector types.

        Args:
            connector_type: Filter by connector type.
            resource_type: Filter by resource type.
            auth_method: Filter by authentication method.

        Returns:
            List of service connector types.
        """
        return self.zen_store.list_service_connector_types(
            connector_type=connector_type,
            resource_type=resource_type,
            auth_method=auth_method,
        )

    def get_service_connector_type(
        self,
        connector_type: str,
    ) -> ServiceConnectorTypeModel:
        """Returns the requested service connector type.

        Args:
            connector_type: the service connector type identifier.

        Returns:
            The requested service connector type.
        """
        return self.zen_store.get_service_connector_type(
            connector_type=connector_type,
        )

    #########
    # Model
    #########

    def create_model(
        self,
        name: str,
        license: Optional[str] = None,
        description: Optional[str] = None,
        audience: Optional[str] = None,
        use_cases: Optional[str] = None,
        limitations: Optional[str] = None,
        trade_offs: Optional[str] = None,
        ethics: Optional[str] = None,
        tags: Optional[List[str]] = None,
        save_models_to_registry: bool = True,
    ) -> ModelResponse:
        """Creates a new model in Model Control Plane.

        Args:
            name: The name of the model.
            license: The license under which the model is created.
            description: The description of the model.
            audience: The target audience of the model.
            use_cases: The use cases of the model.
            limitations: The known limitations of the model.
            trade_offs: The tradeoffs of the model.
            ethics: The ethical implications of the model.
            tags: Tags associated with the model.
            save_models_to_registry: Whether to save the model to the
                registry.

        Returns:
            The newly created model.
        """
        return self.zen_store.create_model(
            model=ModelRequest(
                name=name,
                license=license,
                description=description,
                audience=audience,
                use_cases=use_cases,
                limitations=limitations,
                trade_offs=trade_offs,
                ethics=ethics,
                tags=tags,
                project=self.active_project.id,
                save_models_to_registry=save_models_to_registry,
            )
        )

    def delete_model(
        self,
        model_name_or_id: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
    ) -> None:
        """Deletes a model from Model Control Plane.

        Args:
            model_name_or_id: name or id of the model to be deleted.
            project: The project name/ID to filter by.
        """
        model = self.get_model(
            model_name_or_id=model_name_or_id, project=project
        )
        self.zen_store.delete_model(model_id=model.id)

    def update_model(
        self,
        model_name_or_id: Union[str, UUID],
        name: Optional[str] = None,
        license: Optional[str] = None,
        description: Optional[str] = None,
        audience: Optional[str] = None,
        use_cases: Optional[str] = None,
        limitations: Optional[str] = None,
        trade_offs: Optional[str] = None,
        ethics: Optional[str] = None,
        add_tags: Optional[List[str]] = None,
        remove_tags: Optional[List[str]] = None,
        save_models_to_registry: Optional[bool] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> ModelResponse:
        """Updates an existing model in Model Control Plane.

        Args:
            model_name_or_id: name or id of the model to be deleted.
            name: The name of the model.
            license: The license under which the model is created.
            description: The description of the model.
            audience: The target audience of the model.
            use_cases: The use cases of the model.
            limitations: The known limitations of the model.
            trade_offs: The tradeoffs of the model.
            ethics: The ethical implications of the model.
            add_tags: Tags to add to the model.
            remove_tags: Tags to remove from to the model.
            save_models_to_registry: Whether to save the model to the
                registry.
            project: The project name/ID to filter by.

        Returns:
            The updated model.
        """
        model = self.get_model(
            model_name_or_id=model_name_or_id, project=project
        )
        return self.zen_store.update_model(
            model_id=model.id,
            model_update=ModelUpdate(
                name=name,
                license=license,
                description=description,
                audience=audience,
                use_cases=use_cases,
                limitations=limitations,
                trade_offs=trade_offs,
                ethics=ethics,
                add_tags=add_tags,
                remove_tags=remove_tags,
                save_models_to_registry=save_models_to_registry,
            ),
        )

    def get_model(
        self,
        model_name_or_id: Union[str, UUID],
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
        bypass_lazy_loader: bool = False,
    ) -> ModelResponse:
        """Get an existing model from Model Control Plane.

        Args:
            model_name_or_id: name or id of the model to be retrieved.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            bypass_lazy_loader: Whether to bypass the lazy loader.

        Returns:
            The model of interest.
        """
        if not bypass_lazy_loader:
            if cll := client_lazy_loader(
                "get_model",
                model_name_or_id=model_name_or_id,
                hydrate=hydrate,
                project=project,
            ):
                return cll  # type: ignore[return-value]

        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_model,
            list_method=self.list_models,
            name_id_or_prefix=model_name_or_id,
            project=project,
            hydrate=hydrate,
        )

    def list_models(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        id: Optional[Union[UUID, str]] = None,
        user: Optional[Union[UUID, str]] = None,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = False,
        tag: Optional[str] = None,
        tags: Optional[List[str]] = None,
    ) -> Page[ModelResponse]:
        """Get models by filter from Model Control Plane.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: The name of the model to filter by.
            id: The id of the model to filter by.
            user: Filter by user name/ID.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            tag: The tag of the model to filter by.
            tags: Tags to filter by.

        Returns:
            A page object with all models.
        """
        filter = ModelFilter(
            name=name,
            id=id,
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            created=created,
            updated=updated,
            tag=tag,
            tags=tags,
            user=user,
            project=project or self.active_project.id,
        )

        return self.zen_store.list_models(
            model_filter_model=filter, hydrate=hydrate
        )

    #################
    # Model Versions
    #################

    def create_model_version(
        self,
        model_name_or_id: Union[str, UUID],
        name: Optional[str] = None,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> ModelVersionResponse:
        """Creates a new model version in Model Control Plane.

        Args:
            model_name_or_id: the name or id of the model to create model
                version in.
            name: the name of the Model Version to be created.
            description: the description of the Model Version to be created.
            tags: Tags associated with the model.
            project: The project name/ID to filter by.

        Returns:
            The newly created model version.
        """
        model = self.get_model(
            model_name_or_id=model_name_or_id, project=project
        )
        return self.zen_store.create_model_version(
            model_version=ModelVersionRequest(
                name=name,
                description=description,
                project=model.project.id,
                model=model.id,
                tags=tags,
            )
        )

    def delete_model_version(
        self,
        model_version_id: UUID,
    ) -> None:
        """Deletes a model version from Model Control Plane.

        Args:
            model_version_id: Id of the model version to be deleted.
        """
        self.zen_store.delete_model_version(
            model_version_id=model_version_id,
        )

    def get_model_version(
        self,
        model_name_or_id: Optional[Union[str, UUID]] = None,
        model_version_name_or_number_or_id: Optional[
            Union[str, int, ModelStages, UUID]
        ] = None,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> ModelVersionResponse:
        """Get an existing model version from Model Control Plane.

        Args:
            model_name_or_id: name or id of the model containing the model
                version.
            model_version_name_or_number_or_id: name, id, stage or number of
                the model version to be retrieved. If skipped - latest version
                is retrieved.
            project: The project name/ID to filter by.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The model version of interest.

        Raises:
            RuntimeError: In case method inputs don't adhere to restrictions.
            KeyError: In case no model version with the identifiers exists.
            ValueError: In case retrieval is attempted using non UUID model version
                identifier and no model identifier provided.
        """
        if (
            not is_valid_uuid(model_version_name_or_number_or_id)
            and model_name_or_id is None
        ):
            raise ValueError(
                "No model identifier provided and model version identifier "
                f"`{model_version_name_or_number_or_id}` is not a valid UUID."
            )
        if cll := client_lazy_loader(
            "get_model_version",
            model_name_or_id=model_name_or_id,
            model_version_name_or_number_or_id=model_version_name_or_number_or_id,
            project=project,
            hydrate=hydrate,
        ):
            return cll  # type: ignore[return-value]

        if model_version_name_or_number_or_id is None:
            model_version_name_or_number_or_id = ModelStages.LATEST

        if isinstance(model_version_name_or_number_or_id, UUID):
            return self.zen_store.get_model_version(
                model_version_id=model_version_name_or_number_or_id,
                hydrate=hydrate,
            )
        elif isinstance(model_version_name_or_number_or_id, int):
            model_versions = self.zen_store.list_model_versions(
                model_version_filter_model=ModelVersionFilter(
                    model=model_name_or_id,
                    number=model_version_name_or_number_or_id,
                    project=project or self.active_project.id,
                ),
                hydrate=hydrate,
            ).items
        elif isinstance(model_version_name_or_number_or_id, str):
            if model_version_name_or_number_or_id == ModelStages.LATEST:
                model_versions = self.zen_store.list_model_versions(
                    model_version_filter_model=ModelVersionFilter(
                        model=model_name_or_id,
                        sort_by=f"{SorterOps.DESCENDING}:number",
                        project=project or self.active_project.id,
                    ),
                    hydrate=hydrate,
                ).items

                if len(model_versions) > 0:
                    model_versions = [model_versions[0]]
                else:
                    model_versions = []
            elif model_version_name_or_number_or_id in ModelStages.values():
                model_versions = self.zen_store.list_model_versions(
                    model_version_filter_model=ModelVersionFilter(
                        model=model_name_or_id,
                        stage=model_version_name_or_number_or_id,
                        project=project or self.active_project.id,
                    ),
                    hydrate=hydrate,
                ).items
            else:
                model_versions = self.zen_store.list_model_versions(
                    model_version_filter_model=ModelVersionFilter(
                        model=model_name_or_id,
                        name=model_version_name_or_number_or_id,
                        project=project or self.active_project.id,
                    ),
                    hydrate=hydrate,
                ).items
        else:
            raise RuntimeError(
                f"The model version identifier "
                f"`{model_version_name_or_number_or_id}` is not"
                f"of the correct type."
            )

        if len(model_versions) == 1:
            return model_versions[0]
        elif len(model_versions) == 0:
            raise KeyError(
                f"No model version found for model "
                f"`{model_name_or_id}` with version identifier "
                f"`{model_version_name_or_number_or_id}`."
            )
        else:
            raise RuntimeError(
                f"The model version identifier "
                f"`{model_version_name_or_number_or_id}` is not"
                f"unique for model `{model_name_or_id}`."
            )

    def list_model_versions(
        self,
        model_name_or_id: Union[str, UUID],
        sort_by: str = "number",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        id: Optional[Union[UUID, str]] = None,
        number: Optional[int] = None,
        stage: Optional[Union[str, ModelStages]] = None,
        run_metadata: Optional[List[str]] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
        tag: Optional[str] = None,
        tags: Optional[List[str]] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> Page[ModelVersionResponse]:
        """Get model versions by filter from Model Control Plane.

        Args:
            model_name_or_id: name or id of the model containing the model
                version.
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: name or id of the model version.
            id: id of the model version.
            number: number of the model version.
            stage: stage of the model version.
            run_metadata: run metadata of the model version.
            user: Filter by user name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            tag: The tag to filter by.
            tags: Tags to filter by.
            project: The project name/ID to filter by.

        Returns:
            A page object with all model versions.
        """
        model_version_filter_model = ModelVersionFilter(
            page=page,
            size=size,
            sort_by=sort_by,
            logical_operator=logical_operator,
            created=created,
            updated=updated,
            name=name,
            id=id,
            number=number,
            stage=stage,
            run_metadata=run_metadata,
            tag=tag,
            tags=tags,
            user=user,
            model=model_name_or_id,
            project=project or self.active_project.id,
        )

        return self.zen_store.list_model_versions(
            model_version_filter_model=model_version_filter_model,
            hydrate=hydrate,
        )

    def update_model_version(
        self,
        model_name_or_id: Union[str, UUID],
        version_name_or_id: Union[str, UUID],
        stage: Optional[Union[str, ModelStages]] = None,
        force: bool = False,
        name: Optional[str] = None,
        description: Optional[str] = None,
        add_tags: Optional[List[str]] = None,
        remove_tags: Optional[List[str]] = None,
        project: Optional[Union[str, UUID]] = None,
    ) -> ModelVersionResponse:
        """Get all model versions by filter.

        Args:
            model_name_or_id: The name or ID of the model containing model version.
            version_name_or_id: The name or ID of model version to be updated.
            stage: Target model version stage to be set.
            force: Whether existing model version in target stage should be
                silently archived or an error should be raised.
            name: Target model version name to be set.
            description: Target model version description to be set.
            add_tags: Tags to add to the model version.
            remove_tags: Tags to remove from to the model version.
            project: The project name/ID to filter by.

        Returns:
            An updated model version.
        """
        if not is_valid_uuid(model_name_or_id):
            model = self.get_model(model_name_or_id, project=project)
            model_name_or_id = model.id
            project = project or model.project.id
        if not is_valid_uuid(version_name_or_id):
            version_name_or_id = self.get_model_version(
                model_name_or_id, version_name_or_id, project=project
            ).id

        return self.zen_store.update_model_version(
            model_version_id=version_name_or_id,  # type:ignore[arg-type]
            model_version_update_model=ModelVersionUpdate(
                stage=stage,
                force=force,
                name=name,
                description=description,
                add_tags=add_tags,
                remove_tags=remove_tags,
            ),
        )

    #################################################
    # Model Versions Artifacts
    #################################################

    def list_model_version_artifact_links(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        model_version_id: Optional[Union[UUID, str]] = None,
        artifact_version_id: Optional[Union[UUID, str]] = None,
        artifact_name: Optional[str] = None,
        only_data_artifacts: Optional[bool] = None,
        only_model_artifacts: Optional[bool] = None,
        only_deployment_artifacts: Optional[bool] = None,
        has_custom_name: Optional[bool] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[ModelVersionArtifactResponse]:
        """Get model version to artifact links by filter in Model Control Plane.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            model_version_id: Use the model version id for filtering
            artifact_version_id: Use the artifact id for filtering
            artifact_name: Use the artifact name for filtering
            only_data_artifacts: Use to filter by data artifacts
            only_model_artifacts: Use to filter by model artifacts
            only_deployment_artifacts: Use to filter by deployment artifacts
            has_custom_name: Filter artifacts with/without custom names.
            user: Filter by user name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of all model version to artifact links.
        """
        return self.zen_store.list_model_version_artifact_links(
            ModelVersionArtifactFilter(
                sort_by=sort_by,
                logical_operator=logical_operator,
                page=page,
                size=size,
                created=created,
                updated=updated,
                model_version_id=model_version_id,
                artifact_version_id=artifact_version_id,
                artifact_name=artifact_name,
                only_data_artifacts=only_data_artifacts,
                only_model_artifacts=only_model_artifacts,
                only_deployment_artifacts=only_deployment_artifacts,
                has_custom_name=has_custom_name,
                user=user,
            ),
            hydrate=hydrate,
        )

    def delete_model_version_artifact_link(
        self, model_version_id: UUID, artifact_version_id: UUID
    ) -> None:
        """Delete model version to artifact link in Model Control Plane.

        Args:
            model_version_id: The id of the model version holding the link.
            artifact_version_id: The id of the artifact version to be deleted.

        Raises:
            RuntimeError: If more than one artifact link is found for given filters.
        """
        artifact_links = self.list_model_version_artifact_links(
            model_version_id=model_version_id,
            artifact_version_id=artifact_version_id,
        )
        if artifact_links.items:
            if artifact_links.total > 1:
                raise RuntimeError(
                    "More than one artifact link found for give model version "
                    f"`{model_version_id}` and artifact version "
                    f"`{artifact_version_id}`. This should not be happening and "
                    "might indicate a corrupted state of your ZenML database. "
                    "Please seek support via Community Slack."
                )
            self.zen_store.delete_model_version_artifact_link(
                model_version_id=model_version_id,
                model_version_artifact_link_name_or_id=artifact_links.items[
                    0
                ].id,
            )

    def delete_all_model_version_artifact_links(
        self, model_version_id: UUID, only_links: bool
    ) -> None:
        """Delete all model version to artifact links in Model Control Plane.

        Args:
            model_version_id: The id of the model version holding the link.
            only_links: If true, only delete the link to the artifact.
        """
        self.zen_store.delete_all_model_version_artifact_links(
            model_version_id, only_links
        )

    #################################################
    # Model Versions Pipeline Runs
    #
    # Only view capabilities are exposed via client.
    #################################################

    def list_model_version_pipeline_run_links(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        model_version_id: Optional[Union[UUID, str]] = None,
        pipeline_run_id: Optional[Union[UUID, str]] = None,
        pipeline_run_name: Optional[str] = None,
        user: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[ModelVersionPipelineRunResponse]:
        """Get all model version to pipeline run links by filter.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            model_version_id: Use the model version id for filtering
            pipeline_run_id: Use the pipeline run id for filtering
            pipeline_run_name: Use the pipeline run name for filtering
            user: Filter by user name or ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response

        Returns:
            A page of all model version to pipeline run links.
        """
        return self.zen_store.list_model_version_pipeline_run_links(
            ModelVersionPipelineRunFilter(
                sort_by=sort_by,
                logical_operator=logical_operator,
                page=page,
                size=size,
                created=created,
                updated=updated,
                model_version_id=model_version_id,
                pipeline_run_id=pipeline_run_id,
                pipeline_run_name=pipeline_run_name,
                user=user,
            ),
            hydrate=hydrate,
        )

    # --------------------------- Authorized Devices ---------------------------

    def list_authorized_devices(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        expires: Optional[Union[datetime, str]] = None,
        client_id: Union[UUID, str, None] = None,
        status: Union[OAuthDeviceStatus, str, None] = None,
        trusted_device: Union[bool, str, None] = None,
        user: Optional[Union[UUID, str]] = None,
        failed_auth_attempts: Union[int, str, None] = None,
        last_login: Optional[Union[datetime, str, None]] = None,
        hydrate: bool = False,
    ) -> Page[OAuthDeviceResponse]:
        """List all authorized devices.

        Args:
            sort_by: The column to sort by.
            page: The page of items.
            size: The maximum size of all pages.
            logical_operator: Which logical operator to use [and, or].
            id: Use the id of the code repository to filter by.
            created: Use to filter by time of creation.
            updated: Use the last updated date for filtering.
            expires: Use the expiration date for filtering.
            client_id: Use the client id for filtering.
            status: Use the status for filtering.
            user: Filter by user name/ID.
            trusted_device: Use the trusted device flag for filtering.
            failed_auth_attempts: Use the failed auth attempts for filtering.
            last_login: Use the last login date for filtering.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of authorized devices matching the filter.
        """
        filter_model = OAuthDeviceFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            expires=expires,
            client_id=client_id,
            user=user,
            status=status,
            trusted_device=trusted_device,
            failed_auth_attempts=failed_auth_attempts,
            last_login=last_login,
        )
        return self.zen_store.list_authorized_devices(
            filter_model=filter_model,
            hydrate=hydrate,
        )

    def get_authorized_device(
        self,
        id_or_prefix: Union[UUID, str],
        allow_id_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> OAuthDeviceResponse:
        """Get an authorized device by id or prefix.

        Args:
            id_or_prefix: The ID or ID prefix of the authorized device.
            allow_id_prefix_match: If True, allow matching by ID prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The requested authorized device.

        Raises:
            KeyError: If no authorized device is found with the given ID or
                prefix.
        """
        if isinstance(id_or_prefix, str):
            try:
                id_or_prefix = UUID(id_or_prefix)
            except ValueError:
                if not allow_id_prefix_match:
                    raise KeyError(
                        f"No authorized device found with id or prefix "
                        f"'{id_or_prefix}'."
                    )
        if isinstance(id_or_prefix, UUID):
            return self.zen_store.get_authorized_device(
                id_or_prefix, hydrate=hydrate
            )
        return self._get_entity_by_prefix(
            get_method=self.zen_store.get_authorized_device,
            list_method=self.list_authorized_devices,
            partial_id_or_name=id_or_prefix,
            allow_name_prefix_match=False,
            hydrate=hydrate,
        )

    def update_authorized_device(
        self,
        id_or_prefix: Union[UUID, str],
        locked: Optional[bool] = None,
    ) -> OAuthDeviceResponse:
        """Update an authorized device.

        Args:
            id_or_prefix: The ID or ID prefix of the authorized device.
            locked: Whether to lock or unlock the authorized device.

        Returns:
            The updated authorized device.
        """
        device = self.get_authorized_device(
            id_or_prefix=id_or_prefix, allow_id_prefix_match=False
        )
        return self.zen_store.update_authorized_device(
            device_id=device.id,
            update=OAuthDeviceUpdate(
                locked=locked,
            ),
        )

    def delete_authorized_device(
        self,
        id_or_prefix: Union[str, UUID],
    ) -> None:
        """Delete an authorized device.

        Args:
            id_or_prefix: The ID or ID prefix of the authorized device.
        """
        device = self.get_authorized_device(
            id_or_prefix=id_or_prefix,
            allow_id_prefix_match=False,
        )
        self.zen_store.delete_authorized_device(device.id)

    # --------------------------- Trigger Executions ---------------------------

    def get_trigger_execution(
        self,
        trigger_execution_id: UUID,
        hydrate: bool = True,
    ) -> TriggerExecutionResponse:
        """Get a trigger execution by ID.

        Args:
            trigger_execution_id: The ID of the trigger execution to get.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The trigger execution.
        """
        return self.zen_store.get_trigger_execution(
            trigger_execution_id=trigger_execution_id, hydrate=hydrate
        )

    def list_trigger_executions(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        trigger_id: Optional[UUID] = None,
        user: Optional[Union[UUID, str]] = None,
        project: Optional[Union[UUID, str]] = None,
        hydrate: bool = False,
    ) -> Page[TriggerExecutionResponse]:
        """List all trigger executions matching the given filter criteria.

        Args:
            sort_by: The column to sort by.
            page: The page of items.
            size: The maximum size of all pages.
            logical_operator: Which logical operator to use [and, or].
            trigger_id: ID of the trigger to filter by.
            user: Filter by user name/ID.
            project: Filter by project name/ID.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A list of all trigger executions matching the filter criteria.
        """
        filter_model = TriggerExecutionFilter(
            trigger_id=trigger_id,
            sort_by=sort_by,
            page=page,
            size=size,
            user=user,
            logical_operator=logical_operator,
            project=project or self.active_project.id,
        )
        return self.zen_store.list_trigger_executions(
            trigger_execution_filter_model=filter_model, hydrate=hydrate
        )

    def delete_trigger_execution(self, trigger_execution_id: UUID) -> None:
        """Delete a trigger execution.

        Args:
            trigger_execution_id: The ID of the trigger execution to delete.
        """
        self.zen_store.delete_trigger_execution(
            trigger_execution_id=trigger_execution_id
        )

    # ---- utility prefix matching get functions -----

    def _get_entity_by_id_or_name_or_prefix(
        self,
        get_method: Callable[..., AnyResponse],
        list_method: Callable[..., Page[AnyResponse]],
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> AnyResponse:
        """Fetches an entity using the id, name, or partial id/name.

        Args:
            get_method: The method to use to fetch the entity by id.
            list_method: The method to use to fetch all entities.
            name_id_or_prefix: The id, name or partial id of the entity to
                fetch.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            project: The project name/ID to filter by.

        Returns:
            The entity with the given name, id or partial id.

        Raises:
            ZenKeyError: If there is more than one entity with that name
                or id prefix.
        """
        from zenml.utils.uuid_utils import is_valid_uuid

        entity_label = get_method.__name__.replace("get_", "") + "s"

        # First interpret as full UUID
        if is_valid_uuid(name_id_or_prefix):
            return get_method(name_id_or_prefix, hydrate=hydrate)

        # If not a UUID, try to find by name
        assert not isinstance(name_id_or_prefix, UUID)
        list_kwargs: Dict[str, Any] = dict(
            name=f"equals:{name_id_or_prefix}",
            hydrate=hydrate,
        )
        scope = ""
        if project:
            scope = f"in project {project} "
            list_kwargs["project"] = project
        entity = list_method(**list_kwargs)

        # If only a single entity is found, return it
        if entity.total == 1:
            return entity.items[0]

        # If still no match, try with prefix now
        if entity.total == 0:
            return self._get_entity_by_prefix(
                get_method=get_method,
                list_method=list_method,
                partial_id_or_name=name_id_or_prefix,
                allow_name_prefix_match=allow_name_prefix_match,
                project=project,
                hydrate=hydrate,
            )

        # If more than one entity with the same name is found, raise an error.
        formatted_entity_items = [
            f"- {item.name}: (id: {item.id})\n"
            if hasattr(item, "name")
            else f"- {item.id}\n"
            for item in entity.items
        ]
        raise ZenKeyError(
            f"{entity.total} {entity_label} have been found {scope}that have "
            f"a name that matches the provided "
            f"string '{name_id_or_prefix}':\n"
            f"{formatted_entity_items}.\n"
            f"Please use the id to uniquely identify "
            f"only one of the {entity_label}s."
        )

    def _get_entity_version_by_id_or_name_or_prefix(
        self,
        get_method: Callable[..., AnyResponse],
        list_method: Callable[..., Page[AnyResponse]],
        name_id_or_prefix: Union[str, UUID],
        version: Optional[str],
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> "AnyResponse":
        from zenml.utils.uuid_utils import is_valid_uuid

        entity_label = get_method.__name__.replace("get_", "") + "s"

        if is_valid_uuid(name_id_or_prefix):
            if version:
                logger.warning(
                    "You specified both an ID as well as a version of the "
                    f"{entity_label}. Ignoring the version and fetching the "
                    f"{entity_label} by ID."
                )
            if not isinstance(name_id_or_prefix, UUID):
                name_id_or_prefix = UUID(name_id_or_prefix, version=4)

            return get_method(name_id_or_prefix, hydrate=hydrate)

        assert not isinstance(name_id_or_prefix, UUID)
        list_kwargs: Dict[str, Any] = dict(
            size=1,
            sort_by="desc:created",
            name=name_id_or_prefix,
            version=version,
            hydrate=hydrate,
        )
        scope = ""
        if project:
            scope = f" in project {project}"
            list_kwargs["project"] = project
        exact_name_matches = list_method(**list_kwargs)

        if len(exact_name_matches) == 1:
            # If the name matches exactly, use the explicitly specified version
            # or fallback to the latest if not given
            return exact_name_matches.items[0]

        partial_id_matches = list_method(
            id=f"startswith:{name_id_or_prefix}",
            hydrate=hydrate,
        )
        if partial_id_matches.total == 1:
            if version:
                logger.warning(
                    "You specified both a partial ID as well as a version of "
                    f"the {entity_label}. Ignoring the version and fetching "
                    f"the {entity_label} by partial ID."
                )
            return partial_id_matches[0]
        elif partial_id_matches.total == 0:
            raise KeyError(
                f"No {entity_label} found for name, ID or prefix "
                f"{name_id_or_prefix}{scope}."
            )
        else:
            raise ZenKeyError(
                f"{partial_id_matches.total} {entity_label} have been found"
                f"{scope} that have an id prefix that matches the provided "
                f"string '{name_id_or_prefix}':\n"
                f"{partial_id_matches.items}.\n"
                f"Please provide more characters to uniquely identify "
                f"only one of the {entity_label}s."
            )

    def _get_entity_by_prefix(
        self,
        get_method: Callable[..., AnyResponse],
        list_method: Callable[..., Page[AnyResponse]],
        partial_id_or_name: str,
        allow_name_prefix_match: bool,
        project: Optional[Union[str, UUID]] = None,
        hydrate: bool = True,
    ) -> AnyResponse:
        """Fetches an entity using a partial ID or name.

        Args:
            get_method: The method to use to fetch the entity by id.
            list_method: The method to use to fetch all entities.
            partial_id_or_name: The partial ID or name of the entity to fetch.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            project: The project name/ID to filter by.

        Returns:
            The entity with the given partial ID or name.

        Raises:
            KeyError: If no entity with the given partial ID or name is found.
            ZenKeyError: If there is more than one entity with that partial ID
                or name.
        """
        list_method_args: Dict[str, Any] = {
            "logical_operator": LogicalOperators.OR,
            "id": f"startswith:{partial_id_or_name}",
            "hydrate": hydrate,
        }
        if allow_name_prefix_match:
            list_method_args["name"] = f"startswith:{partial_id_or_name}"
        scope = ""
        if project:
            scope = f"in project {project} "
            list_method_args["project"] = project

        entity = list_method(**list_method_args)

        # If only a single entity is found, return it.
        if entity.total == 1:
            return entity.items[0]

        irregular_plurals = {"code_repository": "code_repositories"}
        entity_label = irregular_plurals.get(
            get_method.__name__.replace("get_", ""),
            get_method.__name__.replace("get_", "") + "s",
        )

        prefix_description = (
            "a name/ID prefix" if allow_name_prefix_match else "an ID prefix"
        )
        # If no entity is found, raise an error.
        if entity.total == 0:
            raise KeyError(
                f"No {entity_label} have been found{scope} that have "
                f"{prefix_description} that matches the provided string "
                f"'{partial_id_or_name}'."
            )

        # If more than one entity is found, raise an error.
        ambiguous_entities: List[str] = []
        for model in entity.items:
            model_name = getattr(model, "name", None)
            if model_name:
                ambiguous_entities.append(f"{model_name}: {model.id}")
            else:
                ambiguous_entities.append(str(model.id))
        raise ZenKeyError(
            f"{entity.total} {entity_label} have been found{scope} that have "
            f"{prefix_description} that matches the provided "
            f"string '{partial_id_or_name}':\n"
            f"{ambiguous_entities}.\n"
            f"Please provide more characters to uniquely identify "
            f"only one of the {entity_label}s."
        )

    # ---------------------------- Service Accounts ----------------------------

    def create_service_account(
        self,
        name: str,
        description: str = "",
    ) -> ServiceAccountResponse:
        """Create a new service account.

        Args:
            name: The name of the service account.
            description: The description of the service account.

        Returns:
            The created service account.
        """
        service_account = ServiceAccountRequest(
            name=name, description=description, active=True
        )
        created_service_account = self.zen_store.create_service_account(
            service_account=service_account
        )

        return created_service_account

    def get_service_account(
        self,
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> ServiceAccountResponse:
        """Gets a service account.

        Args:
            name_id_or_prefix: The name or ID of the service account.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The ServiceAccount
        """
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_service_account,
            list_method=self.list_service_accounts,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )

    def list_service_accounts(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        description: Optional[str] = None,
        active: Optional[bool] = None,
        hydrate: bool = False,
    ) -> Page[ServiceAccountResponse]:
        """List all service accounts.

        Args:
            sort_by: The column to sort by
            page: The page of items
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or]
            id: Use the id of stacks to filter by.
            created: Use to filter by time of creation
            updated: Use the last updated date for filtering
            name: Use the service account name for filtering
            description: Use the service account description for filtering
            active: Use the service account active status for filtering
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The list of service accounts matching the filter description.
        """
        return self.zen_store.list_service_accounts(
            ServiceAccountFilter(
                sort_by=sort_by,
                page=page,
                size=size,
                logical_operator=logical_operator,
                id=id,
                created=created,
                updated=updated,
                name=name,
                description=description,
                active=active,
            ),
            hydrate=hydrate,
        )

    def update_service_account(
        self,
        name_id_or_prefix: Union[str, UUID],
        updated_name: Optional[str] = None,
        description: Optional[str] = None,
        active: Optional[bool] = None,
    ) -> ServiceAccountResponse:
        """Update a service account.

        Args:
            name_id_or_prefix: The name or ID of the service account to update.
            updated_name: The new name of the service account.
            description: The new description of the service account.
            active: The new active status of the service account.

        Returns:
            The updated service account.
        """
        service_account = self.get_service_account(
            name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
        )
        service_account_update = ServiceAccountUpdate(
            name=updated_name,
            description=description,
            active=active,
        )

        return self.zen_store.update_service_account(
            service_account_name_or_id=service_account.id,
            service_account_update=service_account_update,
        )

    def delete_service_account(
        self,
        name_id_or_prefix: Union[str, UUID],
    ) -> None:
        """Delete a service account.

        Args:
            name_id_or_prefix: The name or ID of the service account to delete.
        """
        service_account = self.get_service_account(
            name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
        )
        self.zen_store.delete_service_account(
            service_account_name_or_id=service_account.id
        )

    # -------------------------------- API Keys --------------------------------

    def create_api_key(
        self,
        service_account_name_id_or_prefix: Union[str, UUID],
        name: str,
        description: str = "",
        set_key: bool = False,
    ) -> APIKeyResponse:
        """Create a new API key and optionally set it as the active API key.

        Args:
            service_account_name_id_or_prefix: The name, ID or prefix of the
                service account to create the API key for.
            name: Name of the API key.
            description: The description of the API key.
            set_key: Whether to set the created API key as the active API key.

        Returns:
            The created API key.
        """
        service_account = self.get_service_account(
            name_id_or_prefix=service_account_name_id_or_prefix,
            allow_name_prefix_match=False,
        )
        request = APIKeyRequest(
            name=name,
            description=description,
        )
        api_key = self.zen_store.create_api_key(
            service_account_id=service_account.id, api_key=request
        )
        assert api_key.key is not None

        if set_key:
            self.set_api_key(key=api_key.key)

        return api_key

    def set_api_key(self, key: str) -> None:
        """Configure the client with an API key.

        Args:
            key: The API key to use.

        Raises:
            NotImplementedError: If the client is not connected to a ZenML
                server.
        """
        from zenml.login.credentials_store import get_credentials_store
        from zenml.zen_stores.rest_zen_store import RestZenStore

        zen_store = self.zen_store
        if not zen_store.TYPE == StoreType.REST:
            raise NotImplementedError(
                "API key configuration is only supported if connected to a "
                "ZenML server."
            )

        credentials_store = get_credentials_store()
        assert isinstance(zen_store, RestZenStore)

        credentials_store.set_api_key(server_url=zen_store.url, api_key=key)

        # Force a re-authentication to start using the new API key
        # right away.
        zen_store.authenticate(force=True)

    def list_api_keys(
        self,
        service_account_name_id_or_prefix: Union[str, UUID],
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        description: Optional[str] = None,
        active: Optional[bool] = None,
        last_login: Optional[Union[datetime, str]] = None,
        last_rotated: Optional[Union[datetime, str]] = None,
        hydrate: bool = False,
    ) -> Page[APIKeyResponse]:
        """List all API keys.

        Args:
            service_account_name_id_or_prefix: The name, ID or prefix of the
                service account to list the API keys for.
            sort_by: The column to sort by.
            page: The page of items.
            size: The maximum size of all pages.
            logical_operator: Which logical operator to use [and, or].
            id: Use the id of the API key to filter by.
            created: Use to filter by time of creation.
            updated: Use the last updated date for filtering.
            name: The name of the API key to filter by.
            description: The description of the API key to filter by.
            active: Whether the API key is active or not.
            last_login: The last time the API key was used.
            last_rotated: The last time the API key was rotated.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of API keys matching the filter description.
        """
        service_account = self.get_service_account(
            name_id_or_prefix=service_account_name_id_or_prefix,
            allow_name_prefix_match=False,
        )
        filter_model = APIKeyFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            description=description,
            active=active,
            last_login=last_login,
            last_rotated=last_rotated,
        )
        return self.zen_store.list_api_keys(
            service_account_id=service_account.id,
            filter_model=filter_model,
            hydrate=hydrate,
        )

    def get_api_key(
        self,
        service_account_name_id_or_prefix: Union[str, UUID],
        name_id_or_prefix: Union[str, UUID],
        allow_name_prefix_match: bool = True,
        hydrate: bool = True,
    ) -> APIKeyResponse:
        """Get an API key by name, id or prefix.

        Args:
            service_account_name_id_or_prefix: The name, ID or prefix of the
                service account to get the API key for.
            name_id_or_prefix: The name, ID or ID prefix of the API key.
            allow_name_prefix_match: If True, allow matching by name prefix.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The API key.
        """
        service_account = self.get_service_account(
            name_id_or_prefix=service_account_name_id_or_prefix,
            allow_name_prefix_match=False,
        )

        def get_api_key_method(
            api_key_name_or_id: str, hydrate: bool = True
        ) -> APIKeyResponse:
            return self.zen_store.get_api_key(
                service_account_id=service_account.id,
                api_key_name_or_id=api_key_name_or_id,
                hydrate=hydrate,
            )

        def list_api_keys_method(
            hydrate: bool = True,
            **filter_args: Any,
        ) -> Page[APIKeyResponse]:
            return self.list_api_keys(
                service_account_name_id_or_prefix=service_account.id,
                hydrate=hydrate,
                **filter_args,
            )

        return self._get_entity_by_id_or_name_or_prefix(
            get_method=get_api_key_method,
            list_method=list_api_keys_method,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )

    def update_api_key(
        self,
        service_account_name_id_or_prefix: Union[str, UUID],
        name_id_or_prefix: Union[UUID, str],
        name: Optional[str] = None,
        description: Optional[str] = None,
        active: Optional[bool] = None,
    ) -> APIKeyResponse:
        """Update an API key.

        Args:
            service_account_name_id_or_prefix: The name, ID or prefix of the
                service account to update the API key for.
            name_id_or_prefix: Name, ID or prefix of the API key to update.
            name: New name of the API key.
            description: New description of the API key.
            active: Whether the API key is active or not.

        Returns:
            The updated API key.
        """
        api_key = self.get_api_key(
            service_account_name_id_or_prefix=service_account_name_id_or_prefix,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
        )
        update = APIKeyUpdate(
            name=name, description=description, active=active
        )
        return self.zen_store.update_api_key(
            service_account_id=api_key.service_account.id,
            api_key_name_or_id=api_key.id,
            api_key_update=update,
        )

    def rotate_api_key(
        self,
        service_account_name_id_or_prefix: Union[str, UUID],
        name_id_or_prefix: Union[UUID, str],
        retain_period_minutes: int = 0,
        set_key: bool = False,
    ) -> APIKeyResponse:
        """Rotate an API key.

        Args:
            service_account_name_id_or_prefix: The name, ID or prefix of the
                service account to rotate the API key for.
            name_id_or_prefix: Name, ID or prefix of the API key to update.
            retain_period_minutes: The number of minutes to retain the old API
                key for. If set to 0, the old API key will be invalidated.
            set_key: Whether to set the rotated API key as the active API key.

        Returns:
            The updated API key.
        """
        api_key = self.get_api_key(
            service_account_name_id_or_prefix=service_account_name_id_or_prefix,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
        )
        rotate_request = APIKeyRotateRequest(
            retain_period_minutes=retain_period_minutes
        )
        new_key = self.zen_store.rotate_api_key(
            service_account_id=api_key.service_account.id,
            api_key_name_or_id=api_key.id,
            rotate_request=rotate_request,
        )
        assert new_key.key is not None
        if set_key:
            self.set_api_key(key=new_key.key)

        return new_key

    def delete_api_key(
        self,
        service_account_name_id_or_prefix: Union[str, UUID],
        name_id_or_prefix: Union[str, UUID],
    ) -> None:
        """Delete an API key.

        Args:
            service_account_name_id_or_prefix: The name, ID or prefix of the
                service account to delete the API key for.
            name_id_or_prefix: The name, ID or prefix of the API key.
        """
        api_key = self.get_api_key(
            service_account_name_id_or_prefix=service_account_name_id_or_prefix,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=False,
        )
        self.zen_store.delete_api_key(
            service_account_id=api_key.service_account.id,
            api_key_name_or_id=api_key.id,
        )

    # ---------------------------------- Tags ----------------------------------
    def create_tag(
        self,
        name: str,
        exclusive: bool = False,
        color: Optional[Union[str, ColorVariants]] = None,
    ) -> TagResponse:
        """Creates a new tag.

        Args:
            name: the name of the tag.
            exclusive: the boolean to decide whether the tag is an exclusive tag.
                An exclusive tag means that the tag can exist only for a single:
                    - pipeline run within the scope of a pipeline
                    - artifact version within the scope of an artifact
                    - run template
            color: the color of the tag

        Returns:
            The newly created tag.
        """
        request_model = TagRequest(name=name, exclusive=exclusive)

        if color is not None:
            request_model.color = ColorVariants(color)

        return self.zen_store.create_tag(tag=request_model)

    def delete_tag(
        self,
        tag_name_or_id: Union[str, UUID],
    ) -> None:
        """Deletes a tag.

        Args:
            tag_name_or_id: name or id of the tag to be deleted.
        """
        self.zen_store.delete_tag(
            tag_name_or_id=tag_name_or_id,
        )

    def update_tag(
        self,
        tag_name_or_id: Union[str, UUID],
        name: Optional[str] = None,
        exclusive: Optional[bool] = None,
        color: Optional[Union[str, ColorVariants]] = None,
    ) -> TagResponse:
        """Updates an existing tag.

        Args:
            tag_name_or_id: name or UUID of the tag to be updated.
            name: the name of the tag.
            exclusive: the boolean to decide whether the tag is an exclusive tag.
                An exclusive tag means that the tag can exist only for a single:
                    - pipeline run within the scope of a pipeline
                    - artifact version within the scope of an artifact
                    - run template
            color: the color of the tag

        Returns:
            The updated tag.
        """
        update_model = TagUpdate()

        if name is not None:
            update_model.name = name

        if exclusive is not None:
            update_model.exclusive = exclusive

        if color is not None:
            if isinstance(color, str):
                update_model.color = ColorVariants(color)
            else:
                update_model.color = color

        return self.zen_store.update_tag(
            tag_name_or_id=tag_name_or_id,
            tag_update_model=update_model,
        )

    def get_tag(
        self,
        tag_name_or_id: Union[str, UUID],
        hydrate: bool = True,
    ) -> TagResponse:
        """Get an existing tag.

        Args:
            tag_name_or_id: name or id of the tag to be retrieved.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            The tag of interest.
        """
        return self.zen_store.get_tag(
            tag_name_or_id=tag_name_or_id,
            hydrate=hydrate,
        )

    def list_tags(
        self,
        sort_by: str = "created",
        page: int = PAGINATION_STARTING_PAGE,
        size: int = PAGE_SIZE_DEFAULT,
        logical_operator: LogicalOperators = LogicalOperators.AND,
        id: Optional[Union[UUID, str]] = None,
        user: Optional[Union[UUID, str]] = None,
        created: Optional[Union[datetime, str]] = None,
        updated: Optional[Union[datetime, str]] = None,
        name: Optional[str] = None,
        color: Optional[Union[str, ColorVariants]] = None,
        exclusive: Optional[bool] = None,
        resource_type: Optional[Union[str, TaggableResourceTypes]] = None,
        hydrate: bool = False,
    ) -> Page[TagResponse]:
        """Get tags by filter.

        Args:
            sort_by: The column to sort by.
            page: The page of items.
            size: The maximum size of all pages
            logical_operator: Which logical operator to use [and, or].
            id: Use the id of stacks to filter by.
            user: Use the user to filter by.
            created: Use to filter by time of creation.
            updated: Use the last updated date for filtering.
            name: The name of the tag.
            color: The color of the tag.
            exclusive: Flag indicating whether the tag is exclusive.
            resource_type: Filter tags associated with a specific resource type.
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.

        Returns:
            A page of all tags.
        """
        return self.zen_store.list_tags(
            tag_filter_model=TagFilter(
                sort_by=sort_by,
                page=page,
                size=size,
                logical_operator=logical_operator,
                id=id,
                user=user,
                created=created,
                updated=updated,
                name=name,
                color=color,
                exclusive=exclusive,
                resource_type=resource_type,
            ),
            hydrate=hydrate,
        )

    def attach_tag(
        self,
        tag_name_or_id: Union[str, UUID],
        resources: List[TagResource],
    ) -> None:
        """Attach a tag to resources.

        Args:
            tag_name_or_id: name or id of the tag to be attached.
            resources: the resources to attach the tag to.
        """
        if isinstance(tag_name_or_id, str):
            try:
                tag_model = self.create_tag(name=tag_name_or_id)
            except EntityExistsError:
                tag_model = self.get_tag(tag_name_or_id)
        else:
            tag_model = self.get_tag(tag_name_or_id)

        self.zen_store.batch_create_tag_resource(
            tag_resources=[
                TagResourceRequest(
                    tag_id=tag_model.id,
                    resource_id=resource.id,
                    resource_type=resource.type,
                )
                for resource in resources
            ]
        )

    def detach_tag(
        self,
        tag_name_or_id: Union[str, UUID],
        resources: List[TagResource],
    ) -> None:
        """Detach a tag from resources.

        Args:
            tag_name_or_id: name or id of the tag to be detached.
            resources: the resources to detach the tag from.
        """
        tag_model = self.get_tag(tag_name_or_id)

        self.zen_store.batch_delete_tag_resource(
            tag_resources=[
                TagResourceRequest(
                    tag_id=tag_model.id,
                    resource_id=resource.id,
                    resource_type=resource.type,
                )
                for resource in resources
            ]
        )

active_project property

Get the currently active project of the local client.

If no active project is configured locally for the client, the active project in the global configuration is used instead.

Returns:

Type	Description
ProjectResponse	The active project.

Raises:

Type	Description
RuntimeError	If the active project is not set.

active_stack property

The active stack for this client.

Returns:

Type	Description
Stack	The active stack for this client.

active_stack_model property

The model of the active stack for this client.

If no active stack is configured locally for the client, the active stack in the global configuration is used instead.

Returns:

Type	Description
StackResponse	The model of the active stack for this client.

Raises:

Type	Description
RuntimeError	If the active stack is not set.

active_user property

Get the user that is currently in use.

Returns:

Type	Description
UserResponse	The active user.

config_directory property

The configuration directory of this client.

Returns:

Type	Description
Optional[Path]	The configuration directory of this client, or None, if the
Optional[Path]	client doesn't have an active root.

root property

The root directory of this client.

Returns:

Type	Description
Optional[Path]	The root directory of this client, or None, if the client
Optional[Path]	has not been initialized.

uses_local_configuration property

Check if the client is using a local configuration.

Returns:

Type	Description
bool	True if the client is using a local configuration,
bool	False otherwise.

zen_store property

Shortcut to return the global zen store.

Returns:

Type	Description
BaseZenStore	The global zen store.

__init__(root=None)

Initializes the global client instance.

Client is a singleton class: only one instance can exist. Calling this constructor multiple times will always yield the same instance (see the exception below).

The root argument is only meant for internal use and testing purposes. User code must never pass them to the constructor. When a custom root value is passed, an anonymous Client instance is created and returned independently of the Client singleton and that will have no effect as far as the rest of the ZenML core code is concerned.

Instead of creating a new Client instance to reflect a different repository root, to change the active root in the global Client, call Client().activate_root(<new-root>).

Parameters:

Name	Type	Description	Default
root	Optional[Path]	(internal use) custom root directory for the client. If no path is given, the repository root is determined using the environment variable ZENML_REPOSITORY_PATH (if set) and by recursively searching in the parent directories of the current working directory. Only used to initialize new clients internally.	None

Source code in src/zenml/client.py

def __init__(
    self,
    root: Optional[Path] = None,
) -> None:
    """Initializes the global client instance.

    Client is a singleton class: only one instance can exist. Calling
    this constructor multiple times will always yield the same instance (see
    the exception below).

    The `root` argument is only meant for internal use and testing purposes.
    User code must never pass them to the constructor.
    When a custom `root` value is passed, an anonymous Client instance
    is created and returned independently of the Client singleton and
    that will have no effect as far as the rest of the ZenML core code is
    concerned.

    Instead of creating a new Client instance to reflect a different
    repository root, to change the active root in the global Client,
    call `Client().activate_root(<new-root>)`.

    Args:
        root: (internal use) custom root directory for the client. If
            no path is given, the repository root is determined using the
            environment variable `ZENML_REPOSITORY_PATH` (if set) and by
            recursively searching in the parent directories of the
            current working directory. Only used to initialize new
            clients internally.
    """
    self._root: Optional[Path] = None
    self._config: Optional[ClientConfiguration] = None

    self._set_active_root(root)

activate_root(root=None)

Set the active repository root directory.

Parameters:

Name	Type	Description	Default
root	Optional[Path]	The path to set as the active repository root. If not set, the repository root is determined using the environment variable ZENML_REPOSITORY_PATH (if set) and by recursively searching in the parent directories of the current working directory.	None

Source code in src/zenml/client.py

def activate_root(self, root: Optional[Path] = None) -> None:
    """Set the active repository root directory.

    Args:
        root: The path to set as the active repository root. If not set,
            the repository root is determined using the environment
            variable `ZENML_REPOSITORY_PATH` (if set) and by recursively
            searching in the parent directories of the current working
            directory.
    """
    self._set_active_root(root)

activate_stack(stack_name_id_or_prefix)

Sets the stack as active.

Parameters:

Name	Type	Description	Default
stack_name_id_or_prefix	Union[str, UUID]	Model of the stack to activate.	required

Raises:

Type	Description
KeyError	If the stack is not registered.

Source code in src/zenml/client.py

def activate_stack(
    self, stack_name_id_or_prefix: Union[str, UUID]
) -> None:
    """Sets the stack as active.

    Args:
        stack_name_id_or_prefix: Model of the stack to activate.

    Raises:
        KeyError: If the stack is not registered.
    """
    # Make sure the stack is registered
    try:
        stack = self.get_stack(name_id_or_prefix=stack_name_id_or_prefix)
    except KeyError as e:
        raise KeyError(
            f"Stack '{stack_name_id_or_prefix}' cannot be activated since "
            f"it is not registered yet. Please register it first."
        ) from e

    if self._config:
        self._config.set_active_stack(stack=stack)

    else:
        # set the active stack globally only if the client doesn't use
        # a local configuration
        GlobalConfiguration().set_active_stack(stack=stack)

attach_tag(tag_name_or_id, resources)

Attach a tag to resources.

Parameters:

Name	Type	Description	Default
tag_name_or_id	Union[str, UUID]	name or id of the tag to be attached.	required
resources	List[TagResource]	the resources to attach the tag to.	required

Source code in src/zenml/client.py

def attach_tag(
    self,
    tag_name_or_id: Union[str, UUID],
    resources: List[TagResource],
) -> None:
    """Attach a tag to resources.

    Args:
        tag_name_or_id: name or id of the tag to be attached.
        resources: the resources to attach the tag to.
    """
    if isinstance(tag_name_or_id, str):
        try:
            tag_model = self.create_tag(name=tag_name_or_id)
        except EntityExistsError:
            tag_model = self.get_tag(tag_name_or_id)
    else:
        tag_model = self.get_tag(tag_name_or_id)

    self.zen_store.batch_create_tag_resource(
        tag_resources=[
            TagResourceRequest(
                tag_id=tag_model.id,
                resource_id=resource.id,
                resource_type=resource.type,
            )
            for resource in resources
        ]
    )

backup_secrets(ignore_errors=True, delete_secrets=False)

Backs up all secrets to the configured backup secrets store.

Parameters:

Name	Type	Description	Default
ignore_errors	bool	Whether to ignore individual errors during the backup process and attempt to backup all secrets.	True
delete_secrets	bool	Whether to delete the secrets that have been successfully backed up from the primary secrets store. Setting this flag effectively moves all secrets from the primary secrets store to the backup secrets store.	False

Source code in src/zenml/client.py

def backup_secrets(
    self,
    ignore_errors: bool = True,
    delete_secrets: bool = False,
) -> None:
    """Backs up all secrets to the configured backup secrets store.

    Args:
        ignore_errors: Whether to ignore individual errors during the backup
            process and attempt to backup all secrets.
        delete_secrets: Whether to delete the secrets that have been
            successfully backed up from the primary secrets store. Setting
            this flag effectively moves all secrets from the primary secrets
            store to the backup secrets store.
    """
    self.zen_store.backup_secrets(
        ignore_errors=ignore_errors, delete_secrets=delete_secrets
    )

create_action(name, flavor, action_type, configuration, service_account_id, auth_window=None, description='')

Create an action.

Parameters:

Name	Type	Description	Default
name	str	The name of the action.	required
flavor	str	The flavor of the action,	required
action_type	PluginSubType	The action subtype.	required
configuration	Dict[str, Any]	The action configuration.	required
service_account_id	UUID	The service account that is used to execute the action.	required
auth_window	Optional[int]	The time window in minutes for which the service account is authorized to execute the action. Set this to 0 to authorize the service account indefinitely (not recommended).	None
description	str	The description of the action.	''

Returns:

Type	Description
ActionResponse	The created action

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def create_action(
    self,
    name: str,
    flavor: str,
    action_type: PluginSubType,
    configuration: Dict[str, Any],
    service_account_id: UUID,
    auth_window: Optional[int] = None,
    description: str = "",
) -> ActionResponse:
    """Create an action.

    Args:
        name: The name of the action.
        flavor: The flavor of the action,
        action_type: The action subtype.
        configuration: The action configuration.
        service_account_id: The service account that is used to execute the
            action.
        auth_window: The time window in minutes for which the service
            account is authorized to execute the action. Set this to 0 to
            authorize the service account indefinitely (not recommended).
        description: The description of the action.

    Returns:
        The created action
    """
    action = ActionRequest(
        name=name,
        description=description,
        flavor=flavor,
        plugin_subtype=action_type,
        configuration=configuration,
        service_account_id=service_account_id,
        auth_window=auth_window,
        project=self.active_project.id,
    )

    return self.zen_store.create_action(action=action)

create_api_key(service_account_name_id_or_prefix, name, description='', set_key=False)

Create a new API key and optionally set it as the active API key.

Parameters:

Name	Type	Description	Default
service_account_name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the service account to create the API key for.	required
name	str	Name of the API key.	required
description	str	The description of the API key.	''
set_key	bool	Whether to set the created API key as the active API key.	False

Returns:

Type	Description
APIKeyResponse	The created API key.

Source code in src/zenml/client.py

def create_api_key(
    self,
    service_account_name_id_or_prefix: Union[str, UUID],
    name: str,
    description: str = "",
    set_key: bool = False,
) -> APIKeyResponse:
    """Create a new API key and optionally set it as the active API key.

    Args:
        service_account_name_id_or_prefix: The name, ID or prefix of the
            service account to create the API key for.
        name: Name of the API key.
        description: The description of the API key.
        set_key: Whether to set the created API key as the active API key.

    Returns:
        The created API key.
    """
    service_account = self.get_service_account(
        name_id_or_prefix=service_account_name_id_or_prefix,
        allow_name_prefix_match=False,
    )
    request = APIKeyRequest(
        name=name,
        description=description,
    )
    api_key = self.zen_store.create_api_key(
        service_account_id=service_account.id, api_key=request
    )
    assert api_key.key is not None

    if set_key:
        self.set_api_key(key=api_key.key)

    return api_key

create_code_repository(name, config, source, description=None, logo_url=None)

Create a new code repository.

Parameters:

Name	Type	Description	Default
name	str	Name of the code repository.	required
config	Dict[str, Any]	The configuration for the code repository.	required
source	Source	The code repository implementation source.	required
description	Optional[str]	The code repository description.	None
logo_url	Optional[str]	URL of a logo (png, jpg or svg) for the code repository.	None

Returns:

Type	Description
CodeRepositoryResponse	The created code repository.

Source code in src/zenml/client.py

def create_code_repository(
    self,
    name: str,
    config: Dict[str, Any],
    source: Source,
    description: Optional[str] = None,
    logo_url: Optional[str] = None,
) -> CodeRepositoryResponse:
    """Create a new code repository.

    Args:
        name: Name of the code repository.
        config: The configuration for the code repository.
        source: The code repository implementation source.
        description: The code repository description.
        logo_url: URL of a logo (png, jpg or svg) for the code repository.

    Returns:
        The created code repository.
    """
    self._validate_code_repository_config(source=source, config=config)
    repo_request = CodeRepositoryRequest(
        project=self.active_project.id,
        name=name,
        config=config,
        source=source,
        description=description,
        logo_url=logo_url,
    )
    return self.zen_store.create_code_repository(
        code_repository=repo_request
    )

create_event_source(name, configuration, flavor, event_source_subtype, description='')

Registers an event source.

Parameters:

Name	Type	Description	Default
name	str	The name of the event source to create.	required
configuration	Dict[str, Any]	Configuration for this event source.	required
flavor	str	The flavor of event source.	required
event_source_subtype	PluginSubType	The event source subtype.	required
description	str	The description of the event source.	''

Returns:

Type	Description
EventSourceResponse	The model of the registered event source.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def create_event_source(
    self,
    name: str,
    configuration: Dict[str, Any],
    flavor: str,
    event_source_subtype: PluginSubType,
    description: str = "",
) -> EventSourceResponse:
    """Registers an event source.

    Args:
        name: The name of the event source to create.
        configuration: Configuration for this event source.
        flavor: The flavor of event source.
        event_source_subtype: The event source subtype.
        description: The description of the event source.

    Returns:
        The model of the registered event source.
    """
    event_source = EventSourceRequest(
        name=name,
        configuration=configuration,
        description=description,
        flavor=flavor,
        plugin_type=PluginType.EVENT_SOURCE,
        plugin_subtype=event_source_subtype,
        project=self.active_project.id,
    )

    return self.zen_store.create_event_source(event_source=event_source)

create_flavor(source, component_type)

Creates a new flavor.

Parameters:

Name	Type	Description	Default
source	str	The flavor to create.	required
component_type	StackComponentType	The type of the flavor.	required

Returns:

Type	Description
FlavorResponse	The created flavor (in model form).

Raises:

Type	Description
ValueError	in case the config_schema of the flavor is too large.

Source code in src/zenml/client.py

def create_flavor(
    self,
    source: str,
    component_type: StackComponentType,
) -> FlavorResponse:
    """Creates a new flavor.

    Args:
        source: The flavor to create.
        component_type: The type of the flavor.

    Returns:
        The created flavor (in model form).

    Raises:
        ValueError: in case the config_schema of the flavor is too large.
    """
    from zenml.stack.flavor import validate_flavor_source

    flavor = validate_flavor_source(
        source=source, component_type=component_type
    )()

    if len(flavor.config_schema) > TEXT_FIELD_MAX_LENGTH:
        raise ValueError(
            "Json representation of configuration schema"
            "exceeds max length. This could be caused by an"
            "overly long docstring on the flavors "
            "configuration class' docstring."
        )

    flavor_request = flavor.to_model(integration="custom", is_custom=True)
    return self.zen_store.create_flavor(flavor=flavor_request)

create_model(name, license=None, description=None, audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=None, save_models_to_registry=True)

Creates a new model in Model Control Plane.

Parameters:

Name	Type	Description	Default
name	str	The name of the model.	required
license	Optional[str]	The license under which the model is created.	None
description	Optional[str]	The description of the model.	None
audience	Optional[str]	The target audience of the model.	None
use_cases	Optional[str]	The use cases of the model.	None
limitations	Optional[str]	The known limitations of the model.	None
trade_offs	Optional[str]	The tradeoffs of the model.	None
ethics	Optional[str]	The ethical implications of the model.	None
tags	Optional[List[str]]	Tags associated with the model.	None
save_models_to_registry	bool	Whether to save the model to the registry.	True

Returns:

Type	Description
ModelResponse	The newly created model.

Source code in src/zenml/client.py

def create_model(
    self,
    name: str,
    license: Optional[str] = None,
    description: Optional[str] = None,
    audience: Optional[str] = None,
    use_cases: Optional[str] = None,
    limitations: Optional[str] = None,
    trade_offs: Optional[str] = None,
    ethics: Optional[str] = None,
    tags: Optional[List[str]] = None,
    save_models_to_registry: bool = True,
) -> ModelResponse:
    """Creates a new model in Model Control Plane.

    Args:
        name: The name of the model.
        license: The license under which the model is created.
        description: The description of the model.
        audience: The target audience of the model.
        use_cases: The use cases of the model.
        limitations: The known limitations of the model.
        trade_offs: The tradeoffs of the model.
        ethics: The ethical implications of the model.
        tags: Tags associated with the model.
        save_models_to_registry: Whether to save the model to the
            registry.

    Returns:
        The newly created model.
    """
    return self.zen_store.create_model(
        model=ModelRequest(
            name=name,
            license=license,
            description=description,
            audience=audience,
            use_cases=use_cases,
            limitations=limitations,
            trade_offs=trade_offs,
            ethics=ethics,
            tags=tags,
            project=self.active_project.id,
            save_models_to_registry=save_models_to_registry,
        )
    )

create_model_version(model_name_or_id, name=None, description=None, tags=None, project=None)

Creates a new model version in Model Control Plane.

Parameters:

Name	Type	Description	Default
model_name_or_id	Union[str, UUID]	the name or id of the model to create model version in.	required
name	Optional[str]	the name of the Model Version to be created.	None
description	Optional[str]	the description of the Model Version to be created.	None
tags	Optional[List[str]]	Tags associated with the model.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
ModelVersionResponse	The newly created model version.

Source code in src/zenml/client.py

def create_model_version(
    self,
    model_name_or_id: Union[str, UUID],
    name: Optional[str] = None,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
    project: Optional[Union[str, UUID]] = None,
) -> ModelVersionResponse:
    """Creates a new model version in Model Control Plane.

    Args:
        model_name_or_id: the name or id of the model to create model
            version in.
        name: the name of the Model Version to be created.
        description: the description of the Model Version to be created.
        tags: Tags associated with the model.
        project: The project name/ID to filter by.

    Returns:
        The newly created model version.
    """
    model = self.get_model(
        model_name_or_id=model_name_or_id, project=project
    )
    return self.zen_store.create_model_version(
        model_version=ModelVersionRequest(
            name=name,
            description=description,
            project=model.project.id,
            model=model.id,
            tags=tags,
        )
    )

create_project(name, description, display_name=None)

Create a new project.

Parameters:

Name	Type	Description	Default
name	str	Name of the project.	required
description	str	Description of the project.	required
display_name	Optional[str]	Display name of the project.	None

Returns:

Type	Description
ProjectResponse	The created project.

Source code in src/zenml/client.py

def create_project(
    self,
    name: str,
    description: str,
    display_name: Optional[str] = None,
) -> ProjectResponse:
    """Create a new project.

    Args:
        name: Name of the project.
        description: Description of the project.
        display_name: Display name of the project.

    Returns:
        The created project.
    """
    return self.zen_store.create_project(
        ProjectRequest(
            name=name,
            description=description,
            display_name=display_name or "",
        )
    )

create_run_metadata(metadata, resources, stack_component_id=None, publisher_step_id=None)

Create run metadata.

Parameters:

Name	Type	Description	Default
metadata	Dict[str, MetadataType]	The metadata to create as a dictionary of key-value pairs.	required
resources	List[RunMetadataResource]	The list of IDs and types of the resources for that the metadata was produced.	required
stack_component_id	Optional[UUID]	The ID of the stack component that produced the metadata.	None
publisher_step_id	Optional[UUID]	The ID of the step execution that publishes this metadata automatically.	None

Source code in src/zenml/client.py

def create_run_metadata(
    self,
    metadata: Dict[str, "MetadataType"],
    resources: List[RunMetadataResource],
    stack_component_id: Optional[UUID] = None,
    publisher_step_id: Optional[UUID] = None,
) -> None:
    """Create run metadata.

    Args:
        metadata: The metadata to create as a dictionary of key-value pairs.
        resources: The list of IDs and types of the resources for that the
            metadata was produced.
        stack_component_id: The ID of the stack component that produced
            the metadata.
        publisher_step_id: The ID of the step execution that publishes
            this metadata automatically.
    """
    from zenml.metadata.metadata_types import get_metadata_type

    values: Dict[str, "MetadataType"] = {}
    types: Dict[str, "MetadataTypeEnum"] = {}
    for key, value in metadata.items():
        # Skip metadata that is too large to be stored in the database.
        if len(json.dumps(value)) > TEXT_FIELD_MAX_LENGTH:
            logger.warning(
                f"Metadata value for key '{key}' is too large to be "
                "stored in the database. Skipping."
            )
            continue
        # Skip metadata that is not of a supported type.
        try:
            metadata_type = get_metadata_type(value)
        except ValueError as e:
            logger.warning(
                f"Metadata value for key '{key}' is not of a supported "
                f"type. Skipping. Full error: {e}"
            )
            continue
        values[key] = value
        types[key] = metadata_type

    run_metadata = RunMetadataRequest(
        project=self.active_project.id,
        resources=resources,
        stack_component_id=stack_component_id,
        publisher_step_id=publisher_step_id,
        values=values,
        types=types,
    )
    self.zen_store.create_run_metadata(run_metadata)

create_run_template(name, deployment_id, description=None, tags=None)

Create a run template.

Parameters:

Name	Type	Description	Default
name	str	The name of the run template.	required
deployment_id	UUID	ID of the deployment which this template should be based off of.	required
description	Optional[str]	The description of the run template.	None
tags	Optional[List[str]]	Tags associated with the run template.	None

Returns:

Type	Description
RunTemplateResponse	The created run template.

Source code in src/zenml/client.py

def create_run_template(
    self,
    name: str,
    deployment_id: UUID,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
) -> RunTemplateResponse:
    """Create a run template.

    Args:
        name: The name of the run template.
        deployment_id: ID of the deployment which this template should be
            based off of.
        description: The description of the run template.
        tags: Tags associated with the run template.

    Returns:
        The created run template.
    """
    return self.zen_store.create_run_template(
        template=RunTemplateRequest(
            name=name,
            description=description,
            source_deployment_id=deployment_id,
            tags=tags,
            project=self.active_project.id,
        )
    )

create_secret(name, values, private=False)

Creates a new secret.

Parameters:

Name	Type	Description	Default
name	str	The name of the secret.	required
values	Dict[str, str]	The values of the secret.	required
private	bool	Whether the secret is private. A private secret is only accessible to the user who created it.	False

Returns:

Type	Description
SecretResponse	The created secret (in model form).

Raises:

Type	Description
NotImplementedError	If centralized secrets management is not enabled.

Source code in src/zenml/client.py

def create_secret(
    self,
    name: str,
    values: Dict[str, str],
    private: bool = False,
) -> SecretResponse:
    """Creates a new secret.

    Args:
        name: The name of the secret.
        values: The values of the secret.
        private: Whether the secret is private. A private secret is only
            accessible to the user who created it.

    Returns:
        The created secret (in model form).

    Raises:
        NotImplementedError: If centralized secrets management is not
            enabled.
    """
    create_secret_request = SecretRequest(
        name=name,
        values=values,
        private=private,
    )
    try:
        return self.zen_store.create_secret(secret=create_secret_request)
    except NotImplementedError:
        raise NotImplementedError(
            "centralized secrets management is not supported or explicitly "
            "disabled in the target ZenML deployment."
        )

create_service(config, service_type, model_version_id=None)

Registers a service.

Parameters:

Name	Type	Description	Default
config	ServiceConfig	The configuration of the service.	required
service_type	ServiceType	The type of the service.	required
model_version_id	Optional[UUID]	The ID of the model version to associate with the service.	None

Returns:

Type	Description
ServiceResponse	The registered service.

Source code in src/zenml/client.py

def create_service(
    self,
    config: "ServiceConfig",
    service_type: ServiceType,
    model_version_id: Optional[UUID] = None,
) -> ServiceResponse:
    """Registers a service.

    Args:
        config: The configuration of the service.
        service_type: The type of the service.
        model_version_id: The ID of the model version to associate with the
            service.

    Returns:
        The registered service.
    """
    service_request = ServiceRequest(
        name=config.service_name,
        service_type=service_type,
        config=config.model_dump(),
        project=self.active_project.id,
        model_version_id=model_version_id,
    )
    # Register the service
    return self.zen_store.create_service(service_request)

create_service_account(name, description='')

Create a new service account.

Parameters:

Name	Type	Description	Default
name	str	The name of the service account.	required
description	str	The description of the service account.	''

Returns:

Type	Description
ServiceAccountResponse	The created service account.

Source code in src/zenml/client.py

def create_service_account(
    self,
    name: str,
    description: str = "",
) -> ServiceAccountResponse:
    """Create a new service account.

    Args:
        name: The name of the service account.
        description: The description of the service account.

    Returns:
        The created service account.
    """
    service_account = ServiceAccountRequest(
        name=name, description=description, active=True
    )
    created_service_account = self.zen_store.create_service_account(
        service_account=service_account
    )

    return created_service_account

create_service_connector(name, connector_type, resource_type=None, auth_method=None, configuration=None, resource_id=None, description='', expiration_seconds=None, expires_at=None, expires_skew_tolerance=None, labels=None, auto_configure=False, verify=True, list_resources=True, register=True)

Create, validate and/or register a service connector.

Parameters:

Name	Type	Description	Default
name	str	The name of the service connector.	required
connector_type	str	The service connector type.	required
auth_method	Optional[str]	The authentication method of the service connector. May be omitted if auto-configuration is used.	None
resource_type	Optional[str]	The resource type for the service connector.	None
configuration	Optional[Dict[str, str]]	The configuration of the service connector.	None
resource_id	Optional[str]	The resource id of the service connector.	None
description	str	The description of the service connector.	''
expiration_seconds	Optional[int]	The expiration time of the service connector.	None
expires_at	Optional[datetime]	The expiration time of the service connector.	None
expires_skew_tolerance	Optional[int]	The allowed expiration skew for the service connector credentials.	None
labels	Optional[Dict[str, str]]	The labels of the service connector.	None
auto_configure	bool	Whether to automatically configure the service connector from the local environment.	False
verify	bool	Whether to verify that the service connector configuration and credentials can be used to gain access to the resource.	True
list_resources	bool	Whether to also list the resources that the service connector can give access to (if verify is True).	True
register	bool	Whether to register the service connector or not.	True

Returns:

Type	Description
Optional[Union[ServiceConnectorResponse, ServiceConnectorRequest]]	The model of the registered service connector and the resources
Optional[ServiceConnectorResourcesModel]	that the service connector can give access to (if verify is True).

Raises:

Type	Description
ValueError	If the arguments are invalid.
KeyError	If the service connector type is not found.
NotImplementedError	If auto-configuration is not supported or not implemented for the service connector type.
AuthorizationException	If the connector verification failed due to authorization issues.

Source code in src/zenml/client.py

def create_service_connector(
    self,
    name: str,
    connector_type: str,
    resource_type: Optional[str] = None,
    auth_method: Optional[str] = None,
    configuration: Optional[Dict[str, str]] = None,
    resource_id: Optional[str] = None,
    description: str = "",
    expiration_seconds: Optional[int] = None,
    expires_at: Optional[datetime] = None,
    expires_skew_tolerance: Optional[int] = None,
    labels: Optional[Dict[str, str]] = None,
    auto_configure: bool = False,
    verify: bool = True,
    list_resources: bool = True,
    register: bool = True,
) -> Tuple[
    Optional[
        Union[
            ServiceConnectorResponse,
            ServiceConnectorRequest,
        ]
    ],
    Optional[ServiceConnectorResourcesModel],
]:
    """Create, validate and/or register a service connector.

    Args:
        name: The name of the service connector.
        connector_type: The service connector type.
        auth_method: The authentication method of the service connector.
            May be omitted if auto-configuration is used.
        resource_type: The resource type for the service connector.
        configuration: The configuration of the service connector.
        resource_id: The resource id of the service connector.
        description: The description of the service connector.
        expiration_seconds: The expiration time of the service connector.
        expires_at: The expiration time of the service connector.
        expires_skew_tolerance: The allowed expiration skew for the service
            connector credentials.
        labels: The labels of the service connector.
        auto_configure: Whether to automatically configure the service
            connector from the local environment.
        verify: Whether to verify that the service connector configuration
            and credentials can be used to gain access to the resource.
        list_resources: Whether to also list the resources that the service
            connector can give access to (if verify is True).
        register: Whether to register the service connector or not.

    Returns:
        The model of the registered service connector and the resources
        that the service connector can give access to (if verify is True).

    Raises:
        ValueError: If the arguments are invalid.
        KeyError: If the service connector type is not found.
        NotImplementedError: If auto-configuration is not supported or
            not implemented for the service connector type.
        AuthorizationException: If the connector verification failed due
            to authorization issues.
    """
    from zenml.service_connectors.service_connector_registry import (
        service_connector_registry,
    )

    connector_instance: Optional[ServiceConnector] = None
    connector_resources: Optional[ServiceConnectorResourcesModel] = None

    # Get the service connector type class
    try:
        connector = self.zen_store.get_service_connector_type(
            connector_type=connector_type,
        )
    except KeyError:
        raise KeyError(
            f"Service connector type {connector_type} not found."
            "Please check that you have installed all required "
            "Python packages and ZenML integrations and try again."
        )

    if not resource_type and len(connector.resource_types) == 1:
        resource_type = connector.resource_types[0].resource_type

    # If auto_configure is set, we will try to automatically configure the
    # service connector from the local environment
    if auto_configure:
        if not connector.supports_auto_configuration:
            raise NotImplementedError(
                f"The {connector.name} service connector type "
                "does not support auto-configuration."
            )
        if not connector.local:
            raise NotImplementedError(
                f"The {connector.name} service connector type "
                "implementation is not available locally. Please "
                "check that you have installed all required Python "
                "packages and ZenML integrations and try again, or "
                "skip auto-configuration."
            )

        assert connector.connector_class is not None

        connector_instance = connector.connector_class.auto_configure(
            resource_type=resource_type,
            auth_method=auth_method,
            resource_id=resource_id,
        )
        assert connector_instance is not None
        connector_request = connector_instance.to_model(
            name=name,
            description=description or "",
            labels=labels,
        )

        if verify:
            # Prefer to verify the connector config server-side if the
            # implementation if available there, because it ensures
            # that the connector can be shared with other users or used
            # from other machines and because some auth methods rely on the
            # server-side authentication environment
            if connector.remote:
                connector_resources = (
                    self.zen_store.verify_service_connector_config(
                        connector_request,
                        list_resources=list_resources,
                    )
                )
            else:
                connector_resources = connector_instance.verify(
                    list_resources=list_resources,
                )

            if connector_resources.error:
                # Raise an exception if the connector verification failed
                raise AuthorizationException(connector_resources.error)

    else:
        if not auth_method:
            if len(connector.auth_methods) == 1:
                auth_method = connector.auth_methods[0].auth_method
            else:
                raise ValueError(
                    f"Multiple authentication methods are available for "
                    f"the {connector.name} service connector type. Please "
                    f"specify one of the following: "
                    f"{list(connector.auth_method_dict.keys())}."
                )

        connector_request = ServiceConnectorRequest(
            name=name,
            connector_type=connector_type,
            description=description,
            auth_method=auth_method,
            expiration_seconds=expiration_seconds,
            expires_at=expires_at,
            expires_skew_tolerance=expires_skew_tolerance,
            labels=labels or {},
        )
        # Validate and configure the resources
        connector_request.validate_and_configure_resources(
            connector_type=connector,
            resource_types=resource_type,
            resource_id=resource_id,
            configuration=configuration,
        )
        if verify:
            # Prefer to verify the connector config server-side if the
            # implementation if available there, because it ensures
            # that the connector can be shared with other users or used
            # from other machines and because some auth methods rely on the
            # server-side authentication environment
            if connector.remote:
                connector_resources = (
                    self.zen_store.verify_service_connector_config(
                        connector_request,
                        list_resources=list_resources,
                    )
                )
            else:
                connector_instance = (
                    service_connector_registry.instantiate_connector(
                        model=connector_request
                    )
                )
                connector_resources = connector_instance.verify(
                    list_resources=list_resources,
                )

            if connector_resources.error:
                # Raise an exception if the connector verification failed
                raise AuthorizationException(connector_resources.error)

            # For resource types that don't support multi-instances, it's
            # better to save the default resource ID in the connector, if
            # available. Otherwise, we'll need to instantiate the connector
            # again to get the default resource ID.
            connector_request.resource_id = (
                connector_request.resource_id
                or connector_resources.get_default_resource_id()
            )

    if not register:
        return connector_request, connector_resources

    # Register the new model
    connector_response = self.zen_store.create_service_connector(
        service_connector=connector_request
    )

    if connector_resources:
        connector_resources.id = connector_response.id
        connector_resources.name = connector_response.name
        connector_resources.connector_type = (
            connector_response.connector_type
        )

    return connector_response, connector_resources

create_stack(name, components, stack_spec_file=None, labels=None)

Registers a stack and its components.

Parameters:

Name	Type	Description	Default
name	str	The name of the stack to register.	required
components	Mapping[StackComponentType, Union[str, UUID]]	dictionary which maps component types to component names	required
stack_spec_file	Optional[str]	path to the stack spec file	None
labels	Optional[Dict[str, Any]]	The labels of the stack.	None

Returns:

Type	Description
StackResponse	The model of the registered stack.

Source code in src/zenml/client.py

def create_stack(
    self,
    name: str,
    components: Mapping[StackComponentType, Union[str, UUID]],
    stack_spec_file: Optional[str] = None,
    labels: Optional[Dict[str, Any]] = None,
) -> StackResponse:
    """Registers a stack and its components.

    Args:
        name: The name of the stack to register.
        components: dictionary which maps component types to component names
        stack_spec_file: path to the stack spec file
        labels: The labels of the stack.

    Returns:
        The model of the registered stack.
    """
    stack_components = {}

    for c_type, c_identifier in components.items():
        # Skip non-existent components.
        if not c_identifier:
            continue

        # Get the component.
        component = self.get_stack_component(
            name_id_or_prefix=c_identifier,
            component_type=c_type,
        )
        stack_components[c_type] = [component.id]

    stack = StackRequest(
        name=name,
        components=stack_components,
        stack_spec_path=stack_spec_file,
        labels=labels,
    )

    self._validate_stack_configuration(stack=stack)

    return self.zen_store.create_stack(stack=stack)

create_stack_component(name, flavor, component_type, configuration, labels=None)

Registers a stack component.

Parameters:

Name	Type	Description	Default
name	str	The name of the stack component.	required
flavor	str	The flavor of the stack component.	required
component_type	StackComponentType	The type of the stack component.	required
configuration	Dict[str, str]	The configuration of the stack component.	required
labels	Optional[Dict[str, Any]]	The labels of the stack component.	None

Returns:

Type	Description
ComponentResponse	The model of the registered component.

Source code in src/zenml/client.py

def create_stack_component(
    self,
    name: str,
    flavor: str,
    component_type: StackComponentType,
    configuration: Dict[str, str],
    labels: Optional[Dict[str, Any]] = None,
) -> "ComponentResponse":
    """Registers a stack component.

    Args:
        name: The name of the stack component.
        flavor: The flavor of the stack component.
        component_type: The type of the stack component.
        configuration: The configuration of the stack component.
        labels: The labels of the stack component.

    Returns:
        The model of the registered component.
    """
    from zenml.stack.utils import (
        validate_stack_component_config,
        warn_if_config_server_mismatch,
    )

    validated_config = validate_stack_component_config(
        configuration_dict=configuration,
        flavor=flavor,
        component_type=component_type,
        # Always enforce validation of custom flavors
        validate_custom_flavors=True,
    )
    # Guaranteed to not be None by setting
    # `validate_custom_flavors=True` above
    assert validated_config is not None
    warn_if_config_server_mismatch(validated_config)

    create_component_model = ComponentRequest(
        name=name,
        type=component_type,
        flavor=flavor,
        configuration=configuration,
        labels=labels,
    )

    # Register the new model
    return self.zen_store.create_stack_component(
        component=create_component_model
    )

create_tag(name, exclusive=False, color=None)

Creates a new tag.

Parameters:

Name	Type	Description	Default
name	str	the name of the tag.	required
exclusive	bool	the boolean to decide whether the tag is an exclusive tag. An exclusive tag means that the tag can exist only for a single: - pipeline run within the scope of a pipeline - artifact version within the scope of an artifact - run template	False
color	Optional[Union[str, ColorVariants]]	the color of the tag	None

Returns:

Type	Description
TagResponse	The newly created tag.

Source code in src/zenml/client.py

def create_tag(
    self,
    name: str,
    exclusive: bool = False,
    color: Optional[Union[str, ColorVariants]] = None,
) -> TagResponse:
    """Creates a new tag.

    Args:
        name: the name of the tag.
        exclusive: the boolean to decide whether the tag is an exclusive tag.
            An exclusive tag means that the tag can exist only for a single:
                - pipeline run within the scope of a pipeline
                - artifact version within the scope of an artifact
                - run template
        color: the color of the tag

    Returns:
        The newly created tag.
    """
    request_model = TagRequest(name=name, exclusive=exclusive)

    if color is not None:
        request_model.color = ColorVariants(color)

    return self.zen_store.create_tag(tag=request_model)

create_trigger(name, event_source_id, event_filter, action_id, description='')

Registers a trigger.

Parameters:

Name	Type	Description	Default
name	str	The name of the trigger to create.	required
event_source_id	UUID	The id of the event source id	required
event_filter	Dict[str, Any]	The event filter	required
action_id	UUID	The ID of the action that should be triggered.	required
description	str	The description of the trigger	''

Returns:

Type	Description
TriggerResponse	The created trigger.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def create_trigger(
    self,
    name: str,
    event_source_id: UUID,
    event_filter: Dict[str, Any],
    action_id: UUID,
    description: str = "",
) -> TriggerResponse:
    """Registers a trigger.

    Args:
        name: The name of the trigger to create.
        event_source_id: The id of the event source id
        event_filter: The event filter
        action_id: The ID of the action that should be triggered.
        description: The description of the trigger

    Returns:
        The created trigger.
    """
    trigger = TriggerRequest(
        name=name,
        description=description,
        event_source_id=event_source_id,
        event_filter=event_filter,
        action_id=action_id,
        project=self.active_project.id,
    )

    return self.zen_store.create_trigger(trigger=trigger)

create_user(name, password=None, is_admin=False)

Create a new user.

Parameters:

Name	Type	Description	Default
name	str	The name of the user.	required
password	Optional[str]	The password of the user. If not provided, the user will be created with empty password.	None
is_admin	bool	Whether the user should be an admin.	False

Returns:

Type	Description
UserResponse	The model of the created user.

Source code in src/zenml/client.py

def create_user(
    self,
    name: str,
    password: Optional[str] = None,
    is_admin: bool = False,
) -> UserResponse:
    """Create a new user.

    Args:
        name: The name of the user.
        password: The password of the user. If not provided, the user will
            be created with empty password.
        is_admin: Whether the user should be an admin.

    Returns:
        The model of the created user.
    """
    user = UserRequest(
        name=name, password=password or None, is_admin=is_admin
    )
    user.active = (
        password != "" if self.zen_store.type != StoreType.REST else True
    )
    created_user = self.zen_store.create_user(user=user)

    return created_user

deactivate_user(name_id_or_prefix)

Deactivate a user and generate an activation token.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	str	The name or ID of the user to reset.	required

Returns:

Type	Description
UserResponse	The deactivated user.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def deactivate_user(self, name_id_or_prefix: str) -> "UserResponse":
    """Deactivate a user and generate an activation token.

    Args:
        name_id_or_prefix: The name or ID of the user to reset.

    Returns:
        The deactivated user.
    """
    from zenml.zen_stores.rest_zen_store import RestZenStore

    user = self.get_user(name_id_or_prefix, allow_name_prefix_match=False)
    assert isinstance(self.zen_store, RestZenStore)
    return self.zen_store.deactivate_user(user_name_or_id=user.name)

delete_action(name_id_or_prefix, project=None)

Delete an action.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, id or prefix id of the action to delete.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def delete_action(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete an action.

    Args:
        name_id_or_prefix: The name, id or prefix id of the action
            to delete.
        project: The project name/ID to filter by.
    """
    action = self.get_action(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )

    self.zen_store.delete_action(action_id=action.id)
    logger.info("Deleted action with name '%s'.", action.name)

delete_all_model_version_artifact_links(model_version_id, only_links)

Delete all model version to artifact links in Model Control Plane.

Parameters:

Name	Type	Description	Default
model_version_id	UUID	The id of the model version holding the link.	required
only_links	bool	If true, only delete the link to the artifact.	required

Source code in src/zenml/client.py

def delete_all_model_version_artifact_links(
    self, model_version_id: UUID, only_links: bool
) -> None:
    """Delete all model version to artifact links in Model Control Plane.

    Args:
        model_version_id: The id of the model version holding the link.
        only_links: If true, only delete the link to the artifact.
    """
    self.zen_store.delete_all_model_version_artifact_links(
        model_version_id, only_links
    )

delete_api_key(service_account_name_id_or_prefix, name_id_or_prefix)

Delete an API key.

Parameters:

Name	Type	Description	Default
service_account_name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the service account to delete the API key for.	required
name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the API key.	required

Source code in src/zenml/client.py

def delete_api_key(
    self,
    service_account_name_id_or_prefix: Union[str, UUID],
    name_id_or_prefix: Union[str, UUID],
) -> None:
    """Delete an API key.

    Args:
        service_account_name_id_or_prefix: The name, ID or prefix of the
            service account to delete the API key for.
        name_id_or_prefix: The name, ID or prefix of the API key.
    """
    api_key = self.get_api_key(
        service_account_name_id_or_prefix=service_account_name_id_or_prefix,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
    )
    self.zen_store.delete_api_key(
        service_account_id=api_key.service_account.id,
        api_key_name_or_id=api_key.id,
    )

delete_artifact(name_id_or_prefix, project=None)

Delete an artifact.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the artifact to delete.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_artifact(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete an artifact.

    Args:
        name_id_or_prefix: The name, ID or prefix of the artifact to delete.
        project: The project name/ID to filter by.
    """
    artifact = self.get_artifact(
        name_id_or_prefix=name_id_or_prefix,
        project=project,
    )
    self.zen_store.delete_artifact(artifact_id=artifact.id)
    logger.info(f"Deleted artifact '{artifact.name}'.")

delete_artifact_version(name_id_or_prefix, version=None, delete_metadata=True, delete_from_artifact_store=False, project=None)

Delete an artifact version.

By default, this will delete only the metadata of the artifact from the database, not the actual object stored in the artifact store.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The ID of artifact version or name or prefix of the artifact to delete.	required
version	Optional[str]	The version of the artifact to delete.	None
delete_metadata	bool	If True, delete the metadata of the artifact version from the database.	True
delete_from_artifact_store	bool	If True, delete the artifact object itself from the artifact store.	False
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_artifact_version(
    self,
    name_id_or_prefix: Union[str, UUID],
    version: Optional[str] = None,
    delete_metadata: bool = True,
    delete_from_artifact_store: bool = False,
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete an artifact version.

    By default, this will delete only the metadata of the artifact from the
    database, not the actual object stored in the artifact store.

    Args:
        name_id_or_prefix: The ID of artifact version or name or prefix of the artifact to
            delete.
        version: The version of the artifact to delete.
        delete_metadata: If True, delete the metadata of the artifact
            version from the database.
        delete_from_artifact_store: If True, delete the artifact object
                itself from the artifact store.
        project: The project name/ID to filter by.
    """
    artifact_version = self.get_artifact_version(
        name_id_or_prefix=name_id_or_prefix,
        version=version,
        project=project,
    )
    if delete_from_artifact_store:
        self._delete_artifact_from_artifact_store(
            artifact_version=artifact_version
        )
    if delete_metadata:
        self._delete_artifact_version(artifact_version=artifact_version)

delete_authorized_device(id_or_prefix)

Delete an authorized device.

Parameters:

Name	Type	Description	Default
id_or_prefix	Union[str, UUID]	The ID or ID prefix of the authorized device.	required

Source code in src/zenml/client.py

def delete_authorized_device(
    self,
    id_or_prefix: Union[str, UUID],
) -> None:
    """Delete an authorized device.

    Args:
        id_or_prefix: The ID or ID prefix of the authorized device.
    """
    device = self.get_authorized_device(
        id_or_prefix=id_or_prefix,
        allow_id_prefix_match=False,
    )
    self.zen_store.delete_authorized_device(device.id)

delete_build(id_or_prefix, project=None)

Delete a build.

Parameters:

Name	Type	Description	Default
id_or_prefix	str	The id or id prefix of the build.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_build(
    self, id_or_prefix: str, project: Optional[Union[str, UUID]] = None
) -> None:
    """Delete a build.

    Args:
        id_or_prefix: The id or id prefix of the build.
        project: The project name/ID to filter by.
    """
    build = self.get_build(id_or_prefix=id_or_prefix, project=project)
    self.zen_store.delete_build(build_id=build.id)

delete_code_repository(name_id_or_prefix, project=None)

Delete a code repository.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the code repository.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_code_repository(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete a code repository.

    Args:
        name_id_or_prefix: The name, ID or prefix of the code repository.
        project: The project name/ID to filter by.
    """
    repo = self.get_code_repository(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )
    self.zen_store.delete_code_repository(code_repository_id=repo.id)

delete_deployment(id_or_prefix, project=None)

Delete a deployment.

Parameters:

Name	Type	Description	Default
id_or_prefix	str	The id or id prefix of the deployment.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_deployment(
    self,
    id_or_prefix: str,
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete a deployment.

    Args:
        id_or_prefix: The id or id prefix of the deployment.
        project: The project name/ID to filter by.
    """
    deployment = self.get_deployment(
        id_or_prefix=id_or_prefix,
        project=project,
        hydrate=False,
    )
    self.zen_store.delete_deployment(deployment_id=deployment.id)

delete_event_source(name_id_or_prefix, project=None)

Deletes an event_source.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, id or prefix id of the event_source to deregister.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def delete_event_source(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Deletes an event_source.

    Args:
        name_id_or_prefix: The name, id or prefix id of the event_source
            to deregister.
        project: The project name/ID to filter by.
    """
    event_source = self.get_event_source(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )

    self.zen_store.delete_event_source(event_source_id=event_source.id)
    logger.info("Deleted event_source with name '%s'.", event_source.name)

delete_flavor(name_id_or_prefix)

Deletes a flavor.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	str	The name, id or prefix of the id for the flavor to delete.	required

Source code in src/zenml/client.py

def delete_flavor(self, name_id_or_prefix: str) -> None:
    """Deletes a flavor.

    Args:
        name_id_or_prefix: The name, id or prefix of the id for the
            flavor to delete.
    """
    flavor = self.get_flavor(
        name_id_or_prefix, allow_name_prefix_match=False
    )
    self.zen_store.delete_flavor(flavor_id=flavor.id)

    logger.info(f"Deleted flavor '{flavor.name}' of type '{flavor.type}'.")

delete_model(model_name_or_id, project=None)

Deletes a model from Model Control Plane.

Parameters:

Name	Type	Description	Default
model_name_or_id	Union[str, UUID]	name or id of the model to be deleted.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_model(
    self,
    model_name_or_id: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Deletes a model from Model Control Plane.

    Args:
        model_name_or_id: name or id of the model to be deleted.
        project: The project name/ID to filter by.
    """
    model = self.get_model(
        model_name_or_id=model_name_or_id, project=project
    )
    self.zen_store.delete_model(model_id=model.id)

delete_model_version(model_version_id)

Deletes a model version from Model Control Plane.

Parameters:

Name	Type	Description	Default
model_version_id	UUID	Id of the model version to be deleted.	required

Source code in src/zenml/client.py

def delete_model_version(
    self,
    model_version_id: UUID,
) -> None:
    """Deletes a model version from Model Control Plane.

    Args:
        model_version_id: Id of the model version to be deleted.
    """
    self.zen_store.delete_model_version(
        model_version_id=model_version_id,
    )

delete_model_version_artifact_link(model_version_id, artifact_version_id)

Delete model version to artifact link in Model Control Plane.

Parameters:

Name	Type	Description	Default
model_version_id	UUID	The id of the model version holding the link.	required
artifact_version_id	UUID	The id of the artifact version to be deleted.	required

Raises:

Type	Description
RuntimeError	If more than one artifact link is found for given filters.

Source code in src/zenml/client.py

def delete_model_version_artifact_link(
    self, model_version_id: UUID, artifact_version_id: UUID
) -> None:
    """Delete model version to artifact link in Model Control Plane.

    Args:
        model_version_id: The id of the model version holding the link.
        artifact_version_id: The id of the artifact version to be deleted.

    Raises:
        RuntimeError: If more than one artifact link is found for given filters.
    """
    artifact_links = self.list_model_version_artifact_links(
        model_version_id=model_version_id,
        artifact_version_id=artifact_version_id,
    )
    if artifact_links.items:
        if artifact_links.total > 1:
            raise RuntimeError(
                "More than one artifact link found for give model version "
                f"`{model_version_id}` and artifact version "
                f"`{artifact_version_id}`. This should not be happening and "
                "might indicate a corrupted state of your ZenML database. "
                "Please seek support via Community Slack."
            )
        self.zen_store.delete_model_version_artifact_link(
            model_version_id=model_version_id,
            model_version_artifact_link_name_or_id=artifact_links.items[
                0
            ].id,
        )

delete_pipeline(name_id_or_prefix, project=None)

Delete a pipeline.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or ID prefix of the pipeline.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_pipeline(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete a pipeline.

    Args:
        name_id_or_prefix: The name, ID or ID prefix of the pipeline.
        project: The project name/ID to filter by.
    """
    pipeline = self.get_pipeline(
        name_id_or_prefix=name_id_or_prefix, project=project
    )
    self.zen_store.delete_pipeline(pipeline_id=pipeline.id)

delete_pipeline_run(name_id_or_prefix, project=None)

Deletes a pipeline run.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	Name, ID, or prefix of the pipeline run.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_pipeline_run(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Deletes a pipeline run.

    Args:
        name_id_or_prefix: Name, ID, or prefix of the pipeline run.
        project: The project name/ID to filter by.
    """
    run = self.get_pipeline_run(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )
    self.zen_store.delete_run(run_id=run.id)

delete_project(name_id_or_prefix)

Delete a project.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	str	The name or ID of the project to delete.	required

Raises:

Type	Description
IllegalOperationError	If the project to delete is the active project.

Source code in src/zenml/client.py

def delete_project(self, name_id_or_prefix: str) -> None:
    """Delete a project.

    Args:
        name_id_or_prefix: The name or ID of the project to delete.

    Raises:
        IllegalOperationError: If the project to delete is the active
            project.
    """
    project = self.get_project(
        name_id_or_prefix, allow_name_prefix_match=False
    )
    if self.active_project.id == project.id:
        raise IllegalOperationError(
            f"Project '{name_id_or_prefix}' cannot be deleted since "
            "it is currently active. Please set another project as "
            "active first."
        )
    self.zen_store.delete_project(project_name_or_id=project.id)

delete_run_template(name_id_or_prefix, project=None)

Delete a run template.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	Name/ID/ID prefix of the template to delete.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_run_template(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete a run template.

    Args:
        name_id_or_prefix: Name/ID/ID prefix of the template to delete.
        project: The project name/ID to filter by.
    """
    if is_valid_uuid(name_id_or_prefix):
        template_id = (
            UUID(name_id_or_prefix)
            if isinstance(name_id_or_prefix, str)
            else name_id_or_prefix
        )
    else:
        template_id = self.get_run_template(
            name_id_or_prefix,
            project=project,
            hydrate=False,
        ).id

    self.zen_store.delete_run_template(template_id=template_id)

delete_schedule(name_id_or_prefix, project=None)

Delete a schedule.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, id or prefix id of the schedule to delete.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_schedule(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete a schedule.

    Args:
        name_id_or_prefix: The name, id or prefix id of the schedule
            to delete.
        project: The project name/ID to filter by.
    """
    schedule = self.get_schedule(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )
    logger.warning(
        f"Deleting schedule '{name_id_or_prefix}'... This will only delete "
        "the reference of the schedule from ZenML. Please make sure to "
        "manually stop/delete this schedule in your orchestrator as well!"
    )
    self.zen_store.delete_schedule(schedule_id=schedule.id)

delete_secret(name_id_or_prefix, private=None)

Deletes a secret.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	str	The name or ID of the secret.	required
private	Optional[bool]	The private status of the secret to delete.	None

Source code in src/zenml/client.py

def delete_secret(
    self, name_id_or_prefix: str, private: Optional[bool] = None
) -> None:
    """Deletes a secret.

    Args:
        name_id_or_prefix: The name or ID of the secret.
        private: The private status of the secret to delete.
    """
    secret = self.get_secret(
        name_id_or_prefix=name_id_or_prefix,
        private=private,
        # Don't allow partial name matches, but allow partial ID matches
        allow_partial_name_match=False,
        allow_partial_id_match=True,
    )

    self.zen_store.delete_secret(secret_id=secret.id)

delete_service(name_id_or_prefix, project=None)

Delete a service.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	UUID	The name or ID of the service to delete.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def delete_service(
    self,
    name_id_or_prefix: UUID,
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete a service.

    Args:
        name_id_or_prefix: The name or ID of the service to delete.
        project: The project name/ID to filter by.
    """
    service = self.get_service(
        name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )
    self.zen_store.delete_service(service_id=service.id)

delete_service_account(name_id_or_prefix)

Delete a service account.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name or ID of the service account to delete.	required

Source code in src/zenml/client.py

def delete_service_account(
    self,
    name_id_or_prefix: Union[str, UUID],
) -> None:
    """Delete a service account.

    Args:
        name_id_or_prefix: The name or ID of the service account to delete.
    """
    service_account = self.get_service_account(
        name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
    )
    self.zen_store.delete_service_account(
        service_account_name_or_id=service_account.id
    )

delete_service_connector(name_id_or_prefix)

Deletes a registered service connector.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The ID or name of the service connector to delete.	required

Source code in src/zenml/client.py

def delete_service_connector(
    self,
    name_id_or_prefix: Union[str, UUID],
) -> None:
    """Deletes a registered service connector.

    Args:
        name_id_or_prefix: The ID or name of the service connector to delete.
    """
    service_connector = self.get_service_connector(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
    )

    self.zen_store.delete_service_connector(
        service_connector_id=service_connector.id
    )
    logger.info(
        "Removed service connector (type: %s) with name '%s'.",
        service_connector.type,
        service_connector.name,
    )

delete_stack(name_id_or_prefix, recursive=False)

Deregisters a stack.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, id or prefix id of the stack to deregister.	required
recursive	bool	If True, all components of the stack which are not associated with any other stack will also be deleted.	False

Raises:

Type	Description
ValueError	If the stack is the currently active stack for this client.

Source code in src/zenml/client.py

def delete_stack(
    self, name_id_or_prefix: Union[str, UUID], recursive: bool = False
) -> None:
    """Deregisters a stack.

    Args:
        name_id_or_prefix: The name, id or prefix id of the stack
            to deregister.
        recursive: If `True`, all components of the stack which are not
            associated with any other stack will also be deleted.

    Raises:
        ValueError: If the stack is the currently active stack for this
            client.
    """
    stack = self.get_stack(
        name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
    )

    if stack.id == self.active_stack_model.id:
        raise ValueError(
            f"Unable to deregister active stack '{stack.name}'. Make "
            f"sure to designate a new active stack before deleting this "
            f"one."
        )

    cfg = GlobalConfiguration()
    if stack.id == cfg.active_stack_id:
        raise ValueError(
            f"Unable to deregister '{stack.name}' as it is the active "
            f"stack within your global configuration. Make "
            f"sure to designate a new active stack before deleting this "
            f"one."
        )

    if recursive:
        stack_components_free_for_deletion = []

        # Get all stack components associated with this stack
        for component_type, component_model in stack.components.items():
            # Get stack associated with the stack component

            stacks = self.list_stacks(
                component_id=component_model[0].id, size=2, page=1
            )

            # Check if the stack component is part of another stack
            if len(stacks) == 1 and stack.id == stacks[0].id:
                stack_components_free_for_deletion.append(
                    (component_type, component_model)
                )

        self.delete_stack(stack.id)

        for (
            stack_component_type,
            stack_component_model,
        ) in stack_components_free_for_deletion:
            self.delete_stack_component(
                stack_component_model[0].name, stack_component_type
            )

        logger.info("Deregistered stack with name '%s'.", stack.name)
        return

    self.zen_store.delete_stack(stack_id=stack.id)
    logger.info("Deregistered stack with name '%s'.", stack.name)

delete_stack_component(name_id_or_prefix, component_type)

Deletes a registered stack component.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The model of the component to delete.	required
component_type	StackComponentType	The type of the component to delete.	required

Source code in src/zenml/client.py

def delete_stack_component(
    self,
    name_id_or_prefix: Union[str, UUID],
    component_type: StackComponentType,
) -> None:
    """Deletes a registered stack component.

    Args:
        name_id_or_prefix: The model of the component to delete.
        component_type: The type of the component to delete.
    """
    component = self.get_stack_component(
        name_id_or_prefix=name_id_or_prefix,
        component_type=component_type,
        allow_name_prefix_match=False,
    )

    self.zen_store.delete_stack_component(component_id=component.id)
    logger.info(
        "Deregistered stack component (type: %s) with name '%s'.",
        component.type,
        component.name,
    )

delete_tag(tag_name_or_id)

Deletes a tag.

Parameters:

Name	Type	Description	Default
tag_name_or_id	Union[str, UUID]	name or id of the tag to be deleted.	required

Source code in src/zenml/client.py

def delete_tag(
    self,
    tag_name_or_id: Union[str, UUID],
) -> None:
    """Deletes a tag.

    Args:
        tag_name_or_id: name or id of the tag to be deleted.
    """
    self.zen_store.delete_tag(
        tag_name_or_id=tag_name_or_id,
    )

delete_trigger(name_id_or_prefix, project=None)

Deletes an trigger.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, id or prefix id of the trigger to deregister.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def delete_trigger(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Deletes an trigger.

    Args:
        name_id_or_prefix: The name, id or prefix id of the trigger
            to deregister.
        project: The project name/ID to filter by.
    """
    trigger = self.get_trigger(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )

    self.zen_store.delete_trigger(trigger_id=trigger.id)
    logger.info("Deleted trigger with name '%s'.", trigger.name)

delete_trigger_execution(trigger_execution_id)

Delete a trigger execution.

Parameters:

Name	Type	Description	Default
trigger_execution_id	UUID	The ID of the trigger execution to delete.	required

Source code in src/zenml/client.py

def delete_trigger_execution(self, trigger_execution_id: UUID) -> None:
    """Delete a trigger execution.

    Args:
        trigger_execution_id: The ID of the trigger execution to delete.
    """
    self.zen_store.delete_trigger_execution(
        trigger_execution_id=trigger_execution_id
    )

delete_user(name_id_or_prefix)

Delete a user.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	str	The name or ID of the user to delete.	required

Source code in src/zenml/client.py

def delete_user(self, name_id_or_prefix: str) -> None:
    """Delete a user.

    Args:
        name_id_or_prefix: The name or ID of the user to delete.
    """
    user = self.get_user(name_id_or_prefix, allow_name_prefix_match=False)
    self.zen_store.delete_user(user_name_or_id=user.name)

detach_tag(tag_name_or_id, resources)

Detach a tag from resources.

Parameters:

Name	Type	Description	Default
tag_name_or_id	Union[str, UUID]	name or id of the tag to be detached.	required
resources	List[TagResource]	the resources to detach the tag from.	required

Source code in src/zenml/client.py

def detach_tag(
    self,
    tag_name_or_id: Union[str, UUID],
    resources: List[TagResource],
) -> None:
    """Detach a tag from resources.

    Args:
        tag_name_or_id: name or id of the tag to be detached.
        resources: the resources to detach the tag from.
    """
    tag_model = self.get_tag(tag_name_or_id)

    self.zen_store.batch_delete_tag_resource(
        tag_resources=[
            TagResourceRequest(
                tag_id=tag_model.id,
                resource_id=resource.id,
                resource_type=resource.type,
            )
            for resource in resources
        ]
    )

find_repository(path=None, enable_warnings=False) staticmethod

Search for a ZenML repository directory.

Parameters:

Name	Type	Description	Default
path	Optional[Path]	Optional path to look for the repository. If no path is given, this function tries to find the repository using the environment variable ZENML_REPOSITORY_PATH (if set) and recursively searching in the parent directories of the current working directory.	None
enable_warnings	bool	If True, warnings are printed if the repository root cannot be found.	False

Returns:

Type	Description
Optional[Path]	Absolute path to a ZenML repository directory or None if no
Optional[Path]	repository directory was found.

Source code in src/zenml/client.py

@staticmethod
def find_repository(
    path: Optional[Path] = None, enable_warnings: bool = False
) -> Optional[Path]:
    """Search for a ZenML repository directory.

    Args:
        path: Optional path to look for the repository. If no path is
            given, this function tries to find the repository using the
            environment variable `ZENML_REPOSITORY_PATH` (if set) and
            recursively searching in the parent directories of the current
            working directory.
        enable_warnings: If `True`, warnings are printed if the repository
            root cannot be found.

    Returns:
        Absolute path to a ZenML repository directory or None if no
        repository directory was found.
    """
    if not path:
        # try to get path from the environment variable
        env_var_path = os.getenv(ENV_ZENML_REPOSITORY_PATH)
        if env_var_path:
            path = Path(env_var_path)

    if path:
        # explicit path via parameter or environment variable, don't search
        # parent directories
        search_parent_directories = False
        warning_message = (
            f"Unable to find ZenML repository at path '{path}'. Make sure "
            f"to create a ZenML repository by calling `zenml init` when "
            f"specifying an explicit repository path in code or via the "
            f"environment variable '{ENV_ZENML_REPOSITORY_PATH}'."
        )
    else:
        # try to find the repository in the parent directories of the
        # current working directory
        path = Path.cwd()
        search_parent_directories = True
        warning_message = (
            f"Unable to find ZenML repository in your current working "
            f"directory ({path}) or any parent directories. If you "
            f"want to use an existing repository which is in a different "
            f"location, set the environment variable "
            f"'{ENV_ZENML_REPOSITORY_PATH}'. If you want to create a new "
            f"repository, run `zenml init`."
        )

    def _find_repository_helper(path_: Path) -> Optional[Path]:
        """Recursively search parent directories for a ZenML repository.

        Args:
            path_: The path to search.

        Returns:
            Absolute path to a ZenML repository directory or None if no
            repository directory was found.
        """
        if Client.is_repository_directory(path_):
            return path_

        if not search_parent_directories or io_utils.is_root(str(path_)):
            return None

        return _find_repository_helper(path_.parent)

    repository_path = _find_repository_helper(path)

    if repository_path:
        return repository_path.resolve()
    if enable_warnings:
        logger.warning(warning_message)
    return None

get_action(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)

Get an action by name, ID or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, ID or prefix of the action.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ActionResponse	The action.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def get_action(
    self,
    name_id_or_prefix: Union[UUID, str],
    allow_name_prefix_match: bool = True,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> ActionResponse:
    """Get an action by name, ID or prefix.

    Args:
        name_id_or_prefix: The name, ID or prefix of the action.
        allow_name_prefix_match: If True, allow matching by name prefix.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The action.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_action,
        list_method=self.list_actions,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        project=project,
        hydrate=hydrate,
    )

get_api_key(service_account_name_id_or_prefix, name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)

Get an API key by name, id or prefix.

Parameters:

Name	Type	Description	Default
service_account_name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the service account to get the API key for.	required
name_id_or_prefix	Union[str, UUID]	The name, ID or ID prefix of the API key.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
APIKeyResponse	The API key.

Source code in src/zenml/client.py

def get_api_key(
    self,
    service_account_name_id_or_prefix: Union[str, UUID],
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
) -> APIKeyResponse:
    """Get an API key by name, id or prefix.

    Args:
        service_account_name_id_or_prefix: The name, ID or prefix of the
            service account to get the API key for.
        name_id_or_prefix: The name, ID or ID prefix of the API key.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The API key.
    """
    service_account = self.get_service_account(
        name_id_or_prefix=service_account_name_id_or_prefix,
        allow_name_prefix_match=False,
    )

    def get_api_key_method(
        api_key_name_or_id: str, hydrate: bool = True
    ) -> APIKeyResponse:
        return self.zen_store.get_api_key(
            service_account_id=service_account.id,
            api_key_name_or_id=api_key_name_or_id,
            hydrate=hydrate,
        )

    def list_api_keys_method(
        hydrate: bool = True,
        **filter_args: Any,
    ) -> Page[APIKeyResponse]:
        return self.list_api_keys(
            service_account_name_id_or_prefix=service_account.id,
            hydrate=hydrate,
            **filter_args,
        )

    return self._get_entity_by_id_or_name_or_prefix(
        get_method=get_api_key_method,
        list_method=list_api_keys_method,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
    )

get_artifact(name_id_or_prefix, project=None, hydrate=False)

Get an artifact by name, id or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the artifact to get.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
ArtifactResponse	The artifact.

Source code in src/zenml/client.py

def get_artifact(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = False,
) -> ArtifactResponse:
    """Get an artifact by name, id or prefix.

    Args:
        name_id_or_prefix: The name, ID or prefix of the artifact to get.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The artifact.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_artifact,
        list_method=self.list_artifacts,
        name_id_or_prefix=name_id_or_prefix,
        project=project,
        hydrate=hydrate,
    )

get_artifact_version(name_id_or_prefix, version=None, project=None, hydrate=True)

Get an artifact version by ID or artifact name.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	Either the ID of the artifact version or the name of the artifact.	required
version	Optional[str]	The version of the artifact to get. Only used if name_id_or_prefix is the name of the artifact. If not specified, the latest version is returned.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ArtifactVersionResponse	The artifact version.

Source code in src/zenml/client.py

def get_artifact_version(
    self,
    name_id_or_prefix: Union[str, UUID],
    version: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> ArtifactVersionResponse:
    """Get an artifact version by ID or artifact name.

    Args:
        name_id_or_prefix: Either the ID of the artifact version or the
            name of the artifact.
        version: The version of the artifact to get. Only used if
            `name_id_or_prefix` is the name of the artifact. If not
            specified, the latest version is returned.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The artifact version.
    """
    from zenml import get_step_context

    if cll := client_lazy_loader(
        method_name="get_artifact_version",
        name_id_or_prefix=name_id_or_prefix,
        version=version,
        project=project,
        hydrate=hydrate,
    ):
        return cll  # type: ignore[return-value]

    artifact = self._get_entity_version_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_artifact_version,
        list_method=self.list_artifact_versions,
        name_id_or_prefix=name_id_or_prefix,
        version=version,
        project=project,
        hydrate=hydrate,
    )
    try:
        step_run = get_step_context().step_run
        client = Client()
        client.zen_store.update_run_step(
            step_run_id=step_run.id,
            step_run_update=StepRunUpdate(
                loaded_artifact_versions={artifact.name: artifact.id}
            ),
        )
    except RuntimeError:
        pass  # Cannot link to step run if called outside a step
    return artifact

get_authorized_device(id_or_prefix, allow_id_prefix_match=True, hydrate=True)

Get an authorized device by id or prefix.

Parameters:

Name	Type	Description	Default
id_or_prefix	Union[UUID, str]	The ID or ID prefix of the authorized device.	required
allow_id_prefix_match	bool	If True, allow matching by ID prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
OAuthDeviceResponse	The requested authorized device.

Raises:

Type	Description
KeyError	If no authorized device is found with the given ID or prefix.

Source code in src/zenml/client.py

def get_authorized_device(
    self,
    id_or_prefix: Union[UUID, str],
    allow_id_prefix_match: bool = True,
    hydrate: bool = True,
) -> OAuthDeviceResponse:
    """Get an authorized device by id or prefix.

    Args:
        id_or_prefix: The ID or ID prefix of the authorized device.
        allow_id_prefix_match: If True, allow matching by ID prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The requested authorized device.

    Raises:
        KeyError: If no authorized device is found with the given ID or
            prefix.
    """
    if isinstance(id_or_prefix, str):
        try:
            id_or_prefix = UUID(id_or_prefix)
        except ValueError:
            if not allow_id_prefix_match:
                raise KeyError(
                    f"No authorized device found with id or prefix "
                    f"'{id_or_prefix}'."
                )
    if isinstance(id_or_prefix, UUID):
        return self.zen_store.get_authorized_device(
            id_or_prefix, hydrate=hydrate
        )
    return self._get_entity_by_prefix(
        get_method=self.zen_store.get_authorized_device,
        list_method=self.list_authorized_devices,
        partial_id_or_name=id_or_prefix,
        allow_name_prefix_match=False,
        hydrate=hydrate,
    )

get_build(id_or_prefix, project=None, hydrate=True)

Get a build by id or prefix.

Parameters:

Name	Type	Description	Default
id_or_prefix	Union[str, UUID]	The id or id prefix of the build.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
PipelineBuildResponse	The build.

Raises:

Type	Description
KeyError	If no build was found for the given id or prefix.
ZenKeyError	If multiple builds were found that match the given id or prefix.

Source code in src/zenml/client.py

def get_build(
    self,
    id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> PipelineBuildResponse:
    """Get a build by id or prefix.

    Args:
        id_or_prefix: The id or id prefix of the build.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The build.

    Raises:
        KeyError: If no build was found for the given id or prefix.
        ZenKeyError: If multiple builds were found that match the given
            id or prefix.
    """
    from zenml.utils.uuid_utils import is_valid_uuid

    # First interpret as full UUID
    if is_valid_uuid(id_or_prefix):
        if not isinstance(id_or_prefix, UUID):
            id_or_prefix = UUID(id_or_prefix, version=4)

        return self.zen_store.get_build(
            id_or_prefix,
            hydrate=hydrate,
        )

    list_kwargs: Dict[str, Any] = dict(
        id=f"startswith:{id_or_prefix}",
        hydrate=hydrate,
    )
    scope = ""
    if project:
        list_kwargs["project"] = project
        scope = f" in project {project}"

    entity = self.list_builds(**list_kwargs)

    # If only a single entity is found, return it.
    if entity.total == 1:
        return entity.items[0]

    # If no entity is found, raise an error.
    if entity.total == 0:
        raise KeyError(
            f"No builds have been found that have either an id or prefix "
            f"that matches the provided string '{id_or_prefix}'{scope}."
        )

    raise ZenKeyError(
        f"{entity.total} builds have been found{scope} that have "
        f"an ID that matches the provided "
        f"string '{id_or_prefix}':\n"
        f"{[entity.items]}.\n"
        f"Please use the id to uniquely identify "
        f"only one of the builds."
    )

get_code_repository(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)

Get a code repository by name, id or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or ID prefix of the code repository.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
CodeRepositoryResponse	The code repository.

Source code in src/zenml/client.py

def get_code_repository(
    self,
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> CodeRepositoryResponse:
    """Get a code repository by name, id or prefix.

    Args:
        name_id_or_prefix: The name, ID or ID prefix of the code repository.
        allow_name_prefix_match: If True, allow matching by name prefix.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The code repository.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_code_repository,
        list_method=self.list_code_repositories,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
        project=project,
    )

get_deployment(id_or_prefix, project=None, hydrate=True)

Get a deployment by id or prefix.

Parameters:

Name	Type	Description	Default
id_or_prefix	Union[str, UUID]	The id or id prefix of the deployment.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
PipelineDeploymentResponse	The deployment.

Raises:

Type	Description
KeyError	If no deployment was found for the given id or prefix.
ZenKeyError	If multiple deployments were found that match the given id or prefix.

Source code in src/zenml/client.py

def get_deployment(
    self,
    id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> PipelineDeploymentResponse:
    """Get a deployment by id or prefix.

    Args:
        id_or_prefix: The id or id prefix of the deployment.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The deployment.

    Raises:
        KeyError: If no deployment was found for the given id or prefix.
        ZenKeyError: If multiple deployments were found that match the given
            id or prefix.
    """
    from zenml.utils.uuid_utils import is_valid_uuid

    # First interpret as full UUID
    if is_valid_uuid(id_or_prefix):
        id_ = (
            UUID(id_or_prefix)
            if isinstance(id_or_prefix, str)
            else id_or_prefix
        )
        return self.zen_store.get_deployment(id_, hydrate=hydrate)

    list_kwargs: Dict[str, Any] = dict(
        id=f"startswith:{id_or_prefix}",
        hydrate=hydrate,
    )
    scope = ""
    if project:
        list_kwargs["project"] = project
        scope = f" in project {project}"

    entity = self.list_deployments(**list_kwargs)

    # If only a single entity is found, return it.
    if entity.total == 1:
        return entity.items[0]

    # If no entity is found, raise an error.
    if entity.total == 0:
        raise KeyError(
            f"No deployment have been found that have either an id or "
            f"prefix that matches the provided string '{id_or_prefix}'{scope}."
        )

    raise ZenKeyError(
        f"{entity.total} deployments have been found{scope} that have "
        f"an ID that matches the provided "
        f"string '{id_or_prefix}':\n"
        f"{[entity.items]}.\n"
        f"Please use the id to uniquely identify "
        f"only one of the deployments."
    )

get_event_source(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)

Get an event source by name, ID or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, ID or prefix of the stack.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
EventSourceResponse	The event_source.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def get_event_source(
    self,
    name_id_or_prefix: Union[UUID, str],
    allow_name_prefix_match: bool = True,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> EventSourceResponse:
    """Get an event source by name, ID or prefix.

    Args:
        name_id_or_prefix: The name, ID or prefix of the stack.
        allow_name_prefix_match: If True, allow matching by name prefix.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The event_source.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_event_source,
        list_method=self.list_event_sources,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        project=project,
        hydrate=hydrate,
    )

get_flavor(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)

Get a stack component flavor.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	str	The name, ID or prefix to the id of the flavor to get.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
FlavorResponse	The stack component flavor.

Source code in src/zenml/client.py

def get_flavor(
    self,
    name_id_or_prefix: str,
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
) -> FlavorResponse:
    """Get a stack component flavor.

    Args:
        name_id_or_prefix: The name, ID or prefix to the id of the flavor
            to get.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The stack component flavor.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_flavor,
        list_method=self.list_flavors,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
    )

get_flavor_by_name_and_type(name, component_type)

Fetches a registered flavor.

Parameters:

Name	Type	Description	Default
component_type	StackComponentType	The type of the component to fetch.	required
name	str	The name of the flavor to fetch.	required

Returns:

Type	Description
FlavorResponse	The registered flavor.

Raises:

Type	Description
KeyError	If no flavor exists for the given type and name.

Source code in src/zenml/client.py

def get_flavor_by_name_and_type(
    self, name: str, component_type: "StackComponentType"
) -> FlavorResponse:
    """Fetches a registered flavor.

    Args:
        component_type: The type of the component to fetch.
        name: The name of the flavor to fetch.

    Returns:
        The registered flavor.

    Raises:
        KeyError: If no flavor exists for the given type and name.
    """
    logger.debug(
        f"Fetching the flavor of type {component_type} with name {name}."
    )

    if not (
        flavors := self.list_flavors(
            type=component_type, name=name, hydrate=True
        ).items
    ):
        raise KeyError(
            f"No flavor with name '{name}' and type '{component_type}' "
            "exists."
        )
    if len(flavors) > 1:
        raise KeyError(
            f"More than one flavor with name {name} and type "
            f"{component_type} exists."
        )

    return flavors[0]

get_flavors_by_type(component_type)

Fetches the list of flavor for a stack component type.

Parameters:

Name	Type	Description	Default
component_type	StackComponentType	The type of the component to fetch.	required

Returns:

Type	Description
Page[FlavorResponse]	The list of flavors.

Source code in src/zenml/client.py

def get_flavors_by_type(
    self, component_type: "StackComponentType"
) -> Page[FlavorResponse]:
    """Fetches the list of flavor for a stack component type.

    Args:
        component_type: The type of the component to fetch.

    Returns:
        The list of flavors.
    """
    logger.debug(f"Fetching the flavors of type {component_type}.")

    return self.list_flavors(
        type=component_type,
    )

get_instance() classmethod

Return the Client singleton instance.

Returns:

Type	Description
Optional[Client]	The Client singleton instance or None, if the Client hasn't
Optional[Client]	been initialized yet.

Source code in src/zenml/client.py

@classmethod
def get_instance(cls) -> Optional["Client"]:
    """Return the Client singleton instance.

    Returns:
        The Client singleton instance or None, if the Client hasn't
        been initialized yet.
    """
    return cls._global_client

get_model(model_name_or_id, project=None, hydrate=True, bypass_lazy_loader=False)

Get an existing model from Model Control Plane.

Parameters:

Name	Type	Description	Default
model_name_or_id	Union[str, UUID]	name or id of the model to be retrieved.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True
bypass_lazy_loader	bool	Whether to bypass the lazy loader.	False

Returns:

Type	Description
ModelResponse	The model of interest.

Source code in src/zenml/client.py

def get_model(
    self,
    model_name_or_id: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
    bypass_lazy_loader: bool = False,
) -> ModelResponse:
    """Get an existing model from Model Control Plane.

    Args:
        model_name_or_id: name or id of the model to be retrieved.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.
        bypass_lazy_loader: Whether to bypass the lazy loader.

    Returns:
        The model of interest.
    """
    if not bypass_lazy_loader:
        if cll := client_lazy_loader(
            "get_model",
            model_name_or_id=model_name_or_id,
            hydrate=hydrate,
            project=project,
        ):
            return cll  # type: ignore[return-value]

    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_model,
        list_method=self.list_models,
        name_id_or_prefix=model_name_or_id,
        project=project,
        hydrate=hydrate,
    )

get_model_version(model_name_or_id=None, model_version_name_or_number_or_id=None, project=None, hydrate=True)

Get an existing model version from Model Control Plane.

Parameters:

Name	Type	Description	Default
model_name_or_id	Optional[Union[str, UUID]]	name or id of the model containing the model version.	None
model_version_name_or_number_or_id	Optional[Union[str, int, ModelStages, UUID]]	name, id, stage or number of the model version to be retrieved. If skipped - latest version is retrieved.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ModelVersionResponse	The model version of interest.

Raises:

Type	Description
RuntimeError	In case method inputs don't adhere to restrictions.
KeyError	In case no model version with the identifiers exists.
ValueError	In case retrieval is attempted using non UUID model version identifier and no model identifier provided.

Source code in src/zenml/client.py

def get_model_version(
    self,
    model_name_or_id: Optional[Union[str, UUID]] = None,
    model_version_name_or_number_or_id: Optional[
        Union[str, int, ModelStages, UUID]
    ] = None,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> ModelVersionResponse:
    """Get an existing model version from Model Control Plane.

    Args:
        model_name_or_id: name or id of the model containing the model
            version.
        model_version_name_or_number_or_id: name, id, stage or number of
            the model version to be retrieved. If skipped - latest version
            is retrieved.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The model version of interest.

    Raises:
        RuntimeError: In case method inputs don't adhere to restrictions.
        KeyError: In case no model version with the identifiers exists.
        ValueError: In case retrieval is attempted using non UUID model version
            identifier and no model identifier provided.
    """
    if (
        not is_valid_uuid(model_version_name_or_number_or_id)
        and model_name_or_id is None
    ):
        raise ValueError(
            "No model identifier provided and model version identifier "
            f"`{model_version_name_or_number_or_id}` is not a valid UUID."
        )
    if cll := client_lazy_loader(
        "get_model_version",
        model_name_or_id=model_name_or_id,
        model_version_name_or_number_or_id=model_version_name_or_number_or_id,
        project=project,
        hydrate=hydrate,
    ):
        return cll  # type: ignore[return-value]

    if model_version_name_or_number_or_id is None:
        model_version_name_or_number_or_id = ModelStages.LATEST

    if isinstance(model_version_name_or_number_or_id, UUID):
        return self.zen_store.get_model_version(
            model_version_id=model_version_name_or_number_or_id,
            hydrate=hydrate,
        )
    elif isinstance(model_version_name_or_number_or_id, int):
        model_versions = self.zen_store.list_model_versions(
            model_version_filter_model=ModelVersionFilter(
                model=model_name_or_id,
                number=model_version_name_or_number_or_id,
                project=project or self.active_project.id,
            ),
            hydrate=hydrate,
        ).items
    elif isinstance(model_version_name_or_number_or_id, str):
        if model_version_name_or_number_or_id == ModelStages.LATEST:
            model_versions = self.zen_store.list_model_versions(
                model_version_filter_model=ModelVersionFilter(
                    model=model_name_or_id,
                    sort_by=f"{SorterOps.DESCENDING}:number",
                    project=project or self.active_project.id,
                ),
                hydrate=hydrate,
            ).items

            if len(model_versions) > 0:
                model_versions = [model_versions[0]]
            else:
                model_versions = []
        elif model_version_name_or_number_or_id in ModelStages.values():
            model_versions = self.zen_store.list_model_versions(
                model_version_filter_model=ModelVersionFilter(
                    model=model_name_or_id,
                    stage=model_version_name_or_number_or_id,
                    project=project or self.active_project.id,
                ),
                hydrate=hydrate,
            ).items
        else:
            model_versions = self.zen_store.list_model_versions(
                model_version_filter_model=ModelVersionFilter(
                    model=model_name_or_id,
                    name=model_version_name_or_number_or_id,
                    project=project or self.active_project.id,
                ),
                hydrate=hydrate,
            ).items
    else:
        raise RuntimeError(
            f"The model version identifier "
            f"`{model_version_name_or_number_or_id}` is not"
            f"of the correct type."
        )

    if len(model_versions) == 1:
        return model_versions[0]
    elif len(model_versions) == 0:
        raise KeyError(
            f"No model version found for model "
            f"`{model_name_or_id}` with version identifier "
            f"`{model_version_name_or_number_or_id}`."
        )
    else:
        raise RuntimeError(
            f"The model version identifier "
            f"`{model_version_name_or_number_or_id}` is not"
            f"unique for model `{model_name_or_id}`."
        )

get_pipeline(name_id_or_prefix, project=None, hydrate=True)

Get a pipeline by name, id or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or ID prefix of the pipeline.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
PipelineResponse	The pipeline.

Source code in src/zenml/client.py

def get_pipeline(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> PipelineResponse:
    """Get a pipeline by name, id or prefix.

    Args:
        name_id_or_prefix: The name, ID or ID prefix of the pipeline.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The pipeline.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_pipeline,
        list_method=self.list_pipelines,
        name_id_or_prefix=name_id_or_prefix,
        project=project,
        hydrate=hydrate,
    )

get_pipeline_run(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)

Gets a pipeline run by name, ID, or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	Name, ID, or prefix of the pipeline run.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
PipelineRunResponse	The pipeline run.

Source code in src/zenml/client.py

def get_pipeline_run(
    self,
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> PipelineRunResponse:
    """Gets a pipeline run by name, ID, or prefix.

    Args:
        name_id_or_prefix: Name, ID, or prefix of the pipeline run.
        allow_name_prefix_match: If True, allow matching by name prefix.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The pipeline run.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_run,
        list_method=self.list_pipeline_runs,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        project=project,
        hydrate=hydrate,
    )

get_project(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)

Gets a project.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Optional[Union[UUID, str]]	The name or ID of the project.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ProjectResponse	The project

Source code in src/zenml/client.py

def get_project(
    self,
    name_id_or_prefix: Optional[Union[UUID, str]],
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
) -> ProjectResponse:
    """Gets a project.

    Args:
        name_id_or_prefix: The name or ID of the project.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The project
    """
    if not name_id_or_prefix:
        return self.active_project
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_project,
        list_method=self.list_projects,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
    )

get_run_step(step_run_id, hydrate=True)

Get a step run by ID.

Parameters:

Name	Type	Description	Default
step_run_id	UUID	The ID of the step run to get.	required
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
StepRunResponse	The step run.

Source code in src/zenml/client.py

def get_run_step(
    self,
    step_run_id: UUID,
    hydrate: bool = True,
) -> StepRunResponse:
    """Get a step run by ID.

    Args:
        step_run_id: The ID of the step run to get.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The step run.
    """
    return self.zen_store.get_run_step(
        step_run_id,
        hydrate=hydrate,
    )

get_run_template(name_id_or_prefix, project=None, hydrate=True)

Get a run template.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	Name/ID/ID prefix of the template to get.	required
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
RunTemplateResponse	The run template.

Source code in src/zenml/client.py

def get_run_template(
    self,
    name_id_or_prefix: Union[str, UUID],
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> RunTemplateResponse:
    """Get a run template.

    Args:
        name_id_or_prefix: Name/ID/ID prefix of the template to get.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The run template.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_run_template,
        list_method=self.list_run_templates,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
        hydrate=hydrate,
    )

get_schedule(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)

Get a schedule by name, id or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, id or prefix of the schedule.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ScheduleResponse	The schedule.

Source code in src/zenml/client.py

def get_schedule(
    self,
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> ScheduleResponse:
    """Get a schedule by name, id or prefix.

    Args:
        name_id_or_prefix: The name, id or prefix of the schedule.
        allow_name_prefix_match: If True, allow matching by name prefix.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The schedule.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_schedule,
        list_method=self.list_schedules,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        project=project,
        hydrate=hydrate,
    )

get_secret(name_id_or_prefix, private=None, allow_partial_name_match=True, allow_partial_id_match=True, hydrate=True)

Get a secret.

Get a secret identified by a name, ID or prefix of the name or ID and optionally a scope.

If a private status is not provided, privately scoped secrets will be searched for first, followed by publicly scoped secrets. When a name or prefix is used instead of a UUID value, each scope is first searched for an exact match, then for a ID prefix or name substring match before moving on to the next scope.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or prefix to the id of the secret to get.	required
private	Optional[bool]	Whether the secret is private. If not set, all secrets will be searched for, prioritizing privately scoped secrets.	None
allow_partial_name_match	bool	If True, allow partial name matches.	True
allow_partial_id_match	bool	If True, allow partial ID matches.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
SecretResponse	The secret.

Raises:

Type	Description
KeyError	If no secret is found.
ZenKeyError	If multiple secrets are found.
NotImplementedError	If centralized secrets management is not enabled.

Source code in src/zenml/client.py

def get_secret(
    self,
    name_id_or_prefix: Union[str, UUID],
    private: Optional[bool] = None,
    allow_partial_name_match: bool = True,
    allow_partial_id_match: bool = True,
    hydrate: bool = True,
) -> SecretResponse:
    """Get a secret.

    Get a secret identified by a name, ID or prefix of the name or ID and
    optionally a scope.

    If a private status is not provided, privately scoped secrets will be
    searched for first, followed by publicly scoped secrets. When a name or
    prefix is used instead of a UUID value, each scope is first searched for
    an exact match, then for a ID prefix or name substring match before
    moving on to the next scope.

    Args:
        name_id_or_prefix: The name, ID or prefix to the id of the secret
            to get.
        private: Whether the secret is private. If not set, all secrets will
            be searched for, prioritizing privately scoped secrets.
        allow_partial_name_match: If True, allow partial name matches.
        allow_partial_id_match: If True, allow partial ID matches.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The secret.

    Raises:
        KeyError: If no secret is found.
        ZenKeyError: If multiple secrets are found.
        NotImplementedError: If centralized secrets management is not
            enabled.
    """
    from zenml.utils.uuid_utils import is_valid_uuid

    try:
        # First interpret as full UUID
        if is_valid_uuid(name_id_or_prefix):
            # Fetch by ID; filter by scope if provided
            secret = self.zen_store.get_secret(
                secret_id=UUID(name_id_or_prefix)
                if isinstance(name_id_or_prefix, str)
                else name_id_or_prefix,
                hydrate=hydrate,
            )
            if private is not None and secret.private != private:
                raise KeyError(
                    f"No secret found with ID {str(name_id_or_prefix)}"
                )

            return secret
    except NotImplementedError:
        raise NotImplementedError(
            "centralized secrets management is not supported or explicitly "
            "disabled in the target ZenML deployment."
        )

    # If not a UUID, try to find by name and then by prefix
    assert not isinstance(name_id_or_prefix, UUID)

    # Private statuses to search in order of priority
    search_private_statuses = (
        [False, True] if private is None else [private]
    )

    secrets = self.list_secrets(
        logical_operator=LogicalOperators.OR,
        name=f"contains:{name_id_or_prefix}"
        if allow_partial_name_match
        else f"equals:{name_id_or_prefix}",
        id=f"startswith:{name_id_or_prefix}"
        if allow_partial_id_match
        else None,
        hydrate=hydrate,
    )

    for search_private_status in search_private_statuses:
        partial_matches: List[SecretResponse] = []
        for secret in secrets.items:
            if secret.private != search_private_status:
                continue
            # Exact match
            if secret.name == name_id_or_prefix:
                # Need to fetch the secret again to get the secret values
                return self.zen_store.get_secret(
                    secret_id=secret.id,
                    hydrate=hydrate,
                )
            # Partial match
            partial_matches.append(secret)

        if len(partial_matches) > 1:
            match_summary = "\n".join(
                [
                    f"[{secret.id}]: name = {secret.name}"
                    for secret in partial_matches
                ]
            )
            raise ZenKeyError(
                f"{len(partial_matches)} secrets have been found that have "
                f"a name or ID that matches the provided "
                f"string '{name_id_or_prefix}':\n"
                f"{match_summary}.\n"
                f"Please use the id to uniquely identify "
                f"only one of the secrets."
            )

        # If only a single secret is found, return it
        if len(partial_matches) == 1:
            # Need to fetch the secret again to get the secret values
            return self.zen_store.get_secret(
                secret_id=partial_matches[0].id,
                hydrate=hydrate,
            )
    private_status = ""
    if private is not None:
        private_status = "private " if private else "public "
    msg = (
        f"No {private_status}secret found with name, ID or prefix "
        f"'{name_id_or_prefix}'"
    )

    raise KeyError(msg)

get_secret_by_name_and_private_status(name, private=None, hydrate=True)

Fetches a registered secret with a given name and optional private status.

This is a version of get_secret that restricts the search to a given name and an optional private status, without doing any prefix or UUID matching.

If no private status is provided, the search will be done first for private secrets, then for public secrets.

Parameters:

Name	Type	Description	Default
name	str	The name of the secret to get.	required
private	Optional[bool]	The private status of the secret to get.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
SecretResponse	The registered secret.

Raises:

Type	Description
KeyError	If no secret exists for the given name in the given scope.

Source code in src/zenml/client.py

def get_secret_by_name_and_private_status(
    self,
    name: str,
    private: Optional[bool] = None,
    hydrate: bool = True,
) -> SecretResponse:
    """Fetches a registered secret with a given name and optional private status.

    This is a version of get_secret that restricts the search to a given
    name and an optional private status, without doing any prefix or UUID
    matching.

    If no private status is provided, the search will be done first for
    private secrets, then for public secrets.

    Args:
        name: The name of the secret to get.
        private: The private status of the secret to get.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The registered secret.

    Raises:
        KeyError: If no secret exists for the given name in the given scope.
    """
    logger.debug(
        f"Fetching the secret with name '{name}' and private status "
        f"'{private}'."
    )

    # Private statuses to search in order of priority
    search_private_statuses = (
        [False, True] if private is None else [private]
    )

    for search_private_status in search_private_statuses:
        secrets = self.list_secrets(
            logical_operator=LogicalOperators.AND,
            name=f"equals:{name}",
            private=search_private_status,
            hydrate=hydrate,
        )

        if len(secrets.items) >= 1:
            # Need to fetch the secret again to get the secret values
            return self.zen_store.get_secret(
                secret_id=secrets.items[0].id, hydrate=hydrate
            )

    private_status = ""
    if private is not None:
        private_status = "private " if private else "public "
    msg = f"No {private_status}secret with name '{name}' was found"

    raise KeyError(msg)

get_service(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True, type=None, project=None)

Gets a service.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name or ID of the service.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True
type	Optional[str]	The type of the service.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
ServiceResponse	The Service

Source code in src/zenml/client.py

def get_service(
    self,
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
    type: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
) -> ServiceResponse:
    """Gets a service.

    Args:
        name_id_or_prefix: The name or ID of the service.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.
        type: The type of the service.
        project: The project name/ID to filter by.

    Returns:
        The Service
    """

    def type_scoped_list_method(
        hydrate: bool = True,
        **kwargs: Any,
    ) -> Page[ServiceResponse]:
        """Call `zen_store.list_services` with type scoping.

        Args:
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            **kwargs: Keyword arguments to pass to `ServiceFilterModel`.

        Returns:
            The type-scoped list of services.
        """
        service_filter_model = ServiceFilter(**kwargs)
        if type:
            service_filter_model.set_type(type=type)
        return self.zen_store.list_services(
            filter_model=service_filter_model,
            hydrate=hydrate,
        )

    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_service,
        list_method=type_scoped_list_method,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        project=project,
        hydrate=hydrate,
    )

get_service_account(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)

Gets a service account.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name or ID of the service account.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ServiceAccountResponse	The ServiceAccount

Source code in src/zenml/client.py

def get_service_account(
    self,
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
) -> ServiceAccountResponse:
    """Gets a service account.

    Args:
        name_id_or_prefix: The name or ID of the service account.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The ServiceAccount
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_service_account,
        list_method=self.list_service_accounts,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
    )

get_service_connector(name_id_or_prefix, allow_name_prefix_match=True, load_secrets=False, hydrate=True)

Fetches a registered service connector.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The id of the service connector to fetch.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
load_secrets	bool	If True, load the secrets for the service connector.	False
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ServiceConnectorResponse	The registered service connector.

Source code in src/zenml/client.py

def get_service_connector(
    self,
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    load_secrets: bool = False,
    hydrate: bool = True,
) -> ServiceConnectorResponse:
    """Fetches a registered service connector.

    Args:
        name_id_or_prefix: The id of the service connector to fetch.
        allow_name_prefix_match: If True, allow matching by name prefix.
        load_secrets: If True, load the secrets for the service connector.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The registered service connector.
    """
    connector = self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_service_connector,
        list_method=self.list_service_connectors,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
    )

    if load_secrets and connector.secret_id:
        client = Client()
        try:
            secret = client.get_secret(
                name_id_or_prefix=connector.secret_id,
                allow_partial_id_match=False,
                allow_partial_name_match=False,
            )
        except KeyError as err:
            logger.error(
                "Unable to retrieve secret values associated with "
                f"service connector '{connector.name}': {err}"
            )
        else:
            # Add secret values to connector configuration
            connector.secrets.update(secret.values)

    return connector

get_service_connector_client(name_id_or_prefix, resource_type=None, resource_id=None, verify=False)

Get the client side of a service connector instance to use with a local client.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, id or prefix of the service connector to use.	required
resource_type	Optional[str]	The type of the resource to connect to. If not provided, the resource type from the service connector configuration will be used.	None
resource_id	Optional[str]	The ID of a particular resource instance to configure the local client to connect to. If the connector instance is already configured with a resource ID that is not the same or equivalent to the one requested, a ValueError exception is raised. May be omitted for connectors and resource types that do not support multiple resource instances.	None
verify	bool	Whether to verify that the service connector configuration and credentials can be used to gain access to the resource.	False

Returns:

Type	Description
ServiceConnector	The client side of the indicated service connector instance that can
ServiceConnector	be used to connect to the resource locally.

Source code in src/zenml/client.py

def get_service_connector_client(
    self,
    name_id_or_prefix: Union[UUID, str],
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
    verify: bool = False,
) -> "ServiceConnector":
    """Get the client side of a service connector instance to use with a local client.

    Args:
        name_id_or_prefix: The name, id or prefix of the service connector
            to use.
        resource_type: The type of the resource to connect to. If not
            provided, the resource type from the service connector
            configuration will be used.
        resource_id: The ID of a particular resource instance to configure
            the local client to connect to. If the connector instance is
            already configured with a resource ID that is not the same or
            equivalent to the one requested, a `ValueError` exception is
            raised. May be omitted for connectors and resource types that do
            not support multiple resource instances.
        verify: Whether to verify that the service connector configuration
            and credentials can be used to gain access to the resource.

    Returns:
        The client side of the indicated service connector instance that can
        be used to connect to the resource locally.
    """
    from zenml.service_connectors.service_connector_registry import (
        service_connector_registry,
    )

    # Get the service connector model
    service_connector = self.get_service_connector(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
    )

    connector_type = self.get_service_connector_type(
        service_connector.type
    )

    # Prefer to fetch the connector client from the server if the
    # implementation if available there, because some auth methods rely on
    # the server-side authentication environment
    if connector_type.remote:
        connector_client_model = (
            self.zen_store.get_service_connector_client(
                service_connector_id=service_connector.id,
                resource_type=resource_type,
                resource_id=resource_id,
            )
        )

        connector_client = (
            service_connector_registry.instantiate_connector(
                model=connector_client_model
            )
        )

        if verify:
            # Verify the connector client on the local machine, because the
            # server-side implementation may not be able to do so
            connector_client.verify()
    else:
        connector_instance = (
            service_connector_registry.instantiate_connector(
                model=service_connector
            )
        )

        # Fetch the connector client
        connector_client = connector_instance.get_connector_client(
            resource_type=resource_type,
            resource_id=resource_id,
        )

    return connector_client

get_service_connector_type(connector_type)

Returns the requested service connector type.

Parameters:

Name	Type	Description	Default
connector_type	str	the service connector type identifier.	required

Returns:

Type	Description
ServiceConnectorTypeModel	The requested service connector type.

Source code in src/zenml/client.py

def get_service_connector_type(
    self,
    connector_type: str,
) -> ServiceConnectorTypeModel:
    """Returns the requested service connector type.

    Args:
        connector_type: the service connector type identifier.

    Returns:
        The requested service connector type.
    """
    return self.zen_store.get_service_connector_type(
        connector_type=connector_type,
    )

get_settings(hydrate=True)

Get the server settings.

Parameters:

Name	Type	Description	Default
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ServerSettingsResponse	The server settings.

Source code in src/zenml/client.py

def get_settings(self, hydrate: bool = True) -> ServerSettingsResponse:
    """Get the server settings.

    Args:
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The server settings.
    """
    return self.zen_store.get_server_settings(hydrate=hydrate)

get_stack(name_id_or_prefix=None, allow_name_prefix_match=True, hydrate=True)

Get a stack by name, ID or prefix.

If no name, ID or prefix is provided, the active stack is returned.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Optional[Union[UUID, str]]	The name, ID or prefix of the stack.	None
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
StackResponse	The stack.

Source code in src/zenml/client.py

def get_stack(
    self,
    name_id_or_prefix: Optional[Union[UUID, str]] = None,
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
) -> StackResponse:
    """Get a stack by name, ID or prefix.

    If no name, ID or prefix is provided, the active stack is returned.

    Args:
        name_id_or_prefix: The name, ID or prefix of the stack.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The stack.
    """
    if name_id_or_prefix is not None:
        return self._get_entity_by_id_or_name_or_prefix(
            get_method=self.zen_store.get_stack,
            list_method=self.list_stacks,
            name_id_or_prefix=name_id_or_prefix,
            allow_name_prefix_match=allow_name_prefix_match,
            hydrate=hydrate,
        )
    else:
        return self.active_stack_model

get_stack_component(component_type, name_id_or_prefix=None, allow_name_prefix_match=True, hydrate=True)

Fetches a registered stack component.

If the name_id_or_prefix is provided, it will try to fetch the component with the corresponding identifier. If not, it will try to fetch the active component of the given type.

Parameters:

Name	Type	Description	Default
component_type	StackComponentType	The type of the component to fetch	required
name_id_or_prefix	Optional[Union[str, UUID]]	The id of the component to fetch.	None
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
ComponentResponse	The registered stack component.

Raises:

Type	Description
KeyError	If no name_id_or_prefix is provided and no such component is part of the active stack.

Source code in src/zenml/client.py

def get_stack_component(
    self,
    component_type: StackComponentType,
    name_id_or_prefix: Optional[Union[str, UUID]] = None,
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
) -> ComponentResponse:
    """Fetches a registered stack component.

    If the name_id_or_prefix is provided, it will try to fetch the component
    with the corresponding identifier. If not, it will try to fetch the
    active component of the given type.

    Args:
        component_type: The type of the component to fetch
        name_id_or_prefix: The id of the component to fetch.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The registered stack component.

    Raises:
        KeyError: If no name_id_or_prefix is provided and no such component
            is part of the active stack.
    """
    # If no `name_id_or_prefix` provided, try to get the active component.
    if not name_id_or_prefix:
        components = self.active_stack_model.components.get(
            component_type, None
        )
        if components:
            return components[0]
        raise KeyError(
            "No name_id_or_prefix provided and there is no active "
            f"{component_type} in the current active stack."
        )

    # Else, try to fetch the component with an explicit type filter
    def type_scoped_list_method(
        hydrate: bool = False,
        **kwargs: Any,
    ) -> Page[ComponentResponse]:
        """Call `zen_store.list_stack_components` with type scoping.

        Args:
            hydrate: Flag deciding whether to hydrate the output model(s)
                by including metadata fields in the response.
            **kwargs: Keyword arguments to pass to `ComponentFilterModel`.

        Returns:
            The type-scoped list of components.
        """
        component_filter_model = ComponentFilter(**kwargs)
        component_filter_model.set_scope_type(
            component_type=component_type
        )
        return self.zen_store.list_stack_components(
            component_filter_model=component_filter_model,
            hydrate=hydrate,
        )

    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_stack_component,
        list_method=type_scoped_list_method,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
    )

get_tag(tag_name_or_id, hydrate=True)

Get an existing tag.

Parameters:

Name	Type	Description	Default
tag_name_or_id	Union[str, UUID]	name or id of the tag to be retrieved.	required
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
TagResponse	The tag of interest.

Source code in src/zenml/client.py

def get_tag(
    self,
    tag_name_or_id: Union[str, UUID],
    hydrate: bool = True,
) -> TagResponse:
    """Get an existing tag.

    Args:
        tag_name_or_id: name or id of the tag to be retrieved.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The tag of interest.
    """
    return self.zen_store.get_tag(
        tag_name_or_id=tag_name_or_id,
        hydrate=hydrate,
    )

get_trigger(name_id_or_prefix, allow_name_prefix_match=True, project=None, hydrate=True)

Get a trigger by name, ID or prefix.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, ID or prefix of the trigger.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
TriggerResponse	The trigger.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def get_trigger(
    self,
    name_id_or_prefix: Union[UUID, str],
    allow_name_prefix_match: bool = True,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = True,
) -> TriggerResponse:
    """Get a trigger by name, ID or prefix.

    Args:
        name_id_or_prefix: The name, ID or prefix of the trigger.
        allow_name_prefix_match: If True, allow matching by name prefix.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The trigger.
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_trigger,
        list_method=self.list_triggers,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        project=project,
        hydrate=hydrate,
    )

get_trigger_execution(trigger_execution_id, hydrate=True)

Get a trigger execution by ID.

Parameters:

Name	Type	Description	Default
trigger_execution_id	UUID	The ID of the trigger execution to get.	required
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
TriggerExecutionResponse	The trigger execution.

Source code in src/zenml/client.py

def get_trigger_execution(
    self,
    trigger_execution_id: UUID,
    hydrate: bool = True,
) -> TriggerExecutionResponse:
    """Get a trigger execution by ID.

    Args:
        trigger_execution_id: The ID of the trigger execution to get.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The trigger execution.
    """
    return self.zen_store.get_trigger_execution(
        trigger_execution_id=trigger_execution_id, hydrate=hydrate
    )

get_user(name_id_or_prefix, allow_name_prefix_match=True, hydrate=True)

Gets a user.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name or ID of the user.	required
allow_name_prefix_match	bool	If True, allow matching by name prefix.	True
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	True

Returns:

Type	Description
UserResponse	The User

Source code in src/zenml/client.py

def get_user(
    self,
    name_id_or_prefix: Union[str, UUID],
    allow_name_prefix_match: bool = True,
    hydrate: bool = True,
) -> UserResponse:
    """Gets a user.

    Args:
        name_id_or_prefix: The name or ID of the user.
        allow_name_prefix_match: If True, allow matching by name prefix.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The User
    """
    return self._get_entity_by_id_or_name_or_prefix(
        get_method=self.zen_store.get_user,
        list_method=self.list_users,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=allow_name_prefix_match,
        hydrate=hydrate,
    )

initialize(root=None) staticmethod

Initializes a new ZenML repository at the given path.

Parameters:

Name	Type	Description	Default
root	Optional[Path]	The root directory where the repository should be created. If None, the current working directory is used.	None

Raises:

Type	Description
InitializationException	If the root directory already contains a ZenML repository.

Source code in src/zenml/client.py

@staticmethod
def initialize(
    root: Optional[Path] = None,
) -> None:
    """Initializes a new ZenML repository at the given path.

    Args:
        root: The root directory where the repository should be created.
            If None, the current working directory is used.

    Raises:
        InitializationException: If the root directory already contains a
            ZenML repository.
    """
    root = root or Path.cwd()
    logger.debug("Initializing new repository at path %s.", root)
    if Client.is_repository_directory(root):
        raise InitializationException(
            f"Found existing ZenML repository at path '{root}'."
        )

    config_directory = str(root / REPOSITORY_DIRECTORY_NAME)
    io_utils.create_dir_recursive_if_not_exists(config_directory)
    # Initialize the repository configuration at the custom path
    Client(root=root)

is_inside_repository(file_path) staticmethod

Returns whether a file is inside the active ZenML repository.

Parameters:

Name	Type	Description	Default
file_path	str	A file path.	required

Returns:

Type	Description
bool	True if the file is inside the active ZenML repository, False
bool	otherwise.

Source code in src/zenml/client.py

@staticmethod
def is_inside_repository(file_path: str) -> bool:
    """Returns whether a file is inside the active ZenML repository.

    Args:
        file_path: A file path.

    Returns:
        True if the file is inside the active ZenML repository, False
        otherwise.
    """
    if repo_path := Client.find_repository():
        return repo_path in Path(file_path).resolve().parents
    return False

is_repository_directory(path) staticmethod

Checks whether a ZenML client exists at the given path.

Parameters:

Name	Type	Description	Default
path	Path	The path to check.	required

Returns:

Type	Description
bool	True if a ZenML client exists at the given path,
bool	False otherwise.

Source code in src/zenml/client.py

@staticmethod
def is_repository_directory(path: Path) -> bool:
    """Checks whether a ZenML client exists at the given path.

    Args:
        path: The path to check.

    Returns:
        True if a ZenML client exists at the given path,
        False otherwise.
    """
    config_dir = path / REPOSITORY_DIRECTORY_NAME
    return fileio.isdir(str(config_dir))

list_actions(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, flavor=None, action_type=None, project=None, user=None, hydrate=False)

List actions.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of the action to filter by.	None
created	Optional[datetime]	Use to filter by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
name	Optional[str]	The name of the action to filter by.	None
flavor	Optional[str]	The flavor of the action to filter by.	None
action_type	Optional[str]	The type of the action to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[ActionResponse]	A page of actions.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def list_actions(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    name: Optional[str] = None,
    flavor: Optional[str] = None,
    action_type: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[ActionResponse]:
    """List actions.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of the action to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        name: The name of the action to filter by.
        flavor: The flavor of the action to filter by.
        action_type: The type of the action to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of actions.
    """
    filter_model = ActionFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        project=project or self.active_project.id,
        user=user,
        name=name,
        id=id,
        flavor=flavor,
        plugin_subtype=action_type,
        created=created,
        updated=updated,
    )
    return self.zen_store.list_actions(filter_model, hydrate=hydrate)

list_api_keys(service_account_name_id_or_prefix, sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, description=None, active=None, last_login=None, last_rotated=None, hydrate=False)

List all API keys.

Parameters:

Name	Type	Description	Default
service_account_name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the service account to list the API keys for.	required
sort_by	str	The column to sort by.	'created'
page	int	The page of items.	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages.	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or].	AND
id	Optional[Union[UUID, str]]	Use the id of the API key to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation.	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering.	None
name	Optional[str]	The name of the API key to filter by.	None
description	Optional[str]	The description of the API key to filter by.	None
active	Optional[bool]	Whether the API key is active or not.	None
last_login	Optional[Union[datetime, str]]	The last time the API key was used.	None
last_rotated	Optional[Union[datetime, str]]	The last time the API key was rotated.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[APIKeyResponse]	A page of API keys matching the filter description.

Source code in src/zenml/client.py

def list_api_keys(
    self,
    service_account_name_id_or_prefix: Union[str, UUID],
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    description: Optional[str] = None,
    active: Optional[bool] = None,
    last_login: Optional[Union[datetime, str]] = None,
    last_rotated: Optional[Union[datetime, str]] = None,
    hydrate: bool = False,
) -> Page[APIKeyResponse]:
    """List all API keys.

    Args:
        service_account_name_id_or_prefix: The name, ID or prefix of the
            service account to list the API keys for.
        sort_by: The column to sort by.
        page: The page of items.
        size: The maximum size of all pages.
        logical_operator: Which logical operator to use [and, or].
        id: Use the id of the API key to filter by.
        created: Use to filter by time of creation.
        updated: Use the last updated date for filtering.
        name: The name of the API key to filter by.
        description: The description of the API key to filter by.
        active: Whether the API key is active or not.
        last_login: The last time the API key was used.
        last_rotated: The last time the API key was rotated.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of API keys matching the filter description.
    """
    service_account = self.get_service_account(
        name_id_or_prefix=service_account_name_id_or_prefix,
        allow_name_prefix_match=False,
    )
    filter_model = APIKeyFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        name=name,
        description=description,
        active=active,
        last_login=last_login,
        last_rotated=last_rotated,
    )
    return self.zen_store.list_api_keys(
        service_account_id=service_account.id,
        filter_model=filter_model,
        hydrate=hydrate,
    )

list_artifact_versions(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, artifact=None, name=None, version=None, version_number=None, artifact_store_id=None, type=None, data_type=None, uri=None, materializer=None, project=None, model_version_id=None, only_unused=False, has_custom_name=None, user=None, model=None, pipeline_run=None, run_metadata=None, tag=None, tags=None, hydrate=False)

Get a list of artifact versions.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of artifact version to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
artifact	Optional[Union[str, UUID]]	The name or ID of the artifact to filter by.	None
name	Optional[str]	The name of the artifact to filter by.	None
version	Optional[Union[str, int]]	The version of the artifact to filter by.	None
version_number	Optional[int]	The version number of the artifact to filter by.	None
artifact_store_id	Optional[Union[str, UUID]]	The id of the artifact store to filter by.	None
type	Optional[ArtifactType]	The type of the artifact to filter by.	None
data_type	Optional[str]	The data type of the artifact to filter by.	None
uri	Optional[str]	The uri of the artifact to filter by.	None
materializer	Optional[str]	The materializer of the artifact to filter by.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
model_version_id	Optional[Union[str, UUID]]	Filter by model version ID.	None
only_unused	Optional[bool]	Only return artifact versions that are not used in any pipeline runs.	False
has_custom_name	Optional[bool]	Filter artifacts with/without custom names.	None
tag	Optional[str]	A tag to filter by.	None
tags	Optional[List[str]]	Tags to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name or ID.	None
model	Optional[Union[UUID, str]]	Filter by model name or ID.	None
pipeline_run	Optional[Union[UUID, str]]	Filter by pipeline run name or ID.	None
run_metadata	Optional[List[str]]	Filter by run metadata.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[ArtifactVersionResponse]	A list of artifact versions.

Source code in src/zenml/client.py

def list_artifact_versions(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    artifact: Optional[Union[str, UUID]] = None,
    name: Optional[str] = None,
    version: Optional[Union[str, int]] = None,
    version_number: Optional[int] = None,
    artifact_store_id: Optional[Union[str, UUID]] = None,
    type: Optional[ArtifactType] = None,
    data_type: Optional[str] = None,
    uri: Optional[str] = None,
    materializer: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    model_version_id: Optional[Union[str, UUID]] = None,
    only_unused: Optional[bool] = False,
    has_custom_name: Optional[bool] = None,
    user: Optional[Union[UUID, str]] = None,
    model: Optional[Union[UUID, str]] = None,
    pipeline_run: Optional[Union[UUID, str]] = None,
    run_metadata: Optional[List[str]] = None,
    tag: Optional[str] = None,
    tags: Optional[List[str]] = None,
    hydrate: bool = False,
) -> Page[ArtifactVersionResponse]:
    """Get a list of artifact versions.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of artifact version to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        artifact: The name or ID of the artifact to filter by.
        name: The name of the artifact to filter by.
        version: The version of the artifact to filter by.
        version_number: The version number of the artifact to filter by.
        artifact_store_id: The id of the artifact store to filter by.
        type: The type of the artifact to filter by.
        data_type: The data type of the artifact to filter by.
        uri: The uri of the artifact to filter by.
        materializer: The materializer of the artifact to filter by.
        project: The project name/ID to filter by.
        model_version_id: Filter by model version ID.
        only_unused: Only return artifact versions that are not used in
            any pipeline runs.
        has_custom_name: Filter artifacts with/without custom names.
        tag: A tag to filter by.
        tags: Tags to filter by.
        user: Filter by user name or ID.
        model: Filter by model name or ID.
        pipeline_run: Filter by pipeline run name or ID.
        run_metadata: Filter by run metadata.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A list of artifact versions.
    """
    if name:
        artifact = name

    artifact_version_filter_model = ArtifactVersionFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        artifact=artifact,
        version=str(version) if version else None,
        version_number=version_number,
        artifact_store_id=artifact_store_id,
        type=type,
        data_type=data_type,
        uri=uri,
        materializer=materializer,
        project=project or self.active_project.id,
        model_version_id=model_version_id,
        only_unused=only_unused,
        has_custom_name=has_custom_name,
        tag=tag,
        tags=tags,
        user=user,
        model=model,
        pipeline_run=pipeline_run,
        run_metadata=run_metadata,
    )
    return self.zen_store.list_artifact_versions(
        artifact_version_filter_model,
        hydrate=hydrate,
    )

list_artifacts(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, has_custom_name=None, user=None, project=None, hydrate=False, tag=None, tags=None)

Get a list of artifacts.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of artifact to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	The name of the artifact to filter by.	None
has_custom_name	Optional[bool]	Filter artifacts with/without custom names.	None
user	Optional[Union[UUID, str]]	Filter by user name or ID.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False
tag	Optional[str]	Filter artifacts by tag.	None
tags	Optional[List[str]]	Tags to filter by.	None

Returns:

Type	Description
Page[ArtifactResponse]	A list of artifacts.

Source code in src/zenml/client.py

def list_artifacts(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    has_custom_name: Optional[bool] = None,
    user: Optional[Union[UUID, str]] = None,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = False,
    tag: Optional[str] = None,
    tags: Optional[List[str]] = None,
) -> Page[ArtifactResponse]:
    """Get a list of artifacts.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of artifact to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: The name of the artifact to filter by.
        has_custom_name: Filter artifacts with/without custom names.
        user: Filter by user name or ID.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.
        tag: Filter artifacts by tag.
        tags: Tags to filter by.

    Returns:
        A list of artifacts.
    """
    artifact_filter_model = ArtifactFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        name=name,
        has_custom_name=has_custom_name,
        tag=tag,
        tags=tags,
        user=user,
        project=project or self.active_project.id,
    )
    return self.zen_store.list_artifacts(
        artifact_filter_model,
        hydrate=hydrate,
    )

list_authorized_devices(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, expires=None, client_id=None, status=None, trusted_device=None, user=None, failed_auth_attempts=None, last_login=None, hydrate=False)

List all authorized devices.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by.	'created'
page	int	The page of items.	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages.	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or].	AND
id	Optional[Union[UUID, str]]	Use the id of the code repository to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation.	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering.	None
expires	Optional[Union[datetime, str]]	Use the expiration date for filtering.	None
client_id	Union[UUID, str, None]	Use the client id for filtering.	None
status	Union[OAuthDeviceStatus, str, None]	Use the status for filtering.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
trusted_device	Union[bool, str, None]	Use the trusted device flag for filtering.	None
failed_auth_attempts	Union[int, str, None]	Use the failed auth attempts for filtering.	None
last_login	Optional[Union[datetime, str, None]]	Use the last login date for filtering.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[OAuthDeviceResponse]	A page of authorized devices matching the filter.

Source code in src/zenml/client.py

def list_authorized_devices(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    expires: Optional[Union[datetime, str]] = None,
    client_id: Union[UUID, str, None] = None,
    status: Union[OAuthDeviceStatus, str, None] = None,
    trusted_device: Union[bool, str, None] = None,
    user: Optional[Union[UUID, str]] = None,
    failed_auth_attempts: Union[int, str, None] = None,
    last_login: Optional[Union[datetime, str, None]] = None,
    hydrate: bool = False,
) -> Page[OAuthDeviceResponse]:
    """List all authorized devices.

    Args:
        sort_by: The column to sort by.
        page: The page of items.
        size: The maximum size of all pages.
        logical_operator: Which logical operator to use [and, or].
        id: Use the id of the code repository to filter by.
        created: Use to filter by time of creation.
        updated: Use the last updated date for filtering.
        expires: Use the expiration date for filtering.
        client_id: Use the client id for filtering.
        status: Use the status for filtering.
        user: Filter by user name/ID.
        trusted_device: Use the trusted device flag for filtering.
        failed_auth_attempts: Use the failed auth attempts for filtering.
        last_login: Use the last login date for filtering.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of authorized devices matching the filter.
    """
    filter_model = OAuthDeviceFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        expires=expires,
        client_id=client_id,
        user=user,
        status=status,
        trusted_device=trusted_device,
        failed_auth_attempts=failed_auth_attempts,
        last_login=last_login,
    )
    return self.zen_store.list_authorized_devices(
        filter_model=filter_model,
        hydrate=hydrate,
    )

list_builds(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, project=None, user=None, pipeline_id=None, stack_id=None, container_registry_id=None, is_local=None, contains_code=None, zenml_version=None, python_version=None, checksum=None, stack_checksum=None, duration=None, hydrate=False)

List all builds.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of build to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
pipeline_id	Optional[Union[str, UUID]]	The id of the pipeline to filter by.	None
stack_id	Optional[Union[str, UUID]]	The id of the stack to filter by.	None
container_registry_id	Optional[Union[UUID, str]]	The id of the container registry to filter by.	None
is_local	Optional[bool]	Use to filter local builds.	None
contains_code	Optional[bool]	Use to filter builds that contain code.	None
zenml_version	Optional[str]	The version of ZenML to filter by.	None
python_version	Optional[str]	The Python version to filter by.	None
checksum	Optional[str]	The build checksum to filter by.	None
stack_checksum	Optional[str]	The stack checksum to filter by.	None
duration	Optional[Union[int, str]]	The duration of the build in seconds to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[PipelineBuildResponse]	A page with builds fitting the filter description

Source code in src/zenml/client.py

def list_builds(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    pipeline_id: Optional[Union[str, UUID]] = None,
    stack_id: Optional[Union[str, UUID]] = None,
    container_registry_id: Optional[Union[UUID, str]] = None,
    is_local: Optional[bool] = None,
    contains_code: Optional[bool] = None,
    zenml_version: Optional[str] = None,
    python_version: Optional[str] = None,
    checksum: Optional[str] = None,
    stack_checksum: Optional[str] = None,
    duration: Optional[Union[int, str]] = None,
    hydrate: bool = False,
) -> Page[PipelineBuildResponse]:
    """List all builds.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of build to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        pipeline_id: The id of the pipeline to filter by.
        stack_id: The id of the stack to filter by.
        container_registry_id: The id of the container registry to
            filter by.
        is_local: Use to filter local builds.
        contains_code: Use to filter builds that contain code.
        zenml_version: The version of ZenML to filter by.
        python_version: The Python version to filter by.
        checksum: The build checksum to filter by.
        stack_checksum: The stack checksum to filter by.
        duration: The duration of the build in seconds to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page with builds fitting the filter description
    """
    build_filter_model = PipelineBuildFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        project=project or self.active_project.id,
        user=user,
        pipeline_id=pipeline_id,
        stack_id=stack_id,
        container_registry_id=container_registry_id,
        is_local=is_local,
        contains_code=contains_code,
        zenml_version=zenml_version,
        python_version=python_version,
        checksum=checksum,
        stack_checksum=stack_checksum,
        duration=duration,
    )
    return self.zen_store.list_builds(
        build_filter_model=build_filter_model,
        hydrate=hydrate,
    )

list_code_repositories(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, project=None, user=None, hydrate=False)

List all code repositories.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by.	'created'
page	int	The page of items.	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages.	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or].	AND
id	Optional[Union[UUID, str]]	Use the id of the code repository to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation.	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering.	None
name	Optional[str]	The name of the code repository to filter by.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[CodeRepositoryResponse]	A page of code repositories matching the filter description.

Source code in src/zenml/client.py

def list_code_repositories(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[CodeRepositoryResponse]:
    """List all code repositories.

    Args:
        sort_by: The column to sort by.
        page: The page of items.
        size: The maximum size of all pages.
        logical_operator: Which logical operator to use [and, or].
        id: Use the id of the code repository to filter by.
        created: Use to filter by time of creation.
        updated: Use the last updated date for filtering.
        name: The name of the code repository to filter by.
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of code repositories matching the filter description.
    """
    filter_model = CodeRepositoryFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        name=name,
        project=project or self.active_project.id,
        user=user,
    )
    return self.zen_store.list_code_repositories(
        filter_model=filter_model,
        hydrate=hydrate,
    )

list_deployments(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, project=None, user=None, pipeline_id=None, stack_id=None, build_id=None, template_id=None, hydrate=False)

List all deployments.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of build to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
pipeline_id	Optional[Union[str, UUID]]	The id of the pipeline to filter by.	None
stack_id	Optional[Union[str, UUID]]	The id of the stack to filter by.	None
build_id	Optional[Union[str, UUID]]	The id of the build to filter by.	None
template_id	Optional[Union[str, UUID]]	The ID of the template to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[PipelineDeploymentResponse]	A page with deployments fitting the filter description

Source code in src/zenml/client.py

def list_deployments(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    pipeline_id: Optional[Union[str, UUID]] = None,
    stack_id: Optional[Union[str, UUID]] = None,
    build_id: Optional[Union[str, UUID]] = None,
    template_id: Optional[Union[str, UUID]] = None,
    hydrate: bool = False,
) -> Page[PipelineDeploymentResponse]:
    """List all deployments.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of build to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        pipeline_id: The id of the pipeline to filter by.
        stack_id: The id of the stack to filter by.
        build_id: The id of the build to filter by.
        template_id: The ID of the template to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page with deployments fitting the filter description
    """
    deployment_filter_model = PipelineDeploymentFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        project=project or self.active_project.id,
        user=user,
        pipeline_id=pipeline_id,
        stack_id=stack_id,
        build_id=build_id,
        template_id=template_id,
    )
    return self.zen_store.list_deployments(
        deployment_filter_model=deployment_filter_model,
        hydrate=hydrate,
    )

list_event_sources(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, flavor=None, event_source_type=None, project=None, user=None, hydrate=False)

Lists all event_sources.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of event_sources to filter by.	None
created	Optional[datetime]	Use to filter by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
name	Optional[str]	The name of the event_source to filter by.	None
flavor	Optional[str]	The flavor of the event_source to filter by.	None
event_source_type	Optional[str]	The subtype of the event_source to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[EventSourceResponse]	A page of event_sources.

Source code in src/zenml/client.py

def list_event_sources(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    name: Optional[str] = None,
    flavor: Optional[str] = None,
    event_source_type: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[EventSourceResponse]:
    """Lists all event_sources.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of event_sources to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        name: The name of the event_source to filter by.
        flavor: The flavor of the event_source to filter by.
        event_source_type: The subtype of the event_source to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of event_sources.
    """
    event_source_filter_model = EventSourceFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        project=project or self.active_project.id,
        user=user,
        name=name,
        flavor=flavor,
        plugin_subtype=event_source_type,
        id=id,
        created=created,
        updated=updated,
    )
    return self.zen_store.list_event_sources(
        event_source_filter_model, hydrate=hydrate
    )

list_flavors(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, type=None, integration=None, user=None, hydrate=False)

Fetches all the flavor models.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of flavors to filter by.	None
created	Optional[datetime]	Use to flavors by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
name	Optional[str]	The name of the flavor to filter by.	None
type	Optional[str]	The type of the flavor to filter by.	None
integration	Optional[str]	The integration of the flavor to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[FlavorResponse]	A list of all the flavor models.

Source code in src/zenml/client.py

def list_flavors(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    name: Optional[str] = None,
    type: Optional[str] = None,
    integration: Optional[str] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[FlavorResponse]:
    """Fetches all the flavor models.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of flavors to filter by.
        created: Use to flavors by time of creation
        updated: Use the last updated date for filtering
        user: Filter by user name/ID.
        name: The name of the flavor to filter by.
        type: The type of the flavor to filter by.
        integration: The integration of the flavor to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A list of all the flavor models.
    """
    flavor_filter_model = FlavorFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        user=user,
        name=name,
        type=type,
        integration=integration,
        id=id,
        created=created,
        updated=updated,
    )
    return self.zen_store.list_flavors(
        flavor_filter_model=flavor_filter_model, hydrate=hydrate
    )

list_model_version_artifact_links(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, model_version_id=None, artifact_version_id=None, artifact_name=None, only_data_artifacts=None, only_model_artifacts=None, only_deployment_artifacts=None, has_custom_name=None, user=None, hydrate=False)

Get model version to artifact links by filter in Model Control Plane.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
model_version_id	Optional[Union[UUID, str]]	Use the model version id for filtering	None
artifact_version_id	Optional[Union[UUID, str]]	Use the artifact id for filtering	None
artifact_name	Optional[str]	Use the artifact name for filtering	None
only_data_artifacts	Optional[bool]	Use to filter by data artifacts	None
only_model_artifacts	Optional[bool]	Use to filter by model artifacts	None
only_deployment_artifacts	Optional[bool]	Use to filter by deployment artifacts	None
has_custom_name	Optional[bool]	Filter artifacts with/without custom names.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[ModelVersionArtifactResponse]	A page of all model version to artifact links.

Source code in src/zenml/client.py

def list_model_version_artifact_links(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    model_version_id: Optional[Union[UUID, str]] = None,
    artifact_version_id: Optional[Union[UUID, str]] = None,
    artifact_name: Optional[str] = None,
    only_data_artifacts: Optional[bool] = None,
    only_model_artifacts: Optional[bool] = None,
    only_deployment_artifacts: Optional[bool] = None,
    has_custom_name: Optional[bool] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[ModelVersionArtifactResponse]:
    """Get model version to artifact links by filter in Model Control Plane.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        model_version_id: Use the model version id for filtering
        artifact_version_id: Use the artifact id for filtering
        artifact_name: Use the artifact name for filtering
        only_data_artifacts: Use to filter by data artifacts
        only_model_artifacts: Use to filter by model artifacts
        only_deployment_artifacts: Use to filter by deployment artifacts
        has_custom_name: Filter artifacts with/without custom names.
        user: Filter by user name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of all model version to artifact links.
    """
    return self.zen_store.list_model_version_artifact_links(
        ModelVersionArtifactFilter(
            sort_by=sort_by,
            logical_operator=logical_operator,
            page=page,
            size=size,
            created=created,
            updated=updated,
            model_version_id=model_version_id,
            artifact_version_id=artifact_version_id,
            artifact_name=artifact_name,
            only_data_artifacts=only_data_artifacts,
            only_model_artifacts=only_model_artifacts,
            only_deployment_artifacts=only_deployment_artifacts,
            has_custom_name=has_custom_name,
            user=user,
        ),
        hydrate=hydrate,
    )

list_model_version_pipeline_run_links(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, model_version_id=None, pipeline_run_id=None, pipeline_run_name=None, user=None, hydrate=False)

Get all model version to pipeline run links by filter.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
model_version_id	Optional[Union[UUID, str]]	Use the model version id for filtering	None
pipeline_run_id	Optional[Union[UUID, str]]	Use the pipeline run id for filtering	None
pipeline_run_name	Optional[str]	Use the pipeline run name for filtering	None
user	Optional[Union[UUID, str]]	Filter by user name or ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response	False

Returns:

Type	Description
Page[ModelVersionPipelineRunResponse]	A page of all model version to pipeline run links.

Source code in src/zenml/client.py

def list_model_version_pipeline_run_links(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    model_version_id: Optional[Union[UUID, str]] = None,
    pipeline_run_id: Optional[Union[UUID, str]] = None,
    pipeline_run_name: Optional[str] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[ModelVersionPipelineRunResponse]:
    """Get all model version to pipeline run links by filter.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        model_version_id: Use the model version id for filtering
        pipeline_run_id: Use the pipeline run id for filtering
        pipeline_run_name: Use the pipeline run name for filtering
        user: Filter by user name or ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response

    Returns:
        A page of all model version to pipeline run links.
    """
    return self.zen_store.list_model_version_pipeline_run_links(
        ModelVersionPipelineRunFilter(
            sort_by=sort_by,
            logical_operator=logical_operator,
            page=page,
            size=size,
            created=created,
            updated=updated,
            model_version_id=model_version_id,
            pipeline_run_id=pipeline_run_id,
            pipeline_run_name=pipeline_run_name,
            user=user,
        ),
        hydrate=hydrate,
    )

list_model_versions(model_name_or_id, sort_by='number', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, name=None, id=None, number=None, stage=None, run_metadata=None, user=None, hydrate=False, tag=None, tags=None, project=None)

Get model versions by filter from Model Control Plane.

Parameters:

Name	Type	Description	Default
model_name_or_id	Union[str, UUID]	name or id of the model containing the model version.	required
sort_by	str	The column to sort by	'number'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	name or id of the model version.	None
id	Optional[Union[UUID, str]]	id of the model version.	None
number	Optional[int]	number of the model version.	None
stage	Optional[Union[str, ModelStages]]	stage of the model version.	None
run_metadata	Optional[List[str]]	run metadata of the model version.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False
tag	Optional[str]	The tag to filter by.	None
tags	Optional[List[str]]	Tags to filter by.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
Page[ModelVersionResponse]	A page object with all model versions.

Source code in src/zenml/client.py

def list_model_versions(
    self,
    model_name_or_id: Union[str, UUID],
    sort_by: str = "number",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    id: Optional[Union[UUID, str]] = None,
    number: Optional[int] = None,
    stage: Optional[Union[str, ModelStages]] = None,
    run_metadata: Optional[List[str]] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
    tag: Optional[str] = None,
    tags: Optional[List[str]] = None,
    project: Optional[Union[str, UUID]] = None,
) -> Page[ModelVersionResponse]:
    """Get model versions by filter from Model Control Plane.

    Args:
        model_name_or_id: name or id of the model containing the model
            version.
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: name or id of the model version.
        id: id of the model version.
        number: number of the model version.
        stage: stage of the model version.
        run_metadata: run metadata of the model version.
        user: Filter by user name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.
        tag: The tag to filter by.
        tags: Tags to filter by.
        project: The project name/ID to filter by.

    Returns:
        A page object with all model versions.
    """
    model_version_filter_model = ModelVersionFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        created=created,
        updated=updated,
        name=name,
        id=id,
        number=number,
        stage=stage,
        run_metadata=run_metadata,
        tag=tag,
        tags=tags,
        user=user,
        model=model_name_or_id,
        project=project or self.active_project.id,
    )

    return self.zen_store.list_model_versions(
        model_version_filter_model=model_version_filter_model,
        hydrate=hydrate,
    )

list_models(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, name=None, id=None, user=None, project=None, hydrate=False, tag=None, tags=None)

Get models by filter from Model Control Plane.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	The name of the model to filter by.	None
id	Optional[Union[UUID, str]]	The id of the model to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False
tag	Optional[str]	The tag of the model to filter by.	None
tags	Optional[List[str]]	Tags to filter by.	None

Returns:

Type	Description
Page[ModelResponse]	A page object with all models.

Source code in src/zenml/client.py

def list_models(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    id: Optional[Union[UUID, str]] = None,
    user: Optional[Union[UUID, str]] = None,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = False,
    tag: Optional[str] = None,
    tags: Optional[List[str]] = None,
) -> Page[ModelResponse]:
    """Get models by filter from Model Control Plane.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: The name of the model to filter by.
        id: The id of the model to filter by.
        user: Filter by user name/ID.
        project: The project name/ID to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.
        tag: The tag of the model to filter by.
        tags: Tags to filter by.

    Returns:
        A page object with all models.
    """
    filter = ModelFilter(
        name=name,
        id=id,
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        created=created,
        updated=updated,
        tag=tag,
        tags=tags,
        user=user,
        project=project or self.active_project.id,
    )

    return self.zen_store.list_models(
        model_filter_model=filter, hydrate=hydrate
    )

list_pipeline_runs(sort_by='desc:created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, project=None, pipeline_id=None, pipeline_name=None, stack_id=None, schedule_id=None, build_id=None, deployment_id=None, code_repository_id=None, template_id=None, model_version_id=None, orchestrator_run_id=None, status=None, start_time=None, end_time=None, unlisted=None, templatable=None, tag=None, tags=None, user=None, run_metadata=None, pipeline=None, code_repository=None, model=None, stack=None, stack_component=None, hydrate=False)

List all pipeline runs.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'desc:created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	The id of the runs to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
pipeline_id	Optional[Union[str, UUID]]	The id of the pipeline to filter by.	None
pipeline_name	Optional[str]	DEPRECATED. Use pipeline instead to filter by pipeline name.	None
stack_id	Optional[Union[str, UUID]]	The id of the stack to filter by.	None
schedule_id	Optional[Union[str, UUID]]	The id of the schedule to filter by.	None
build_id	Optional[Union[str, UUID]]	The id of the build to filter by.	None
deployment_id	Optional[Union[str, UUID]]	The id of the deployment to filter by.	None
code_repository_id	Optional[Union[str, UUID]]	The id of the code repository to filter by.	None
template_id	Optional[Union[str, UUID]]	The ID of the template to filter by.	None
model_version_id	Optional[Union[str, UUID]]	The ID of the model version to filter by.	None
orchestrator_run_id	Optional[str]	The run id of the orchestrator to filter by.	None
name	Optional[str]	The name of the run to filter by.	None
status	Optional[str]	The status of the pipeline run	None
start_time	Optional[Union[datetime, str]]	The start_time for the pipeline run	None
end_time	Optional[Union[datetime, str]]	The end_time for the pipeline run	None
unlisted	Optional[bool]	If the runs should be unlisted or not.	None
templatable	Optional[bool]	If the runs should be templatable or not.	None
tag	Optional[str]	Tag to filter by.	None
tags	Optional[List[str]]	Tags to filter by.	None
user	Optional[Union[UUID, str]]	The name/ID of the user to filter by.	None
run_metadata	Optional[List[str]]	The run_metadata of the run to filter by.	None
pipeline	Optional[Union[UUID, str]]	The name/ID of the pipeline to filter by.	None
code_repository	Optional[Union[UUID, str]]	Filter by code repository name/ID.	None
model	Optional[Union[UUID, str]]	Filter by model name/ID.	None
stack	Optional[Union[UUID, str]]	Filter by stack name/ID.	None
stack_component	Optional[Union[UUID, str]]	Filter by stack component name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[PipelineRunResponse]	A page with Pipeline Runs fitting the filter description

Source code in src/zenml/client.py

def list_pipeline_runs(
    self,
    sort_by: str = "desc:created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    pipeline_id: Optional[Union[str, UUID]] = None,
    pipeline_name: Optional[str] = None,
    stack_id: Optional[Union[str, UUID]] = None,
    schedule_id: Optional[Union[str, UUID]] = None,
    build_id: Optional[Union[str, UUID]] = None,
    deployment_id: Optional[Union[str, UUID]] = None,
    code_repository_id: Optional[Union[str, UUID]] = None,
    template_id: Optional[Union[str, UUID]] = None,
    model_version_id: Optional[Union[str, UUID]] = None,
    orchestrator_run_id: Optional[str] = None,
    status: Optional[str] = None,
    start_time: Optional[Union[datetime, str]] = None,
    end_time: Optional[Union[datetime, str]] = None,
    unlisted: Optional[bool] = None,
    templatable: Optional[bool] = None,
    tag: Optional[str] = None,
    tags: Optional[List[str]] = None,
    user: Optional[Union[UUID, str]] = None,
    run_metadata: Optional[List[str]] = None,
    pipeline: Optional[Union[UUID, str]] = None,
    code_repository: Optional[Union[UUID, str]] = None,
    model: Optional[Union[UUID, str]] = None,
    stack: Optional[Union[UUID, str]] = None,
    stack_component: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[PipelineRunResponse]:
    """List all pipeline runs.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: The id of the runs to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        project: The project name/ID to filter by.
        pipeline_id: The id of the pipeline to filter by.
        pipeline_name: DEPRECATED. Use `pipeline` instead to filter by
            pipeline name.
        stack_id: The id of the stack to filter by.
        schedule_id: The id of the schedule to filter by.
        build_id: The id of the build to filter by.
        deployment_id: The id of the deployment to filter by.
        code_repository_id: The id of the code repository to filter by.
        template_id: The ID of the template to filter by.
        model_version_id: The ID of the model version to filter by.
        orchestrator_run_id: The run id of the orchestrator to filter by.
        name: The name of the run to filter by.
        status: The status of the pipeline run
        start_time: The start_time for the pipeline run
        end_time: The end_time for the pipeline run
        unlisted: If the runs should be unlisted or not.
        templatable: If the runs should be templatable or not.
        tag: Tag to filter by.
        tags: Tags to filter by.
        user: The name/ID of the user to filter by.
        run_metadata: The run_metadata of the run to filter by.
        pipeline: The name/ID of the pipeline to filter by.
        code_repository: Filter by code repository name/ID.
        model: Filter by model name/ID.
        stack: Filter by stack name/ID.
        stack_component: Filter by stack component name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page with Pipeline Runs fitting the filter description
    """
    runs_filter_model = PipelineRunFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        name=name,
        project=project or self.active_project.id,
        pipeline_id=pipeline_id,
        pipeline_name=pipeline_name,
        schedule_id=schedule_id,
        build_id=build_id,
        deployment_id=deployment_id,
        code_repository_id=code_repository_id,
        template_id=template_id,
        model_version_id=model_version_id,
        orchestrator_run_id=orchestrator_run_id,
        stack_id=stack_id,
        status=status,
        start_time=start_time,
        end_time=end_time,
        tag=tag,
        tags=tags,
        unlisted=unlisted,
        user=user,
        run_metadata=run_metadata,
        pipeline=pipeline,
        code_repository=code_repository,
        stack=stack,
        model=model,
        stack_component=stack_component,
        templatable=templatable,
    )
    return self.zen_store.list_runs(
        runs_filter_model=runs_filter_model,
        hydrate=hydrate,
    )

list_pipelines(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, latest_run_status=None, project=None, user=None, tag=None, tags=None, hydrate=False)

List all pipelines.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of pipeline to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	The name of the pipeline to filter by.	None
latest_run_status	Optional[str]	Filter by the status of the latest run of a pipeline.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	The name/ID of the user to filter by.	None
tag	Optional[str]	Tag to filter by.	None
tags	Optional[List[str]]	Tags to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[PipelineResponse]	A page with Pipeline fitting the filter description

Source code in src/zenml/client.py

def list_pipelines(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    latest_run_status: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    tag: Optional[str] = None,
    tags: Optional[List[str]] = None,
    hydrate: bool = False,
) -> Page[PipelineResponse]:
    """List all pipelines.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of pipeline to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: The name of the pipeline to filter by.
        latest_run_status: Filter by the status of the latest run of a
            pipeline.
        project: The project name/ID to filter by.
        user: The name/ID of the user to filter by.
        tag: Tag to filter by.
        tags: Tags to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page with Pipeline fitting the filter description
    """
    pipeline_filter_model = PipelineFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        name=name,
        latest_run_status=latest_run_status,
        project=project or self.active_project.id,
        user=user,
        tag=tag,
        tags=tags,
    )
    return self.zen_store.list_pipelines(
        pipeline_filter_model=pipeline_filter_model,
        hydrate=hydrate,
    )

list_projects(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, display_name=None, hydrate=False)

List all projects.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the project ID to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	Use the project name for filtering	None
display_name	Optional[str]	Use the project display name for filtering	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[ProjectResponse]	Page of projects

Source code in src/zenml/client.py

def list_projects(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    display_name: Optional[str] = None,
    hydrate: bool = False,
) -> Page[ProjectResponse]:
    """List all projects.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the project ID to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: Use the project name for filtering
        display_name: Use the project display name for filtering
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        Page of projects
    """
    return self.zen_store.list_projects(
        ProjectFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            display_name=display_name,
        ),
        hydrate=hydrate,
    )

list_run_steps(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, cache_key=None, code_hash=None, status=None, start_time=None, end_time=None, pipeline_run_id=None, deployment_id=None, original_step_run_id=None, project=None, user=None, model_version_id=None, model=None, run_metadata=None, hydrate=False)

List all pipelines.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of runs to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
start_time	Optional[Union[datetime, str]]	Use to filter by the time when the step started running	None
end_time	Optional[Union[datetime, str]]	Use to filter by the time when the step finished running	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
pipeline_run_id	Optional[Union[str, UUID]]	The id of the pipeline run to filter by.	None
deployment_id	Optional[Union[str, UUID]]	The id of the deployment to filter by.	None
original_step_run_id	Optional[Union[str, UUID]]	The id of the original step run to filter by.	None
model_version_id	Optional[Union[str, UUID]]	The ID of the model version to filter by.	None
model	Optional[Union[UUID, str]]	Filter by model name/ID.	None
name	Optional[str]	The name of the step run to filter by.	None
cache_key	Optional[str]	The cache key of the step run to filter by.	None
code_hash	Optional[str]	The code hash of the step run to filter by.	None
status	Optional[str]	The name of the run to filter by.	None
run_metadata	Optional[List[str]]	Filter by run metadata.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[StepRunResponse]	A page with Pipeline fitting the filter description

Source code in src/zenml/client.py

def list_run_steps(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    cache_key: Optional[str] = None,
    code_hash: Optional[str] = None,
    status: Optional[str] = None,
    start_time: Optional[Union[datetime, str]] = None,
    end_time: Optional[Union[datetime, str]] = None,
    pipeline_run_id: Optional[Union[str, UUID]] = None,
    deployment_id: Optional[Union[str, UUID]] = None,
    original_step_run_id: Optional[Union[str, UUID]] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    model_version_id: Optional[Union[str, UUID]] = None,
    model: Optional[Union[UUID, str]] = None,
    run_metadata: Optional[List[str]] = None,
    hydrate: bool = False,
) -> Page[StepRunResponse]:
    """List all pipelines.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of runs to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        start_time: Use to filter by the time when the step started running
        end_time: Use to filter by the time when the step finished running
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        pipeline_run_id: The id of the pipeline run to filter by.
        deployment_id: The id of the deployment to filter by.
        original_step_run_id: The id of the original step run to filter by.
        model_version_id: The ID of the model version to filter by.
        model: Filter by model name/ID.
        name: The name of the step run to filter by.
        cache_key: The cache key of the step run to filter by.
        code_hash: The code hash of the step run to filter by.
        status: The name of the run to filter by.
        run_metadata: Filter by run metadata.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page with Pipeline fitting the filter description
    """
    step_run_filter_model = StepRunFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        cache_key=cache_key,
        code_hash=code_hash,
        pipeline_run_id=pipeline_run_id,
        deployment_id=deployment_id,
        original_step_run_id=original_step_run_id,
        status=status,
        created=created,
        updated=updated,
        start_time=start_time,
        end_time=end_time,
        name=name,
        project=project or self.active_project.id,
        user=user,
        model_version_id=model_version_id,
        model=model,
        run_metadata=run_metadata,
    )
    return self.zen_store.list_run_steps(
        step_run_filter_model=step_run_filter_model,
        hydrate=hydrate,
    )

list_run_templates(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, created=None, updated=None, id=None, name=None, hidden=False, tag=None, project=None, pipeline_id=None, build_id=None, stack_id=None, code_repository_id=None, user=None, pipeline=None, stack=None, hydrate=False)

Get a page of run templates.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by.	'created'
page	int	The page of items.	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages.	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or].	AND
created	Optional[Union[datetime, str]]	Filter by the creation date.	None
updated	Optional[Union[datetime, str]]	Filter by the last updated date.	None
id	Optional[Union[UUID, str]]	Filter by run template ID.	None
name	Optional[str]	Filter by run template name.	None
hidden	Optional[bool]	Filter by run template hidden status.	False
tag	Optional[str]	Filter by run template tags.	None
project	Optional[Union[str, UUID]]	Filter by project name/ID.	None
pipeline_id	Optional[Union[str, UUID]]	Filter by pipeline ID.	None
build_id	Optional[Union[str, UUID]]	Filter by build ID.	None
stack_id	Optional[Union[str, UUID]]	Filter by stack ID.	None
code_repository_id	Optional[Union[str, UUID]]	Filter by code repository ID.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
pipeline	Optional[Union[UUID, str]]	Filter by pipeline name/ID.	None
stack	Optional[Union[UUID, str]]	Filter by stack name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[RunTemplateResponse]	A page of run templates.

Source code in src/zenml/client.py

def list_run_templates(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    id: Optional[Union[UUID, str]] = None,
    name: Optional[str] = None,
    hidden: Optional[bool] = False,
    tag: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    pipeline_id: Optional[Union[str, UUID]] = None,
    build_id: Optional[Union[str, UUID]] = None,
    stack_id: Optional[Union[str, UUID]] = None,
    code_repository_id: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    pipeline: Optional[Union[UUID, str]] = None,
    stack: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[RunTemplateResponse]:
    """Get a page of run templates.

    Args:
        sort_by: The column to sort by.
        page: The page of items.
        size: The maximum size of all pages.
        logical_operator: Which logical operator to use [and, or].
        created: Filter by the creation date.
        updated: Filter by the last updated date.
        id: Filter by run template ID.
        name: Filter by run template name.
        hidden: Filter by run template hidden status.
        tag: Filter by run template tags.
        project: Filter by project name/ID.
        pipeline_id: Filter by pipeline ID.
        build_id: Filter by build ID.
        stack_id: Filter by stack ID.
        code_repository_id: Filter by code repository ID.
        user: Filter by user name/ID.
        pipeline: Filter by pipeline name/ID.
        stack: Filter by stack name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of run templates.
    """
    filter = RunTemplateFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        created=created,
        updated=updated,
        id=id,
        name=name,
        hidden=hidden,
        tag=tag,
        project=project,
        pipeline_id=pipeline_id,
        build_id=build_id,
        stack_id=stack_id,
        code_repository_id=code_repository_id,
        user=user,
        pipeline=pipeline,
        stack=stack,
    )

    return self.zen_store.list_run_templates(
        template_filter_model=filter, hydrate=hydrate
    )

list_schedules(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, project=None, user=None, pipeline_id=None, orchestrator_id=None, active=None, cron_expression=None, start_time=None, end_time=None, interval_second=None, catchup=None, hydrate=False, run_once_start_time=None)

List schedules.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of stacks to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	The name of the stack to filter by.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
pipeline_id	Optional[Union[str, UUID]]	The id of the pipeline to filter by.	None
orchestrator_id	Optional[Union[str, UUID]]	The id of the orchestrator to filter by.	None
active	Optional[Union[str, bool]]	Use to filter by active status.	None
cron_expression	Optional[str]	Use to filter by cron expression.	None
start_time	Optional[Union[datetime, str]]	Use to filter by start time.	None
end_time	Optional[Union[datetime, str]]	Use to filter by end time.	None
interval_second	Optional[int]	Use to filter by interval second.	None
catchup	Optional[Union[str, bool]]	Use to filter by catchup.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False
run_once_start_time	Optional[Union[datetime, str]]	Use to filter by run once start time.	None

Returns:

Type	Description
Page[ScheduleResponse]	A list of schedules.

Source code in src/zenml/client.py

def list_schedules(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    pipeline_id: Optional[Union[str, UUID]] = None,
    orchestrator_id: Optional[Union[str, UUID]] = None,
    active: Optional[Union[str, bool]] = None,
    cron_expression: Optional[str] = None,
    start_time: Optional[Union[datetime, str]] = None,
    end_time: Optional[Union[datetime, str]] = None,
    interval_second: Optional[int] = None,
    catchup: Optional[Union[str, bool]] = None,
    hydrate: bool = False,
    run_once_start_time: Optional[Union[datetime, str]] = None,
) -> Page[ScheduleResponse]:
    """List schedules.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of stacks to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: The name of the stack to filter by.
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        pipeline_id: The id of the pipeline to filter by.
        orchestrator_id: The id of the orchestrator to filter by.
        active: Use to filter by active status.
        cron_expression: Use to filter by cron expression.
        start_time: Use to filter by start time.
        end_time: Use to filter by end time.
        interval_second: Use to filter by interval second.
        catchup: Use to filter by catchup.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.
        run_once_start_time: Use to filter by run once start time.

    Returns:
        A list of schedules.
    """
    schedule_filter_model = ScheduleFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        name=name,
        project=project or self.active_project.id,
        user=user,
        pipeline_id=pipeline_id,
        orchestrator_id=orchestrator_id,
        active=active,
        cron_expression=cron_expression,
        start_time=start_time,
        end_time=end_time,
        interval_second=interval_second,
        catchup=catchup,
        run_once_start_time=run_once_start_time,
    )
    return self.zen_store.list_schedules(
        schedule_filter_model=schedule_filter_model,
        hydrate=hydrate,
    )

list_secrets(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, private=None, user=None, hydrate=False)

Fetches all the secret models.

The returned secrets do not contain the secret values. To get the secret values, use get_secret individually for each secret.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of secrets to filter by.	None
created	Optional[datetime]	Use to secrets by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
name	Optional[str]	The name of the secret to filter by.	None
private	Optional[bool]	The private status of the secret to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[SecretResponse]	A list of all the secret models without the secret values.

Raises:

Type	Description
NotImplementedError	If centralized secrets management is not enabled.

Source code in src/zenml/client.py

def list_secrets(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    name: Optional[str] = None,
    private: Optional[bool] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[SecretResponse]:
    """Fetches all the secret models.

    The returned secrets do not contain the secret values. To get the
    secret values, use `get_secret` individually for each secret.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of secrets to filter by.
        created: Use to secrets by time of creation
        updated: Use the last updated date for filtering
        name: The name of the secret to filter by.
        private: The private status of the secret to filter by.
        user: Filter by user name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A list of all the secret models without the secret values.

    Raises:
        NotImplementedError: If centralized secrets management is not
            enabled.
    """
    secret_filter_model = SecretFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        user=user,
        name=name,
        private=private,
        id=id,
        created=created,
        updated=updated,
    )
    try:
        return self.zen_store.list_secrets(
            secret_filter_model=secret_filter_model,
            hydrate=hydrate,
        )
    except NotImplementedError:
        raise NotImplementedError(
            "centralized secrets management is not supported or explicitly "
            "disabled in the target ZenML deployment."
        )

list_secrets_by_private_status(private, hydrate=False)

Fetches the list of secrets with a given private status.

The returned secrets do not contain the secret values. To get the secret values, use get_secret individually for each secret.

Parameters:

Name	Type	Description	Default
private	bool	The private status of the secrets to search for.	required
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[SecretResponse]	The list of secrets in the given scope without the secret values.

Source code in src/zenml/client.py

def list_secrets_by_private_status(
    self,
    private: bool,
    hydrate: bool = False,
) -> Page[SecretResponse]:
    """Fetches the list of secrets with a given private status.

    The returned secrets do not contain the secret values. To get the
    secret values, use `get_secret` individually for each secret.

    Args:
        private: The private status of the secrets to search for.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The list of secrets in the given scope without the secret values.
    """
    logger.debug(f"Fetching the secrets with private status '{private}'.")

    return self.list_secrets(private=private, hydrate=hydrate)

list_service_accounts(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, description=None, active=None, hydrate=False)

List all service accounts.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of stacks to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	Use the service account name for filtering	None
description	Optional[str]	Use the service account description for filtering	None
active	Optional[bool]	Use the service account active status for filtering	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[ServiceAccountResponse]	The list of service accounts matching the filter description.

Source code in src/zenml/client.py

def list_service_accounts(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    description: Optional[str] = None,
    active: Optional[bool] = None,
    hydrate: bool = False,
) -> Page[ServiceAccountResponse]:
    """List all service accounts.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of stacks to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: Use the service account name for filtering
        description: Use the service account description for filtering
        active: Use the service account active status for filtering
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The list of service accounts matching the filter description.
    """
    return self.zen_store.list_service_accounts(
        ServiceAccountFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            created=created,
            updated=updated,
            name=name,
            description=description,
            active=active,
        ),
        hydrate=hydrate,
    )

list_service_connector_resources(connector_type=None, resource_type=None, resource_id=None)

List resources that can be accessed by service connectors.

Parameters:

Name	Type	Description	Default
connector_type	Optional[str]	The type of service connector to filter by.	None
resource_type	Optional[str]	The type of resource to filter by.	None
resource_id	Optional[str]	The ID of a particular resource instance to filter by.	None

Returns:

Type	Description
List[ServiceConnectorResourcesModel]	The matching list of resources that available service
List[ServiceConnectorResourcesModel]	connectors have access to.

Source code in src/zenml/client.py

def list_service_connector_resources(
    self,
    connector_type: Optional[str] = None,
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
) -> List[ServiceConnectorResourcesModel]:
    """List resources that can be accessed by service connectors.

    Args:
        connector_type: The type of service connector to filter by.
        resource_type: The type of resource to filter by.
        resource_id: The ID of a particular resource instance to filter by.

    Returns:
        The matching list of resources that available service
        connectors have access to.
    """
    return self.zen_store.list_service_connector_resources(
        ServiceConnectorFilter(
            connector_type=connector_type,
            resource_type=resource_type,
            resource_id=resource_id,
        )
    )

list_service_connector_types(connector_type=None, resource_type=None, auth_method=None)

Get a list of service connector types.

Parameters:

Name	Type	Description	Default
connector_type	Optional[str]	Filter by connector type.	None
resource_type	Optional[str]	Filter by resource type.	None
auth_method	Optional[str]	Filter by authentication method.	None

Returns:

Type	Description
List[ServiceConnectorTypeModel]	List of service connector types.

Source code in src/zenml/client.py

def list_service_connector_types(
    self,
    connector_type: Optional[str] = None,
    resource_type: Optional[str] = None,
    auth_method: Optional[str] = None,
) -> List[ServiceConnectorTypeModel]:
    """Get a list of service connector types.

    Args:
        connector_type: Filter by connector type.
        resource_type: Filter by resource type.
        auth_method: Filter by authentication method.

    Returns:
        List of service connector types.
    """
    return self.zen_store.list_service_connector_types(
        connector_type=connector_type,
        resource_type=resource_type,
        auth_method=auth_method,
    )

list_service_connectors(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, connector_type=None, auth_method=None, resource_type=None, resource_id=None, user=None, labels=None, secret_id=None, hydrate=False)

Lists all registered service connectors.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	The id of the service connector to filter by.	None
created	Optional[datetime]	Filter service connectors by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
connector_type	Optional[str]	Use the service connector type for filtering	None
auth_method	Optional[str]	Use the service connector auth method for filtering	None
resource_type	Optional[str]	Filter service connectors by the resource type that they can give access to.	None
resource_id	Optional[str]	Filter service connectors by the resource id that they can give access to.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
name	Optional[str]	The name of the service connector to filter by.	None
labels	Optional[Dict[str, Optional[str]]]	The labels of the service connector to filter by.	None
secret_id	Optional[Union[str, UUID]]	Filter by the id of the secret that is referenced by the service connector.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[ServiceConnectorResponse]	A page of service connectors.

Source code in src/zenml/client.py

def list_service_connectors(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    name: Optional[str] = None,
    connector_type: Optional[str] = None,
    auth_method: Optional[str] = None,
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
    user: Optional[Union[UUID, str]] = None,
    labels: Optional[Dict[str, Optional[str]]] = None,
    secret_id: Optional[Union[str, UUID]] = None,
    hydrate: bool = False,
) -> Page[ServiceConnectorResponse]:
    """Lists all registered service connectors.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: The id of the service connector to filter by.
        created: Filter service connectors by time of creation
        updated: Use the last updated date for filtering
        connector_type: Use the service connector type for filtering
        auth_method: Use the service connector auth method for filtering
        resource_type: Filter service connectors by the resource type that
            they can give access to.
        resource_id: Filter service connectors by the resource id that
            they can give access to.
        user: Filter by user name/ID.
        name: The name of the service connector to filter by.
        labels: The labels of the service connector to filter by.
        secret_id: Filter by the id of the secret that is referenced by the
            service connector.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of service connectors.
    """
    connector_filter_model = ServiceConnectorFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        user=user,
        name=name,
        connector_type=connector_type,
        auth_method=auth_method,
        resource_type=resource_type,
        resource_id=resource_id,
        id=id,
        created=created,
        updated=updated,
        labels=labels,
        secret_id=secret_id,
    )
    return self.zen_store.list_service_connectors(
        filter_model=connector_filter_model,
        hydrate=hydrate,
    )

list_services(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, type=None, flavor=None, user=None, project=None, hydrate=False, running=None, service_name=None, pipeline_name=None, pipeline_run_id=None, pipeline_step_name=None, model_version_id=None, config=None)

List all services.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of services to filter by.	None
created	Optional[datetime]	Use to filter by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
type	Optional[str]	Use the service type for filtering	None
flavor	Optional[str]	Use the service flavor for filtering	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False
running	Optional[bool]	Use the running status for filtering	None
pipeline_name	Optional[str]	Use the pipeline name for filtering	None
service_name	Optional[str]	Use the service name or model name for filtering	None
pipeline_step_name	Optional[str]	Use the pipeline step name for filtering	None
model_version_id	Optional[Union[str, UUID]]	Use the model version id for filtering	None
config	Optional[Dict[str, Any]]	Use the config for filtering	None
pipeline_run_id	Optional[str]	Use the pipeline run id for filtering	None

Returns:

Type	Description
Page[ServiceResponse]	The Service response page.

Source code in src/zenml/client.py

def list_services(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    type: Optional[str] = None,
    flavor: Optional[str] = None,
    user: Optional[Union[UUID, str]] = None,
    project: Optional[Union[str, UUID]] = None,
    hydrate: bool = False,
    running: Optional[bool] = None,
    service_name: Optional[str] = None,
    pipeline_name: Optional[str] = None,
    pipeline_run_id: Optional[str] = None,
    pipeline_step_name: Optional[str] = None,
    model_version_id: Optional[Union[str, UUID]] = None,
    config: Optional[Dict[str, Any]] = None,
) -> Page[ServiceResponse]:
    """List all services.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of services to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        type: Use the service type for filtering
        flavor: Use the service flavor for filtering
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.
        running: Use the running status for filtering
        pipeline_name: Use the pipeline name for filtering
        service_name: Use the service name or model name
            for filtering
        pipeline_step_name: Use the pipeline step name for filtering
        model_version_id: Use the model version id for filtering
        config: Use the config for filtering
        pipeline_run_id: Use the pipeline run id for filtering

    Returns:
        The Service response page.
    """
    service_filter_model = ServiceFilter(
        sort_by=sort_by,
        page=page,
        size=size,
        logical_operator=logical_operator,
        id=id,
        created=created,
        updated=updated,
        type=type,
        flavor=flavor,
        project=project or self.active_project.id,
        user=user,
        running=running,
        name=service_name,
        pipeline_name=pipeline_name,
        pipeline_step_name=pipeline_step_name,
        model_version_id=model_version_id,
        pipeline_run_id=pipeline_run_id,
        config=dict_to_bytes(config) if config else None,
    )
    return self.zen_store.list_services(
        filter_model=service_filter_model, hydrate=hydrate
    )

list_stack_components(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, flavor=None, type=None, connector_id=None, stack_id=None, user=None, hydrate=False)

Lists all registered stack components.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of component to filter by.	None
created	Optional[datetime]	Use to component by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
flavor	Optional[str]	Use the component flavor for filtering	None
type	Optional[str]	Use the component type for filtering	None
connector_id	Optional[Union[str, UUID]]	The id of the connector to filter by.	None
stack_id	Optional[Union[str, UUID]]	The id of the stack to filter by.	None
name	Optional[str]	The name of the component to filter by.	None
user	Optional[Union[UUID, str]]	The ID of name of the user to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[ComponentResponse]	A page of stack components.

Source code in src/zenml/client.py

def list_stack_components(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    name: Optional[str] = None,
    flavor: Optional[str] = None,
    type: Optional[str] = None,
    connector_id: Optional[Union[str, UUID]] = None,
    stack_id: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[ComponentResponse]:
    """Lists all registered stack components.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of component to filter by.
        created: Use to component by time of creation
        updated: Use the last updated date for filtering
        flavor: Use the component flavor for filtering
        type: Use the component type for filtering
        connector_id: The id of the connector to filter by.
        stack_id: The id of the stack to filter by.
        name: The name of the component to filter by.
        user: The ID of name of the user to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of stack components.
    """
    component_filter_model = ComponentFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        connector_id=connector_id,
        stack_id=stack_id,
        name=name,
        flavor=flavor,
        type=type,
        id=id,
        created=created,
        updated=updated,
        user=user,
    )

    return self.zen_store.list_stack_components(
        component_filter_model=component_filter_model, hydrate=hydrate
    )

list_stacks(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, description=None, component_id=None, user=None, component=None, hydrate=False)

Lists all stacks.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of stacks to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
description	Optional[str]	Use the stack description for filtering	None
component_id	Optional[Union[str, UUID]]	The id of the component to filter by.	None
user	Optional[Union[UUID, str]]	The name/ID of the user to filter by.	None
component	Optional[Union[UUID, str]]	The name/ID of the component to filter by.	None
name	Optional[str]	The name of the stack to filter by.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[StackResponse]	A page of stacks.

Source code in src/zenml/client.py

def list_stacks(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    description: Optional[str] = None,
    component_id: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    component: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[StackResponse]:
    """Lists all stacks.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of stacks to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        description: Use the stack description for filtering
        component_id: The id of the component to filter by.
        user: The name/ID of the user to filter by.
        component: The name/ID of the component to filter by.
        name: The name of the stack to filter by.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of stacks.
    """
    stack_filter_model = StackFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        component_id=component_id,
        user=user,
        component=component,
        name=name,
        description=description,
        id=id,
        created=created,
        updated=updated,
    )
    return self.zen_store.list_stacks(stack_filter_model, hydrate=hydrate)

list_tags(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, user=None, created=None, updated=None, name=None, color=None, exclusive=None, resource_type=None, hydrate=False)

Get tags by filter.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by.	'created'
page	int	The page of items.	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or].	AND
id	Optional[Union[UUID, str]]	Use the id of stacks to filter by.	None
user	Optional[Union[UUID, str]]	Use the user to filter by.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation.	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering.	None
name	Optional[str]	The name of the tag.	None
color	Optional[Union[str, ColorVariants]]	The color of the tag.	None
exclusive	Optional[bool]	Flag indicating whether the tag is exclusive.	None
resource_type	Optional[Union[str, TaggableResourceTypes]]	Filter tags associated with a specific resource type.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[TagResponse]	A page of all tags.

Source code in src/zenml/client.py

def list_tags(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    user: Optional[Union[UUID, str]] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    color: Optional[Union[str, ColorVariants]] = None,
    exclusive: Optional[bool] = None,
    resource_type: Optional[Union[str, TaggableResourceTypes]] = None,
    hydrate: bool = False,
) -> Page[TagResponse]:
    """Get tags by filter.

    Args:
        sort_by: The column to sort by.
        page: The page of items.
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or].
        id: Use the id of stacks to filter by.
        user: Use the user to filter by.
        created: Use to filter by time of creation.
        updated: Use the last updated date for filtering.
        name: The name of the tag.
        color: The color of the tag.
        exclusive: Flag indicating whether the tag is exclusive.
        resource_type: Filter tags associated with a specific resource type.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of all tags.
    """
    return self.zen_store.list_tags(
        tag_filter_model=TagFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            user=user,
            created=created,
            updated=updated,
            name=name,
            color=color,
            exclusive=exclusive,
            resource_type=resource_type,
        ),
        hydrate=hydrate,
    )

list_trigger_executions(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, trigger_id=None, user=None, project=None, hydrate=False)

List all trigger executions matching the given filter criteria.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by.	'created'
page	int	The page of items.	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages.	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or].	AND
trigger_id	Optional[UUID]	ID of the trigger to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
project	Optional[Union[UUID, str]]	Filter by project name/ID.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[TriggerExecutionResponse]	A list of all trigger executions matching the filter criteria.

Source code in src/zenml/client.py

def list_trigger_executions(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    trigger_id: Optional[UUID] = None,
    user: Optional[Union[UUID, str]] = None,
    project: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[TriggerExecutionResponse]:
    """List all trigger executions matching the given filter criteria.

    Args:
        sort_by: The column to sort by.
        page: The page of items.
        size: The maximum size of all pages.
        logical_operator: Which logical operator to use [and, or].
        trigger_id: ID of the trigger to filter by.
        user: Filter by user name/ID.
        project: Filter by project name/ID.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A list of all trigger executions matching the filter criteria.
    """
    filter_model = TriggerExecutionFilter(
        trigger_id=trigger_id,
        sort_by=sort_by,
        page=page,
        size=size,
        user=user,
        logical_operator=logical_operator,
        project=project or self.active_project.id,
    )
    return self.zen_store.list_trigger_executions(
        trigger_execution_filter_model=filter_model, hydrate=hydrate
    )

list_triggers(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, created=None, updated=None, name=None, event_source_id=None, action_id=None, event_source_flavor=None, event_source_subtype=None, action_flavor=None, action_subtype=None, project=None, user=None, hydrate=False)

Lists all triggers.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of triggers to filter by.	None
created	Optional[datetime]	Use to filter by time of creation	None
updated	Optional[datetime]	Use the last updated date for filtering	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None
user	Optional[Union[UUID, str]]	Filter by user name/ID.	None
name	Optional[str]	The name of the trigger to filter by.	None
event_source_id	Optional[UUID]	The event source associated with the trigger.	None
action_id	Optional[UUID]	The action associated with the trigger.	None
event_source_flavor	Optional[str]	Flavor of the event source associated with the trigger.	None
event_source_subtype	Optional[str]	Type of the event source associated with the trigger.	None
action_flavor	Optional[str]	Flavor of the action associated with the trigger.	None
action_subtype	Optional[str]	Type of the action associated with the trigger.	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[TriggerResponse]	A page of triggers.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def list_triggers(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    created: Optional[datetime] = None,
    updated: Optional[datetime] = None,
    name: Optional[str] = None,
    event_source_id: Optional[UUID] = None,
    action_id: Optional[UUID] = None,
    event_source_flavor: Optional[str] = None,
    event_source_subtype: Optional[str] = None,
    action_flavor: Optional[str] = None,
    action_subtype: Optional[str] = None,
    project: Optional[Union[str, UUID]] = None,
    user: Optional[Union[UUID, str]] = None,
    hydrate: bool = False,
) -> Page[TriggerResponse]:
    """Lists all triggers.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of triggers to filter by.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        project: The project name/ID to filter by.
        user: Filter by user name/ID.
        name: The name of the trigger to filter by.
        event_source_id: The event source associated with the trigger.
        action_id: The action associated with the trigger.
        event_source_flavor: Flavor of the event source associated with the
            trigger.
        event_source_subtype: Type of the event source associated with the
            trigger.
        action_flavor: Flavor of the action associated with the trigger.
        action_subtype: Type of the action associated with the trigger.
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        A page of triggers.
    """
    trigger_filter_model = TriggerFilter(
        page=page,
        size=size,
        sort_by=sort_by,
        logical_operator=logical_operator,
        project=project or self.active_project.id,
        user=user,
        name=name,
        event_source_id=event_source_id,
        action_id=action_id,
        event_source_flavor=event_source_flavor,
        event_source_subtype=event_source_subtype,
        action_flavor=action_flavor,
        action_subtype=action_subtype,
        id=id,
        created=created,
        updated=updated,
    )
    return self.zen_store.list_triggers(
        trigger_filter_model, hydrate=hydrate
    )

list_users(sort_by='created', page=PAGINATION_STARTING_PAGE, size=PAGE_SIZE_DEFAULT, logical_operator=LogicalOperators.AND, id=None, external_user_id=None, created=None, updated=None, name=None, full_name=None, email=None, active=None, email_opted_in=None, hydrate=False)

List all users.

Parameters:

Name	Type	Description	Default
sort_by	str	The column to sort by	'created'
page	int	The page of items	PAGINATION_STARTING_PAGE
size	int	The maximum size of all pages	PAGE_SIZE_DEFAULT
logical_operator	LogicalOperators	Which logical operator to use [and, or]	AND
id	Optional[Union[UUID, str]]	Use the id of stacks to filter by.	None
external_user_id	Optional[str]	Use the external user id for filtering.	None
created	Optional[Union[datetime, str]]	Use to filter by time of creation	None
updated	Optional[Union[datetime, str]]	Use the last updated date for filtering	None
name	Optional[str]	Use the username for filtering	None
full_name	Optional[str]	Use the user full name for filtering	None
email	Optional[str]	Use the user email for filtering	None
active	Optional[bool]	User the user active status for filtering	None
email_opted_in	Optional[bool]	Use the user opt in status for filtering	None
hydrate	bool	Flag deciding whether to hydrate the output model(s) by including metadata fields in the response.	False

Returns:

Type	Description
Page[UserResponse]	The User

Source code in src/zenml/client.py

def list_users(
    self,
    sort_by: str = "created",
    page: int = PAGINATION_STARTING_PAGE,
    size: int = PAGE_SIZE_DEFAULT,
    logical_operator: LogicalOperators = LogicalOperators.AND,
    id: Optional[Union[UUID, str]] = None,
    external_user_id: Optional[str] = None,
    created: Optional[Union[datetime, str]] = None,
    updated: Optional[Union[datetime, str]] = None,
    name: Optional[str] = None,
    full_name: Optional[str] = None,
    email: Optional[str] = None,
    active: Optional[bool] = None,
    email_opted_in: Optional[bool] = None,
    hydrate: bool = False,
) -> Page[UserResponse]:
    """List all users.

    Args:
        sort_by: The column to sort by
        page: The page of items
        size: The maximum size of all pages
        logical_operator: Which logical operator to use [and, or]
        id: Use the id of stacks to filter by.
        external_user_id: Use the external user id for filtering.
        created: Use to filter by time of creation
        updated: Use the last updated date for filtering
        name: Use the username for filtering
        full_name: Use the user full name for filtering
        email: Use the user email for filtering
        active: User the user active status for filtering
        email_opted_in: Use the user opt in status for filtering
        hydrate: Flag deciding whether to hydrate the output model(s)
            by including metadata fields in the response.

    Returns:
        The User
    """
    return self.zen_store.list_users(
        UserFilter(
            sort_by=sort_by,
            page=page,
            size=size,
            logical_operator=logical_operator,
            id=id,
            external_user_id=external_user_id,
            created=created,
            updated=updated,
            name=name,
            full_name=full_name,
            email=email,
            active=active,
            email_opted_in=email_opted_in,
        ),
        hydrate=hydrate,
    )

login_service_connector(name_id_or_prefix, resource_type=None, resource_id=None, **kwargs)

Use a service connector to authenticate a local client/SDK.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, id or prefix of the service connector to use.	required
resource_type	Optional[str]	The type of the resource to connect to. If not provided, the resource type from the service connector configuration will be used.	None
resource_id	Optional[str]	The ID of a particular resource instance to configure the local client to connect to. If the connector instance is already configured with a resource ID that is not the same or equivalent to the one requested, a ValueError exception is raised. May be omitted for connectors and resource types that do not support multiple resource instances.	None
kwargs	Any	Additional implementation specific keyword arguments to use to configure the client.	{}

Returns:

Type	Description
ServiceConnector	The service connector client instance that was used to configure the
ServiceConnector	local client.

Source code in src/zenml/client.py

def login_service_connector(
    self,
    name_id_or_prefix: Union[UUID, str],
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
    **kwargs: Any,
) -> "ServiceConnector":
    """Use a service connector to authenticate a local client/SDK.

    Args:
        name_id_or_prefix: The name, id or prefix of the service connector
            to use.
        resource_type: The type of the resource to connect to. If not
            provided, the resource type from the service connector
            configuration will be used.
        resource_id: The ID of a particular resource instance to configure
            the local client to connect to. If the connector instance is
            already configured with a resource ID that is not the same or
            equivalent to the one requested, a `ValueError` exception is
            raised. May be omitted for connectors and resource types that do
            not support multiple resource instances.
        kwargs: Additional implementation specific keyword arguments to use
            to configure the client.

    Returns:
        The service connector client instance that was used to configure the
        local client.
    """
    connector_client = self.get_service_connector_client(
        name_id_or_prefix=name_id_or_prefix,
        resource_type=resource_type,
        resource_id=resource_id,
        verify=False,
    )

    connector_client.configure_local_client(
        **kwargs,
    )

    return connector_client

prune_artifacts(only_versions=True, delete_from_artifact_store=False, project=None)

Delete all unused artifacts and artifact versions.

Parameters:

Name	Type	Description	Default
only_versions	bool	Only delete artifact versions, keeping artifacts	True
delete_from_artifact_store	bool	Delete data from artifact metadata	False
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Source code in src/zenml/client.py

def prune_artifacts(
    self,
    only_versions: bool = True,
    delete_from_artifact_store: bool = False,
    project: Optional[Union[str, UUID]] = None,
) -> None:
    """Delete all unused artifacts and artifact versions.

    Args:
        only_versions: Only delete artifact versions, keeping artifacts
        delete_from_artifact_store: Delete data from artifact metadata
        project: The project name/ID to filter by.
    """
    if delete_from_artifact_store:
        unused_artifact_versions = depaginate(
            self.list_artifact_versions,
            only_unused=True,
            project=project,
        )
        for unused_artifact_version in unused_artifact_versions:
            self._delete_artifact_from_artifact_store(
                unused_artifact_version
            )

    project = project or self.active_project.id

    self.zen_store.prune_artifact_versions(
        project_name_or_id=project, only_versions=only_versions
    )
    logger.info("All unused artifacts and artifact versions deleted.")

restore_secrets(ignore_errors=False, delete_secrets=False)

Restore all secrets from the configured backup secrets store.

Parameters:

Name	Type	Description	Default
ignore_errors	bool	Whether to ignore individual errors during the restore process and attempt to restore all secrets.	False
delete_secrets	bool	Whether to delete the secrets that have been successfully restored from the backup secrets store. Setting this flag effectively moves all secrets from the backup secrets store to the primary secrets store.	False

Source code in src/zenml/client.py

def restore_secrets(
    self,
    ignore_errors: bool = False,
    delete_secrets: bool = False,
) -> None:
    """Restore all secrets from the configured backup secrets store.

    Args:
        ignore_errors: Whether to ignore individual errors during the
            restore process and attempt to restore all secrets.
        delete_secrets: Whether to delete the secrets that have been
            successfully restored from the backup secrets store. Setting
            this flag effectively moves all secrets from the backup secrets
            store to the primary secrets store.
    """
    self.zen_store.restore_secrets(
        ignore_errors=ignore_errors, delete_secrets=delete_secrets
    )

rotate_api_key(service_account_name_id_or_prefix, name_id_or_prefix, retain_period_minutes=0, set_key=False)

Rotate an API key.

Parameters:

Name	Type	Description	Default
service_account_name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the service account to rotate the API key for.	required
name_id_or_prefix	Union[UUID, str]	Name, ID or prefix of the API key to update.	required
retain_period_minutes	int	The number of minutes to retain the old API key for. If set to 0, the old API key will be invalidated.	0
set_key	bool	Whether to set the rotated API key as the active API key.	False

Returns:

Type	Description
APIKeyResponse	The updated API key.

Source code in src/zenml/client.py

def rotate_api_key(
    self,
    service_account_name_id_or_prefix: Union[str, UUID],
    name_id_or_prefix: Union[UUID, str],
    retain_period_minutes: int = 0,
    set_key: bool = False,
) -> APIKeyResponse:
    """Rotate an API key.

    Args:
        service_account_name_id_or_prefix: The name, ID or prefix of the
            service account to rotate the API key for.
        name_id_or_prefix: Name, ID or prefix of the API key to update.
        retain_period_minutes: The number of minutes to retain the old API
            key for. If set to 0, the old API key will be invalidated.
        set_key: Whether to set the rotated API key as the active API key.

    Returns:
        The updated API key.
    """
    api_key = self.get_api_key(
        service_account_name_id_or_prefix=service_account_name_id_or_prefix,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
    )
    rotate_request = APIKeyRotateRequest(
        retain_period_minutes=retain_period_minutes
    )
    new_key = self.zen_store.rotate_api_key(
        service_account_id=api_key.service_account.id,
        api_key_name_or_id=api_key.id,
        rotate_request=rotate_request,
    )
    assert new_key.key is not None
    if set_key:
        self.set_api_key(key=new_key.key)

    return new_key

set_active_project(project_name_or_id)

Set the project for the local client.

Parameters:

Name	Type	Description	Default
project_name_or_id	Union[str, UUID]	The name or ID of the project to set active.	required

Returns:

Type	Description
ProjectResponse	The model of the active project.

Source code in src/zenml/client.py

def set_active_project(
    self, project_name_or_id: Union[str, UUID]
) -> "ProjectResponse":
    """Set the project for the local client.

    Args:
        project_name_or_id: The name or ID of the project to set active.

    Returns:
        The model of the active project.
    """
    project = self.zen_store.get_project(
        project_name_or_id=project_name_or_id
    )  # raises KeyError
    if self._config:
        self._config.set_active_project(project)
        # Sanitize the client configuration to reflect the current
        # settings
        self._sanitize_config()
    else:
        # set the active project globally only if the client doesn't use
        # a local configuration
        GlobalConfiguration().set_active_project(project)
    return project

set_api_key(key)

Configure the client with an API key.

Parameters:

Name	Type	Description	Default
key	str	The API key to use.	required

Raises:

Type	Description
NotImplementedError	If the client is not connected to a ZenML server.

Source code in src/zenml/client.py

def set_api_key(self, key: str) -> None:
    """Configure the client with an API key.

    Args:
        key: The API key to use.

    Raises:
        NotImplementedError: If the client is not connected to a ZenML
            server.
    """
    from zenml.login.credentials_store import get_credentials_store
    from zenml.zen_stores.rest_zen_store import RestZenStore

    zen_store = self.zen_store
    if not zen_store.TYPE == StoreType.REST:
        raise NotImplementedError(
            "API key configuration is only supported if connected to a "
            "ZenML server."
        )

    credentials_store = get_credentials_store()
    assert isinstance(zen_store, RestZenStore)

    credentials_store.set_api_key(server_url=zen_store.url, api_key=key)

    # Force a re-authentication to start using the new API key
    # right away.
    zen_store.authenticate(force=True)

trigger_pipeline(pipeline_name_or_id=None, run_configuration=None, config_path=None, template_id=None, stack_name_or_id=None, synchronous=False, project=None)

Trigger a pipeline from the server.

Usage examples: * Run the latest runnable template for a pipeline:

Client().trigger_pipeline(pipeline_name_or_id=<NAME>)

• Run the latest runnable template for a pipeline on a specific stack:

Client().trigger_pipeline(
    pipeline_name_or_id=<NAME>,
    stack_name_or_id=<STACK_NAME_OR_ID>
)

• Run a specific template:

Client().trigger_pipeline(template_id=<ID>)

Parameters:

Name	Type	Description	Default
pipeline_name_or_id	Union[str, UUID, None]	Name or ID of the pipeline. If this is specified, the latest runnable template for this pipeline will be used for the run (Runnable here means that the build associated with the template is for a remote stack without any custom flavor stack components). If not given, a template ID that should be run needs to be specified.	None
run_configuration	Union[PipelineRunConfiguration, Dict[str, Any], None]	Configuration for the run. Either this or a path to a config file can be specified.	None
config_path	Optional[str]	Path to a YAML configuration file. This file will be parsed as a PipelineRunConfiguration object. Either this or the configuration in code can be specified.	None
template_id	Optional[UUID]	ID of the template to run. Either this or a pipeline can be specified.	None
stack_name_or_id	Union[str, UUID, None]	Name or ID of the stack on which to run the pipeline. If not specified, this method will try to find a runnable template on any stack.	None
synchronous	bool	If True, this method will wait until the triggered run is finished.	False
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Raises:

Type	Description
RuntimeError	If triggering the pipeline failed.

Returns:

Type	Description
PipelineRunResponse	Model of the pipeline run.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def trigger_pipeline(
    self,
    pipeline_name_or_id: Union[str, UUID, None] = None,
    run_configuration: Union[
        PipelineRunConfiguration, Dict[str, Any], None
    ] = None,
    config_path: Optional[str] = None,
    template_id: Optional[UUID] = None,
    stack_name_or_id: Union[str, UUID, None] = None,
    synchronous: bool = False,
    project: Optional[Union[str, UUID]] = None,
) -> PipelineRunResponse:
    """Trigger a pipeline from the server.

    Usage examples:
    * Run the latest runnable template for a pipeline:
    ```python
    Client().trigger_pipeline(pipeline_name_or_id=<NAME>)
    ```
    * Run the latest runnable template for a pipeline on a specific stack:
    ```python
    Client().trigger_pipeline(
        pipeline_name_or_id=<NAME>,
        stack_name_or_id=<STACK_NAME_OR_ID>
    )
    ```
    * Run a specific template:
    ```python
    Client().trigger_pipeline(template_id=<ID>)
    ```

    Args:
        pipeline_name_or_id: Name or ID of the pipeline. If this is
            specified, the latest runnable template for this pipeline will
            be used for the run (Runnable here means that the build
            associated with the template is for a remote stack without any
            custom flavor stack components). If not given, a template ID
            that should be run needs to be specified.
        run_configuration: Configuration for the run. Either this or a
            path to a config file can be specified.
        config_path: Path to a YAML configuration file. This file will be
            parsed as a `PipelineRunConfiguration` object. Either this or
            the configuration in code can be specified.
        template_id: ID of the template to run. Either this or a pipeline
            can be specified.
        stack_name_or_id: Name or ID of the stack on which to run the
            pipeline. If not specified, this method will try to find a
            runnable template on any stack.
        synchronous: If `True`, this method will wait until the triggered
            run is finished.
        project: The project name/ID to filter by.

    Raises:
        RuntimeError: If triggering the pipeline failed.

    Returns:
        Model of the pipeline run.
    """
    from zenml.pipelines.run_utils import (
        validate_run_config_is_runnable_from_server,
        validate_stack_is_runnable_from_server,
        wait_for_pipeline_run_to_finish,
    )

    if Counter([template_id, pipeline_name_or_id])[None] != 1:
        raise RuntimeError(
            "You need to specify exactly one of pipeline or template "
            "to trigger."
        )

    if run_configuration and config_path:
        raise RuntimeError(
            "Only config path or runtime configuration can be specified."
        )

    if config_path:
        run_configuration = PipelineRunConfiguration.from_yaml(config_path)

    if isinstance(run_configuration, Dict):
        run_configuration = PipelineRunConfiguration.model_validate(
            run_configuration
        )

    if run_configuration:
        validate_run_config_is_runnable_from_server(run_configuration)

    if template_id:
        if stack_name_or_id:
            logger.warning(
                "Template ID and stack specified, ignoring the stack and "
                "using stack associated with the template instead."
            )

        run = self.zen_store.run_template(
            template_id=template_id,
            run_configuration=run_configuration,
        )
    else:
        assert pipeline_name_or_id
        pipeline = self.get_pipeline(name_id_or_prefix=pipeline_name_or_id)

        stack = None
        if stack_name_or_id:
            stack = self.get_stack(
                stack_name_or_id, allow_name_prefix_match=False
            )
            validate_stack_is_runnable_from_server(
                zen_store=self.zen_store, stack=stack
            )

        templates = depaginate(
            self.list_run_templates,
            pipeline_id=pipeline.id,
            stack_id=stack.id if stack else None,
            project=project or pipeline.project.id,
        )

        for template in templates:
            if not template.build:
                continue

            stack = template.build.stack
            if not stack:
                continue

            try:
                validate_stack_is_runnable_from_server(
                    zen_store=self.zen_store, stack=stack
                )
            except ValueError:
                continue

            run = self.zen_store.run_template(
                template_id=template.id,
                run_configuration=run_configuration,
            )
            break
        else:
            raise RuntimeError(
                "Unable to find a runnable template for the given stack "
                "and pipeline."
            )

    if synchronous:
        run = wait_for_pipeline_run_to_finish(run_id=run.id)

    return run

update_action(name_id_or_prefix, name=None, description=None, configuration=None, service_account_id=None, auth_window=None, project=None)

Update an action.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, id or prefix of the action to update.	required
name	Optional[str]	The new name of the action.	None
description	Optional[str]	The new description of the action.	None
configuration	Optional[Dict[str, Any]]	The new configuration of the action.	None
service_account_id	Optional[UUID]	The new service account that is used to execute the action.	None
auth_window	Optional[int]	The new time window in minutes for which the service account is authorized to execute the action. Set this to 0 to authorize the service account indefinitely (not recommended).	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
ActionResponse	The updated action.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def update_action(
    self,
    name_id_or_prefix: Union[UUID, str],
    name: Optional[str] = None,
    description: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    service_account_id: Optional[UUID] = None,
    auth_window: Optional[int] = None,
    project: Optional[Union[str, UUID]] = None,
) -> ActionResponse:
    """Update an action.

    Args:
        name_id_or_prefix: The name, id or prefix of the action to update.
        name: The new name of the action.
        description: The new description of the action.
        configuration: The new configuration of the action.
        service_account_id: The new service account that is used to execute
            the action.
        auth_window: The new time window in minutes for which the service
            account is authorized to execute the action. Set this to 0 to
            authorize the service account indefinitely (not recommended).
        project: The project name/ID to filter by.

    Returns:
        The updated action.
    """
    action = self.get_action(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )

    update_model = ActionUpdate(
        name=name,
        description=description,
        configuration=configuration,
        service_account_id=service_account_id,
        auth_window=auth_window,
    )

    return self.zen_store.update_action(
        action_id=action.id,
        action_update=update_model,
    )

update_api_key(service_account_name_id_or_prefix, name_id_or_prefix, name=None, description=None, active=None)

Update an API key.

Parameters:

Name	Type	Description	Default
service_account_name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the service account to update the API key for.	required
name_id_or_prefix	Union[UUID, str]	Name, ID or prefix of the API key to update.	required
name	Optional[str]	New name of the API key.	None
description	Optional[str]	New description of the API key.	None
active	Optional[bool]	Whether the API key is active or not.	None

Returns:

Type	Description
APIKeyResponse	The updated API key.

Source code in src/zenml/client.py

def update_api_key(
    self,
    service_account_name_id_or_prefix: Union[str, UUID],
    name_id_or_prefix: Union[UUID, str],
    name: Optional[str] = None,
    description: Optional[str] = None,
    active: Optional[bool] = None,
) -> APIKeyResponse:
    """Update an API key.

    Args:
        service_account_name_id_or_prefix: The name, ID or prefix of the
            service account to update the API key for.
        name_id_or_prefix: Name, ID or prefix of the API key to update.
        name: New name of the API key.
        description: New description of the API key.
        active: Whether the API key is active or not.

    Returns:
        The updated API key.
    """
    api_key = self.get_api_key(
        service_account_name_id_or_prefix=service_account_name_id_or_prefix,
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
    )
    update = APIKeyUpdate(
        name=name, description=description, active=active
    )
    return self.zen_store.update_api_key(
        service_account_id=api_key.service_account.id,
        api_key_name_or_id=api_key.id,
        api_key_update=update,
    )

update_artifact(name_id_or_prefix, new_name=None, add_tags=None, remove_tags=None, has_custom_name=None, project=None)

Update an artifact.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the artifact to update.	required
new_name	Optional[str]	The new name of the artifact.	None
add_tags	Optional[List[str]]	Tags to add to the artifact.	None
remove_tags	Optional[List[str]]	Tags to remove from the artifact.	None
has_custom_name	Optional[bool]	Whether the artifact has a custom name.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
ArtifactResponse	The updated artifact.

Source code in src/zenml/client.py

def update_artifact(
    self,
    name_id_or_prefix: Union[str, UUID],
    new_name: Optional[str] = None,
    add_tags: Optional[List[str]] = None,
    remove_tags: Optional[List[str]] = None,
    has_custom_name: Optional[bool] = None,
    project: Optional[Union[str, UUID]] = None,
) -> ArtifactResponse:
    """Update an artifact.

    Args:
        name_id_or_prefix: The name, ID or prefix of the artifact to update.
        new_name: The new name of the artifact.
        add_tags: Tags to add to the artifact.
        remove_tags: Tags to remove from the artifact.
        has_custom_name: Whether the artifact has a custom name.
        project: The project name/ID to filter by.

    Returns:
        The updated artifact.
    """
    artifact = self.get_artifact(
        name_id_or_prefix=name_id_or_prefix,
        project=project,
    )
    artifact_update = ArtifactUpdate(
        name=new_name,
        add_tags=add_tags,
        remove_tags=remove_tags,
        has_custom_name=has_custom_name,
    )
    return self.zen_store.update_artifact(
        artifact_id=artifact.id, artifact_update=artifact_update
    )

update_artifact_version(name_id_or_prefix, version=None, add_tags=None, remove_tags=None, project=None)

Update an artifact version.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, ID or prefix of the artifact to update.	required
version	Optional[str]	The version of the artifact to update. Only used if name_id_or_prefix is the name of the artifact. If not specified, the latest version is updated.	None
add_tags	Optional[List[str]]	Tags to add to the artifact version.	None
remove_tags	Optional[List[str]]	Tags to remove from the artifact version.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
ArtifactVersionResponse	The updated artifact version.

Source code in src/zenml/client.py

def update_artifact_version(
    self,
    name_id_or_prefix: Union[str, UUID],
    version: Optional[str] = None,
    add_tags: Optional[List[str]] = None,
    remove_tags: Optional[List[str]] = None,
    project: Optional[Union[str, UUID]] = None,
) -> ArtifactVersionResponse:
    """Update an artifact version.

    Args:
        name_id_or_prefix: The name, ID or prefix of the artifact to update.
        version: The version of the artifact to update. Only used if
            `name_id_or_prefix` is the name of the artifact. If not
            specified, the latest version is updated.
        add_tags: Tags to add to the artifact version.
        remove_tags: Tags to remove from the artifact version.
        project: The project name/ID to filter by.

    Returns:
        The updated artifact version.
    """
    artifact_version = self.get_artifact_version(
        name_id_or_prefix=name_id_or_prefix,
        version=version,
        project=project,
    )
    artifact_version_update = ArtifactVersionUpdate(
        add_tags=add_tags, remove_tags=remove_tags
    )
    return self.zen_store.update_artifact_version(
        artifact_version_id=artifact_version.id,
        artifact_version_update=artifact_version_update,
    )

update_authorized_device(id_or_prefix, locked=None)

Update an authorized device.

Parameters:

Name	Type	Description	Default
id_or_prefix	Union[UUID, str]	The ID or ID prefix of the authorized device.	required
locked	Optional[bool]	Whether to lock or unlock the authorized device.	None

Returns:

Type	Description
OAuthDeviceResponse	The updated authorized device.

Source code in src/zenml/client.py

def update_authorized_device(
    self,
    id_or_prefix: Union[UUID, str],
    locked: Optional[bool] = None,
) -> OAuthDeviceResponse:
    """Update an authorized device.

    Args:
        id_or_prefix: The ID or ID prefix of the authorized device.
        locked: Whether to lock or unlock the authorized device.

    Returns:
        The updated authorized device.
    """
    device = self.get_authorized_device(
        id_or_prefix=id_or_prefix, allow_id_prefix_match=False
    )
    return self.zen_store.update_authorized_device(
        device_id=device.id,
        update=OAuthDeviceUpdate(
            locked=locked,
        ),
    )

update_code_repository(name_id_or_prefix, name=None, description=None, logo_url=None, config=None, project=None)

Update a code repository.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	Name, ID or prefix of the code repository to update.	required
name	Optional[str]	New name of the code repository.	None
description	Optional[str]	New description of the code repository.	None
logo_url	Optional[str]	New logo URL of the code repository.	None
config	Optional[Dict[str, Any]]	New configuration options for the code repository. Will be used to update the existing configuration values. To remove values from the existing configuration, set the value for that key to None.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
CodeRepositoryResponse	The updated code repository.

Source code in src/zenml/client.py

def update_code_repository(
    self,
    name_id_or_prefix: Union[UUID, str],
    name: Optional[str] = None,
    description: Optional[str] = None,
    logo_url: Optional[str] = None,
    config: Optional[Dict[str, Any]] = None,
    project: Optional[Union[str, UUID]] = None,
) -> CodeRepositoryResponse:
    """Update a code repository.

    Args:
        name_id_or_prefix: Name, ID or prefix of the code repository to
            update.
        name: New name of the code repository.
        description: New description of the code repository.
        logo_url: New logo URL of the code repository.
        config: New configuration options for the code repository. Will
            be used to update the existing configuration values. To remove
            values from the existing configuration, set the value for that
            key to `None`.
        project: The project name/ID to filter by.

    Returns:
        The updated code repository.
    """
    repo = self.get_code_repository(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )
    update = CodeRepositoryUpdate(
        name=name, description=description, logo_url=logo_url
    )
    if config is not None:
        combined_config = repo.config
        combined_config.update(config)
        combined_config = {
            k: v for k, v in combined_config.items() if v is not None
        }

        self._validate_code_repository_config(
            source=repo.source, config=combined_config
        )
        update.config = combined_config

    return self.zen_store.update_code_repository(
        code_repository_id=repo.id, update=update
    )

update_event_source(name_id_or_prefix, name=None, description=None, configuration=None, rotate_secret=None, is_active=None, project=None)

Updates an event_source.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, id or prefix of the event_source to update.	required
name	Optional[str]	the new name of the event_source.	None
description	Optional[str]	the new description of the event_source.	None
configuration	Optional[Dict[str, Any]]	The event source configuration.	None
rotate_secret	Optional[bool]	Allows rotating of secret, if true, the response will contain the new secret value	None
is_active	Optional[bool]	Optional[bool] = Allows for activation/deactivating the event source	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
EventSourceResponse	The model of the updated event_source.

Raises:

Type	Description
EntityExistsError	If the event_source name is already taken.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def update_event_source(
    self,
    name_id_or_prefix: Union[UUID, str],
    name: Optional[str] = None,
    description: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    rotate_secret: Optional[bool] = None,
    is_active: Optional[bool] = None,
    project: Optional[Union[str, UUID]] = None,
) -> EventSourceResponse:
    """Updates an event_source.

    Args:
        name_id_or_prefix: The name, id or prefix of the event_source to update.
        name: the new name of the event_source.
        description: the new description of the event_source.
        configuration: The event source configuration.
        rotate_secret: Allows rotating of secret, if true, the response will
            contain the new secret value
        is_active: Optional[bool] = Allows for activation/deactivating the
            event source
        project: The project name/ID to filter by.

    Returns:
        The model of the updated event_source.

    Raises:
        EntityExistsError: If the event_source name is already taken.
    """
    # First, get the eve
    event_source = self.get_event_source(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )

    # Create the update model
    update_model = EventSourceUpdate(
        name=name,
        description=description,
        configuration=configuration,
        rotate_secret=rotate_secret,
        is_active=is_active,
    )

    if name:
        if self.list_event_sources(name=name):
            raise EntityExistsError(
                "There are already existing event_sources with the name "
                f"'{name}'."
            )

    updated_event_source = self.zen_store.update_event_source(
        event_source_id=event_source.id,
        event_source_update=update_model,
    )
    return updated_event_source

update_model(model_name_or_id, name=None, license=None, description=None, audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, add_tags=None, remove_tags=None, save_models_to_registry=None, project=None)

Updates an existing model in Model Control Plane.

Parameters:

Name	Type	Description	Default
model_name_or_id	Union[str, UUID]	name or id of the model to be deleted.	required
name	Optional[str]	The name of the model.	None
license	Optional[str]	The license under which the model is created.	None
description	Optional[str]	The description of the model.	None
audience	Optional[str]	The target audience of the model.	None
use_cases	Optional[str]	The use cases of the model.	None
limitations	Optional[str]	The known limitations of the model.	None
trade_offs	Optional[str]	The tradeoffs of the model.	None
ethics	Optional[str]	The ethical implications of the model.	None
add_tags	Optional[List[str]]	Tags to add to the model.	None
remove_tags	Optional[List[str]]	Tags to remove from to the model.	None
save_models_to_registry	Optional[bool]	Whether to save the model to the registry.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
ModelResponse	The updated model.

Source code in src/zenml/client.py

def update_model(
    self,
    model_name_or_id: Union[str, UUID],
    name: Optional[str] = None,
    license: Optional[str] = None,
    description: Optional[str] = None,
    audience: Optional[str] = None,
    use_cases: Optional[str] = None,
    limitations: Optional[str] = None,
    trade_offs: Optional[str] = None,
    ethics: Optional[str] = None,
    add_tags: Optional[List[str]] = None,
    remove_tags: Optional[List[str]] = None,
    save_models_to_registry: Optional[bool] = None,
    project: Optional[Union[str, UUID]] = None,
) -> ModelResponse:
    """Updates an existing model in Model Control Plane.

    Args:
        model_name_or_id: name or id of the model to be deleted.
        name: The name of the model.
        license: The license under which the model is created.
        description: The description of the model.
        audience: The target audience of the model.
        use_cases: The use cases of the model.
        limitations: The known limitations of the model.
        trade_offs: The tradeoffs of the model.
        ethics: The ethical implications of the model.
        add_tags: Tags to add to the model.
        remove_tags: Tags to remove from to the model.
        save_models_to_registry: Whether to save the model to the
            registry.
        project: The project name/ID to filter by.

    Returns:
        The updated model.
    """
    model = self.get_model(
        model_name_or_id=model_name_or_id, project=project
    )
    return self.zen_store.update_model(
        model_id=model.id,
        model_update=ModelUpdate(
            name=name,
            license=license,
            description=description,
            audience=audience,
            use_cases=use_cases,
            limitations=limitations,
            trade_offs=trade_offs,
            ethics=ethics,
            add_tags=add_tags,
            remove_tags=remove_tags,
            save_models_to_registry=save_models_to_registry,
        ),
    )

update_model_version(model_name_or_id, version_name_or_id, stage=None, force=False, name=None, description=None, add_tags=None, remove_tags=None, project=None)

Get all model versions by filter.

Parameters:

Name	Type	Description	Default
model_name_or_id	Union[str, UUID]	The name or ID of the model containing model version.	required
version_name_or_id	Union[str, UUID]	The name or ID of model version to be updated.	required
stage	Optional[Union[str, ModelStages]]	Target model version stage to be set.	None
force	bool	Whether existing model version in target stage should be silently archived or an error should be raised.	False
name	Optional[str]	Target model version name to be set.	None
description	Optional[str]	Target model version description to be set.	None
add_tags	Optional[List[str]]	Tags to add to the model version.	None
remove_tags	Optional[List[str]]	Tags to remove from to the model version.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
ModelVersionResponse	An updated model version.

Source code in src/zenml/client.py

def update_model_version(
    self,
    model_name_or_id: Union[str, UUID],
    version_name_or_id: Union[str, UUID],
    stage: Optional[Union[str, ModelStages]] = None,
    force: bool = False,
    name: Optional[str] = None,
    description: Optional[str] = None,
    add_tags: Optional[List[str]] = None,
    remove_tags: Optional[List[str]] = None,
    project: Optional[Union[str, UUID]] = None,
) -> ModelVersionResponse:
    """Get all model versions by filter.

    Args:
        model_name_or_id: The name or ID of the model containing model version.
        version_name_or_id: The name or ID of model version to be updated.
        stage: Target model version stage to be set.
        force: Whether existing model version in target stage should be
            silently archived or an error should be raised.
        name: Target model version name to be set.
        description: Target model version description to be set.
        add_tags: Tags to add to the model version.
        remove_tags: Tags to remove from to the model version.
        project: The project name/ID to filter by.

    Returns:
        An updated model version.
    """
    if not is_valid_uuid(model_name_or_id):
        model = self.get_model(model_name_or_id, project=project)
        model_name_or_id = model.id
        project = project or model.project.id
    if not is_valid_uuid(version_name_or_id):
        version_name_or_id = self.get_model_version(
            model_name_or_id, version_name_or_id, project=project
        ).id

    return self.zen_store.update_model_version(
        model_version_id=version_name_or_id,  # type:ignore[arg-type]
        model_version_update_model=ModelVersionUpdate(
            stage=stage,
            force=force,
            name=name,
            description=description,
            add_tags=add_tags,
            remove_tags=remove_tags,
        ),
    )

update_project(name_id_or_prefix, new_name=None, new_display_name=None, new_description=None)

Update a project.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Optional[Union[UUID, str]]	Name, ID or prefix of the project to update.	required
new_name	Optional[str]	New name of the project.	None
new_display_name	Optional[str]	New display name of the project.	None
new_description	Optional[str]	New description of the project.	None

Returns:

Type	Description
ProjectResponse	The updated project.

Source code in src/zenml/client.py

def update_project(
    self,
    name_id_or_prefix: Optional[Union[UUID, str]],
    new_name: Optional[str] = None,
    new_display_name: Optional[str] = None,
    new_description: Optional[str] = None,
) -> ProjectResponse:
    """Update a project.

    Args:
        name_id_or_prefix: Name, ID or prefix of the project to update.
        new_name: New name of the project.
        new_display_name: New display name of the project.
        new_description: New description of the project.

    Returns:
        The updated project.
    """
    project = self.get_project(
        name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
    )
    project_update = ProjectUpdate(
        name=new_name or project.name,
        display_name=new_display_name or project.display_name,
    )
    if new_description:
        project_update.description = new_description
    return self.zen_store.update_project(
        project_id=project.id,
        project_update=project_update,
    )

update_run_template(name_id_or_prefix, name=None, description=None, hidden=None, add_tags=None, remove_tags=None, project=None)

Update a run template.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	Name/ID/ID prefix of the template to update.	required
name	Optional[str]	The new name of the run template.	None
description	Optional[str]	The new description of the run template.	None
hidden	Optional[bool]	The new hidden status of the run template.	None
add_tags	Optional[List[str]]	Tags to add to the run template.	None
remove_tags	Optional[List[str]]	Tags to remove from the run template.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
RunTemplateResponse	The updated run template.

Source code in src/zenml/client.py

def update_run_template(
    self,
    name_id_or_prefix: Union[str, UUID],
    name: Optional[str] = None,
    description: Optional[str] = None,
    hidden: Optional[bool] = None,
    add_tags: Optional[List[str]] = None,
    remove_tags: Optional[List[str]] = None,
    project: Optional[Union[str, UUID]] = None,
) -> RunTemplateResponse:
    """Update a run template.

    Args:
        name_id_or_prefix: Name/ID/ID prefix of the template to update.
        name: The new name of the run template.
        description: The new description of the run template.
        hidden: The new hidden status of the run template.
        add_tags: Tags to add to the run template.
        remove_tags: Tags to remove from the run template.
        project: The project name/ID to filter by.

    Returns:
        The updated run template.
    """
    if is_valid_uuid(name_id_or_prefix):
        template_id = (
            UUID(name_id_or_prefix)
            if isinstance(name_id_or_prefix, str)
            else name_id_or_prefix
        )
    else:
        template_id = self.get_run_template(
            name_id_or_prefix,
            project=project,
            hydrate=False,
        ).id

    return self.zen_store.update_run_template(
        template_id=template_id,
        template_update=RunTemplateUpdate(
            name=name,
            description=description,
            hidden=hidden,
            add_tags=add_tags,
            remove_tags=remove_tags,
        ),
    )

update_secret(name_id_or_prefix, private=None, new_name=None, update_private=None, add_or_update_values=None, remove_values=None)

Updates a secret.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name, id or prefix of the id for the secret to update.	required
private	Optional[bool]	The private status of the secret to update.	None
new_name	Optional[str]	The new name of the secret.	None
update_private	Optional[bool]	New value used to update the private status of the secret.	None
add_or_update_values	Optional[Dict[str, str]]	The values to add or update.	None
remove_values	Optional[List[str]]	The values to remove.	None

Returns:

Type	Description
SecretResponse	The updated secret.

Raises:

Type	Description
KeyError	If trying to remove a value that doesn't exist.
ValueError	If a key is provided in both add_or_update_values and remove_values.

Source code in src/zenml/client.py

def update_secret(
    self,
    name_id_or_prefix: Union[str, UUID],
    private: Optional[bool] = None,
    new_name: Optional[str] = None,
    update_private: Optional[bool] = None,
    add_or_update_values: Optional[Dict[str, str]] = None,
    remove_values: Optional[List[str]] = None,
) -> SecretResponse:
    """Updates a secret.

    Args:
        name_id_or_prefix: The name, id or prefix of the id for the
            secret to update.
        private: The private status of the secret to update.
        new_name: The new name of the secret.
        update_private: New value used to update the private status of the
            secret.
        add_or_update_values: The values to add or update.
        remove_values: The values to remove.

    Returns:
        The updated secret.

    Raises:
        KeyError: If trying to remove a value that doesn't exist.
        ValueError: If a key is provided in both add_or_update_values and
            remove_values.
    """
    secret = self.get_secret(
        name_id_or_prefix=name_id_or_prefix,
        private=private,
        # Don't allow partial name matches, but allow partial ID matches
        allow_partial_name_match=False,
        allow_partial_id_match=True,
        hydrate=True,
    )

    secret_update = SecretUpdate(name=new_name or secret.name)

    if update_private:
        secret_update.private = update_private
    values: Dict[str, Optional[SecretStr]] = {}
    if add_or_update_values:
        values.update(
            {
                key: SecretStr(value)
                for key, value in add_or_update_values.items()
            }
        )
    if remove_values:
        for key in remove_values:
            if key not in secret.values:
                raise KeyError(
                    f"Cannot remove value '{key}' from secret "
                    f"'{secret.name}' because it does not exist."
                )
            if key in values:
                raise ValueError(
                    f"Key '{key}' is supplied both in the values to add or "
                    f"update and the values to be removed."
                )
            values[key] = None
    if values:
        secret_update.values = values

    return Client().zen_store.update_secret(
        secret_id=secret.id, secret_update=secret_update
    )

update_server_settings(updated_name=None, updated_logo_url=None, updated_enable_analytics=None, updated_enable_announcements=None, updated_enable_updates=None, updated_onboarding_state=None)

Update the server settings.

Parameters:

Name	Type	Description	Default
updated_name	Optional[str]	Updated name for the server.	None
updated_logo_url	Optional[str]	Updated logo URL for the server.	None
updated_enable_analytics	Optional[bool]	Updated value whether to enable analytics for the server.	None
updated_enable_announcements	Optional[bool]	Updated value whether to display announcements about ZenML.	None
updated_enable_updates	Optional[bool]	Updated value whether to display updates about ZenML.	None
updated_onboarding_state	Optional[Dict[str, Any]]	Updated onboarding state for the server.	None

Returns:

Type	Description
ServerSettingsResponse	The updated server settings.

Source code in src/zenml/client.py

def update_server_settings(
    self,
    updated_name: Optional[str] = None,
    updated_logo_url: Optional[str] = None,
    updated_enable_analytics: Optional[bool] = None,
    updated_enable_announcements: Optional[bool] = None,
    updated_enable_updates: Optional[bool] = None,
    updated_onboarding_state: Optional[Dict[str, Any]] = None,
) -> ServerSettingsResponse:
    """Update the server settings.

    Args:
        updated_name: Updated name for the server.
        updated_logo_url: Updated logo URL for the server.
        updated_enable_analytics: Updated value whether to enable
            analytics for the server.
        updated_enable_announcements: Updated value whether to display
            announcements about ZenML.
        updated_enable_updates: Updated value whether to display updates
            about ZenML.
        updated_onboarding_state: Updated onboarding state for the server.

    Returns:
        The updated server settings.
    """
    update_model = ServerSettingsUpdate(
        server_name=updated_name,
        logo_url=updated_logo_url,
        enable_analytics=updated_enable_analytics,
        display_announcements=updated_enable_announcements,
        display_updates=updated_enable_updates,
        onboarding_state=updated_onboarding_state,
    )
    return self.zen_store.update_server_settings(update_model)

update_service(id, name=None, service_source=None, admin_state=None, status=None, endpoint=None, labels=None, prediction_url=None, health_check_url=None, model_version_id=None)

Update a service.

Parameters:

Name	Type	Description	Default
id	UUID	The ID of the service to update.	required
name	Optional[str]	The new name of the service.	None
admin_state	Optional[ServiceState]	The new admin state of the service.	None
status	Optional[Dict[str, Any]]	The new status of the service.	None
endpoint	Optional[Dict[str, Any]]	The new endpoint of the service.	None
service_source	Optional[str]	The new service source of the service.	None
labels	Optional[Dict[str, str]]	The new labels of the service.	None
prediction_url	Optional[str]	The new prediction url of the service.	None
health_check_url	Optional[str]	The new health check url of the service.	None
model_version_id	Optional[UUID]	The new model version id of the service.	None

Returns:

Type	Description
ServiceResponse	The updated service.

Source code in src/zenml/client.py

def update_service(
    self,
    id: UUID,
    name: Optional[str] = None,
    service_source: Optional[str] = None,
    admin_state: Optional[ServiceState] = None,
    status: Optional[Dict[str, Any]] = None,
    endpoint: Optional[Dict[str, Any]] = None,
    labels: Optional[Dict[str, str]] = None,
    prediction_url: Optional[str] = None,
    health_check_url: Optional[str] = None,
    model_version_id: Optional[UUID] = None,
) -> ServiceResponse:
    """Update a service.

    Args:
        id: The ID of the service to update.
        name: The new name of the service.
        admin_state: The new admin state of the service.
        status: The new status of the service.
        endpoint: The new endpoint of the service.
        service_source: The new service source of the service.
        labels: The new labels of the service.
        prediction_url: The new prediction url of the service.
        health_check_url: The new health check url of the service.
        model_version_id: The new model version id of the service.

    Returns:
        The updated service.
    """
    service_update = ServiceUpdate()
    if name:
        service_update.name = name
    if service_source:
        service_update.service_source = service_source
    if admin_state:
        service_update.admin_state = admin_state
    if status:
        service_update.status = status
    if endpoint:
        service_update.endpoint = endpoint
    if labels:
        service_update.labels = labels
    if prediction_url:
        service_update.prediction_url = prediction_url
    if health_check_url:
        service_update.health_check_url = health_check_url
    if model_version_id:
        service_update.model_version_id = model_version_id
    return self.zen_store.update_service(
        service_id=id, update=service_update
    )

update_service_account(name_id_or_prefix, updated_name=None, description=None, active=None)

Update a service account.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name or ID of the service account to update.	required
updated_name	Optional[str]	The new name of the service account.	None
description	Optional[str]	The new description of the service account.	None
active	Optional[bool]	The new active status of the service account.	None

Returns:

Type	Description
ServiceAccountResponse	The updated service account.

Source code in src/zenml/client.py

def update_service_account(
    self,
    name_id_or_prefix: Union[str, UUID],
    updated_name: Optional[str] = None,
    description: Optional[str] = None,
    active: Optional[bool] = None,
) -> ServiceAccountResponse:
    """Update a service account.

    Args:
        name_id_or_prefix: The name or ID of the service account to update.
        updated_name: The new name of the service account.
        description: The new description of the service account.
        active: The new active status of the service account.

    Returns:
        The updated service account.
    """
    service_account = self.get_service_account(
        name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
    )
    service_account_update = ServiceAccountUpdate(
        name=updated_name,
        description=description,
        active=active,
    )

    return self.zen_store.update_service_account(
        service_account_name_or_id=service_account.id,
        service_account_update=service_account_update,
    )

update_service_connector(name_id_or_prefix, name=None, auth_method=None, resource_type=None, configuration=None, resource_id=None, description=None, expires_at=None, expires_skew_tolerance=None, expiration_seconds=None, labels=None, verify=True, list_resources=True, update=True)

Validate and/or register an updated service connector.

If the resource_type, resource_id and expiration_seconds parameters are set to their "empty" values (empty string for resource type and resource ID, 0 for expiration seconds), the existing values will be removed from the service connector. Setting them to None or omitting them will not affect the existing values.

If supplied, the configuration parameter is a full replacement of the existing configuration rather than a partial update.

Labels can be updated or removed by setting the label value to None.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, id or prefix of the service connector to update.	required
name	Optional[str]	The new name of the service connector.	None
auth_method	Optional[str]	The new authentication method of the service connector.	None
resource_type	Optional[str]	The new resource type for the service connector. If set to the empty string, the existing resource type will be removed.	None
configuration	Optional[Dict[str, str]]	The new configuration of the service connector. If set, this needs to be a full replacement of the existing configuration rather than a partial update.	None
resource_id	Optional[str]	The new resource id of the service connector. If set to the empty string, the existing resource ID will be removed.	None
description	Optional[str]	The description of the service connector.	None
expires_at	Optional[datetime]	The new UTC expiration time of the service connector.	None
expires_skew_tolerance	Optional[int]	The allowed expiration skew for the service connector credentials.	None
expiration_seconds	Optional[int]	The expiration time of the service connector. If set to 0, the existing expiration time will be removed.	None
labels	Optional[Dict[str, Optional[str]]]	The service connector to update or remove. If a label value is set to None, the label will be removed.	None
verify	bool	Whether to verify that the service connector configuration and credentials can be used to gain access to the resource.	True
list_resources	bool	Whether to also list the resources that the service connector can give access to (if verify is True).	True
update	bool	Whether to update the service connector or not.	True

Returns:

Type	Description
Optional[Union[ServiceConnectorResponse, ServiceConnectorUpdate]]	The model of the registered service connector and the resources
Optional[ServiceConnectorResourcesModel]	that the service connector can give access to (if verify is True).

Raises:

Type	Description
AuthorizationException	If the service connector verification fails due to invalid credentials or insufficient permissions.

Source code in src/zenml/client.py

def update_service_connector(
    self,
    name_id_or_prefix: Union[UUID, str],
    name: Optional[str] = None,
    auth_method: Optional[str] = None,
    resource_type: Optional[str] = None,
    configuration: Optional[Dict[str, str]] = None,
    resource_id: Optional[str] = None,
    description: Optional[str] = None,
    expires_at: Optional[datetime] = None,
    expires_skew_tolerance: Optional[int] = None,
    expiration_seconds: Optional[int] = None,
    labels: Optional[Dict[str, Optional[str]]] = None,
    verify: bool = True,
    list_resources: bool = True,
    update: bool = True,
) -> Tuple[
    Optional[
        Union[
            ServiceConnectorResponse,
            ServiceConnectorUpdate,
        ]
    ],
    Optional[ServiceConnectorResourcesModel],
]:
    """Validate and/or register an updated service connector.

    If the `resource_type`, `resource_id` and `expiration_seconds`
    parameters are set to their "empty" values (empty string for resource
    type and resource ID, 0 for expiration seconds), the existing values
    will be removed from the service connector. Setting them to None or
    omitting them will not affect the existing values.

    If supplied, the `configuration` parameter is a full replacement of the
    existing configuration rather than a partial update.

    Labels can be updated or removed by setting the label value to None.

    Args:
        name_id_or_prefix: The name, id or prefix of the service connector
            to update.
        name: The new name of the service connector.
        auth_method: The new authentication method of the service connector.
        resource_type: The new resource type for the service connector.
            If set to the empty string, the existing resource type will be
            removed.
        configuration: The new configuration of the service connector. If
            set, this needs to be a full replacement of the existing
            configuration rather than a partial update.
        resource_id: The new resource id of the service connector.
            If set to the empty string, the existing resource ID will be
            removed.
        description: The description of the service connector.
        expires_at: The new UTC expiration time of the service connector.
        expires_skew_tolerance: The allowed expiration skew for the service
            connector credentials.
        expiration_seconds: The expiration time of the service connector.
            If set to 0, the existing expiration time will be removed.
        labels: The service connector to update or remove. If a label value
            is set to None, the label will be removed.
        verify: Whether to verify that the service connector configuration
            and credentials can be used to gain access to the resource.
        list_resources: Whether to also list the resources that the service
            connector can give access to (if verify is True).
        update: Whether to update the service connector or not.

    Returns:
        The model of the registered service connector and the resources
        that the service connector can give access to (if verify is True).

    Raises:
        AuthorizationException: If the service connector verification
            fails due to invalid credentials or insufficient permissions.
    """
    from zenml.service_connectors.service_connector_registry import (
        service_connector_registry,
    )

    connector_model = self.get_service_connector(
        name_id_or_prefix,
        allow_name_prefix_match=False,
        load_secrets=True,
    )

    connector_instance: Optional[ServiceConnector] = None
    connector_resources: Optional[ServiceConnectorResourcesModel] = None

    if isinstance(connector_model.connector_type, str):
        connector = self.get_service_connector_type(
            connector_model.connector_type
        )
    else:
        connector = connector_model.connector_type

    resource_types: Optional[Union[str, List[str]]] = None
    if resource_type == "":
        resource_types = None
    elif resource_type is None:
        resource_types = connector_model.resource_types
    else:
        resource_types = resource_type

    if not resource_type and len(connector.resource_types) == 1:
        resource_types = connector.resource_types[0].resource_type

    if resource_id == "":
        resource_id = None
    elif resource_id is None:
        resource_id = connector_model.resource_id

    if expiration_seconds == 0:
        expiration_seconds = None
    elif expiration_seconds is None:
        expiration_seconds = connector_model.expiration_seconds

    connector_update = ServiceConnectorUpdate(
        name=name or connector_model.name,
        connector_type=connector.connector_type,
        description=description or connector_model.description,
        auth_method=auth_method or connector_model.auth_method,
        expires_at=expires_at,
        expires_skew_tolerance=expires_skew_tolerance,
        expiration_seconds=expiration_seconds,
    )

    # Validate and configure the resources
    if configuration is not None:
        # The supplied configuration is a drop-in replacement for the
        # existing configuration and secrets
        connector_update.validate_and_configure_resources(
            connector_type=connector,
            resource_types=resource_types,
            resource_id=resource_id,
            configuration=configuration,
        )
    else:
        connector_update.validate_and_configure_resources(
            connector_type=connector,
            resource_types=resource_types,
            resource_id=resource_id,
            configuration=connector_model.configuration,
            secrets=connector_model.secrets,
        )

    # Add the labels
    if labels is not None:
        # Apply the new label values, but don't keep any labels that
        # have been set to None in the update
        connector_update.labels = {
            **{
                label: value
                for label, value in connector_model.labels.items()
                if label not in labels
            },
            **{
                label: value
                for label, value in labels.items()
                if value is not None
            },
        }
    else:
        connector_update.labels = connector_model.labels

    if verify:
        # Prefer to verify the connector config server-side if the
        # implementation, if available there, because it ensures
        # that the connector can be shared with other users or used
        # from other machines and because some auth methods rely on the
        # server-side authentication environment

        # Convert the update model to a request model for validation
        connector_request_dict = connector_update.model_dump()
        connector_request = ServiceConnectorRequest.model_validate(
            connector_request_dict
        )

        if connector.remote:
            connector_resources = (
                self.zen_store.verify_service_connector_config(
                    service_connector=connector_request,
                    list_resources=list_resources,
                )
            )
        else:
            connector_instance = (
                service_connector_registry.instantiate_connector(
                    model=connector_request,
                )
            )
            connector_resources = connector_instance.verify(
                list_resources=list_resources
            )

        if connector_resources.error:
            raise AuthorizationException(connector_resources.error)

        # For resource types that don't support multi-instances, it's
        # better to save the default resource ID in the connector, if
        # available. Otherwise, we'll need to instantiate the connector
        # again to get the default resource ID.
        connector_update.resource_id = (
            connector_update.resource_id
            or connector_resources.get_default_resource_id()
        )

    if not update:
        return connector_update, connector_resources

    # Update the model
    connector_response = self.zen_store.update_service_connector(
        service_connector_id=connector_model.id,
        update=connector_update,
    )

    if connector_resources:
        connector_resources.id = connector_response.id
        connector_resources.name = connector_response.name
        connector_resources.connector_type = (
            connector_response.connector_type
        )

    return connector_response, connector_resources

update_stack(name_id_or_prefix=None, name=None, stack_spec_file=None, labels=None, description=None, component_updates=None)

Updates a stack and its components.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Optional[Union[UUID, str]]	The name, id or prefix of the stack to update.	None
name	Optional[str]	the new name of the stack.	None
stack_spec_file	Optional[str]	path to the stack spec file.	None
labels	Optional[Dict[str, Any]]	The new labels of the stack component.	None
description	Optional[str]	the new description of the stack.	None
component_updates	Optional[Dict[StackComponentType, List[Union[UUID, str]]]]	dictionary which maps stack component types to lists of new stack component names or ids.	None

Returns:

Type	Description
StackResponse	The model of the updated stack.

Raises:

Type	Description
EntityExistsError	If the stack name is already taken.

Source code in src/zenml/client.py

def update_stack(
    self,
    name_id_or_prefix: Optional[Union[UUID, str]] = None,
    name: Optional[str] = None,
    stack_spec_file: Optional[str] = None,
    labels: Optional[Dict[str, Any]] = None,
    description: Optional[str] = None,
    component_updates: Optional[
        Dict[StackComponentType, List[Union[UUID, str]]]
    ] = None,
) -> StackResponse:
    """Updates a stack and its components.

    Args:
        name_id_or_prefix: The name, id or prefix of the stack to update.
        name: the new name of the stack.
        stack_spec_file: path to the stack spec file.
        labels: The new labels of the stack component.
        description: the new description of the stack.
        component_updates: dictionary which maps stack component types to
            lists of new stack component names or ids.

    Returns:
        The model of the updated stack.

    Raises:
        EntityExistsError: If the stack name is already taken.
    """
    # First, get the stack
    stack = self.get_stack(
        name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
    )

    # Create the update model
    update_model = StackUpdate(
        stack_spec_path=stack_spec_file,
    )

    if name:
        if self.list_stacks(name=name):
            raise EntityExistsError(
                "There are already existing stacks with the name "
                f"'{name}'."
            )

        update_model.name = name

    if description:
        update_model.description = description

    # Get the current components
    if component_updates:
        components_dict = stack.components.copy()

        for component_type, component_id_list in component_updates.items():
            if component_id_list is not None:
                components_dict[component_type] = [
                    self.get_stack_component(
                        name_id_or_prefix=component_id,
                        component_type=component_type,
                    )
                    for component_id in component_id_list
                ]

        update_model.components = {
            c_type: [c.id for c in c_list]
            for c_type, c_list in components_dict.items()
        }

    if labels is not None:
        existing_labels = stack.labels or {}
        existing_labels.update(labels)

        existing_labels = {
            k: v for k, v in existing_labels.items() if v is not None
        }
        update_model.labels = existing_labels

    updated_stack = self.zen_store.update_stack(
        stack_id=stack.id,
        stack_update=update_model,
    )
    if updated_stack.id == self.active_stack_model.id:
        if self._config:
            self._config.set_active_stack(updated_stack)
        else:
            GlobalConfiguration().set_active_stack(updated_stack)
    return updated_stack

update_stack_component(name_id_or_prefix, component_type, name=None, configuration=None, labels=None, disconnect=None, connector_id=None, connector_resource_id=None)

Updates a stack component.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Optional[Union[UUID, str]]	The name, id or prefix of the stack component to update.	required
component_type	StackComponentType	The type of the stack component to update.	required
name	Optional[str]	The new name of the stack component.	None
configuration	Optional[Dict[str, Any]]	The new configuration of the stack component.	None
labels	Optional[Dict[str, Any]]	The new labels of the stack component.	None
disconnect	Optional[bool]	Whether to disconnect the stack component from its service connector.	None
connector_id	Optional[UUID]	The new connector id of the stack component.	None
connector_resource_id	Optional[str]	The new connector resource id of the stack component.	None

Returns:

Type	Description
ComponentResponse	The updated stack component.

Raises:

Type	Description
EntityExistsError	If the new name is already taken.

Source code in src/zenml/client.py

def update_stack_component(
    self,
    name_id_or_prefix: Optional[Union[UUID, str]],
    component_type: StackComponentType,
    name: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    labels: Optional[Dict[str, Any]] = None,
    disconnect: Optional[bool] = None,
    connector_id: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
) -> ComponentResponse:
    """Updates a stack component.

    Args:
        name_id_or_prefix: The name, id or prefix of the stack component to
            update.
        component_type: The type of the stack component to update.
        name: The new name of the stack component.
        configuration: The new configuration of the stack component.
        labels: The new labels of the stack component.
        disconnect: Whether to disconnect the stack component from its
            service connector.
        connector_id: The new connector id of the stack component.
        connector_resource_id: The new connector resource id of the
            stack component.

    Returns:
        The updated stack component.

    Raises:
        EntityExistsError: If the new name is already taken.
    """
    # Get the existing component model
    component = self.get_stack_component(
        name_id_or_prefix=name_id_or_prefix,
        component_type=component_type,
        allow_name_prefix_match=False,
    )

    update_model = ComponentUpdate()

    if name is not None:
        existing_components = self.list_stack_components(
            name=name,
            type=component_type,
        )
        if existing_components.total > 0:
            raise EntityExistsError(
                f"There are already existing components with the "
                f"name '{name}'."
            )
        update_model.name = name

    if configuration is not None:
        existing_configuration = component.configuration
        existing_configuration.update(configuration)
        existing_configuration = {
            k: v
            for k, v in existing_configuration.items()
            if v is not None
        }

        from zenml.stack.utils import (
            validate_stack_component_config,
            warn_if_config_server_mismatch,
        )

        validated_config = validate_stack_component_config(
            configuration_dict=existing_configuration,
            flavor=component.flavor,
            component_type=component.type,
            # Always enforce validation of custom flavors
            validate_custom_flavors=True,
        )
        # Guaranteed to not be None by setting
        # `validate_custom_flavors=True` above
        assert validated_config is not None
        warn_if_config_server_mismatch(validated_config)

        update_model.configuration = existing_configuration

    if labels is not None:
        existing_labels = component.labels or {}
        existing_labels.update(labels)

        existing_labels = {
            k: v for k, v in existing_labels.items() if v is not None
        }
        update_model.labels = existing_labels

    if disconnect:
        update_model.connector = None
        update_model.connector_resource_id = None
    else:
        existing_component = self.get_stack_component(
            name_id_or_prefix=name_id_or_prefix,
            component_type=component_type,
            allow_name_prefix_match=False,
        )
        update_model.connector = connector_id
        update_model.connector_resource_id = connector_resource_id
        if connector_id is None and existing_component.connector:
            update_model.connector = existing_component.connector.id
            update_model.connector_resource_id = (
                existing_component.connector_resource_id
            )

    # Send the updated component to the ZenStore
    return self.zen_store.update_stack_component(
        component_id=component.id,
        component_update=update_model,
    )

update_tag(tag_name_or_id, name=None, exclusive=None, color=None)

Updates an existing tag.

Parameters:

Name	Type	Description	Default
tag_name_or_id	Union[str, UUID]	name or UUID of the tag to be updated.	required
name	Optional[str]	the name of the tag.	None
exclusive	Optional[bool]	the boolean to decide whether the tag is an exclusive tag. An exclusive tag means that the tag can exist only for a single: - pipeline run within the scope of a pipeline - artifact version within the scope of an artifact - run template	None
color	Optional[Union[str, ColorVariants]]	the color of the tag	None

Returns:

Type	Description
TagResponse	The updated tag.

Source code in src/zenml/client.py

def update_tag(
    self,
    tag_name_or_id: Union[str, UUID],
    name: Optional[str] = None,
    exclusive: Optional[bool] = None,
    color: Optional[Union[str, ColorVariants]] = None,
) -> TagResponse:
    """Updates an existing tag.

    Args:
        tag_name_or_id: name or UUID of the tag to be updated.
        name: the name of the tag.
        exclusive: the boolean to decide whether the tag is an exclusive tag.
            An exclusive tag means that the tag can exist only for a single:
                - pipeline run within the scope of a pipeline
                - artifact version within the scope of an artifact
                - run template
        color: the color of the tag

    Returns:
        The updated tag.
    """
    update_model = TagUpdate()

    if name is not None:
        update_model.name = name

    if exclusive is not None:
        update_model.exclusive = exclusive

    if color is not None:
        if isinstance(color, str):
            update_model.color = ColorVariants(color)
        else:
            update_model.color = color

    return self.zen_store.update_tag(
        tag_name_or_id=tag_name_or_id,
        tag_update_model=update_model,
    )

update_trigger(name_id_or_prefix, name=None, description=None, event_filter=None, is_active=None, project=None)

Updates a trigger.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, id or prefix of the trigger to update.	required
name	Optional[str]	the new name of the trigger.	None
description	Optional[str]	the new description of the trigger.	None
event_filter	Optional[Dict[str, Any]]	The event filter configuration.	None
is_active	Optional[bool]	Whether the trigger is active or not.	None
project	Optional[Union[str, UUID]]	The project name/ID to filter by.	None

Returns:

Type	Description
TriggerResponse	The model of the updated trigger.

Raises:

Type	Description
EntityExistsError	If the trigger name is already taken.

Source code in src/zenml/client.py

@_fail_for_sql_zen_store
def update_trigger(
    self,
    name_id_or_prefix: Union[UUID, str],
    name: Optional[str] = None,
    description: Optional[str] = None,
    event_filter: Optional[Dict[str, Any]] = None,
    is_active: Optional[bool] = None,
    project: Optional[Union[str, UUID]] = None,
) -> TriggerResponse:
    """Updates a trigger.

    Args:
        name_id_or_prefix: The name, id or prefix of the trigger to update.
        name: the new name of the trigger.
        description: the new description of the trigger.
        event_filter: The event filter configuration.
        is_active: Whether the trigger is active or not.
        project: The project name/ID to filter by.

    Returns:
        The model of the updated trigger.

    Raises:
        EntityExistsError: If the trigger name is already taken.
    """
    # First, get the eve
    trigger = self.get_trigger(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
        project=project,
    )

    # Create the update model
    update_model = TriggerUpdate(
        name=name,
        description=description,
        event_filter=event_filter,
        is_active=is_active,
    )

    if name:
        if self.list_triggers(name=name):
            raise EntityExistsError(
                "There are already is an existing trigger with the name "
                f"'{name}'."
            )

    updated_trigger = self.zen_store.update_trigger(
        trigger_id=trigger.id,
        trigger_update=update_model,
    )
    return updated_trigger

update_user(name_id_or_prefix, updated_name=None, updated_full_name=None, updated_email=None, updated_email_opt_in=None, updated_password=None, old_password=None, updated_is_admin=None, updated_metadata=None, updated_default_project_id=None, active=None)

Update a user.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[str, UUID]	The name or ID of the user to update.	required
updated_name	Optional[str]	The new name of the user.	None
updated_full_name	Optional[str]	The new full name of the user.	None
updated_email	Optional[str]	The new email of the user.	None
updated_email_opt_in	Optional[bool]	The new email opt-in status of the user.	None
updated_password	Optional[str]	The new password of the user.	None
old_password	Optional[str]	The old password of the user. Required for password update.	None
updated_is_admin	Optional[bool]	Whether the user should be an admin.	None
updated_metadata	Optional[Dict[str, Any]]	The new metadata for the user.	None
updated_default_project_id	Optional[UUID]	The new default project ID for the user.	None
active	Optional[bool]	Use to activate or deactivate the user.	None

Returns:

Type	Description
UserResponse	The updated user.

Raises:

Type	Description
ValidationError	If the old password is not provided when updating the password.

Source code in src/zenml/client.py

def update_user(
    self,
    name_id_or_prefix: Union[str, UUID],
    updated_name: Optional[str] = None,
    updated_full_name: Optional[str] = None,
    updated_email: Optional[str] = None,
    updated_email_opt_in: Optional[bool] = None,
    updated_password: Optional[str] = None,
    old_password: Optional[str] = None,
    updated_is_admin: Optional[bool] = None,
    updated_metadata: Optional[Dict[str, Any]] = None,
    updated_default_project_id: Optional[UUID] = None,
    active: Optional[bool] = None,
) -> UserResponse:
    """Update a user.

    Args:
        name_id_or_prefix: The name or ID of the user to update.
        updated_name: The new name of the user.
        updated_full_name: The new full name of the user.
        updated_email: The new email of the user.
        updated_email_opt_in: The new email opt-in status of the user.
        updated_password: The new password of the user.
        old_password: The old password of the user. Required for password
            update.
        updated_is_admin: Whether the user should be an admin.
        updated_metadata: The new metadata for the user.
        updated_default_project_id: The new default project ID for the user.
        active: Use to activate or deactivate the user.

    Returns:
        The updated user.

    Raises:
        ValidationError: If the old password is not provided when updating
            the password.
    """
    user = self.get_user(
        name_id_or_prefix=name_id_or_prefix, allow_name_prefix_match=False
    )
    user_update = UserUpdate(name=updated_name or user.name)
    if updated_full_name:
        user_update.full_name = updated_full_name
    if updated_email is not None:
        user_update.email = updated_email
        user_update.email_opted_in = (
            updated_email_opt_in or user.email_opted_in
        )
    if updated_email_opt_in is not None:
        user_update.email_opted_in = updated_email_opt_in
    if updated_password is not None:
        user_update.password = updated_password
        if old_password is None:
            raise ValidationError(
                "Old password is required to update the password."
            )
        user_update.old_password = old_password
    if updated_is_admin is not None:
        user_update.is_admin = updated_is_admin
    if active is not None:
        user_update.active = active

    if updated_metadata is not None:
        user_update.user_metadata = updated_metadata

    if updated_default_project_id is not None:
        user_update.default_project_id = updated_default_project_id

    return self.zen_store.update_user(
        user_id=user.id, user_update=user_update
    )

verify_service_connector(name_id_or_prefix, resource_type=None, resource_id=None, list_resources=True)

Verifies if a service connector has access to one or more resources.

Parameters:

Name	Type	Description	Default
name_id_or_prefix	Union[UUID, str]	The name, id or prefix of the service connector to verify.	required
resource_type	Optional[str]	The type of the resource for which to verify access. If not provided, the resource type from the service connector configuration will be used.	None
resource_id	Optional[str]	The ID of the resource for which to verify access. If not provided, the resource ID from the service connector configuration will be used.	None
list_resources	bool	Whether to list the resources that the service connector has access to.	True

Returns:

Type	Description
ServiceConnectorResourcesModel	The list of resources that the service connector has access to,
ServiceConnectorResourcesModel	scoped to the supplied resource type and ID, if provided.

Raises:

Type	Description
AuthorizationException	If the service connector does not have access to the resources.

Source code in src/zenml/client.py

def verify_service_connector(
    self,
    name_id_or_prefix: Union[UUID, str],
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
    list_resources: bool = True,
) -> "ServiceConnectorResourcesModel":
    """Verifies if a service connector has access to one or more resources.

    Args:
        name_id_or_prefix: The name, id or prefix of the service connector
            to verify.
        resource_type: The type of the resource for which to verify access.
            If not provided, the resource type from the service connector
            configuration will be used.
        resource_id: The ID of the resource for which to verify access. If
            not provided, the resource ID from the service connector
            configuration will be used.
        list_resources: Whether to list the resources that the service
            connector has access to.

    Returns:
        The list of resources that the service connector has access to,
        scoped to the supplied resource type and ID, if provided.

    Raises:
        AuthorizationException: If the service connector does not have
            access to the resources.
    """
    from zenml.service_connectors.service_connector_registry import (
        service_connector_registry,
    )

    # Get the service connector model
    service_connector = self.get_service_connector(
        name_id_or_prefix=name_id_or_prefix,
        allow_name_prefix_match=False,
    )

    connector_type = self.get_service_connector_type(
        service_connector.type
    )

    # Prefer to verify the connector config server-side if the
    # implementation if available there, because it ensures
    # that the connector can be shared with other users or used
    # from other machines and because some auth methods rely on the
    # server-side authentication environment
    if connector_type.remote:
        connector_resources = self.zen_store.verify_service_connector(
            service_connector_id=service_connector.id,
            resource_type=resource_type,
            resource_id=resource_id,
            list_resources=list_resources,
        )
    else:
        connector_instance = (
            service_connector_registry.instantiate_connector(
                model=service_connector
            )
        )
        connector_resources = connector_instance.verify(
            resource_type=resource_type,
            resource_id=resource_id,
            list_resources=list_resources,
        )

    if connector_resources.error:
        raise AuthorizationException(connector_resources.error)

    return connector_resources

ClientConfiguration

Bases: FileSyncModel

Pydantic object used for serializing client configuration options.

Source code in src/zenml/client.py

class ClientConfiguration(FileSyncModel):
    """Pydantic object used for serializing client configuration options."""

    _active_project: Optional["ProjectResponse"] = None
    active_project_id: Optional[UUID] = None
    active_stack_id: Optional[UUID] = None
    _active_stack: Optional["StackResponse"] = None

    @property
    def active_project(self) -> "ProjectResponse":
        """Get the active project for the local client.

        Returns:
            The active project.

        Raises:
            RuntimeError: If no active project is set.
        """
        if self._active_project:
            return self._active_project
        else:
            raise RuntimeError(
                "No active project is configured. Run "
                "`zenml project set <NAME>` to set the active "
                "project."
            )

    def set_active_project(self, project: "ProjectResponse") -> None:
        """Set the project for the local client.

        Args:
            project: The project to set active.
        """
        self._active_project = project
        self.active_project_id = project.id

    def set_active_stack(self, stack: "StackResponse") -> None:
        """Set the stack for the local client.

        Args:
            stack: The stack to set active.
        """
        self.active_stack_id = stack.id
        self._active_stack = stack

    model_config = ConfigDict(
        # Validate attributes when assigning them. We need to set this in order
        # to have a mix of mutable and immutable attributes
        validate_assignment=True,
        # Allow extra attributes from configs of previous ZenML versions to
        # permit downgrading
        extra="allow",
    )

active_project property

Get the active project for the local client.

Returns:

Type	Description
ProjectResponse	The active project.

Raises:

Type	Description
RuntimeError	If no active project is set.

set_active_project(project)

Set the project for the local client.

Parameters:

Name	Type	Description	Default
project	ProjectResponse	The project to set active.	required

Source code in src/zenml/client.py

def set_active_project(self, project: "ProjectResponse") -> None:
    """Set the project for the local client.

    Args:
        project: The project to set active.
    """
    self._active_project = project
    self.active_project_id = project.id

set_active_stack(stack)

Set the stack for the local client.

Parameters:

Name	Type	Description	Default
stack	StackResponse	The stack to set active.	required

Source code in src/zenml/client.py

def set_active_stack(self, stack: "StackResponse") -> None:
    """Set the stack for the local client.

    Args:
        stack: The stack to set active.
    """
    self.active_stack_id = stack.id
    self._active_stack = stack

ClientMetaClass

Bases: ABCMeta

Client singleton metaclass.

This metaclass is used to enforce a singleton instance of the Client class with the following additional properties:

• the singleton Client instance is created on first access to reflect the global configuration and local client configuration.
• the Client shouldn't be accessed from within pipeline steps (a warning is logged if this is attempted).

Source code in src/zenml/client.py

class ClientMetaClass(ABCMeta):
    """Client singleton metaclass.

    This metaclass is used to enforce a singleton instance of the Client
    class with the following additional properties:

    * the singleton Client instance is created on first access to reflect
    the global configuration and local client configuration.
    * the Client shouldn't be accessed from within pipeline steps (a warning
    is logged if this is attempted).
    """

    def __init__(cls, *args: Any, **kwargs: Any) -> None:
        """Initialize the Client class.

        Args:
            *args: Positional arguments.
            **kwargs: Keyword arguments.
        """
        super().__init__(*args, **kwargs)
        cls._global_client: Optional["Client"] = None

    def __call__(cls, *args: Any, **kwargs: Any) -> "Client":
        """Create or return the global Client instance.

        If the Client constructor is called with custom arguments,
        the singleton functionality of the metaclass is bypassed: a new
        Client instance is created and returned immediately and without
        saving it as the global Client singleton.

        Args:
            *args: Positional arguments.
            **kwargs: Keyword arguments.

        Returns:
            Client: The global Client instance.
        """
        if args or kwargs:
            return cast("Client", super().__call__(*args, **kwargs))

        if not cls._global_client:
            cls._global_client = cast(
                "Client", super().__call__(*args, **kwargs)
            )

        return cls._global_client

__call__(*args, **kwargs)

Create or return the global Client instance.

If the Client constructor is called with custom arguments, the singleton functionality of the metaclass is bypassed: a new Client instance is created and returned immediately and without saving it as the global Client singleton.

Parameters:

Name	Type	Description	Default
*args	Any	Positional arguments.	()
**kwargs	Any	Keyword arguments.	{}

Returns:

Name	Type	Description
Client	Client	The global Client instance.

Source code in src/zenml/client.py

def __call__(cls, *args: Any, **kwargs: Any) -> "Client":
    """Create or return the global Client instance.

    If the Client constructor is called with custom arguments,
    the singleton functionality of the metaclass is bypassed: a new
    Client instance is created and returned immediately and without
    saving it as the global Client singleton.

    Args:
        *args: Positional arguments.
        **kwargs: Keyword arguments.

    Returns:
        Client: The global Client instance.
    """
    if args or kwargs:
        return cast("Client", super().__call__(*args, **kwargs))

    if not cls._global_client:
        cls._global_client = cast(
            "Client", super().__call__(*args, **kwargs)
        )

    return cls._global_client

__init__(*args, **kwargs)

Initialize the Client class.

Parameters:

Name	Type	Description	Default
*args	Any	Positional arguments.	()
**kwargs	Any	Keyword arguments.	{}

Source code in src/zenml/client.py

def __init__(cls, *args: Any, **kwargs: Any) -> None:
    """Initialize the Client class.

    Args:
        *args: Positional arguments.
        **kwargs: Keyword arguments.
    """
    super().__init__(*args, **kwargs)
    cls._global_client: Optional["Client"] = None

Code Repositories

Initialization of the ZenML code repository base abstraction.

BaseCodeRepository

Bases: ABC

Base class for code repositories.

Code repositories are used to connect to a remote code repository and store information about the repository, such as the URL, the owner, the repository name, and the host. They also provide methods to download files from the repository when a pipeline is run remotely.

Source code in src/zenml/code_repositories/base_code_repository.py

class BaseCodeRepository(ABC):
    """Base class for code repositories.

    Code repositories are used to connect to a remote code repository and
    store information about the repository, such as the URL, the owner,
    the repository name, and the host. They also provide methods to
    download files from the repository when a pipeline is run remotely.
    """

    def __init__(
        self,
        id: UUID,
        name: str,
        config: Dict[str, Any],
    ) -> None:
        """Initializes a code repository.

        Args:
            id: The ID of the code repository.
            name: The name of the code repository.
            config: The config of the code repository.
        """
        self._id = id
        self._name = name
        self._config = config
        self.login()

    @property
    def config(self) -> "BaseCodeRepositoryConfig":
        """Config class for Code Repository.

        Returns:
            The config class.
        """
        return BaseCodeRepositoryConfig(**self._config)

    @classmethod
    def from_model(cls, model: CodeRepositoryResponse) -> "BaseCodeRepository":
        """Loads a code repository from a model.

        Args:
            model: The CodeRepositoryResponseModel to load from.

        Returns:
            The loaded code repository object.
        """
        class_: Type[BaseCodeRepository] = (
            source_utils.load_and_validate_class(
                source=model.source, expected_class=BaseCodeRepository
            )
        )
        return class_(id=model.id, name=model.name, config=model.config)

    @classmethod
    def validate_config(cls, config: Dict[str, Any]) -> None:
        """Validate the code repository config.

        This method should check that the config/credentials are valid and
        the configured repository exists.

        Args:
            config: The configuration.
        """
        # The initialization calls the login to verify the credentials
        code_repo = cls(id=uuid4(), name="", config=config)

        # Explicitly access the config for pydantic validation
        _ = code_repo.config

    @property
    def id(self) -> UUID:
        """ID of the code repository.

        Returns:
            The ID of the code repository.
        """
        return self._id

    @property
    def name(self) -> str:
        """Name of the code repository.

        Returns:
            The name of the code repository.
        """
        return self._name

    @property
    def requirements(self) -> Set[str]:
        """Set of PyPI requirements for the repository.

        Returns:
            A set of PyPI requirements for the repository.
        """
        from zenml.integrations.utils import get_requirements_for_module

        return set(get_requirements_for_module(self.__module__))

    @abstractmethod
    def login(self) -> None:
        """Logs into the code repository.

        This method is called when the code repository is initialized.
        It should be used to authenticate with the code repository.

        Raises:
            RuntimeError: If the login fails.
        """
        pass

    @abstractmethod
    def download_files(
        self, commit: str, directory: str, repo_sub_directory: Optional[str]
    ) -> None:
        """Downloads files from the code repository to a local directory.

        Args:
            commit: The commit hash to download files from.
            directory: The directory to download files to.
            repo_sub_directory: The subdirectory in the repository to
                download files from.

        Raises:
            RuntimeError: If the download fails.
        """
        pass

    @abstractmethod
    def get_local_context(
        self, path: str
    ) -> Optional["LocalRepositoryContext"]:
        """Gets a local repository context from a path.

        Args:
            path: The path to the local repository.

        Returns:
            The local repository context object.
        """
        pass

config property

Config class for Code Repository.

Returns:

Type	Description
BaseCodeRepositoryConfig	The config class.

id property

ID of the code repository.

Returns:

Type	Description
UUID	The ID of the code repository.

name property

Name of the code repository.

Returns:

Type	Description
str	The name of the code repository.

requirements property

Set of PyPI requirements for the repository.

Returns:

Type	Description
Set[str]	A set of PyPI requirements for the repository.

__init__(id, name, config)

Initializes a code repository.

Parameters:

Name	Type	Description	Default
id	UUID	The ID of the code repository.	required
name	str	The name of the code repository.	required
config	Dict[str, Any]	The config of the code repository.	required

Source code in src/zenml/code_repositories/base_code_repository.py

def __init__(
    self,
    id: UUID,
    name: str,
    config: Dict[str, Any],
) -> None:
    """Initializes a code repository.

    Args:
        id: The ID of the code repository.
        name: The name of the code repository.
        config: The config of the code repository.
    """
    self._id = id
    self._name = name
    self._config = config
    self.login()

download_files(commit, directory, repo_sub_directory) abstractmethod

Downloads files from the code repository to a local directory.

Parameters:

Name	Type	Description	Default
commit	str	The commit hash to download files from.	required
directory	str	The directory to download files to.	required
repo_sub_directory	Optional[str]	The subdirectory in the repository to download files from.	required

Raises:

Type	Description
RuntimeError	If the download fails.

Source code in src/zenml/code_repositories/base_code_repository.py

@abstractmethod
def download_files(
    self, commit: str, directory: str, repo_sub_directory: Optional[str]
) -> None:
    """Downloads files from the code repository to a local directory.

    Args:
        commit: The commit hash to download files from.
        directory: The directory to download files to.
        repo_sub_directory: The subdirectory in the repository to
            download files from.

    Raises:
        RuntimeError: If the download fails.
    """
    pass

from_model(model) classmethod

Loads a code repository from a model.

Parameters:

Name	Type	Description	Default
model	CodeRepositoryResponse	The CodeRepositoryResponseModel to load from.	required

Returns:

Type	Description
BaseCodeRepository	The loaded code repository object.

Source code in src/zenml/code_repositories/base_code_repository.py

@classmethod
def from_model(cls, model: CodeRepositoryResponse) -> "BaseCodeRepository":
    """Loads a code repository from a model.

    Args:
        model: The CodeRepositoryResponseModel to load from.

    Returns:
        The loaded code repository object.
    """
    class_: Type[BaseCodeRepository] = (
        source_utils.load_and_validate_class(
            source=model.source, expected_class=BaseCodeRepository
        )
    )
    return class_(id=model.id, name=model.name, config=model.config)

get_local_context(path) abstractmethod

Gets a local repository context from a path.

Parameters:

Name	Type	Description	Default
path	str	The path to the local repository.	required

Returns:

Type	Description
Optional[LocalRepositoryContext]	The local repository context object.

Source code in src/zenml/code_repositories/base_code_repository.py

@abstractmethod
def get_local_context(
    self, path: str
) -> Optional["LocalRepositoryContext"]:
    """Gets a local repository context from a path.

    Args:
        path: The path to the local repository.

    Returns:
        The local repository context object.
    """
    pass

login() abstractmethod

Logs into the code repository.

This method is called when the code repository is initialized. It should be used to authenticate with the code repository.

Raises:

Type	Description
RuntimeError	If the login fails.

Source code in src/zenml/code_repositories/base_code_repository.py

@abstractmethod
def login(self) -> None:
    """Logs into the code repository.

    This method is called when the code repository is initialized.
    It should be used to authenticate with the code repository.

    Raises:
        RuntimeError: If the login fails.
    """
    pass

validate_config(config) classmethod

Validate the code repository config.

This method should check that the config/credentials are valid and the configured repository exists.

Parameters:

Name	Type	Description	Default
config	Dict[str, Any]	The configuration.	required

Source code in src/zenml/code_repositories/base_code_repository.py

@classmethod
def validate_config(cls, config: Dict[str, Any]) -> None:
    """Validate the code repository config.

    This method should check that the config/credentials are valid and
    the configured repository exists.

    Args:
        config: The configuration.
    """
    # The initialization calls the login to verify the credentials
    code_repo = cls(id=uuid4(), name="", config=config)

    # Explicitly access the config for pydantic validation
    _ = code_repo.config

LocalRepositoryContext

Bases: ABC

Base class for local repository contexts.

This class is used to represent a local repository. It is used to track the current state of the repository and to provide information about the repository, such as the root path, the current commit, and whether the repository is dirty.

Source code in src/zenml/code_repositories/local_repository_context.py

class LocalRepositoryContext(ABC):
    """Base class for local repository contexts.

    This class is used to represent a local repository. It is used
    to track the current state of the repository and to provide
    information about the repository, such as the root path, the current
    commit, and whether the repository is dirty.
    """

    def __init__(self, code_repository: "BaseCodeRepository") -> None:
        """Initializes a local repository context.

        Args:
            code_repository: The code repository.
        """
        self._code_repository = code_repository

    @property
    def code_repository(self) -> "BaseCodeRepository":
        """Returns the code repository.

        Returns:
            The code repository.
        """
        return self._code_repository

    @property
    @abstractmethod
    def root(self) -> str:
        """Returns the root path of the local repository.

        Returns:
            The root path of the local repository.
        """
        pass

    @property
    @abstractmethod
    def is_dirty(self) -> bool:
        """Returns whether the local repository is dirty.

        A repository counts as dirty if it has any untracked or uncommitted
        changes.

        Returns:
            Whether the local repository is dirty.
        """
        pass

    @property
    @abstractmethod
    def has_local_changes(self) -> bool:
        """Returns whether the local repository has local changes.

        A repository has local changes if it is dirty or there are some commits
        which have not been pushed yet.

        Returns:
            Whether the local repository has local changes.
        """
        pass

    @property
    @abstractmethod
    def current_commit(self) -> str:
        """Returns the current commit of the local repository.

        Returns:
            The current commit of the local repository.
        """
        pass

code_repository property

Returns the code repository.

Returns:

Type	Description
BaseCodeRepository	The code repository.

current_commit abstractmethod property

Returns the current commit of the local repository.

Returns:

Type	Description
str	The current commit of the local repository.

has_local_changes abstractmethod property

Returns whether the local repository has local changes.

A repository has local changes if it is dirty or there are some commits which have not been pushed yet.

Returns:

Type	Description
bool	Whether the local repository has local changes.

is_dirty abstractmethod property

Returns whether the local repository is dirty.

A repository counts as dirty if it has any untracked or uncommitted changes.

Returns:

Type	Description
bool	Whether the local repository is dirty.

root abstractmethod property

Returns the root path of the local repository.

Returns:

Type	Description
str	The root path of the local repository.

__init__(code_repository)

Initializes a local repository context.

Parameters:

Name	Type	Description	Default
code_repository	BaseCodeRepository	The code repository.	required

Source code in src/zenml/code_repositories/local_repository_context.py

def __init__(self, code_repository: "BaseCodeRepository") -> None:
    """Initializes a local repository context.

    Args:
        code_repository: The code repository.
    """
    self._code_repository = code_repository

Config

The config module contains classes and functions that manage user-specific configuration.

ZenML's configuration is stored in a file called config.yaml, located on the user's directory for configuration files. (The exact location differs from operating system to operating system.)

The GlobalConfiguration class is the main class in this module. It provides a Pydantic configuration object that is used to store and retrieve configuration. This GlobalConfiguration object handles the serialization and deserialization of the configuration options that are stored in the file in order to persist the configuration across sessions.

DockerSettings

Bases: BaseSettings

Settings for building Docker images to run ZenML pipelines.

Build process:

• No dockerfile specified: If any of the options regarding requirements, environment variables or copying files require us to build an image, ZenML will build this image. Otherwise, the parent_image will be used to run the pipeline.
• dockerfile specified: ZenML will first build an image based on the specified Dockerfile. If any of the options regarding requirements, environment variables or copying files require an additional image built on top of that, ZenML will build a second image. If not, the image build from the specified Dockerfile will be used to run the pipeline.

Requirements installation order:

Depending on the configuration of this object, requirements will be installed in the following order (each step optional): - The packages installed in your local python environment - The packages required by the stack unless this is disabled by setting install_stack_requirements=False. - The packages specified via the required_integrations - The packages specified via the requirements attribute

Attributes:

Name	Type	Description
parent_image	Optional[str]	Full name of the Docker image that should be used as the parent for the image that will be built. Defaults to a ZenML image built for the active Python and ZenML version. Additional notes: * If you specify a custom image here, you need to make sure it has ZenML installed. * If this is a non-local image, the environment which is running the pipeline and building the Docker image needs to be able to pull this image. * If a custom dockerfile is specified for this settings object, this parent image will be ignored.
dockerfile	Optional[str]	Path to a custom Dockerfile that should be built. Depending on the other values you specify in this object, the resulting image will be used directly to run your pipeline or ZenML will use it as a parent image to build on top of. See the general docstring of this class for more information. Additional notes: * If you specify this, the parent_image attribute will be ignored. * If you specify this, the image built from this Dockerfile needs to have ZenML installed.
build_context_root	Optional[str]	Build context root for the Docker build, only used when the dockerfile attribute is set. If this is left empty, the build context will only contain the Dockerfile.
parent_image_build_config	Optional[DockerBuildConfig]	Configuration for the parent image build.
skip_build	bool	If set to True, the parent image will be used directly to run the steps of your pipeline.
prevent_build_reuse	bool	Prevent the reuse of an existing build.
target_repository	Optional[str]	Name of the Docker repository to which the image should be pushed. This repository will be appended to the registry URI of the container registry of your stack and should therefore not include any registry. If not specified, the default repository name configured in the container registry stack component settings will be used.
python_package_installer	PythonPackageInstaller	The package installer to use for python packages.
python_package_installer_args	Dict[str, Any]	Arguments to pass to the python package installer.
replicate_local_python_environment	Optional[Union[List[str], PythonEnvironmentExportMethod]]	If not None, ZenML will use the specified method to generate a requirements file that replicates the packages installed in the currently running python environment. This requirements file will then be installed in the Docker image.
requirements	Union[None, str, List[str]]	Path to a requirements file or a list of required pip packages. During the image build, these requirements will be installed using pip. If you need to use a different tool to resolve and/or install your packages, please use a custom parent image or specify a custom dockerfile.
required_integrations	List[str]	List of ZenML integrations that should be installed. All requirements for the specified integrations will be installed inside the Docker image.
required_hub_plugins	List[str]	DEPRECATED/UNUSED.
install_stack_requirements	bool	If True, ZenML will automatically detect if components of your active stack are part of a ZenML integration and install the corresponding requirements and apt packages. If you set this to False or use custom components in your stack, you need to make sure these get installed by specifying them in the requirements and apt_packages attributes.
apt_packages	List[str]	APT packages to install inside the Docker image.
environment	Dict[str, Any]	Dictionary of environment variables to set inside the Docker image.
build_config	Optional[DockerBuildConfig]	Configuration for the main image build.
user	Optional[str]	If not None, will set the user, make it owner of the /app directory which contains all the user code and run the container entrypoint as this user.
allow_including_files_in_images	bool	If True, code can be included in the Docker images if code download from a code repository or artifact store is disabled or not possible.
allow_download_from_code_repository	bool	If True, code can be downloaded from a code repository if possible.
allow_download_from_artifact_store	bool	If True, code can be downloaded from the artifact store.
build_options	Dict[str, Any]	DEPRECATED, use parent_image_build_config.build_options instead.
dockerignore	Optional[str]	DEPRECATED, use build_config.dockerignore instead.
copy_files	bool	DEPRECATED/UNUSED.
copy_global_config	bool	DEPRECATED/UNUSED.
source_files	Optional[str]	DEPRECATED. Use allow_including_files_in_images, allow_download_from_code_repository and allow_download_from_artifact_store instead.

Source code in src/zenml/config/docker_settings.py

class DockerSettings(BaseSettings):
    """Settings for building Docker images to run ZenML pipelines.

    Build process:
    --------------
    * No `dockerfile` specified: If any of the options regarding
    requirements, environment variables or copying files require us to build an
    image, ZenML will build this image. Otherwise, the `parent_image` will be
    used to run the pipeline.
    * `dockerfile` specified: ZenML will first build an image based on the
    specified Dockerfile. If any of the options regarding
    requirements, environment variables or copying files require an additional
    image built on top of that, ZenML will build a second image. If not, the
    image build from the specified Dockerfile will be used to run the pipeline.

    Requirements installation order:
    --------------------------------
    Depending on the configuration of this object, requirements will be
    installed in the following order (each step optional):
    - The packages installed in your local python environment
    - The packages required by the stack unless this is disabled by setting
      `install_stack_requirements=False`.
    - The packages specified via the `required_integrations`
    - The packages specified via the `requirements` attribute

    Attributes:
        parent_image: Full name of the Docker image that should be
            used as the parent for the image that will be built. Defaults to
            a ZenML image built for the active Python and ZenML version.

            Additional notes:
            * If you specify a custom image here, you need to make sure it has
            ZenML installed.
            * If this is a non-local image, the environment which is running
            the pipeline and building the Docker image needs to be able to pull
            this image.
            * If a custom `dockerfile` is specified for this settings
            object, this parent image will be ignored.
        dockerfile: Path to a custom Dockerfile that should be built. Depending
            on the other values you specify in this object, the resulting
            image will be used directly to run your pipeline or ZenML will use
            it as a parent image to build on top of. See the general docstring
            of this class for more information.

            Additional notes:
            * If you specify this, the `parent_image` attribute will be ignored.
            * If you specify this, the image built from this Dockerfile needs
            to have ZenML installed.
        build_context_root: Build context root for the Docker build, only used
            when the `dockerfile` attribute is set. If this is left empty, the
            build context will only contain the Dockerfile.
        parent_image_build_config: Configuration for the parent image build.
        skip_build: If set to `True`, the parent image will be used directly to
            run the steps of your pipeline.
        prevent_build_reuse: Prevent the reuse of an existing build.
        target_repository: Name of the Docker repository to which the
            image should be pushed. This repository will be appended to the
            registry URI of the container registry of your stack and should
            therefore **not** include any registry. If not specified, the
            default repository name configured in the container registry
            stack component settings will be used.
        python_package_installer: The package installer to use for python
            packages.
        python_package_installer_args: Arguments to pass to the python package
            installer.
        replicate_local_python_environment: If not `None`, ZenML will use the
            specified method to generate a requirements file that replicates
            the packages installed in the currently running python environment.
            This requirements file will then be installed in the Docker image.
        requirements: Path to a requirements file or a list of required pip
            packages. During the image build, these requirements will be
            installed using pip. If you need to use a different tool to
            resolve and/or install your packages, please use a custom parent
            image or specify a custom `dockerfile`.
        required_integrations: List of ZenML integrations that should be
            installed. All requirements for the specified integrations will
            be installed inside the Docker image.
        required_hub_plugins: DEPRECATED/UNUSED.
        install_stack_requirements: If `True`, ZenML will automatically detect
            if components of your active stack are part of a ZenML integration
            and install the corresponding requirements and apt packages.
            If you set this to `False` or use custom components in your stack,
            you need to make sure these get installed by specifying them in
            the `requirements` and `apt_packages` attributes.
        apt_packages: APT packages to install inside the Docker image.
        environment: Dictionary of environment variables to set inside the
            Docker image.
        build_config: Configuration for the main image build.
        user: If not `None`, will set the user, make it owner of the `/app`
            directory which contains all the user code and run the container
            entrypoint as this user.
        allow_including_files_in_images: If `True`, code can be included in the
            Docker images if code download from a code repository or artifact
            store is disabled or not possible.
        allow_download_from_code_repository: If `True`, code can be downloaded
            from a code repository if possible.
        allow_download_from_artifact_store: If `True`, code can be downloaded
            from the artifact store.
        build_options: DEPRECATED, use parent_image_build_config.build_options
            instead.
        dockerignore: DEPRECATED, use build_config.dockerignore instead.
        copy_files: DEPRECATED/UNUSED.
        copy_global_config: DEPRECATED/UNUSED.
        source_files: DEPRECATED. Use allow_including_files_in_images,
            allow_download_from_code_repository and
            allow_download_from_artifact_store instead.
    """

    parent_image: Optional[str] = None
    dockerfile: Optional[str] = None
    build_context_root: Optional[str] = None
    parent_image_build_config: Optional[DockerBuildConfig] = None
    skip_build: bool = False
    prevent_build_reuse: bool = False
    target_repository: Optional[str] = None
    python_package_installer: PythonPackageInstaller = (
        PythonPackageInstaller.PIP
    )
    python_package_installer_args: Dict[str, Any] = {}
    replicate_local_python_environment: Optional[
        Union[List[str], PythonEnvironmentExportMethod]
    ] = Field(default=None, union_mode="left_to_right")
    requirements: Union[None, str, List[str]] = Field(
        default=None, union_mode="left_to_right"
    )
    required_integrations: List[str] = []
    install_stack_requirements: bool = True
    apt_packages: List[str] = []
    environment: Dict[str, Any] = {}
    user: Optional[str] = None
    build_config: Optional[DockerBuildConfig] = None

    allow_including_files_in_images: bool = True
    allow_download_from_code_repository: bool = True
    allow_download_from_artifact_store: bool = True

    # Deprecated attributes
    build_options: Dict[str, Any] = {}
    dockerignore: Optional[str] = None
    copy_files: bool = True
    copy_global_config: bool = True
    source_files: Optional[str] = None
    required_hub_plugins: List[str] = []

    _deprecation_validator = deprecation_utils.deprecate_pydantic_attributes(
        "copy_files",
        "copy_global_config",
        "source_files",
        "required_hub_plugins",
    )

    @model_validator(mode="before")
    @classmethod
    @before_validator_handler
    def _migrate_source_files(cls, data: Dict[str, Any]) -> Dict[str, Any]:
        """Migrate old source_files values.

        Args:
            data: The model data.

        Raises:
            ValueError: If an invalid source file mode is specified.

        Returns:
            The migrated data.
        """
        source_files = data.get("source_files", None)

        if source_files is None:
            return data

        replacement_attributes = [
            "allow_including_files_in_images",
            "allow_download_from_code_repository",
            "allow_download_from_artifact_store",
        ]
        if any(v in data for v in replacement_attributes):
            logger.warning(
                "Both `source_files` and one of %s specified for the "
                "DockerSettings, ignoring the `source_files` value.",
                replacement_attributes,
            )
            return data

        allow_including_files_in_images = False
        allow_download_from_code_repository = False
        allow_download_from_artifact_store = False

        if source_files == "download":
            allow_download_from_code_repository = True
        elif source_files == "include":
            allow_including_files_in_images = True
        elif source_files == "download_or_include":
            allow_including_files_in_images = True
            allow_download_from_code_repository = True
        elif source_files == "ignore":
            pass
        else:
            raise ValueError(f"Invalid source file mode `{source_files}`.")

        data["allow_including_files_in_images"] = (
            allow_including_files_in_images
        )
        data["allow_download_from_code_repository"] = (
            allow_download_from_code_repository
        )
        data["allow_download_from_artifact_store"] = (
            allow_download_from_artifact_store
        )

        return data

    @model_validator(mode="after")
    def _validate_skip_build(self) -> "DockerSettings":
        """Ensures that a parent image is passed when trying to skip the build.

        Returns:
            The validated settings values.

        Raises:
            ValueError: If the build should be skipped but no parent image
                was specified.
        """
        if self.skip_build and not self.parent_image:
            raise ValueError(
                "Docker settings that specify `skip_build=True` must always "
                "contain a `parent_image`. This parent image will be used "
                "to run the steps of your pipeline directly without additional "
                "Docker builds on top of it."
            )

        return self

    model_config = ConfigDict(
        # public attributes are immutable
        frozen=True,
        # prevent extra attributes during model initialization
        extra="forbid",
    )

ResourceSettings

Bases: BaseSettings

Hardware resource settings.

Attributes:

Name	Type	Description
cpu_count	Optional[PositiveFloat]	The amount of CPU cores that should be configured.
gpu_count	Optional[NonNegativeInt]	The amount of GPUs that should be configured.
memory	Optional[str]	The amount of memory that should be configured.

Source code in src/zenml/config/resource_settings.py

class ResourceSettings(BaseSettings):
    """Hardware resource settings.

    Attributes:
        cpu_count: The amount of CPU cores that should be configured.
        gpu_count: The amount of GPUs that should be configured.
        memory: The amount of memory that should be configured.
    """

    cpu_count: Optional[PositiveFloat] = None
    gpu_count: Optional[NonNegativeInt] = None
    memory: Optional[str] = Field(pattern=MEMORY_REGEX, default=None)

    @property
    def empty(self) -> bool:
        """Returns if this object is "empty" (=no values configured) or not.

        Returns:
            `True` if no values were configured, `False` otherwise.
        """
        # To detect whether this config is empty (= no values specified), we
        # check if there are any attributes which are explicitly set to any
        # value other than `None`.
        return len(self.model_dump(exclude_unset=True, exclude_none=True)) == 0

    def get_memory(
        self, unit: Union[str, ByteUnit] = ByteUnit.GB
    ) -> Optional[float]:
        """Gets the memory configuration in a specific unit.

        Args:
            unit: The unit to which the memory should be converted.

        Raises:
            ValueError: If the memory string is invalid.

        Returns:
            The memory configuration converted to the requested unit, or None
            if no memory was configured.
        """
        if not self.memory:
            return None

        if isinstance(unit, str):
            unit = ByteUnit(unit)

        memory = self.memory
        for memory_unit in ByteUnit:
            if memory.endswith(memory_unit.value):
                memory_value = int(memory[: -len(memory_unit.value)])
                return memory_value * memory_unit.byte_value / unit.byte_value
        else:
            # Should never happen due to the regex validation
            raise ValueError(f"Unable to parse memory unit from '{memory}'.")

    model_config = SettingsConfigDict(
        # public attributes are immutable
        frozen=True,
        # prevent extra attributes during model initialization
        extra="forbid",
    )

empty property

Returns if this object is "empty" (=no values configured) or not.

Returns:

Type	Description
bool	True if no values were configured, False otherwise.

get_memory(unit=ByteUnit.GB)

Gets the memory configuration in a specific unit.

Parameters:

Name	Type	Description	Default
unit	Union[str, ByteUnit]	The unit to which the memory should be converted.	GB

Raises:

Type	Description
ValueError	If the memory string is invalid.

Returns:

Type	Description
Optional[float]	The memory configuration converted to the requested unit, or None
Optional[float]	if no memory was configured.

Source code in src/zenml/config/resource_settings.py

def get_memory(
    self, unit: Union[str, ByteUnit] = ByteUnit.GB
) -> Optional[float]:
    """Gets the memory configuration in a specific unit.

    Args:
        unit: The unit to which the memory should be converted.

    Raises:
        ValueError: If the memory string is invalid.

    Returns:
        The memory configuration converted to the requested unit, or None
        if no memory was configured.
    """
    if not self.memory:
        return None

    if isinstance(unit, str):
        unit = ByteUnit(unit)

    memory = self.memory
    for memory_unit in ByteUnit:
        if memory.endswith(memory_unit.value):
            memory_value = int(memory[: -len(memory_unit.value)])
            return memory_value * memory_unit.byte_value / unit.byte_value
    else:
        # Should never happen due to the regex validation
        raise ValueError(f"Unable to parse memory unit from '{memory}'.")

StepRetryConfig

Bases: StrictBaseModel

Retry configuration for a step.

Delay is an integer (specified in seconds).

Source code in src/zenml/config/retry_config.py

class StepRetryConfig(StrictBaseModel):
    """Retry configuration for a step.

    Delay is an integer (specified in seconds).
    """

    max_retries: int = 1
    delay: int = 0  # in seconds
    backoff: int = 0

Console

ZenML console implementation.

Constants

ZenML constants.

handle_bool_env_var(var, default=False)

Converts normal env var to boolean.

Parameters:

Name	Type	Description	Default
var	str	The environment variable to convert.	required
default	bool	The default value to return if the env var is not set.	False

Returns:

Type	Description
bool	The converted value.

Source code in src/zenml/constants.py

def handle_bool_env_var(var: str, default: bool = False) -> bool:
    """Converts normal env var to boolean.

    Args:
        var: The environment variable to convert.
        default: The default value to return if the env var is not set.

    Returns:
        The converted value.
    """
    value = os.getenv(var)
    if is_true_string_value(value):
        return True
    elif is_false_string_value(value):
        return False
    return default

handle_int_env_var(var, default=0)

Converts normal env var to int.

Parameters:

Name	Type	Description	Default
var	str	The environment variable to convert.	required
default	int	The default value to return if the env var is not set.	0

Returns:

Type	Description
int	The converted value.

Source code in src/zenml/constants.py

def handle_int_env_var(var: str, default: int = 0) -> int:
    """Converts normal env var to int.

    Args:
        var: The environment variable to convert.
        default: The default value to return if the env var is not set.

    Returns:
        The converted value.
    """
    value = os.getenv(var, "")
    try:
        return int(value)
    except (ValueError, TypeError):
        return default

handle_json_env_var(var, expected_type, default=None)

Converts a json env var into a Python object.

Parameters:

Name	Type	Description	Default
var	str	The environment variable to convert.	required
default	Optional[List[str]]	The default value to return if the env var is not set.	None
expected_type	Type[T]	The type of the expected Python object.	required

Returns:

Type	Description
Any	The converted list value.

Raises:

Type	Description
TypeError	In case the value of the environment variable is not of a valid type.

Source code in src/zenml/constants.py

def handle_json_env_var(
    var: str,
    expected_type: Type[T],
    default: Optional[List[str]] = None,
) -> Any:
    """Converts a json env var into a Python object.

    Args:
        var:  The environment variable to convert.
        default: The default value to return if the env var is not set.
        expected_type: The type of the expected Python object.

    Returns:
        The converted list value.

    Raises:
        TypeError: In case the value of the environment variable is not of a
                   valid type.

    """
    # this needs to be here to avoid mutable defaults
    if default is None:
        default = []

    value = os.getenv(var)
    if value:
        try:
            loaded_value = json.loads(value)
            # check if loaded value is of correct type
            if expected_type is None or isinstance(
                loaded_value, expected_type
            ):
                return loaded_value
            else:
                raise TypeError  # if not correct type, raise TypeError
        except (TypeError, json.JSONDecodeError):
            # Use raw logging to avoid cyclic dependency
            logging.warning(
                f"Environment Variable {var} could not be loaded, into type "
                f"{expected_type}, defaulting to: {default}."
            )
            return default
    else:
        return default

is_false_string_value(value)

Checks if the given value is a string representation of 'False'.

Parameters:

Name	Type	Description	Default
value	Any	the value to check.	required

Returns:

Type	Description
bool	Whether the input value represents a string version of 'False'.

Source code in src/zenml/constants.py

def is_false_string_value(value: Any) -> bool:
    """Checks if the given value is a string representation of 'False'.

    Args:
        value: the value to check.

    Returns:
        Whether the input value represents a string version of 'False'.
    """
    return value in ["0", "n", "no", "False", "false"]

is_true_string_value(value)

Checks if the given value is a string representation of 'True'.

Parameters:

Name	Type	Description	Default
value	Any	the value to check.	required

Returns:

Type	Description
bool	Whether the input value represents a string version of 'True'.

Source code in src/zenml/constants.py

def is_true_string_value(value: Any) -> bool:
    """Checks if the given value is a string representation of 'True'.

    Args:
        value: the value to check.

    Returns:
        Whether the input value represents a string version of 'True'.
    """
    return value in ["1", "y", "yes", "True", "true"]

Container Registries

Initialization for ZenML's container registries module.

A container registry is a store for (Docker) containers. A ZenML workflow involving a container registry would automatically containerize your code to be transported across stacks running remotely. As part of the deployment to the cluster, the ZenML base image would be downloaded (from a cloud container registry) and used as the basis for the deployed 'run'.

For instance, when you are running a local container-based stack, you would therefore have a local container registry which stores the container images you create that bundle up your pipeline code. You could also use a remote container registry like the Elastic Container Registry at AWS in a more production setting.

AzureContainerRegistryFlavor

Bases: BaseContainerRegistryFlavor

Class for Azure Container Registry.

Source code in src/zenml/container_registries/azure_container_registry.py

class AzureContainerRegistryFlavor(BaseContainerRegistryFlavor):
    """Class for Azure Container Registry."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return ContainerRegistryFlavor.AZURE.value

    @property
    def service_connector_requirements(
        self,
    ) -> Optional[ServiceConnectorRequirements]:
        """Service connector resource requirements for service connectors.

        Specifies resource requirements that are used to filter the available
        service connector types that are compatible with this flavor.

        Returns:
            Requirements for compatible service connectors, if a service
            connector is required for this flavor.
        """
        return ServiceConnectorRequirements(
            connector_type="azure",
            resource_type=DOCKER_REGISTRY_RESOURCE_TYPE,
            resource_id_attr="uri",
        )

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/container_registry/azure.png"

docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

logo_url property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name property

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

service_connector_requirements property

Service connector resource requirements for service connectors.

Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.

Returns:

Type	Description
Optional[ServiceConnectorRequirements]	Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]	connector is required for this flavor.

BaseContainerRegistry

Bases: AuthenticationMixin

Base class for all ZenML container registries.

Source code in src/zenml/container_registries/base_container_registry.py

class BaseContainerRegistry(AuthenticationMixin):
    """Base class for all ZenML container registries."""

    _docker_client: Optional["DockerClient"] = None

    @property
    def config(self) -> BaseContainerRegistryConfig:
        """Returns the `BaseContainerRegistryConfig` config.

        Returns:
            The configuration.
        """
        return cast(BaseContainerRegistryConfig, self._config)

    @property
    def requires_authentication(self) -> bool:
        """Returns whether the container registry requires authentication.

        Returns:
            `True` if the container registry requires authentication,
            `False` otherwise.
        """
        return bool(self.config.authentication_secret)

    @property
    def credentials(self) -> Optional[Tuple[str, str]]:
        """Username and password to authenticate with this container registry.

        Returns:
            Tuple with username and password if this container registry
            requires authentication, `None` otherwise.
        """
        secret = self.get_typed_authentication_secret(
            expected_schema_type=BasicAuthSecretSchema
        )
        if secret:
            return secret.username, secret.password

        connector = self.get_connector()
        if connector:
            from zenml.service_connectors.docker_service_connector import (
                DockerServiceConnector,
            )

            if isinstance(connector, DockerServiceConnector):
                return (
                    connector.config.username.get_secret_value(),
                    connector.config.password.get_secret_value(),
                )

        return None

    @property
    def docker_client(self) -> "DockerClient":
        """Returns a Docker client for this container registry.

        Returns:
            The Docker client.

        Raises:
            RuntimeError: If the connector does not return a Docker client.
        """
        from docker.client import DockerClient

        # Refresh the client also if the connector has expired
        if self._docker_client and not self.connector_has_expired():
            return self._docker_client

        connector = self.get_connector()
        if connector:
            client = connector.connect()
            if not isinstance(client, DockerClient):
                raise RuntimeError(
                    f"Expected a DockerClient while trying to use the "
                    f"linked connector, but got {type(client)}."
                )
            self._docker_client = client
        else:
            self._docker_client = (
                docker_utils._try_get_docker_client_from_env()
            )

            credentials = self.credentials
            if credentials:
                username, password = credentials
                self._docker_client.login(
                    username=username,
                    password=password,
                    registry=self.config.uri,
                    reauth=True,
                )

        return self._docker_client

    def prepare_image_push(self, image_name: str) -> None:
        """Preparation before an image gets pushed.

        Subclasses can overwrite this to do any necessary checks or
        preparations before an image gets pushed.

        Args:
            image_name: Name of the docker image that will be pushed.
        """

    def push_image(self, image_name: str) -> str:
        """Pushes a docker image.

        Args:
            image_name: Name of the docker image that will be pushed.

        Returns:
            The Docker repository digest of the pushed image.

        Raises:
            ValueError: If the image name is not associated with this
                container registry.
        """
        if not image_name.startswith(self.config.uri):
            raise ValueError(
                f"Docker image `{image_name}` does not belong to container "
                f"registry `{self.config.uri}`."
            )

        self.prepare_image_push(image_name)
        return docker_utils.push_image(
            image_name, docker_client=self.docker_client
        )

config property

Returns the BaseContainerRegistryConfig config.

Returns:

Type	Description
BaseContainerRegistryConfig	The configuration.

credentials property

Username and password to authenticate with this container registry.

Returns:

Type	Description
Optional[Tuple[str, str]]	Tuple with username and password if this container registry
Optional[Tuple[str, str]]	requires authentication, None otherwise.

docker_client property

Returns a Docker client for this container registry.

Returns:

Type	Description
DockerClient	The Docker client.

Raises:

Type	Description
RuntimeError	If the connector does not return a Docker client.

requires_authentication property

Returns whether the container registry requires authentication.

Returns:

Type	Description
bool	True if the container registry requires authentication,
bool	False otherwise.

prepare_image_push(image_name)

Preparation before an image gets pushed.

Subclasses can overwrite this to do any necessary checks or preparations before an image gets pushed.

Parameters:

Name	Type	Description	Default
image_name	str	Name of the docker image that will be pushed.	required

Source code in src/zenml/container_registries/base_container_registry.py

def prepare_image_push(self, image_name: str) -> None:
    """Preparation before an image gets pushed.

    Subclasses can overwrite this to do any necessary checks or
    preparations before an image gets pushed.

    Args:
        image_name: Name of the docker image that will be pushed.
    """

push_image(image_name)

Pushes a docker image.

Parameters:

Name	Type	Description	Default
image_name	str	Name of the docker image that will be pushed.	required

Returns:

Type	Description
str	The Docker repository digest of the pushed image.

Raises:

Type	Description
ValueError	If the image name is not associated with this container registry.

Source code in src/zenml/container_registries/base_container_registry.py

def push_image(self, image_name: str) -> str:
    """Pushes a docker image.

    Args:
        image_name: Name of the docker image that will be pushed.

    Returns:
        The Docker repository digest of the pushed image.

    Raises:
        ValueError: If the image name is not associated with this
            container registry.
    """
    if not image_name.startswith(self.config.uri):
        raise ValueError(
            f"Docker image `{image_name}` does not belong to container "
            f"registry `{self.config.uri}`."
        )

    self.prepare_image_push(image_name)
    return docker_utils.push_image(
        image_name, docker_client=self.docker_client
    )

DefaultContainerRegistryFlavor

Bases: BaseContainerRegistryFlavor

Class for default ZenML container registries.

Source code in src/zenml/container_registries/default_container_registry.py

class DefaultContainerRegistryFlavor(BaseContainerRegistryFlavor):
    """Class for default ZenML container registries."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return ContainerRegistryFlavor.DEFAULT.value

    @property
    def docs_url(self) -> Optional[str]:
        """A URL to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A URL to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A URL to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/container_registry/local.svg"

docs_url property

A URL to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

logo_url property

A URL to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name property

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url property

A URL to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

DockerHubContainerRegistryFlavor

Bases: BaseContainerRegistryFlavor

Class for DockerHub Container Registry.

Source code in src/zenml/container_registries/dockerhub_container_registry.py

class DockerHubContainerRegistryFlavor(BaseContainerRegistryFlavor):
    """Class for DockerHub Container Registry."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return ContainerRegistryFlavor.DOCKERHUB.value

    @property
    def service_connector_requirements(
        self,
    ) -> Optional[ServiceConnectorRequirements]:
        """Service connector resource requirements for service connectors.

        Specifies resource requirements that are used to filter the available
        service connector types that are compatible with this flavor.

        Returns:
            Requirements for compatible service connectors, if a service
            connector is required for this flavor.
        """
        return ServiceConnectorRequirements(
            connector_type="docker",
            resource_type=DOCKER_REGISTRY_RESOURCE_TYPE,
            resource_id_attr="uri",
        )

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/container_registry/docker.png"

docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

logo_url property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name property

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

service_connector_requirements property

Service connector resource requirements for service connectors.

Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.

Returns:

Type	Description
Optional[ServiceConnectorRequirements]	Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]	connector is required for this flavor.

GCPContainerRegistryFlavor

Bases: BaseContainerRegistryFlavor

Class for GCP Container Registry.

Source code in src/zenml/container_registries/gcp_container_registry.py

class GCPContainerRegistryFlavor(BaseContainerRegistryFlavor):
    """Class for GCP Container Registry."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return ContainerRegistryFlavor.GCP.value

    @property
    def service_connector_requirements(
        self,
    ) -> Optional[ServiceConnectorRequirements]:
        """Service connector resource requirements for service connectors.

        Specifies resource requirements that are used to filter the available
        service connector types that are compatible with this flavor.

        Returns:
            Requirements for compatible service connectors, if a service
            connector is required for this flavor.
        """
        return ServiceConnectorRequirements(
            connector_type="gcp",
            resource_type=DOCKER_REGISTRY_RESOURCE_TYPE,
            resource_id_attr="uri",
        )

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/container_registry/gcp.png"

docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

logo_url property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name property

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

service_connector_requirements property

Service connector resource requirements for service connectors.

Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.

Returns:

Type	Description
Optional[ServiceConnectorRequirements]	Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]	connector is required for this flavor.

GitHubContainerRegistryFlavor

Bases: BaseContainerRegistryFlavor

Class for GitHub Container Registry.

Source code in src/zenml/container_registries/github_container_registry.py

class GitHubContainerRegistryFlavor(BaseContainerRegistryFlavor):
    """Class for GitHub Container Registry."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return ContainerRegistryFlavor.GITHUB

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/container_registry/github.png"

docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

logo_url property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name property

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

Data Validators

Data validators are stack components responsible for data profiling and validation.

BaseDataValidator

Bases: StackComponent

Base class for all ZenML data validators.

Source code in src/zenml/data_validators/base_data_validator.py

class BaseDataValidator(StackComponent):
    """Base class for all ZenML data validators."""

    NAME: ClassVar[str]
    FLAVOR: ClassVar[Type["BaseDataValidatorFlavor"]]

    @property
    def config(self) -> BaseDataValidatorConfig:
        """Returns the config of this data validator.

        Returns:
            The config of this data validator.
        """
        return cast(BaseDataValidatorConfig, self._config)

    @classmethod
    def get_active_data_validator(cls) -> "BaseDataValidator":
        """Get the data validator registered in the active stack.

        Returns:
            The data validator registered in the active stack.

        Raises:
            TypeError: if a data validator is not part of the
                active stack.
        """
        flavor: BaseDataValidatorFlavor = cls.FLAVOR()
        client = Client()
        data_validator = client.active_stack.data_validator
        if not data_validator or not isinstance(data_validator, cls):
            raise TypeError(
                f"The active stack needs to have a {cls.NAME} data "
                f"validator component registered to be able to run data validation "
                f"actions with {cls.NAME}. You can create a new stack with "
                f"a {cls.NAME} data validator component or update your "
                f"active stack to add this component, e.g.:\n\n"
                f"  `zenml data-validator register {flavor.name} "
                f"--flavor={flavor.name} ...`\n"
                f"  `zenml stack register <STACK-NAME> -dv {flavor.name} ...`\n"
                f"  or:\n"
                f"  `zenml stack update -dv {flavor.name}`\n\n"
            )

        return data_validator

    def data_profiling(
        self,
        dataset: Any,
        comparison_dataset: Optional[Any] = None,
        profile_list: Optional[Sequence[Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Analyze one or more datasets and generate a data profile.

        This method should be implemented by data validators that support
        analyzing a dataset and generating a data profile (e.g. schema,
        statistical summary, data distribution profile, validation
        rules, data drift reports etc.).
        The method should return a data profile object.

        This method also accepts an optional second dataset argument to
        accommodate different categories of data profiling, e.g.:

        * profiles generated from a single dataset: schema inference, validation
        rules inference, statistical profiles, data integrity reports
        * differential profiles that need a second dataset for comparison:
        differential statistical profiles, data drift reports

        Data validators that support generating multiple categories of data
        profiles should also take in a `profile_list` argument that lists the
        subset of profiles to be generated. If not supplied, the behavior is
        implementation specific, but it is recommended to provide a good default
        (e.g. a single default data profile type may be generated and returned,
        or all available data profiles may be generated and returned as a single
        result).

        Args:
            dataset: Target dataset to be profiled.
            comparison_dataset: Optional second dataset to be used for data
                comparison profiles (e.g data drift reports).
            profile_list: Optional list identifying the categories of data
                profiles to be generated.
            **kwargs: Implementation specific keyword arguments.

        Raises:
            NotImplementedError: if data profiling is not supported by this
                data validator.
        """
        raise NotImplementedError(
            f"Data profiling is not supported by the {self.__class__} data "
            f"validator."
        )

    def data_validation(
        self,
        dataset: Any,
        comparison_dataset: Optional[Any] = None,
        check_list: Optional[Sequence[Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run data validation checks on a dataset.

        This method should be implemented by data validators that support
        running data quality checks an input dataset (e.g. data integrity
        checks, data drift checks).

        This method also accepts an optional second dataset argument to
        accommodate different categories of data validation tests, e.g.:

        * single dataset checks: data integrity checks (e.g. missing
        values, conflicting labels, mixed data types etc.)
        * checks that compare two datasets: data drift checks (e.g. new labels,
        feature drift, label drift etc.)

        Data validators that support running multiple categories of data
        integrity checks should also take in a `check_list` argument that
        lists the subset of checks to be performed. If not supplied, the
        behavior is implementation specific, but it is recommended to provide a
        good default (e.g. a single default validation check may be performed,
        or all available validation checks may be performed and their results
        returned as a list of objects).

        Args:
            dataset: Target dataset to be validated.
            comparison_dataset: Optional second dataset to be used for data
                comparison checks (e.g data drift checks).
            check_list: Optional list identifying the data checks to
                be performed.
            **kwargs: Implementation specific keyword arguments.

        Raises:
            NotImplementedError: if data validation is not
                supported by this data validator.
        """
        raise NotImplementedError(
            f"Data validation not implemented for {self}."
        )

    def model_validation(
        self,
        dataset: Any,
        model: Any,
        comparison_dataset: Optional[Any] = None,
        check_list: Optional[Sequence[Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run model validation checks.

        This method should be implemented by data validators that support
        running model validation checks (e.g. confusion matrix validation,
        performance reports, model error analyzes, etc).

        Unlike `data_validation`, model validation checks require that a model
        be present as an active component during the validation process.

        This method also accepts an optional second dataset argument to
        accommodate different categories of data validation tests, e.g.:

        * single dataset tests: confusion matrix validation,
        performance reports, model error analyzes, etc
        * model comparison tests: tests that identify changes in a model
        behavior by comparing how it performs on two different datasets.

        Data validators that support running multiple categories of model
        validation checks should also take in a `check_list` argument that
        lists the subset of checks to be performed. If not supplied, the
        behavior is implementation specific, but it is recommended to provide a
        good default (e.g. a single default validation check may be performed,
        or all available validation checks may be performed and their results
        returned as a list of objects).

        Args:
            dataset: Target dataset to be validated.
            model: Target model to be validated.
            comparison_dataset: Optional second dataset to be used for model
                comparison checks (e.g model performance comparison checks).
            check_list: Optional list identifying the model validation checks to
                be performed.
            **kwargs: Implementation specific keyword arguments.

        Raises:
            NotImplementedError: if model validation is not supported by this
                data validator.
        """
        raise NotImplementedError(
            f"Model validation not implemented for {self}."
        )

config property

Returns the config of this data validator.

Returns:

Type	Description
BaseDataValidatorConfig	The config of this data validator.

data_profiling(dataset, comparison_dataset=None, profile_list=None, **kwargs)

Analyze one or more datasets and generate a data profile.

This method should be implemented by data validators that support analyzing a dataset and generating a data profile (e.g. schema, statistical summary, data distribution profile, validation rules, data drift reports etc.). The method should return a data profile object.

This method also accepts an optional second dataset argument to accommodate different categories of data profiling, e.g.:

• profiles generated from a single dataset: schema inference, validation rules inference, statistical profiles, data integrity reports
• differential profiles that need a second dataset for comparison: differential statistical profiles, data drift reports

Data validators that support generating multiple categories of data profiles should also take in a profile_list argument that lists the subset of profiles to be generated. If not supplied, the behavior is implementation specific, but it is recommended to provide a good default (e.g. a single default data profile type may be generated and returned, or all available data profiles may be generated and returned as a single result).

Parameters:

Name	Type	Description	Default
dataset	Any	Target dataset to be profiled.	required
comparison_dataset	Optional[Any]	Optional second dataset to be used for data comparison profiles (e.g data drift reports).	None
profile_list	Optional[Sequence[Any]]	Optional list identifying the categories of data profiles to be generated.	None
**kwargs	Any	Implementation specific keyword arguments.	{}

Raises:

Type	Description
NotImplementedError	if data profiling is not supported by this data validator.

Source code in src/zenml/data_validators/base_data_validator.py

def data_profiling(
    self,
    dataset: Any,
    comparison_dataset: Optional[Any] = None,
    profile_list: Optional[Sequence[Any]] = None,
    **kwargs: Any,
) -> Any:
    """Analyze one or more datasets and generate a data profile.

    This method should be implemented by data validators that support
    analyzing a dataset and generating a data profile (e.g. schema,
    statistical summary, data distribution profile, validation
    rules, data drift reports etc.).
    The method should return a data profile object.

    This method also accepts an optional second dataset argument to
    accommodate different categories of data profiling, e.g.:

    * profiles generated from a single dataset: schema inference, validation
    rules inference, statistical profiles, data integrity reports
    * differential profiles that need a second dataset for comparison:
    differential statistical profiles, data drift reports

    Data validators that support generating multiple categories of data
    profiles should also take in a `profile_list` argument that lists the
    subset of profiles to be generated. If not supplied, the behavior is
    implementation specific, but it is recommended to provide a good default
    (e.g. a single default data profile type may be generated and returned,
    or all available data profiles may be generated and returned as a single
    result).

    Args:
        dataset: Target dataset to be profiled.
        comparison_dataset: Optional second dataset to be used for data
            comparison profiles (e.g data drift reports).
        profile_list: Optional list identifying the categories of data
            profiles to be generated.
        **kwargs: Implementation specific keyword arguments.

    Raises:
        NotImplementedError: if data profiling is not supported by this
            data validator.
    """
    raise NotImplementedError(
        f"Data profiling is not supported by the {self.__class__} data "
        f"validator."
    )

data_validation(dataset, comparison_dataset=None, check_list=None, **kwargs)

Run data validation checks on a dataset.

This method should be implemented by data validators that support running data quality checks an input dataset (e.g. data integrity checks, data drift checks).

This method also accepts an optional second dataset argument to accommodate different categories of data validation tests, e.g.:

• single dataset checks: data integrity checks (e.g. missing values, conflicting labels, mixed data types etc.)
• checks that compare two datasets: data drift checks (e.g. new labels, feature drift, label drift etc.)

Data validators that support running multiple categories of data integrity checks should also take in a check_list argument that lists the subset of checks to be performed. If not supplied, the behavior is implementation specific, but it is recommended to provide a good default (e.g. a single default validation check may be performed, or all available validation checks may be performed and their results returned as a list of objects).

Parameters:

Name	Type	Description	Default
dataset	Any	Target dataset to be validated.	required
comparison_dataset	Optional[Any]	Optional second dataset to be used for data comparison checks (e.g data drift checks).	None
check_list	Optional[Sequence[Any]]	Optional list identifying the data checks to be performed.	None
**kwargs	Any	Implementation specific keyword arguments.	{}

Raises:

Type	Description
NotImplementedError	if data validation is not supported by this data validator.

Source code in src/zenml/data_validators/base_data_validator.py

def data_validation(
    self,
    dataset: Any,
    comparison_dataset: Optional[Any] = None,
    check_list: Optional[Sequence[Any]] = None,
    **kwargs: Any,
) -> Any:
    """Run data validation checks on a dataset.

    This method should be implemented by data validators that support
    running data quality checks an input dataset (e.g. data integrity
    checks, data drift checks).

    This method also accepts an optional second dataset argument to
    accommodate different categories of data validation tests, e.g.:

    * single dataset checks: data integrity checks (e.g. missing
    values, conflicting labels, mixed data types etc.)
    * checks that compare two datasets: data drift checks (e.g. new labels,
    feature drift, label drift etc.)

    Data validators that support running multiple categories of data
    integrity checks should also take in a `check_list` argument that
    lists the subset of checks to be performed. If not supplied, the
    behavior is implementation specific, but it is recommended to provide a
    good default (e.g. a single default validation check may be performed,
    or all available validation checks may be performed and their results
    returned as a list of objects).

    Args:
        dataset: Target dataset to be validated.
        comparison_dataset: Optional second dataset to be used for data
            comparison checks (e.g data drift checks).
        check_list: Optional list identifying the data checks to
            be performed.
        **kwargs: Implementation specific keyword arguments.

    Raises:
        NotImplementedError: if data validation is not
            supported by this data validator.
    """
    raise NotImplementedError(
        f"Data validation not implemented for {self}."
    )

get_active_data_validator() classmethod

Get the data validator registered in the active stack.

Returns:

Type	Description
BaseDataValidator	The data validator registered in the active stack.

Raises:

Type	Description
TypeError	if a data validator is not part of the active stack.

Source code in src/zenml/data_validators/base_data_validator.py

@classmethod
def get_active_data_validator(cls) -> "BaseDataValidator":
    """Get the data validator registered in the active stack.

    Returns:
        The data validator registered in the active stack.

    Raises:
        TypeError: if a data validator is not part of the
            active stack.
    """
    flavor: BaseDataValidatorFlavor = cls.FLAVOR()
    client = Client()
    data_validator = client.active_stack.data_validator
    if not data_validator or not isinstance(data_validator, cls):
        raise TypeError(
            f"The active stack needs to have a {cls.NAME} data "
            f"validator component registered to be able to run data validation "
            f"actions with {cls.NAME}. You can create a new stack with "
            f"a {cls.NAME} data validator component or update your "
            f"active stack to add this component, e.g.:\n\n"
            f"  `zenml data-validator register {flavor.name} "
            f"--flavor={flavor.name} ...`\n"
            f"  `zenml stack register <STACK-NAME> -dv {flavor.name} ...`\n"
            f"  or:\n"
            f"  `zenml stack update -dv {flavor.name}`\n\n"
        )

    return data_validator

model_validation(dataset, model, comparison_dataset=None, check_list=None, **kwargs)

Run model validation checks.

This method should be implemented by data validators that support running model validation checks (e.g. confusion matrix validation, performance reports, model error analyzes, etc).

Unlike data_validation, model validation checks require that a model be present as an active component during the validation process.

This method also accepts an optional second dataset argument to accommodate different categories of data validation tests, e.g.:

• single dataset tests: confusion matrix validation, performance reports, model error analyzes, etc
• model comparison tests: tests that identify changes in a model behavior by comparing how it performs on two different datasets.

Data validators that support running multiple categories of model validation checks should also take in a check_list argument that lists the subset of checks to be performed. If not supplied, the behavior is implementation specific, but it is recommended to provide a good default (e.g. a single default validation check may be performed, or all available validation checks may be performed and their results returned as a list of objects).

Parameters:

Name	Type	Description	Default
dataset	Any	Target dataset to be validated.	required
model	Any	Target model to be validated.	required
comparison_dataset	Optional[Any]	Optional second dataset to be used for model comparison checks (e.g model performance comparison checks).	None
check_list	Optional[Sequence[Any]]	Optional list identifying the model validation checks to be performed.	None
**kwargs	Any	Implementation specific keyword arguments.	{}

Raises:

Type	Description
NotImplementedError	if model validation is not supported by this data validator.

Source code in src/zenml/data_validators/base_data_validator.py

def model_validation(
    self,
    dataset: Any,
    model: Any,
    comparison_dataset: Optional[Any] = None,
    check_list: Optional[Sequence[Any]] = None,
    **kwargs: Any,
) -> Any:
    """Run model validation checks.

    This method should be implemented by data validators that support
    running model validation checks (e.g. confusion matrix validation,
    performance reports, model error analyzes, etc).

    Unlike `data_validation`, model validation checks require that a model
    be present as an active component during the validation process.

    This method also accepts an optional second dataset argument to
    accommodate different categories of data validation tests, e.g.:

    * single dataset tests: confusion matrix validation,
    performance reports, model error analyzes, etc
    * model comparison tests: tests that identify changes in a model
    behavior by comparing how it performs on two different datasets.

    Data validators that support running multiple categories of model
    validation checks should also take in a `check_list` argument that
    lists the subset of checks to be performed. If not supplied, the
    behavior is implementation specific, but it is recommended to provide a
    good default (e.g. a single default validation check may be performed,
    or all available validation checks may be performed and their results
    returned as a list of objects).

    Args:
        dataset: Target dataset to be validated.
        model: Target model to be validated.
        comparison_dataset: Optional second dataset to be used for model
            comparison checks (e.g model performance comparison checks).
        check_list: Optional list identifying the model validation checks to
            be performed.
        **kwargs: Implementation specific keyword arguments.

    Raises:
        NotImplementedError: if model validation is not supported by this
            data validator.
    """
    raise NotImplementedError(
        f"Model validation not implemented for {self}."
    )

BaseDataValidatorFlavor

Bases: Flavor

Base class for data validator flavors.

Source code in src/zenml/data_validators/base_data_validator.py

class BaseDataValidatorFlavor(Flavor):
    """Base class for data validator flavors."""

    @property
    def type(self) -> StackComponentType:
        """The type of the component.

        Returns:
            The type of the component.
        """
        return StackComponentType.DATA_VALIDATOR

    @property
    def config_class(self) -> Type[BaseDataValidatorConfig]:
        """Config class for data validator.

        Returns:
            Config class for data validator.
        """
        return BaseDataValidatorConfig

    @property
    def implementation_class(self) -> Type[BaseDataValidator]:
        """Implementation for data validator.

        Returns:
            Implementation for data validator.
        """
        return BaseDataValidator

config_class property

Config class for data validator.

Returns:

Type	Description
Type[BaseDataValidatorConfig]	Config class for data validator.

implementation_class property

Implementation for data validator.

Returns:

Type	Description
Type[BaseDataValidator]	Implementation for data validator.

type property

The type of the component.

Returns:

Type	Description
StackComponentType	The type of the component.

Entrypoints

Initializations for ZenML entrypoints module.

PipelineEntrypointConfiguration

Bases: BaseEntrypointConfiguration

Base class for entrypoint configurations that run an entire pipeline.

Source code in src/zenml/entrypoints/pipeline_entrypoint_configuration.py

class PipelineEntrypointConfiguration(BaseEntrypointConfiguration):
    """Base class for entrypoint configurations that run an entire pipeline."""

    def run(self) -> None:
        """Prepares the environment and runs the configured pipeline."""
        deployment = self.load_deployment()

        # Activate all the integrations. This makes sure that all materializers
        # and stack component flavors are registered.
        integration_registry.activate_integrations()

        self.download_code_if_necessary(deployment=deployment)

        orchestrator = Client().active_stack.orchestrator
        orchestrator._prepare_run(deployment=deployment)

        for step in deployment.step_configurations.values():
            orchestrator.run_step(step)

run()

Prepares the environment and runs the configured pipeline.

Source code in src/zenml/entrypoints/pipeline_entrypoint_configuration.py

def run(self) -> None:
    """Prepares the environment and runs the configured pipeline."""
    deployment = self.load_deployment()

    # Activate all the integrations. This makes sure that all materializers
    # and stack component flavors are registered.
    integration_registry.activate_integrations()

    self.download_code_if_necessary(deployment=deployment)

    orchestrator = Client().active_stack.orchestrator
    orchestrator._prepare_run(deployment=deployment)

    for step in deployment.step_configurations.values():
        orchestrator.run_step(step)

StepEntrypointConfiguration

Bases: BaseEntrypointConfiguration

Base class for entrypoint configurations that run a single step.

If an orchestrator needs to run steps in a separate process or environment (e.g. a docker container), this class can either be used directly or subclassed if custom behavior is necessary.

How to subclass:

Passing additional arguments to the entrypoint: If you need to pass additional arguments to the entrypoint, there are two methods that you need to implement: * get_entrypoint_options(): This method should return all the options that are required in the entrypoint. Make sure to include the result from the superclass method so the options are complete.

    * `get_entrypoint_arguments(...)`: This method should return
        a list of arguments that should be passed to the entrypoint.
        Make sure to include the result from the superclass method so
        the arguments are complete.

You'll be able to access the argument values from `self.entrypoint_args`
inside your `StepEntrypointConfiguration` subclass.

How to use:

After you created your StepEntrypointConfiguration subclass, you only have to run the entrypoint somewhere. To do this, you should execute the command returned by the get_entrypoint_command() method with the arguments returned by the get_entrypoint_arguments(...) method.

Example:

class MyStepEntrypointConfiguration(StepEntrypointConfiguration):
    ...

class MyOrchestrator(BaseOrchestrator):
    def prepare_or_run_pipeline(
        self,
        deployment: "PipelineDeployment",
        stack: "Stack",
        environment: Dict[str, str],
        placeholder_run: Optional["PipelineRunResponse"] = None,
    ) -> Any:
        ...

        cmd = MyStepEntrypointConfiguration.get_entrypoint_command()
        for step_name, step in pipeline.steps.items():
            ...

            args = MyStepEntrypointConfiguration.get_entrypoint_arguments(
                step_name=step_name
            )
            # Run the command and pass it the arguments. Our example
            # orchestrator here executes the entrypoint in a separate
            # process, but in a real-world scenario you would probably run
            # it inside a docker container or a different environment.
            import subprocess
            subprocess.check_call(cmd + args)

Source code in src/zenml/entrypoints/step_entrypoint_configuration.py

class StepEntrypointConfiguration(BaseEntrypointConfiguration):
    """Base class for entrypoint configurations that run a single step.

    If an orchestrator needs to run steps in a separate process or environment
    (e.g. a docker container), this class can either be used directly or
    subclassed if custom behavior is necessary.

    How to subclass:
    ----------------
    Passing additional arguments to the entrypoint:
        If you need to pass additional arguments to the entrypoint, there are
        two methods that you need to implement:
            * `get_entrypoint_options()`: This method should return all
                the options that are required in the entrypoint. Make sure to
                include the result from the superclass method so the options
                are complete.

            * `get_entrypoint_arguments(...)`: This method should return
                a list of arguments that should be passed to the entrypoint.
                Make sure to include the result from the superclass method so
                the arguments are complete.

        You'll be able to access the argument values from `self.entrypoint_args`
        inside your `StepEntrypointConfiguration` subclass.

    How to use:
    -----------
    After you created your `StepEntrypointConfiguration` subclass, you only
    have to run the entrypoint somewhere. To do this, you should execute the
    command returned by the `get_entrypoint_command()` method with the
    arguments returned by the `get_entrypoint_arguments(...)` method.

    Example:
    ```python
    class MyStepEntrypointConfiguration(StepEntrypointConfiguration):
        ...

    class MyOrchestrator(BaseOrchestrator):
        def prepare_or_run_pipeline(
            self,
            deployment: "PipelineDeployment",
            stack: "Stack",
            environment: Dict[str, str],
            placeholder_run: Optional["PipelineRunResponse"] = None,
        ) -> Any:
            ...

            cmd = MyStepEntrypointConfiguration.get_entrypoint_command()
            for step_name, step in pipeline.steps.items():
                ...

                args = MyStepEntrypointConfiguration.get_entrypoint_arguments(
                    step_name=step_name
                )
                # Run the command and pass it the arguments. Our example
                # orchestrator here executes the entrypoint in a separate
                # process, but in a real-world scenario you would probably run
                # it inside a docker container or a different environment.
                import subprocess
                subprocess.check_call(cmd + args)
    ```
    """

    def post_run(
        self,
        pipeline_name: str,
        step_name: str,
    ) -> None:
        """Does cleanup or post-processing after the step finished running.

        Subclasses should overwrite this method if they need to run any
        additional code after the step execution.

        Args:
            pipeline_name: Name of the parent pipeline of the step that was
                executed.
            step_name: Name of the step that was executed.
        """

    @classmethod
    def get_entrypoint_options(cls) -> Set[str]:
        """Gets all options required for running with this configuration.

        Returns:
            The superclass options as well as an option for the name of the
            step to run.
        """
        return super().get_entrypoint_options() | {STEP_NAME_OPTION}

    @classmethod
    def get_entrypoint_arguments(
        cls,
        **kwargs: Any,
    ) -> List[str]:
        """Gets all arguments that the entrypoint command should be called with.

        The argument list should be something that
        `argparse.ArgumentParser.parse_args(...)` can handle (e.g.
        `["--some_option", "some_value"]` or `["--some_option=some_value"]`).
        It needs to provide values for all options returned by the
        `get_entrypoint_options()` method of this class.

        Args:
            **kwargs: Kwargs, must include the step name.

        Returns:
            The superclass arguments as well as arguments for the name of the
            step to run.
        """
        return super().get_entrypoint_arguments(**kwargs) + [
            f"--{STEP_NAME_OPTION}",
            kwargs[STEP_NAME_OPTION],
        ]

    def run(self) -> None:
        """Prepares the environment and runs the configured step."""
        deployment = self.load_deployment()

        # Activate all the integrations. This makes sure that all materializers
        # and stack component flavors are registered.
        integration_registry.activate_integrations()

        step_name = self.entrypoint_args[STEP_NAME_OPTION]

        # Change the working directory to make sure we're in the correct
        # directory where the files in the Docker image should be included.
        # This is necessary as some services overwrite the working directory
        # configured in the Docker image itself.
        os.makedirs("/app", exist_ok=True)
        os.chdir("/app")

        self.download_code_if_necessary(
            deployment=deployment, step_name=step_name
        )

        # If the working directory is not in the sys.path, we include it to make
        # sure user code gets correctly imported.
        cwd = os.getcwd()
        if cwd not in sys.path:
            sys.path.insert(0, cwd)

        pipeline_name = deployment.pipeline_configuration.name

        step = deployment.step_configurations[step_name]
        self._run_step(step, deployment=deployment)

        self.post_run(
            pipeline_name=pipeline_name,
            step_name=step_name,
        )

    def _run_step(
        self,
        step: "Step",
        deployment: "PipelineDeploymentResponse",
    ) -> None:
        """Runs a single step.

        Args:
            step: The step to run.
            deployment: The deployment configuration.
        """
        orchestrator = Client().active_stack.orchestrator
        orchestrator._prepare_run(deployment=deployment)
        orchestrator.run_step(step=step)

get_entrypoint_arguments(**kwargs) classmethod

Gets all arguments that the entrypoint command should be called with.

The argument list should be something that argparse.ArgumentParser.parse_args(...) can handle (e.g. ["--some_option", "some_value"] or ["--some_option=some_value"]). It needs to provide values for all options returned by the get_entrypoint_options() method of this class.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Kwargs, must include the step name.	{}

Returns:

Type	Description
List[str]	The superclass arguments as well as arguments for the name of the
List[str]	step to run.

Source code in src/zenml/entrypoints/step_entrypoint_configuration.py

@classmethod
def get_entrypoint_arguments(
    cls,
    **kwargs: Any,
) -> List[str]:
    """Gets all arguments that the entrypoint command should be called with.

    The argument list should be something that
    `argparse.ArgumentParser.parse_args(...)` can handle (e.g.
    `["--some_option", "some_value"]` or `["--some_option=some_value"]`).
    It needs to provide values for all options returned by the
    `get_entrypoint_options()` method of this class.

    Args:
        **kwargs: Kwargs, must include the step name.

    Returns:
        The superclass arguments as well as arguments for the name of the
        step to run.
    """
    return super().get_entrypoint_arguments(**kwargs) + [
        f"--{STEP_NAME_OPTION}",
        kwargs[STEP_NAME_OPTION],
    ]

get_entrypoint_options() classmethod

Gets all options required for running with this configuration.

Returns:

Type	Description
Set[str]	The superclass options as well as an option for the name of the
Set[str]	step to run.

Source code in src/zenml/entrypoints/step_entrypoint_configuration.py

@classmethod
def get_entrypoint_options(cls) -> Set[str]:
    """Gets all options required for running with this configuration.

    Returns:
        The superclass options as well as an option for the name of the
        step to run.
    """
    return super().get_entrypoint_options() | {STEP_NAME_OPTION}

post_run(pipeline_name, step_name)

Does cleanup or post-processing after the step finished running.

Subclasses should overwrite this method if they need to run any additional code after the step execution.

Parameters:

Name	Type	Description	Default
pipeline_name	str	Name of the parent pipeline of the step that was executed.	required
step_name	str	Name of the step that was executed.	required

Source code in src/zenml/entrypoints/step_entrypoint_configuration.py

def post_run(
    self,
    pipeline_name: str,
    step_name: str,
) -> None:
    """Does cleanup or post-processing after the step finished running.

    Subclasses should overwrite this method if they need to run any
    additional code after the step execution.

    Args:
        pipeline_name: Name of the parent pipeline of the step that was
            executed.
        step_name: Name of the step that was executed.
    """

run()

Prepares the environment and runs the configured step.

Source code in src/zenml/entrypoints/step_entrypoint_configuration.py

def run(self) -> None:
    """Prepares the environment and runs the configured step."""
    deployment = self.load_deployment()

    # Activate all the integrations. This makes sure that all materializers
    # and stack component flavors are registered.
    integration_registry.activate_integrations()

    step_name = self.entrypoint_args[STEP_NAME_OPTION]

    # Change the working directory to make sure we're in the correct
    # directory where the files in the Docker image should be included.
    # This is necessary as some services overwrite the working directory
    # configured in the Docker image itself.
    os.makedirs("/app", exist_ok=True)
    os.chdir("/app")

    self.download_code_if_necessary(
        deployment=deployment, step_name=step_name
    )

    # If the working directory is not in the sys.path, we include it to make
    # sure user code gets correctly imported.
    cwd = os.getcwd()
    if cwd not in sys.path:
        sys.path.insert(0, cwd)

    pipeline_name = deployment.pipeline_configuration.name

    step = deployment.step_configurations[step_name]
    self._run_step(step, deployment=deployment)

    self.post_run(
        pipeline_name=pipeline_name,
        step_name=step_name,
    )

Enums

ZenML enums.

APITokenType

Bases: StrEnum

The API token type.

Source code in src/zenml/enums.py

class APITokenType(StrEnum):
    """The API token type."""

    GENERIC = "generic"
    WORKLOAD = "workload"

AnalyticsEventSource

Bases: StrEnum

Enum to identify analytics events source.

Source code in src/zenml/enums.py

class AnalyticsEventSource(StrEnum):
    """Enum to identify analytics events source."""

    ZENML_GO = "zenml go"
    ZENML_INIT = "zenml init"
    ZENML_SERVER = "zenml server"

AnnotationTasks

Bases: StrEnum

Supported annotation tasks.

Source code in src/zenml/enums.py

class AnnotationTasks(StrEnum):
    """Supported annotation tasks."""

    IMAGE_CLASSIFICATION = "image_classification"
    OBJECT_DETECTION_BOUNDING_BOXES = "object_detection_bounding_boxes"
    OCR = "optical_character_recognition"
    TEXT_CLASSIFICATION = "text_classification"

ArtifactSaveType

Bases: StrEnum

All possible method types of how artifact versions can be saved.

Source code in src/zenml/enums.py

class ArtifactSaveType(StrEnum):
    """All possible method types of how artifact versions can be saved."""

    STEP_OUTPUT = "step_output"  # output of the current step
    MANUAL = "manual"  # manually saved via `zenml.save_artifact()`
    PREEXISTING = "preexisting"  # register via `zenml.register_artifact()`
    EXTERNAL = (
        "external"  # saved via `zenml.ExternalArtifact.upload_by_value()`
    )

ArtifactType

Bases: StrEnum

All possible types an artifact can have.

Source code in src/zenml/enums.py

class ArtifactType(StrEnum):
    """All possible types an artifact can have."""

    DATA_ANALYSIS = "DataAnalysisArtifact"
    DATA = "DataArtifact"
    MODEL = "ModelArtifact"
    SCHEMA = "SchemaArtifact"  # deprecated
    SERVICE = "ServiceArtifact"
    STATISTICS = "StatisticsArtifact"  # deprecated in favor of `DATA_ANALYSIS`
    BASE = "BaseArtifact"

AuthScheme

Bases: StrEnum

The authentication scheme.

Source code in src/zenml/enums.py

class AuthScheme(StrEnum):
    """The authentication scheme."""

    NO_AUTH = "NO_AUTH"
    HTTP_BASIC = "HTTP_BASIC"
    OAUTH2_PASSWORD_BEARER = "OAUTH2_PASSWORD_BEARER"
    EXTERNAL = "EXTERNAL"

CliCategories

Bases: StrEnum

All possible categories for CLI commands.

Note: The order of the categories is important. The same order is used to sort the commands in the CLI help output.

Source code in src/zenml/enums.py

class CliCategories(StrEnum):
    """All possible categories for CLI commands.

    Note: The order of the categories is important. The same
    order is used to sort the commands in the CLI help output.
    """

    STACK_COMPONENTS = "Stack Components"
    MODEL_DEPLOYMENT = "Model Deployment"
    INTEGRATIONS = "Integrations"
    MANAGEMENT_TOOLS = "Management Tools"
    MODEL_CONTROL_PLANE = "Model Control Plane"
    IDENTITY_AND_SECURITY = "Identity and Security"
    OTHER_COMMANDS = "Other Commands"

ColorVariants

Bases: StrEnum

All possible color variants for frontend.

Source code in src/zenml/enums.py

class ColorVariants(StrEnum):
    """All possible color variants for frontend."""

    GREY = "grey"
    PURPLE = "purple"
    RED = "red"
    GREEN = "green"
    YELLOW = "yellow"
    ORANGE = "orange"
    LIME = "lime"
    TEAL = "teal"
    TURQUOISE = "turquoise"
    MAGENTA = "magenta"
    BLUE = "blue"

ContainerRegistryFlavor

Bases: StrEnum

Flavors of container registries.

Source code in src/zenml/enums.py

class ContainerRegistryFlavor(StrEnum):
    """Flavors of container registries."""

    DEFAULT = "default"
    GITHUB = "github"
    DOCKERHUB = "dockerhub"
    GCP = "gcp"
    AZURE = "azure"

DatabaseBackupStrategy

Bases: StrEnum

All available database backup strategies.

Source code in src/zenml/enums.py

class DatabaseBackupStrategy(StrEnum):
    """All available database backup strategies."""

    # Backup disabled
    DISABLED = "disabled"
    # In-memory backup
    IN_MEMORY = "in-memory"
    # Dump the database to a file
    DUMP_FILE = "dump-file"
    # Create a backup of the database in the remote database service
    DATABASE = "database"

EnvironmentType

Bases: StrEnum

Enum for environment types.

Source code in src/zenml/enums.py

class EnvironmentType(StrEnum):
    """Enum for environment types."""

    BITBUCKET_CI = "bitbucket_ci"
    CIRCLE_CI = "circle_ci"
    COLAB = "colab"
    CONTAINER = "container"
    DOCKER = "docker"
    GENERIC_CI = "generic_ci"
    GITHUB_ACTION = "github_action"
    GITLAB_CI = "gitlab_ci"
    KUBERNETES = "kubernetes"
    NATIVE = "native"
    NOTEBOOK = "notebook"
    PAPERSPACE = "paperspace"
    WSL = "wsl"
    LIGHTNING_AI_STUDIO = "lightning_ai_studio"
    GITHUB_CODESPACES = "github_codespaces"
    VSCODE_REMOTE_CONTAINER = "vscode_remote_container"

ExecutionStatus

Bases: StrEnum

Enum that represents the current status of a step or pipeline run.

Source code in src/zenml/enums.py

class ExecutionStatus(StrEnum):
    """Enum that represents the current status of a step or pipeline run."""

    INITIALIZING = "initializing"
    FAILED = "failed"
    COMPLETED = "completed"
    RUNNING = "running"
    CACHED = "cached"

    @property
    def is_finished(self) -> bool:
        """Whether the execution status refers to a finished execution.

        Returns:
            Whether the execution status refers to a finished execution.
        """
        return self in {
            ExecutionStatus.FAILED,
            ExecutionStatus.COMPLETED,
            ExecutionStatus.CACHED,
        }

is_finished property

Whether the execution status refers to a finished execution.

Returns:

Type	Description
bool	Whether the execution status refers to a finished execution.

GenericFilterOps

Bases: StrEnum

Ops for all filters for string values on list methods.

Source code in src/zenml/enums.py

class GenericFilterOps(StrEnum):
    """Ops for all filters for string values on list methods."""

    EQUALS = "equals"
    NOT_EQUALS = "notequals"
    CONTAINS = "contains"
    STARTSWITH = "startswith"
    ENDSWITH = "endswith"
    ONEOF = "oneof"
    GTE = "gte"
    GT = "gt"
    LTE = "lte"
    LT = "lt"
    IN = "in"

LoggingLevels

Bases: Enum

Enum for logging levels.

Source code in src/zenml/enums.py

class LoggingLevels(Enum):
    """Enum for logging levels."""

    NOTSET = logging.NOTSET
    ERROR = logging.ERROR
    WARN = logging.WARN
    INFO = logging.INFO
    DEBUG = logging.DEBUG
    CRITICAL = logging.CRITICAL

LogicalOperators

Bases: StrEnum

Logical Ops to use to combine filters on list methods.

Source code in src/zenml/enums.py

class LogicalOperators(StrEnum):
    """Logical Ops to use to combine filters on list methods."""

    OR = "or"
    AND = "and"

MetadataResourceTypes

Bases: StrEnum

All possible resource types for adding metadata.

Source code in src/zenml/enums.py

class MetadataResourceTypes(StrEnum):
    """All possible resource types for adding metadata."""

    PIPELINE_RUN = "pipeline_run"
    STEP_RUN = "step_run"
    ARTIFACT_VERSION = "artifact_version"
    MODEL_VERSION = "model_version"
    SCHEDULE = "schedule"

ModelStages

Bases: StrEnum

All possible stages of a Model Version.

Source code in src/zenml/enums.py

class ModelStages(StrEnum):
    """All possible stages of a Model Version."""

    NONE = "none"
    STAGING = "staging"
    PRODUCTION = "production"
    ARCHIVED = "archived"
    LATEST = "latest"

OAuthDeviceStatus

Bases: StrEnum

The OAuth device status.

Source code in src/zenml/enums.py

class OAuthDeviceStatus(StrEnum):
    """The OAuth device status."""

    PENDING = "pending"
    VERIFIED = "verified"
    ACTIVE = "active"
    LOCKED = "locked"

OAuthGrantTypes

Bases: StrEnum

The OAuth grant types.

Source code in src/zenml/enums.py

class OAuthGrantTypes(StrEnum):
    """The OAuth grant types."""

    OAUTH_PASSWORD = "password"
    OAUTH_DEVICE_CODE = "urn:ietf:params:oauth:grant-type:device_code"
    ZENML_EXTERNAL = "zenml-external"
    ZENML_API_KEY = "zenml-api-key"

OnboardingStep

Bases: StrEnum

All onboarding steps.

Source code in src/zenml/enums.py

class OnboardingStep(StrEnum):
    """All onboarding steps."""

    DEVICE_VERIFIED = "device_verified"
    PROJECT_CREATED = "project_created"
    PIPELINE_RUN = "pipeline_run"
    STARTER_SETUP_COMPLETED = "starter_setup_completed"
    STACK_WITH_REMOTE_ORCHESTRATOR_CREATED = (
        "stack_with_remote_orchestrator_created"
    )
    PIPELINE_RUN_WITH_REMOTE_ORCHESTRATOR = (
        "pipeline_run_with_remote_orchestrator"
    )
    PRODUCTION_SETUP_COMPLETED = "production_setup_completed"

OperatingSystemType

Bases: StrEnum

Enum for OS types.

Source code in src/zenml/enums.py

class OperatingSystemType(StrEnum):
    """Enum for OS types."""

    LINUX = "Linux"
    WINDOWS = "Windows"
    MACOS = "Darwin"

PluginSubType

Bases: StrEnum

All possible types of Plugins.

Source code in src/zenml/enums.py

class PluginSubType(StrEnum):
    """All possible types of Plugins."""

    # Event Source Subtypes
    WEBHOOK = "webhook"
    # Action Subtypes
    PIPELINE_RUN = "pipeline_run"

PluginType

Bases: StrEnum

All possible types of Plugins.

Source code in src/zenml/enums.py

class PluginType(StrEnum):
    """All possible types of Plugins."""

    EVENT_SOURCE = "event_source"
    ACTION = "action"

ResponseUpdateStrategy

Bases: StrEnum

All available strategies to handle updated properties in the response.

Source code in src/zenml/enums.py

class ResponseUpdateStrategy(StrEnum):
    """All available strategies to handle updated properties in the response."""

    ALLOW = "allow"
    IGNORE = "ignore"
    DENY = "deny"

SecretValidationLevel

Bases: StrEnum

Secret validation levels.

Source code in src/zenml/enums.py

class SecretValidationLevel(StrEnum):
    """Secret validation levels."""

    SECRET_AND_KEY_EXISTS = "SECRET_AND_KEY_EXISTS"
    SECRET_EXISTS = "SECRET_EXISTS"
    NONE = "NONE"

SecretsStoreType

Bases: StrEnum

Secrets Store Backend Types.

Source code in src/zenml/enums.py

class SecretsStoreType(StrEnum):
    """Secrets Store Backend Types."""

    NONE = "none"  # indicates that no secrets store is used
    SQL = "sql"
    AWS = "aws"
    GCP = "gcp"
    AZURE = "azure"
    HASHICORP = "hashicorp"
    CUSTOM = "custom"  # indicates that the secrets store uses a custom backend

ServerProviderType

Bases: StrEnum

ZenML server providers.

Source code in src/zenml/enums.py

class ServerProviderType(StrEnum):
    """ZenML server providers."""

    DAEMON = "daemon"
    DOCKER = "docker"

ServiceState

Bases: StrEnum

Possible states for the service and service endpoint.

Source code in src/zenml/enums.py

class ServiceState(StrEnum):
    """Possible states for the service and service endpoint."""

    INACTIVE = "inactive"
    ACTIVE = "active"
    PENDING_STARTUP = "pending_startup"
    PENDING_SHUTDOWN = "pending_shutdown"
    ERROR = "error"
    SCALED_TO_ZERO = "scaled_to_zero"

SorterOps

Bases: StrEnum

Ops for all filters for string values on list methods.

Source code in src/zenml/enums.py

class SorterOps(StrEnum):
    """Ops for all filters for string values on list methods."""

    ASCENDING = "asc"
    DESCENDING = "desc"

SourceContextTypes

Bases: StrEnum

Enum for event source types.

Source code in src/zenml/enums.py

class SourceContextTypes(StrEnum):
    """Enum for event source types."""

    CLI = "cli"
    PYTHON = "python"
    DASHBOARD = "dashboard"
    DASHBOARD_V2 = "dashboard-v2"
    API = "api"
    UNKNOWN = "unknown"

StackComponentType

Bases: StrEnum

All possible types a StackComponent can have.

Source code in src/zenml/enums.py

class StackComponentType(StrEnum):
    """All possible types a `StackComponent` can have."""

    ALERTER = "alerter"
    ANNOTATOR = "annotator"
    ARTIFACT_STORE = "artifact_store"
    CONTAINER_REGISTRY = "container_registry"
    DATA_VALIDATOR = "data_validator"
    EXPERIMENT_TRACKER = "experiment_tracker"
    FEATURE_STORE = "feature_store"
    IMAGE_BUILDER = "image_builder"
    MODEL_DEPLOYER = "model_deployer"
    ORCHESTRATOR = "orchestrator"
    STEP_OPERATOR = "step_operator"
    MODEL_REGISTRY = "model_registry"

    @property
    def plural(self) -> str:
        """Returns the plural of the enum value.

        Returns:
            The plural of the enum value.
        """
        if self == StackComponentType.CONTAINER_REGISTRY:
            return "container_registries"
        elif self == StackComponentType.MODEL_REGISTRY:
            return "model_registries"

        return f"{self.value}s"

plural property

Returns the plural of the enum value.

Returns:

Type	Description
str	The plural of the enum value.

StackDeploymentProvider

Bases: StrEnum

All possible stack deployment providers.

Source code in src/zenml/enums.py

class StackDeploymentProvider(StrEnum):
    """All possible stack deployment providers."""

    AWS = "aws"
    GCP = "gcp"
    AZURE = "azure"

StepRunInputArtifactType

Bases: StrEnum

All possible types of a step run input artifact.

Source code in src/zenml/enums.py

class StepRunInputArtifactType(StrEnum):
    """All possible types of a step run input artifact."""

    STEP_OUTPUT = (
        "step_output"  # input argument that is the output of a previous step
    )
    MANUAL = "manual"  # manually loaded via `zenml.load_artifact()`
    EXTERNAL = "external"  # loaded via `ExternalArtifact(value=...)`
    LAZY_LOADED = "lazy"  # loaded via various lazy methods

StoreType

Bases: StrEnum

Zen Store Backend Types.

Source code in src/zenml/enums.py

class StoreType(StrEnum):
    """Zen Store Backend Types."""

    SQL = "sql"
    REST = "rest"

TaggableResourceTypes

Bases: StrEnum

All possible resource types for tagging.

Source code in src/zenml/enums.py

class TaggableResourceTypes(StrEnum):
    """All possible resource types for tagging."""

    ARTIFACT = "artifact"
    ARTIFACT_VERSION = "artifact_version"
    MODEL = "model"
    MODEL_VERSION = "model_version"
    PIPELINE = "pipeline"
    PIPELINE_RUN = "pipeline_run"
    RUN_TEMPLATE = "run_template"

VisualizationType

Bases: StrEnum

All currently available visualization types.

Source code in src/zenml/enums.py

class VisualizationType(StrEnum):
    """All currently available visualization types."""

    CSV = "csv"
    HTML = "html"
    IMAGE = "image"
    MARKDOWN = "markdown"
    JSON = "json"

ZenMLServiceType

Bases: StrEnum

All possible types a service can have.

Source code in src/zenml/enums.py

class ZenMLServiceType(StrEnum):
    """All possible types a service can have."""

    ZEN_SERVER = "zen_server"
    MODEL_SERVING = "model-serving"

Environment

Environment implementation.

Environment

Provides environment information.

Individual environment components can be registered separately to extend the global Environment object with additional information (see BaseEnvironmentComponent).

Source code in src/zenml/environment.py

class Environment(metaclass=SingletonMetaClass):
    """Provides environment information.

    Individual environment components can be registered separately to extend
    the global Environment object with additional information (see
    `BaseEnvironmentComponent`).
    """

    def __init__(self) -> None:
        """Initializes an Environment instance.

        Note: Environment is a singleton class, which means this method will
        only get called once. All following `Environment()` calls will return
        the previously initialized instance.
        """

    @staticmethod
    def get_system_info() -> Dict[str, str]:
        """Information about the operating system.

        Returns:
            A dictionary containing information about the operating system.
        """
        system = platform.system()

        if system == "Windows":
            release, version, csd, ptype = platform.win32_ver()

            return {
                "os": "windows",
                "windows_version_release": release,
                "windows_version": version,
                "windows_version_service_pack": csd,
                "windows_version_os_type": ptype,
            }

        if system == "Darwin":
            return {"os": "mac", "mac_version": platform.mac_ver()[0]}

        if system == "Linux":
            return {
                "os": "linux",
                "linux_distro": distro.id(),
                "linux_distro_like": distro.like(),
                "linux_distro_version": distro.version(),
            }

        # We don't collect data for any other system.
        return {"os": "unknown"}

    @staticmethod
    def python_version() -> str:
        """Returns the python version of the running interpreter.

        Returns:
            str: the python version
        """
        return platform.python_version()

    @staticmethod
    def in_container() -> bool:
        """If the current python process is running in a container.

        Returns:
            `True` if the current python process is running in a
            container, `False` otherwise.
        """
        # TODO [ENG-167]: Make this more reliable and add test.
        return INSIDE_ZENML_CONTAINER

    @staticmethod
    def in_docker() -> bool:
        """If the current python process is running in a docker container.

        Returns:
            `True` if the current python process is running in a docker
            container, `False` otherwise.
        """
        if os.path.exists("./dockerenv") or os.path.exists("/.dockerinit"):
            return True

        try:
            with open("/proc/1/cgroup", "rt") as ifh:
                info = ifh.read()
                return "docker" in info
        except (FileNotFoundError, Exception):
            return False

    @staticmethod
    def in_kubernetes() -> bool:
        """If the current python process is running in a kubernetes pod.

        Returns:
            `True` if the current python process is running in a kubernetes
            pod, `False` otherwise.
        """
        if "KUBERNETES_SERVICE_HOST" in os.environ:
            return True

        try:
            with open("/proc/1/cgroup", "rt") as ifh:
                info = ifh.read()
                return "kubepod" in info
        except (FileNotFoundError, Exception):
            return False

    @staticmethod
    def in_google_colab() -> bool:
        """If the current Python process is running in a Google Colab.

        Returns:
            `True` if the current Python process is running in a Google Colab,
            `False` otherwise.
        """
        try:
            import google.colab  # noqa

            return True

        except ModuleNotFoundError:
            return False

    @staticmethod
    def in_notebook() -> bool:
        """If the current Python process is running in a notebook.

        Returns:
            `True` if the current Python process is running in a notebook,
            `False` otherwise.
        """
        if Environment.in_google_colab():
            return True

        try:
            ipython = get_ipython()  # type: ignore[name-defined]
        except NameError:
            return False

        if ipython.__class__.__name__ in [
            "TerminalInteractiveShell",
            "ZMQInteractiveShell",
            "DatabricksShell",
        ]:
            return True
        return False

    @staticmethod
    def in_github_codespaces() -> bool:
        """If the current Python process is running in GitHub Codespaces.

        Returns:
            `True` if the current Python process is running in GitHub Codespaces,
            `False` otherwise.
        """
        return (
            "CODESPACES" in os.environ
            or "GITHUB_CODESPACE_TOKEN" in os.environ
            or "GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN" in os.environ
        )

    @staticmethod
    def in_vscode_remote_container() -> bool:
        """If the current Python process is running in a VS Code Remote Container.

        Returns:
            `True` if the current Python process is running in a VS Code Remote Container,
            `False` otherwise.
        """
        return (
            "REMOTE_CONTAINERS" in os.environ
            or "VSCODE_REMOTE_CONTAINERS_SESSION" in os.environ
        )

    @staticmethod
    def in_paperspace_gradient() -> bool:
        """If the current Python process is running in Paperspace Gradient.

        Returns:
            `True` if the current Python process is running in Paperspace
            Gradient, `False` otherwise.
        """
        return "PAPERSPACE_NOTEBOOK_REPO_ID" in os.environ

    @staticmethod
    def in_github_actions() -> bool:
        """If the current Python process is running in GitHub Actions.

        Returns:
            `True` if the current Python process is running in GitHub
            Actions, `False` otherwise.
        """
        return "GITHUB_ACTIONS" in os.environ

    @staticmethod
    def in_gitlab_ci() -> bool:
        """If the current Python process is running in GitLab CI.

        Returns:
            `True` if the current Python process is running in GitLab
            CI, `False` otherwise.
        """
        return "GITLAB_CI" in os.environ

    @staticmethod
    def in_circle_ci() -> bool:
        """If the current Python process is running in Circle CI.

        Returns:
            `True` if the current Python process is running in Circle
            CI, `False` otherwise.
        """
        return "CIRCLECI" in os.environ

    @staticmethod
    def in_bitbucket_ci() -> bool:
        """If the current Python process is running in Bitbucket CI.

        Returns:
            `True` if the current Python process is running in Bitbucket
            CI, `False` otherwise.
        """
        return "BITBUCKET_BUILD_NUMBER" in os.environ

    @staticmethod
    def in_ci() -> bool:
        """If the current Python process is running in any CI.

        Returns:
            `True` if the current Python process is running in any
            CI, `False` otherwise.
        """
        return "CI" in os.environ

    @staticmethod
    def in_wsl() -> bool:
        """If the current process is running in Windows Subsystem for Linux.

        source: https://www.scivision.dev/python-detect-wsl/

        Returns:
            `True` if the current process is running in WSL, `False` otherwise.
        """
        return "microsoft-standard" in platform.uname().release

    @staticmethod
    def in_lightning_ai_studio() -> bool:
        """If the current Python process is running in Lightning.ai studios.

        Returns:
            `True` if the current Python process is running in Lightning.ai studios,
            `False` otherwise.
        """
        return (
            "LIGHTNING_CLOUD_URL" in os.environ
            and "LIGHTNING_CLOUDSPACE_HOST" in os.environ
        )

__init__()

Initializes an Environment instance.

Note: Environment is a singleton class, which means this method will only get called once. All following Environment() calls will return the previously initialized instance.

Source code in src/zenml/environment.py

def __init__(self) -> None:
    """Initializes an Environment instance.

    Note: Environment is a singleton class, which means this method will
    only get called once. All following `Environment()` calls will return
    the previously initialized instance.
    """

get_system_info() staticmethod

Information about the operating system.

Returns:

Type	Description
Dict[str, str]	A dictionary containing information about the operating system.

Source code in src/zenml/environment.py

@staticmethod
def get_system_info() -> Dict[str, str]:
    """Information about the operating system.

    Returns:
        A dictionary containing information about the operating system.
    """
    system = platform.system()

    if system == "Windows":
        release, version, csd, ptype = platform.win32_ver()

        return {
            "os": "windows",
            "windows_version_release": release,
            "windows_version": version,
            "windows_version_service_pack": csd,
            "windows_version_os_type": ptype,
        }

    if system == "Darwin":
        return {"os": "mac", "mac_version": platform.mac_ver()[0]}

    if system == "Linux":
        return {
            "os": "linux",
            "linux_distro": distro.id(),
            "linux_distro_like": distro.like(),
            "linux_distro_version": distro.version(),
        }

    # We don't collect data for any other system.
    return {"os": "unknown"}

in_bitbucket_ci() staticmethod

If the current Python process is running in Bitbucket CI.

Returns:

Type	Description
bool	True if the current Python process is running in Bitbucket
bool	CI, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_bitbucket_ci() -> bool:
    """If the current Python process is running in Bitbucket CI.

    Returns:
        `True` if the current Python process is running in Bitbucket
        CI, `False` otherwise.
    """
    return "BITBUCKET_BUILD_NUMBER" in os.environ

in_ci() staticmethod

If the current Python process is running in any CI.

Returns:

Type	Description
bool	True if the current Python process is running in any
bool	CI, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_ci() -> bool:
    """If the current Python process is running in any CI.

    Returns:
        `True` if the current Python process is running in any
        CI, `False` otherwise.
    """
    return "CI" in os.environ

in_circle_ci() staticmethod

If the current Python process is running in Circle CI.

Returns:

Type	Description
bool	True if the current Python process is running in Circle
bool	CI, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_circle_ci() -> bool:
    """If the current Python process is running in Circle CI.

    Returns:
        `True` if the current Python process is running in Circle
        CI, `False` otherwise.
    """
    return "CIRCLECI" in os.environ

in_container() staticmethod

If the current python process is running in a container.

Returns:

Type	Description
bool	True if the current python process is running in a
bool	container, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_container() -> bool:
    """If the current python process is running in a container.

    Returns:
        `True` if the current python process is running in a
        container, `False` otherwise.
    """
    # TODO [ENG-167]: Make this more reliable and add test.
    return INSIDE_ZENML_CONTAINER

in_docker() staticmethod

If the current python process is running in a docker container.

Returns:

Type	Description
bool	True if the current python process is running in a docker
bool	container, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_docker() -> bool:
    """If the current python process is running in a docker container.

    Returns:
        `True` if the current python process is running in a docker
        container, `False` otherwise.
    """
    if os.path.exists("./dockerenv") or os.path.exists("/.dockerinit"):
        return True

    try:
        with open("/proc/1/cgroup", "rt") as ifh:
            info = ifh.read()
            return "docker" in info
    except (FileNotFoundError, Exception):
        return False

in_github_actions() staticmethod

If the current Python process is running in GitHub Actions.

Returns:

Type	Description
bool	True if the current Python process is running in GitHub
bool	Actions, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_github_actions() -> bool:
    """If the current Python process is running in GitHub Actions.

    Returns:
        `True` if the current Python process is running in GitHub
        Actions, `False` otherwise.
    """
    return "GITHUB_ACTIONS" in os.environ

in_github_codespaces() staticmethod

If the current Python process is running in GitHub Codespaces.

Returns:

Type	Description
bool	True if the current Python process is running in GitHub Codespaces,
bool	False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_github_codespaces() -> bool:
    """If the current Python process is running in GitHub Codespaces.

    Returns:
        `True` if the current Python process is running in GitHub Codespaces,
        `False` otherwise.
    """
    return (
        "CODESPACES" in os.environ
        or "GITHUB_CODESPACE_TOKEN" in os.environ
        or "GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN" in os.environ
    )

in_gitlab_ci() staticmethod

If the current Python process is running in GitLab CI.

Returns:

Type	Description
bool	True if the current Python process is running in GitLab
bool	CI, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_gitlab_ci() -> bool:
    """If the current Python process is running in GitLab CI.

    Returns:
        `True` if the current Python process is running in GitLab
        CI, `False` otherwise.
    """
    return "GITLAB_CI" in os.environ

in_google_colab() staticmethod

If the current Python process is running in a Google Colab.

Returns:

Type	Description
bool	True if the current Python process is running in a Google Colab,
bool	False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_google_colab() -> bool:
    """If the current Python process is running in a Google Colab.

    Returns:
        `True` if the current Python process is running in a Google Colab,
        `False` otherwise.
    """
    try:
        import google.colab  # noqa

        return True

    except ModuleNotFoundError:
        return False

in_kubernetes() staticmethod

If the current python process is running in a kubernetes pod.

Returns:

Type	Description
bool	True if the current python process is running in a kubernetes
bool	pod, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_kubernetes() -> bool:
    """If the current python process is running in a kubernetes pod.

    Returns:
        `True` if the current python process is running in a kubernetes
        pod, `False` otherwise.
    """
    if "KUBERNETES_SERVICE_HOST" in os.environ:
        return True

    try:
        with open("/proc/1/cgroup", "rt") as ifh:
            info = ifh.read()
            return "kubepod" in info
    except (FileNotFoundError, Exception):
        return False

in_lightning_ai_studio() staticmethod

If the current Python process is running in Lightning.ai studios.

Returns:

Type	Description
bool	True if the current Python process is running in Lightning.ai studios,
bool	False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_lightning_ai_studio() -> bool:
    """If the current Python process is running in Lightning.ai studios.

    Returns:
        `True` if the current Python process is running in Lightning.ai studios,
        `False` otherwise.
    """
    return (
        "LIGHTNING_CLOUD_URL" in os.environ
        and "LIGHTNING_CLOUDSPACE_HOST" in os.environ
    )

in_notebook() staticmethod

If the current Python process is running in a notebook.

Returns:

Type	Description
bool	True if the current Python process is running in a notebook,
bool	False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_notebook() -> bool:
    """If the current Python process is running in a notebook.

    Returns:
        `True` if the current Python process is running in a notebook,
        `False` otherwise.
    """
    if Environment.in_google_colab():
        return True

    try:
        ipython = get_ipython()  # type: ignore[name-defined]
    except NameError:
        return False

    if ipython.__class__.__name__ in [
        "TerminalInteractiveShell",
        "ZMQInteractiveShell",
        "DatabricksShell",
    ]:
        return True
    return False

in_paperspace_gradient() staticmethod

If the current Python process is running in Paperspace Gradient.

Returns:

Type	Description
bool	True if the current Python process is running in Paperspace
bool	Gradient, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_paperspace_gradient() -> bool:
    """If the current Python process is running in Paperspace Gradient.

    Returns:
        `True` if the current Python process is running in Paperspace
        Gradient, `False` otherwise.
    """
    return "PAPERSPACE_NOTEBOOK_REPO_ID" in os.environ

in_vscode_remote_container() staticmethod

If the current Python process is running in a VS Code Remote Container.

Returns:

Type	Description
bool	True if the current Python process is running in a VS Code Remote Container,
bool	False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_vscode_remote_container() -> bool:
    """If the current Python process is running in a VS Code Remote Container.

    Returns:
        `True` if the current Python process is running in a VS Code Remote Container,
        `False` otherwise.
    """
    return (
        "REMOTE_CONTAINERS" in os.environ
        or "VSCODE_REMOTE_CONTAINERS_SESSION" in os.environ
    )

in_wsl() staticmethod

If the current process is running in Windows Subsystem for Linux.

source: https://www.scivision.dev/python-detect-wsl/

Returns:

Type	Description
bool	True if the current process is running in WSL, False otherwise.

Source code in src/zenml/environment.py

@staticmethod
def in_wsl() -> bool:
    """If the current process is running in Windows Subsystem for Linux.

    source: https://www.scivision.dev/python-detect-wsl/

    Returns:
        `True` if the current process is running in WSL, `False` otherwise.
    """
    return "microsoft-standard" in platform.uname().release

python_version() staticmethod

Returns the python version of the running interpreter.

Returns:

Name	Type	Description
str	str	the python version

Source code in src/zenml/environment.py

@staticmethod
def python_version() -> str:
    """Returns the python version of the running interpreter.

    Returns:
        str: the python version
    """
    return platform.python_version()

get_environment()

Returns a string representing the execution environment of the pipeline.

Returns:

Name	Type	Description
str	str	the execution environment

Source code in src/zenml/environment.py

def get_environment() -> str:
    """Returns a string representing the execution environment of the pipeline.

    Returns:
        str: the execution environment
    """
    # Order is important here
    if Environment.in_kubernetes():
        return EnvironmentType.KUBERNETES
    elif Environment.in_github_actions():
        return EnvironmentType.GITHUB_ACTION
    elif Environment.in_gitlab_ci():
        return EnvironmentType.GITLAB_CI
    elif Environment.in_circle_ci():
        return EnvironmentType.CIRCLE_CI
    elif Environment.in_bitbucket_ci():
        return EnvironmentType.BITBUCKET_CI
    elif Environment.in_ci():
        return EnvironmentType.GENERIC_CI
    elif Environment.in_github_codespaces():
        return EnvironmentType.GITHUB_CODESPACES
    elif Environment.in_vscode_remote_container():
        return EnvironmentType.VSCODE_REMOTE_CONTAINER
    elif Environment.in_lightning_ai_studio():
        return EnvironmentType.LIGHTNING_AI_STUDIO
    elif Environment.in_docker():
        return EnvironmentType.DOCKER
    elif Environment.in_container():
        return EnvironmentType.CONTAINER
    elif Environment.in_google_colab():
        return EnvironmentType.COLAB
    elif Environment.in_paperspace_gradient():
        return EnvironmentType.PAPERSPACE
    elif Environment.in_notebook():
        return EnvironmentType.NOTEBOOK
    elif Environment.in_wsl():
        return EnvironmentType.WSL
    else:
        return EnvironmentType.NATIVE

get_run_environment_dict()

Returns a dictionary of the current run environment.

Everything that is returned here will be saved in the DB as pipeline_run.client_environment and pipeline_run.orchestrator_environment for client and orchestrator respectively.

Returns:

Type	Description
Dict[str, str]	A dictionary of the current run environment.

Source code in src/zenml/environment.py

def get_run_environment_dict() -> Dict[str, str]:
    """Returns a dictionary of the current run environment.

    Everything that is returned here will be saved in the DB as
    `pipeline_run.client_environment` and
    `pipeline_run.orchestrator_environment` for client and orchestrator
    respectively.

    Returns:
        A dictionary of the current run environment.
    """
    return {
        "environment": get_environment(),
        **Environment.get_system_info(),
        "python_version": Environment.python_version(),
    }

get_system_details()

Returns OS, python and ZenML information.

Returns:

Name	Type	Description
str	str	OS, python and ZenML information

Source code in src/zenml/environment.py

def get_system_details() -> str:
    """Returns OS, python and ZenML information.

    Returns:
        str: OS, python and ZenML information
    """
    from zenml.integrations.registry import integration_registry

    info = {
        "ZenML version": __version__,
        "Install path": Path(__file__).resolve().parent,
        "Python version": Environment.python_version(),
        "Platform information": Environment.get_system_info(),
        "Environment": get_environment(),
        "Integrations": integration_registry.get_installed_integrations(),
    }
    return "\n".join(
        "{:>10} {}".format(k + ":", str(v).replace("\n", " "))
        for k, v in info.items()
    )

Event Hub

ZenML Event Hub module.

The Event Hub is responsible for receiving all Events and dispatching them to the relevant Subscribers/Triggers.

Event Sources

Base Classes for Event Sources.

Exceptions

ZenML specific exception definitions.

ArtifactInterfaceError

Bases: ZenMLBaseException

Raises exception when interacting with the Artifact interface in an unsupported way.

Source code in src/zenml/exceptions.py

class ArtifactInterfaceError(ZenMLBaseException):
    """Raises exception when interacting with the Artifact interface in an unsupported way."""

ArtifactStoreInterfaceError

Bases: ZenMLBaseException

Raises exception when interacting with the Artifact Store interface in an unsupported way.

Source code in src/zenml/exceptions.py

class ArtifactStoreInterfaceError(ZenMLBaseException):
    """Raises exception when interacting with the Artifact Store interface in an unsupported way."""

AuthorizationException

Bases: ZenMLBaseException

Raised when an authorization error occurred while trying to access a ZenML resource .

Source code in src/zenml/exceptions.py

class AuthorizationException(ZenMLBaseException):
    """Raised when an authorization error occurred while trying to access a ZenML resource ."""

BackupSecretsStoreNotConfiguredError

Bases: NotImplementedError

Raised when a backup secrets store is not configured.

Source code in src/zenml/exceptions.py

class BackupSecretsStoreNotConfiguredError(NotImplementedError):
    """Raised when a backup secrets store is not configured."""

CredentialsNotValid

Bases: AuthorizationException

Raised when the credentials provided are invalid.

This is a subclass of AuthorizationException and should only be raised when the authentication credentials are invalid (e.g. expired API token, invalid username/password, invalid signature). If caught by the ZenML client, it will trigger an invalidation of the currently cached API token and a re-authentication flow.

Source code in src/zenml/exceptions.py

class CredentialsNotValid(AuthorizationException):
    """Raised when the credentials provided are invalid.

    This is a subclass of AuthorizationException and should only be raised when
    the authentication credentials are invalid (e.g. expired API token, invalid
    username/password, invalid signature). If caught by the ZenML client, it
    will trigger an invalidation of the currently cached API token and a
    re-authentication flow.
    """

CustomFlavorImportError

Bases: ImportError

Raised when failing to import a custom flavor.

Source code in src/zenml/exceptions.py

class CustomFlavorImportError(ImportError):
    """Raised when failing to import a custom flavor."""

DoesNotExistException

Bases: ZenMLBaseException

Raises exception when the entity does not exist in the system but an action is being done that requires it to be present.

Source code in src/zenml/exceptions.py

class DoesNotExistException(ZenMLBaseException):
    """Raises exception when the entity does not exist in the system but an action is being done that requires it to be present."""

    def __init__(self, message: str):
        """Initializes the exception.

        Args:
            message: Message with details of exception.
        """
        super().__init__(message)

__init__(message)

Initializes the exception.

Parameters:

Name	Type	Description	Default
message	str	Message with details of exception.	required

Source code in src/zenml/exceptions.py

def __init__(self, message: str):
    """Initializes the exception.

    Args:
        message: Message with details of exception.
    """
    super().__init__(message)

DuplicateRunNameError

Bases: RuntimeError

Raises exception when a run with the same name already exists.

Source code in src/zenml/exceptions.py

class DuplicateRunNameError(RuntimeError):
    """Raises exception when a run with the same name already exists."""

    def __init__(
        self,
        message: str = "Unable to run a pipeline with a run name that "
        "already exists.",
    ):
        """Initializes the exception.

        Args:
            message: Message with details of exception.
        """
        super().__init__(message)

__init__(message='Unable to run a pipeline with a run name that already exists.')

Initializes the exception.

Parameters:

Name	Type	Description	Default
message	str	Message with details of exception.	'Unable to run a pipeline with a run name that already exists.'

Source code in src/zenml/exceptions.py

def __init__(
    self,
    message: str = "Unable to run a pipeline with a run name that "
    "already exists.",
):
    """Initializes the exception.

    Args:
        message: Message with details of exception.
    """
    super().__init__(message)

DuplicatedConfigurationError

Bases: ZenMLBaseException

Raised when a configuration parameter is set twice.

Source code in src/zenml/exceptions.py

class DuplicatedConfigurationError(ZenMLBaseException):
    """Raised when a configuration parameter is set twice."""

EntityCreationError

Bases: ZenMLBaseException, RuntimeError

Raised when failing to create an entity.

Source code in src/zenml/exceptions.py

class EntityCreationError(ZenMLBaseException, RuntimeError):
    """Raised when failing to create an entity."""

EntityExistsError

Bases: ZenMLBaseException

Raised when trying to register an entity that already exists.

Source code in src/zenml/exceptions.py

class EntityExistsError(ZenMLBaseException):
    """Raised when trying to register an entity that already exists."""

GitException

Bases: ZenMLBaseException

Raises exception when a problem occurs in git resolution.

Source code in src/zenml/exceptions.py

class GitException(ZenMLBaseException):
    """Raises exception when a problem occurs in git resolution."""

    def __init__(
        self,
        message: str = "There is a problem with git resolution. "
        "Please make sure that all relevant files "
        "are committed.",
    ):
        """Initializes the exception.

        Args:
            message: Message with details of exception.
        """
        super().__init__(message)

__init__(message='There is a problem with git resolution. Please make sure that all relevant files are committed.')

Initializes the exception.

Parameters:

Name	Type	Description	Default
message	str	Message with details of exception.	'There is a problem with git resolution. Please make sure that all relevant files are committed.'

Source code in src/zenml/exceptions.py

def __init__(
    self,
    message: str = "There is a problem with git resolution. "
    "Please make sure that all relevant files "
    "are committed.",
):
    """Initializes the exception.

    Args:
        message: Message with details of exception.
    """
    super().__init__(message)

GitNotFoundError

Bases: ImportError

Raised when ZenML CLI is used to interact with examples on a machine with no git installation.

Source code in src/zenml/exceptions.py

class GitNotFoundError(ImportError):
    """Raised when ZenML CLI is used to interact with examples on a machine with no git installation."""

HydrationError

Bases: ZenMLBaseException

Raised when the model hydration failed.

Source code in src/zenml/exceptions.py

class HydrationError(ZenMLBaseException):
    """Raised when the model hydration failed."""

IllegalOperationError

Bases: ZenMLBaseException

Raised when an illegal operation is attempted.

Source code in src/zenml/exceptions.py

class IllegalOperationError(ZenMLBaseException):
    """Raised when an illegal operation is attempted."""

InitializationException

Bases: ZenMLBaseException

Raised when an error occurred during initialization of a ZenML repository.

Source code in src/zenml/exceptions.py

class InitializationException(ZenMLBaseException):
    """Raised when an error occurred during initialization of a ZenML repository."""

InputResolutionError

Bases: ZenMLBaseException

Raised when step input resolving failed.

Source code in src/zenml/exceptions.py

class InputResolutionError(ZenMLBaseException):
    """Raised when step input resolving failed."""

IntegrationError

Bases: ZenMLBaseException

Raises exceptions when a requested integration can not be activated.

Source code in src/zenml/exceptions.py

class IntegrationError(ZenMLBaseException):
    """Raises exceptions when a requested integration can not be activated."""

MaterializerInterfaceError

Bases: ZenMLBaseException

Raises exception when interacting with the Materializer interface in an unsupported way.

Source code in src/zenml/exceptions.py

class MaterializerInterfaceError(ZenMLBaseException):
    """Raises exception when interacting with the Materializer interface in an unsupported way."""

MethodNotAllowedError

Bases: ZenMLBaseException

Raised when the server does not allow a request method.

Source code in src/zenml/exceptions.py

class MethodNotAllowedError(ZenMLBaseException):
    """Raised when the server does not allow a request method."""

OAuthError

Bases: ValueError

OAuth2 error.

Source code in src/zenml/exceptions.py

class OAuthError(ValueError):
    """OAuth2 error."""

    def __init__(
        self,
        error: str,
        status_code: int = 400,
        error_description: Optional[str] = None,
        error_uri: Optional[str] = None,
    ) -> None:
        """Initializes the OAuthError.

        Args:
            status_code: HTTP status code.
            error: Error code.
            error_description: Error description.
            error_uri: Error URI.
        """
        self.status_code = status_code
        self.error = error
        self.error_description = error_description
        self.error_uri = error_uri

    def to_dict(self) -> Dict[str, Optional[str]]:
        """Returns the OAuthError as a dictionary.

        Returns:
            The OAuthError as a dictionary.
        """
        return {
            "error": self.error,
            "error_description": self.error_description,
            "error_uri": self.error_uri,
        }

    def __str__(self) -> str:
        """String function.

        Returns:
            the error message
        """
        return f"{self.error}: {self.error_description or ''}"

__init__(error, status_code=400, error_description=None, error_uri=None)

Initializes the OAuthError.

Parameters:

Name	Type	Description	Default
status_code	int	HTTP status code.	400
error	str	Error code.	required
error_description	Optional[str]	Error description.	None
error_uri	Optional[str]	Error URI.	None

Source code in src/zenml/exceptions.py

def __init__(
    self,
    error: str,
    status_code: int = 400,
    error_description: Optional[str] = None,
    error_uri: Optional[str] = None,
) -> None:
    """Initializes the OAuthError.

    Args:
        status_code: HTTP status code.
        error: Error code.
        error_description: Error description.
        error_uri: Error URI.
    """
    self.status_code = status_code
    self.error = error
    self.error_description = error_description
    self.error_uri = error_uri

__str__()

String function.

Returns:

Type	Description
str	the error message

Source code in src/zenml/exceptions.py

def __str__(self) -> str:
    """String function.

    Returns:
        the error message
    """
    return f"{self.error}: {self.error_description or ''}"

to_dict()

Returns the OAuthError as a dictionary.

Returns:

Type	Description
Dict[str, Optional[str]]	The OAuthError as a dictionary.

Source code in src/zenml/exceptions.py

def to_dict(self) -> Dict[str, Optional[str]]:
    """Returns the OAuthError as a dictionary.

    Returns:
        The OAuthError as a dictionary.
    """
    return {
        "error": self.error,
        "error_description": self.error_description,
        "error_uri": self.error_uri,
    }

PipelineConfigurationError

Bases: ZenMLBaseException

Raises exceptions when a pipeline configuration contains invalid values.

Source code in src/zenml/exceptions.py

class PipelineConfigurationError(ZenMLBaseException):
    """Raises exceptions when a pipeline configuration contains invalid values."""

PipelineInterfaceError

Bases: ZenMLBaseException

Raises exception when interacting with the Pipeline interface in an unsupported way.

Source code in src/zenml/exceptions.py

class PipelineInterfaceError(ZenMLBaseException):
    """Raises exception when interacting with the Pipeline interface in an unsupported way."""

PipelineNotSucceededException

Bases: ZenMLBaseException

Raises exception when trying to fetch artifacts from a not succeeded pipeline.

Source code in src/zenml/exceptions.py

class PipelineNotSucceededException(ZenMLBaseException):
    """Raises exception when trying to fetch artifacts from a not succeeded pipeline."""

    def __init__(
        self,
        name: str = "",
        message: str = "{} is not yet completed successfully.",
    ):
        """Initializes the exception.

        Args:
            name: Name of the pipeline.
            message: Message with details of exception.
        """
        super().__init__(message.format(name))

__init__(name='', message='{} is not yet completed successfully.')

Initializes the exception.

Parameters:

Name	Type	Description	Default
name	str	Name of the pipeline.	''
message	str	Message with details of exception.	'{} is not yet completed successfully.'

Source code in src/zenml/exceptions.py

def __init__(
    self,
    name: str = "",
    message: str = "{} is not yet completed successfully.",
):
    """Initializes the exception.

    Args:
        name: Name of the pipeline.
        message: Message with details of exception.
    """
    super().__init__(message.format(name))

ProvisioningError

Bases: ZenMLBaseException

Raised when an error occurs when provisioning resources for a StackComponent.

Source code in src/zenml/exceptions.py

class ProvisioningError(ZenMLBaseException):
    """Raised when an error occurs when provisioning resources for a StackComponent."""

SecretsStoreNotConfiguredError

Bases: NotImplementedError

Raised when a secrets store is not configured.

Source code in src/zenml/exceptions.py

class SecretsStoreNotConfiguredError(NotImplementedError):
    """Raised when a secrets store is not configured."""

SettingsResolvingError

Bases: ZenMLBaseException

Raised when resolving settings failed.

Source code in src/zenml/exceptions.py

class SettingsResolvingError(ZenMLBaseException):
    """Raised when resolving settings failed."""

StackComponentDeploymentError

Bases: ZenMLBaseException

Raises exception when deploying a stack component fails.

Source code in src/zenml/exceptions.py

class StackComponentDeploymentError(ZenMLBaseException):
    """Raises exception when deploying a stack component fails."""

StackComponentInterfaceError

Bases: ZenMLBaseException

Raises exception when interacting with the stack components in an unsupported way.

Source code in src/zenml/exceptions.py

class StackComponentInterfaceError(ZenMLBaseException):
    """Raises exception when interacting with the stack components in an unsupported way."""

StackComponentValidationError

Bases: ZenMLBaseException

Raised when a stack component configuration is not valid.

Source code in src/zenml/exceptions.py

class StackComponentValidationError(ZenMLBaseException):
    """Raised when a stack component configuration is not valid."""

StackValidationError

Bases: ZenMLBaseException

Raised when a stack configuration is not valid.

Source code in src/zenml/exceptions.py

class StackValidationError(ZenMLBaseException):
    """Raised when a stack configuration is not valid."""

StepContextError

Bases: ZenMLBaseException

Raises exception when interacting with a StepContext in an unsupported way.

Source code in src/zenml/exceptions.py

class StepContextError(ZenMLBaseException):
    """Raises exception when interacting with a StepContext in an unsupported way."""

StepInterfaceError

Bases: ZenMLBaseException

Raises exception when interacting with the Step interface in an unsupported way.

Source code in src/zenml/exceptions.py

class StepInterfaceError(ZenMLBaseException):
    """Raises exception when interacting with the Step interface in an unsupported way."""

SubscriptionUpgradeRequiredError

Bases: ZenMLBaseException

Raised when user tries to perform an action outside their current subscription tier.

Source code in src/zenml/exceptions.py

class SubscriptionUpgradeRequiredError(ZenMLBaseException):
    """Raised when user tries to perform an action outside their current subscription tier."""

ValidationError

Bases: ZenMLBaseException

Raised when the Model passed to the ZenStore.

Source code in src/zenml/exceptions.py

class ValidationError(ZenMLBaseException):
    """Raised when the Model passed to the ZenStore."""

WebhookInactiveError

Bases: ZenMLBaseException

Raised when source is inactive.

Source code in src/zenml/exceptions.py

class WebhookInactiveError(ZenMLBaseException):
    """Raised when source is inactive."""

ZenKeyError

Bases: KeyError

Specialized key error which allows error messages with line breaks.

Source code in src/zenml/exceptions.py

class ZenKeyError(KeyError):
    """Specialized key error which allows error messages with line breaks."""

    def __init__(self, message: str) -> None:
        """Initialization.

        Args:
            message:str, the error message
        """
        self.message = message

    def __str__(self) -> str:
        """String function.

        Returns:
            the error message
        """
        return self.message

__init__(message)

Initialization.

Parameters:

Name	Type	Description	Default
message	str	str, the error message	required

Source code in src/zenml/exceptions.py

def __init__(self, message: str) -> None:
    """Initialization.

    Args:
        message:str, the error message
    """
    self.message = message

__str__()

String function.

Returns:

Type	Description
str	the error message

Source code in src/zenml/exceptions.py

def __str__(self) -> str:
    """String function.

    Returns:
        the error message
    """
    return self.message

ZenMLBaseException

Bases: Exception

Base exception for all ZenML Exceptions.

Source code in src/zenml/exceptions.py

class ZenMLBaseException(Exception):
    """Base exception for all ZenML Exceptions."""

    def __init__(
        self,
        message: Optional[str] = None,
        url: Optional[str] = None,
    ):
        """The BaseException used to format messages displayed to the user.

        Args:
            message: Message with details of exception. This message
                     will be appended with another message directing user to
                     `url` for more information. If `None`, then default
                     Exception behavior is used.
            url: URL to point to in exception message. If `None`, then no url
                 is appended.
        """
        if message and url:
            message += f" For more information, visit {url}."
        super().__init__(message)

__init__(message=None, url=None)

The BaseException used to format messages displayed to the user.

Parameters:

Name	Type	Description	Default
message	Optional[str]	Message with details of exception. This message will be appended with another message directing user to url for more information. If None, then default Exception behavior is used.	None
url	Optional[str]	URL to point to in exception message. If None, then no url is appended.	None

Source code in src/zenml/exceptions.py

def __init__(
    self,
    message: Optional[str] = None,
    url: Optional[str] = None,
):
    """The BaseException used to format messages displayed to the user.

    Args:
        message: Message with details of exception. This message
                 will be appended with another message directing user to
                 `url` for more information. If `None`, then default
                 Exception behavior is used.
        url: URL to point to in exception message. If `None`, then no url
             is appended.
    """
    if message and url:
        message += f" For more information, visit {url}."
    super().__init__(message)

Experiment Trackers

Experiment trackers let you track your ML experiments.

They log the parameters used and allow you to compare between runs. In the ZenML world, every pipeline run is considered an experiment, and ZenML facilitates the storage of experiment results through ExperimentTracker stack components. This establishes a clear link between pipeline runs and experiments.

BaseExperimentTracker

Bases: StackComponent, ABC

Base class for all ZenML experiment trackers.

Source code in src/zenml/experiment_trackers/base_experiment_tracker.py

class BaseExperimentTracker(StackComponent, ABC):
    """Base class for all ZenML experiment trackers."""

    @property
    def config(self) -> BaseExperimentTrackerConfig:
        """Returns the config of the experiment tracker.

        Returns:
            The config of the experiment tracker.
        """
        return cast(BaseExperimentTrackerConfig, self._config)

config property

Returns the config of the experiment tracker.

Returns:

Type	Description
BaseExperimentTrackerConfig	The config of the experiment tracker.

Feature Stores

A feature store enables an offline and online serving of feature data.

Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept in sync between the two. It also offers a centralized registry where features (and feature schemas) are stored for use within a team or wider organization.

As a data scientist working on training your model, your requirements for how you access your batch / 'offline' data will almost certainly be different from how you access that data as part of a real-time or online inference setting. Feast solves the problem of developing train-serve skew where those two sources of data diverge from each other.

BaseFeatureStore

Bases: StackComponent, ABC

Base class for all ZenML feature stores.

Source code in src/zenml/feature_stores/base_feature_store.py

class BaseFeatureStore(StackComponent, ABC):
    """Base class for all ZenML feature stores."""

    @property
    def config(self) -> BaseFeatureStoreConfig:
        """Returns the `BaseFeatureStoreConfig` config.

        Returns:
            The configuration.
        """
        return cast(BaseFeatureStoreConfig, self._config)

    @abstractmethod
    def get_historical_features(
        self,
        entity_df: Any,
        features: List[str],
        full_feature_names: bool = False,
    ) -> Any:
        """Returns the historical features for training or batch scoring.

        Args:
            entity_df: The entity DataFrame or entity name.
            features: The features to retrieve.
            full_feature_names: Whether to return the full feature names.

        Returns:
            The historical features.
        """

    @abstractmethod
    def get_online_features(
        self,
        entity_rows: List[Dict[str, Any]],
        features: List[str],
        full_feature_names: bool = False,
    ) -> Dict[str, Any]:
        """Returns the latest online feature data.

        Args:
            entity_rows: The entity rows to retrieve.
            features: The features to retrieve.
            full_feature_names: Whether to return the full feature names.

        Returns:
            The latest online feature data as a dictionary.
        """

config property

Returns the BaseFeatureStoreConfig config.

Returns:

Type	Description
BaseFeatureStoreConfig	The configuration.

get_historical_features(entity_df, features, full_feature_names=False) abstractmethod

Returns the historical features for training or batch scoring.

Parameters:

Name	Type	Description	Default
entity_df	Any	The entity DataFrame or entity name.	required
features	List[str]	The features to retrieve.	required
full_feature_names	bool	Whether to return the full feature names.	False

Returns:

Type	Description
Any	The historical features.

Source code in src/zenml/feature_stores/base_feature_store.py

@abstractmethod
def get_historical_features(
    self,
    entity_df: Any,
    features: List[str],
    full_feature_names: bool = False,
) -> Any:
    """Returns the historical features for training or batch scoring.

    Args:
        entity_df: The entity DataFrame or entity name.
        features: The features to retrieve.
        full_feature_names: Whether to return the full feature names.

    Returns:
        The historical features.
    """

get_online_features(entity_rows, features, full_feature_names=False) abstractmethod

Returns the latest online feature data.

Parameters:

Name	Type	Description	Default
entity_rows	List[Dict[str, Any]]	The entity rows to retrieve.	required
features	List[str]	The features to retrieve.	required
full_feature_names	bool	Whether to return the full feature names.	False

Returns:

Type	Description
Dict[str, Any]	The latest online feature data as a dictionary.

Source code in src/zenml/feature_stores/base_feature_store.py

@abstractmethod
def get_online_features(
    self,
    entity_rows: List[Dict[str, Any]],
    features: List[str],
    full_feature_names: bool = False,
) -> Dict[str, Any]:
    """Returns the latest online feature data.

    Args:
        entity_rows: The entity rows to retrieve.
        features: The features to retrieve.
        full_feature_names: Whether to return the full feature names.

    Returns:
        The latest online feature data as a dictionary.
    """

Hooks

The hooks package exposes some standard hooks that can be used in ZenML.

Hooks are functions that run after a step has exited.

alerter_failure_hook(exception)

Standard failure hook that executes after step fails.

This hook uses any BaseAlerter that is configured within the active stack to post a message.

Parameters:

Name	Type	Description	Default
exception	BaseException	Original exception that lead to step failing.	required

Source code in src/zenml/hooks/alerter_hooks.py

def alerter_failure_hook(exception: BaseException) -> None:
    """Standard failure hook that executes after step fails.

    This hook uses any `BaseAlerter` that is configured within the active stack to post a message.

    Args:
        exception: Original exception that lead to step failing.
    """
    context = get_step_context()
    alerter = Client().active_stack.alerter
    if alerter:
        output_captured = io.StringIO()
        original_stdout = sys.stdout
        sys.stdout = output_captured
        console = Console()
        console.print_exception(show_locals=False)

        sys.stdout = original_stdout
        rich_traceback = output_captured.getvalue()

        message = "*Failure Hook Notification! Step failed!*" + "\n\n"
        message += f"Pipeline name: `{context.pipeline.name}`" + "\n"
        message += f"Run name: `{context.pipeline_run.name}`" + "\n"
        message += f"Step name: `{context.step_run.name}`" + "\n"
        message += f"Parameters: `{context.step_run.config.parameters}`" + "\n"
        message += (
            f"Exception: `({type(exception)}) {rich_traceback}`" + "\n\n"
        )
        alerter.post(message)
    else:
        logger.warning(
            "Specified standard failure hook but no alerter configured in the stack. Skipping.."
        )

alerter_success_hook()

Standard success hook that executes after step finishes successfully.

This hook uses any BaseAlerter that is configured within the active stack to post a message.

Source code in src/zenml/hooks/alerter_hooks.py

def alerter_success_hook() -> None:
    """Standard success hook that executes after step finishes successfully.

    This hook uses any `BaseAlerter` that is configured within the active stack to post a message.
    """
    context = get_step_context()
    alerter = Client().active_stack.alerter
    if alerter:
        message = (
            "*Success Hook Notification! Step completed successfully*" + "\n\n"
        )
        message += f"Pipeline name: `{context.pipeline.name}`" + "\n"
        message += f"Run name: `{context.pipeline_run.name}`" + "\n"
        message += f"Step name: `{context.step_run.name}`" + "\n"
        message += f"Parameters: `{context.step_run.config.parameters}`" + "\n"
        alerter.post(message)
    else:
        logger.warning(
            "Specified standard success hook but no alerter configured in the stack. Skipping.."
        )

resolve_and_validate_hook(hook)

Resolves and validates a hook callback.

Parameters:

Name	Type	Description	Default
hook	HookSpecification	Hook function or source.	required

Returns:

Type	Description
Source	Hook source.

Raises:

Type	Description
ValueError	If hook_func is not a valid callable.

Source code in src/zenml/hooks/hook_validators.py

def resolve_and_validate_hook(hook: "HookSpecification") -> Source:
    """Resolves and validates a hook callback.

    Args:
        hook: Hook function or source.

    Returns:
        Hook source.

    Raises:
        ValueError: If `hook_func` is not a valid callable.
    """
    if isinstance(hook, (str, Source)):
        func = source_utils.load(hook)
    else:
        func = hook

    if not callable(func):
        raise ValueError(f"{func} is not a valid function.")

    sig = inspect.getfullargspec(inspect.unwrap(func))
    sig_annotations = sig.annotations
    if "return" in sig_annotations:
        sig_annotations.pop("return")

    if sig.args and len(sig.args) != len(sig_annotations):
        raise ValueError(
            "You can only pass arguments to a hook that are annotated with a "
            "`BaseException` type."
        )

    if sig_annotations:
        annotations = sig_annotations.values()
        seen_annotations = set()
        for annotation in annotations:
            if annotation:
                if annotation not in (BaseException,):
                    raise ValueError(
                        "Hook arguments must be of type `BaseException`, not "
                        f"`{annotation}`."
                    )

                if annotation in seen_annotations:
                    raise ValueError(
                        "You can only pass one `BaseException` type to a hook."
                        "Currently your function has the following"
                        f"annotations: {sig_annotations}"
                    )
                seen_annotations.add(annotation)

    return source_utils.resolve(func)

Image Builders

Image builders allow you to build container images.

BaseImageBuilder

Bases: StackComponent, ABC

Base class for all ZenML image builders.

Source code in src/zenml/image_builders/base_image_builder.py

class BaseImageBuilder(StackComponent, ABC):
    """Base class for all ZenML image builders."""

    @property
    def config(self) -> BaseImageBuilderConfig:
        """The stack component configuration.

        Returns:
            The configuration.
        """
        return cast(BaseImageBuilderConfig, self._config)

    @property
    def build_context_class(self) -> Type["BuildContext"]:
        """Build context class to use.

        The default build context class creates a build context that works
        for the Docker daemon. Override this method if your image builder
        requires a custom context.

        Returns:
            The build context class.
        """
        from zenml.image_builders import BuildContext

        return BuildContext

    @property
    @abstractmethod
    def is_building_locally(self) -> bool:
        """Whether the image builder builds the images on the client machine.

        Returns:
            True if the image builder builds locally, False otherwise.
        """

    @abstractmethod
    def build(
        self,
        image_name: str,
        build_context: "BuildContext",
        docker_build_options: Dict[str, Any],
        container_registry: Optional["BaseContainerRegistry"] = None,
    ) -> str:
        """Builds a Docker image.

        If a container registry is passed, the image will be pushed to that
        registry.

        Args:
            image_name: Name of the image to build.
            build_context: The build context to use for the image.
            docker_build_options: Docker build options.
            container_registry: Optional container registry to push to.

        Returns:
            The Docker image repo digest or name.
        """

    @staticmethod
    def _upload_build_context(
        build_context: "BuildContext",
        parent_path_directory_name: str,
        archive_type: ArchiveType = ArchiveType.TAR_GZ,
    ) -> str:
        """Uploads a Docker image build context to a remote location.

        Args:
            build_context: The build context to upload.
            parent_path_directory_name: The name of the directory to upload
                the build context to. It will be appended to the artifact
                store path to create the parent path where the build context
                will be uploaded to.
            archive_type: The type of archive to create.

        Returns:
            The path to the uploaded build context.
        """
        artifact_store = Client().active_stack.artifact_store
        parent_path = f"{artifact_store.path}/{parent_path_directory_name}"
        fileio.makedirs(parent_path)

        hash_ = hashlib.sha1()  # nosec
        with tempfile.NamedTemporaryFile(mode="w+b", delete=False) as f:
            build_context.write_archive(f, archive_type)

            while True:
                data = f.read(64 * 1024)
                if not data:
                    break
                hash_.update(data)

            filename = f"{hash_.hexdigest()}.{archive_type.value}"
            filepath = f"{parent_path}/{filename}"
            if not fileio.exists(filepath):
                logger.info("Uploading build context to `%s`.", filepath)
                fileio.copy(f.name, filepath)
            else:
                logger.info("Build context already exists, not uploading.")

        os.unlink(f.name)
        return filepath

build_context_class property

Build context class to use.

The default build context class creates a build context that works for the Docker daemon. Override this method if your image builder requires a custom context.

Returns:

Type	Description
Type[BuildContext]	The build context class.

config property

The stack component configuration.

Returns:

Type	Description
BaseImageBuilderConfig	The configuration.

is_building_locally abstractmethod property

Whether the image builder builds the images on the client machine.

Returns:

Type	Description
bool	True if the image builder builds locally, False otherwise.

build(image_name, build_context, docker_build_options, container_registry=None) abstractmethod

Builds a Docker image.

If a container registry is passed, the image will be pushed to that registry.

Parameters:

Name	Type	Description	Default
image_name	str	Name of the image to build.	required
build_context	BuildContext	The build context to use for the image.	required
docker_build_options	Dict[str, Any]	Docker build options.	required
container_registry	Optional[BaseContainerRegistry]	Optional container registry to push to.	None

Returns:

Type	Description
str	The Docker image repo digest or name.

Source code in src/zenml/image_builders/base_image_builder.py

@abstractmethod
def build(
    self,
    image_name: str,
    build_context: "BuildContext",
    docker_build_options: Dict[str, Any],
    container_registry: Optional["BaseContainerRegistry"] = None,
) -> str:
    """Builds a Docker image.

    If a container registry is passed, the image will be pushed to that
    registry.

    Args:
        image_name: Name of the image to build.
        build_context: The build context to use for the image.
        docker_build_options: Docker build options.
        container_registry: Optional container registry to push to.

    Returns:
        The Docker image repo digest or name.
    """

BaseImageBuilderConfig

Bases: StackComponentConfig

Base config for image builders.

Source code in src/zenml/image_builders/base_image_builder.py

class BaseImageBuilderConfig(StackComponentConfig):
    """Base config for image builders."""

BaseImageBuilderFlavor

Bases: Flavor, ABC

Base class for all ZenML image builder flavors.

Source code in src/zenml/image_builders/base_image_builder.py

class BaseImageBuilderFlavor(Flavor, ABC):
    """Base class for all ZenML image builder flavors."""

    @property
    def type(self) -> StackComponentType:
        """Returns the flavor type.

        Returns:
            The flavor type.
        """
        return StackComponentType.IMAGE_BUILDER

    @property
    def config_class(self) -> Type[BaseImageBuilderConfig]:
        """Config class.

        Returns:
            The config class.
        """
        return BaseImageBuilderConfig

    @property
    def implementation_class(self) -> Type[BaseImageBuilder]:
        """Implementation class.

        Returns:
            The implementation class.
        """
        return BaseImageBuilder

config_class property

Config class.

Returns:

Type	Description
Type[BaseImageBuilderConfig]	The config class.

implementation_class property

Implementation class.

Returns:

Type	Description
Type[BaseImageBuilder]	The implementation class.

type property

Returns the flavor type.

Returns:

Type	Description
StackComponentType	The flavor type.

BuildContext

Bases: Archivable

Image build context.

This class is responsible for creating an archive of the files needed to build a container image.

Source code in src/zenml/image_builders/build_context.py

class BuildContext(Archivable):
    """Image build context.

    This class is responsible for creating an archive of the files needed to
    build a container image.
    """

    def __init__(
        self,
        root: Optional[str] = None,
        dockerignore_file: Optional[str] = None,
    ) -> None:
        """Initializes a build context.

        Args:
            root: Optional root directory for the build context.
            dockerignore_file: Optional path to a dockerignore file. If not
                given, a file called `.dockerignore` in the build context root
                directory will be used instead if it exists.
        """
        super().__init__()
        self._root = root
        self._dockerignore_file = dockerignore_file

    @property
    def dockerignore_file(self) -> Optional[str]:
        """The dockerignore file to use.

        Returns:
            Path to the dockerignore file to use.
        """
        if self._dockerignore_file:
            return self._dockerignore_file

        if self._root:
            default_dockerignore_path = os.path.join(
                self._root, ".dockerignore"
            )
            if fileio.exists(default_dockerignore_path):
                return default_dockerignore_path

        return None

    def write_archive(
        self,
        output_file: IO[bytes],
        archive_type: ArchiveType = ArchiveType.TAR_GZ,
    ) -> None:
        """Writes an archive of the build context to the given file.

        Args:
            output_file: The file to write the archive to.
            archive_type: The type of archive to create.
        """
        super().write_archive(output_file, archive_type)

        build_context_size = os.path.getsize(output_file.name)
        if (
            self._root
            and build_context_size > 50 * 1024 * 1024
            and not self.dockerignore_file
        ):
            # The build context exceeds 50MiB and we didn't find any excludes
            # in dockerignore files -> remind to specify a .dockerignore file
            logger.warning(
                "Build context size for docker image: `%s`. If you believe this is "
                "unreasonably large, make sure to include a `.dockerignore` file "
                "at the root of your build context `%s` or specify a custom file "
                "in the Docker configuration when defining your pipeline.",
                string_utils.get_human_readable_filesize(build_context_size),
                os.path.join(self._root, ".dockerignore"),
            )

    def get_files(self) -> Dict[str, str]:
        """Gets all regular files that should be included in the archive.

        Returns:
            A dict {path_in_archive: path_on_filesystem} for all regular files
            in the archive.
        """
        if self._root:
            from docker.utils import build as docker_build_utils

            exclude_patterns = self._get_exclude_patterns()

            archive_paths = cast(
                Set[str],
                docker_build_utils.exclude_paths(
                    self._root, patterns=exclude_patterns
                ),
            )
            return {
                archive_path: os.path.join(self._root, archive_path)
                for archive_path in archive_paths
            }
        else:
            return {}

    def _get_exclude_patterns(self) -> List[str]:
        """Gets all exclude patterns from the dockerignore file.

        Returns:
            The exclude patterns from the dockerignore file.
        """
        dockerignore = self.dockerignore_file
        if dockerignore:
            patterns = self._parse_dockerignore(dockerignore)
            # Always include the .zen directory
            patterns.append(f"!/{REPOSITORY_DIRECTORY_NAME}")
            return patterns
        else:
            logger.info(
                "No `.dockerignore` found, including all files inside build "
                "context.",
            )
            return []

    @staticmethod
    def _parse_dockerignore(dockerignore_path: str) -> List[str]:
        """Parses a dockerignore file and returns a list of patterns to ignore.

        Args:
            dockerignore_path: Path to the dockerignore file.

        Returns:
            List of patterns to ignore.
        """
        try:
            file_content = io_utils.read_file_contents_as_string(
                dockerignore_path
            )
        except FileNotFoundError:
            logger.warning(
                "Unable to find dockerignore file at path '%s'.",
                dockerignore_path,
            )
            return []

        exclude_patterns = []
        for line in file_content.split("\n"):
            line = line.strip()
            if line and not line.startswith("#"):
                exclude_patterns.append(line)

        return exclude_patterns

dockerignore_file property

The dockerignore file to use.

Returns:

Type	Description
Optional[str]	Path to the dockerignore file to use.

__init__(root=None, dockerignore_file=None)

Initializes a build context.

Parameters:

Name	Type	Description	Default
root	Optional[str]	Optional root directory for the build context.	None
dockerignore_file	Optional[str]	Optional path to a dockerignore file. If not given, a file called .dockerignore in the build context root directory will be used instead if it exists.	None

Source code in src/zenml/image_builders/build_context.py

def __init__(
    self,
    root: Optional[str] = None,
    dockerignore_file: Optional[str] = None,
) -> None:
    """Initializes a build context.

    Args:
        root: Optional root directory for the build context.
        dockerignore_file: Optional path to a dockerignore file. If not
            given, a file called `.dockerignore` in the build context root
            directory will be used instead if it exists.
    """
    super().__init__()
    self._root = root
    self._dockerignore_file = dockerignore_file

get_files()

Gets all regular files that should be included in the archive.

Returns:

Type	Description
Dict[str, str]	A dict {path_in_archive: path_on_filesystem} for all regular files
Dict[str, str]	in the archive.

Source code in src/zenml/image_builders/build_context.py

def get_files(self) -> Dict[str, str]:
    """Gets all regular files that should be included in the archive.

    Returns:
        A dict {path_in_archive: path_on_filesystem} for all regular files
        in the archive.
    """
    if self._root:
        from docker.utils import build as docker_build_utils

        exclude_patterns = self._get_exclude_patterns()

        archive_paths = cast(
            Set[str],
            docker_build_utils.exclude_paths(
                self._root, patterns=exclude_patterns
            ),
        )
        return {
            archive_path: os.path.join(self._root, archive_path)
            for archive_path in archive_paths
        }
    else:
        return {}

write_archive(output_file, archive_type=ArchiveType.TAR_GZ)

Writes an archive of the build context to the given file.

Parameters:

Name	Type	Description	Default
output_file	IO[bytes]	The file to write the archive to.	required
archive_type	ArchiveType	The type of archive to create.	TAR_GZ

Source code in src/zenml/image_builders/build_context.py

def write_archive(
    self,
    output_file: IO[bytes],
    archive_type: ArchiveType = ArchiveType.TAR_GZ,
) -> None:
    """Writes an archive of the build context to the given file.

    Args:
        output_file: The file to write the archive to.
        archive_type: The type of archive to create.
    """
    super().write_archive(output_file, archive_type)

    build_context_size = os.path.getsize(output_file.name)
    if (
        self._root
        and build_context_size > 50 * 1024 * 1024
        and not self.dockerignore_file
    ):
        # The build context exceeds 50MiB and we didn't find any excludes
        # in dockerignore files -> remind to specify a .dockerignore file
        logger.warning(
            "Build context size for docker image: `%s`. If you believe this is "
            "unreasonably large, make sure to include a `.dockerignore` file "
            "at the root of your build context `%s` or specify a custom file "
            "in the Docker configuration when defining your pipeline.",
            string_utils.get_human_readable_filesize(build_context_size),
            os.path.join(self._root, ".dockerignore"),
        )

LocalImageBuilder

Bases: BaseImageBuilder

Local image builder implementation.

Source code in src/zenml/image_builders/local_image_builder.py

class LocalImageBuilder(BaseImageBuilder):
    """Local image builder implementation."""

    @property
    def config(self) -> LocalImageBuilderConfig:
        """The stack component configuration.

        Returns:
            The configuration.
        """
        return cast(LocalImageBuilderConfig, self._config)

    @property
    def is_building_locally(self) -> bool:
        """Whether the image builder builds the images on the client machine.

        Returns:
            True if the image builder builds locally, False otherwise.
        """
        return True

    @staticmethod
    def _check_prerequisites() -> None:
        """Checks that all prerequisites are installed.

        Raises:
            RuntimeError: If any of the prerequisites are not installed or
                running.
        """
        if not shutil.which("docker"):
            raise RuntimeError(
                "`docker` is required to run the local image builder."
            )

        if not docker_utils.check_docker():
            # For 3., this is not supported by the python docker library
            # https://github.com/docker/docker-py/issues/3146
            raise RuntimeError(
                "Unable to connect to the Docker daemon. There are three "
                "common causes for this:\n"
                "1) The Docker daemon isn't running.\n"
                "2) The Docker client isn't configured correctly. The client "
                "loads its configuration from the following file: "
                "$HOME/.docker/config.json. If your configuration file is in a "
                "different location, set the `DOCKER_CONFIG` environment "
                "variable to the directory that contains your `config.json` "
                "file.\n"
                "3) If your Docker CLI is working fine but you ran into this "
                "issue, you might be using a non-default Docker context which "
                "is not supported by the Docker python library. To verify "
                "this, run `docker context ls` and check which context has a "
                "`*` next to it. If this is not the `default` context, copy "
                "the `DOCKER ENDPOINT` value of that context and set the "
                "`DOCKER_HOST` environment variable to that value."
            )

    def build(
        self,
        image_name: str,
        build_context: "BuildContext",
        docker_build_options: Optional[Dict[str, Any]] = None,
        container_registry: Optional["BaseContainerRegistry"] = None,
    ) -> str:
        """Builds and optionally pushes an image using the local Docker client.

        Args:
            image_name: Name of the image to build and push.
            build_context: The build context to use for the image.
            docker_build_options: Docker build options.
            container_registry: Optional container registry to push to.

        Returns:
            The Docker image repo digest.
        """
        self._check_prerequisites()

        if container_registry:
            # Use the container registry's docker client, which may be
            # authenticated to access additional registries
            docker_client = container_registry.docker_client
        else:
            docker_client = docker_utils._try_get_docker_client_from_env()

        with tempfile.TemporaryFile(mode="w+b") as f:
            build_context.write_archive(f)

            # We use the client api directly here, so we can stream the logs
            output_stream = docker_client.images.client.api.build(
                fileobj=f,
                custom_context=True,
                tag=image_name,
                **(docker_build_options or {}),
            )
        docker_utils._process_stream(output_stream)

        if container_registry:
            return container_registry.push_image(image_name)
        else:
            return image_name

config property

The stack component configuration.

Returns:

Type	Description
LocalImageBuilderConfig	The configuration.

is_building_locally property

Whether the image builder builds the images on the client machine.

Returns:

Type	Description
bool	True if the image builder builds locally, False otherwise.

build(image_name, build_context, docker_build_options=None, container_registry=None)

Builds and optionally pushes an image using the local Docker client.

Parameters:

Name	Type	Description	Default
image_name	str	Name of the image to build and push.	required
build_context	BuildContext	The build context to use for the image.	required
docker_build_options	Optional[Dict[str, Any]]	Docker build options.	None
container_registry	Optional[BaseContainerRegistry]	Optional container registry to push to.	None

Returns:

Type	Description
str	The Docker image repo digest.

Source code in src/zenml/image_builders/local_image_builder.py

def build(
    self,
    image_name: str,
    build_context: "BuildContext",
    docker_build_options: Optional[Dict[str, Any]] = None,
    container_registry: Optional["BaseContainerRegistry"] = None,
) -> str:
    """Builds and optionally pushes an image using the local Docker client.

    Args:
        image_name: Name of the image to build and push.
        build_context: The build context to use for the image.
        docker_build_options: Docker build options.
        container_registry: Optional container registry to push to.

    Returns:
        The Docker image repo digest.
    """
    self._check_prerequisites()

    if container_registry:
        # Use the container registry's docker client, which may be
        # authenticated to access additional registries
        docker_client = container_registry.docker_client
    else:
        docker_client = docker_utils._try_get_docker_client_from_env()

    with tempfile.TemporaryFile(mode="w+b") as f:
        build_context.write_archive(f)

        # We use the client api directly here, so we can stream the logs
        output_stream = docker_client.images.client.api.build(
            fileobj=f,
            custom_context=True,
            tag=image_name,
            **(docker_build_options or {}),
        )
    docker_utils._process_stream(output_stream)

    if container_registry:
        return container_registry.push_image(image_name)
    else:
        return image_name

LocalImageBuilderConfig

Bases: BaseImageBuilderConfig

Local image builder configuration.

Source code in src/zenml/image_builders/local_image_builder.py

class LocalImageBuilderConfig(BaseImageBuilderConfig):
    """Local image builder configuration."""

LocalImageBuilderFlavor

Bases: BaseImageBuilderFlavor

Local image builder flavor.

Source code in src/zenml/image_builders/local_image_builder.py

class LocalImageBuilderFlavor(BaseImageBuilderFlavor):
    """Local image builder flavor."""

    @property
    def name(self) -> str:
        """The flavor name.

        Returns:
            The flavor name.
        """
        return "local"

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/image_builder/local.svg"

    @property
    def config_class(self) -> Type[LocalImageBuilderConfig]:
        """Config class.

        Returns:
            The config class.
        """
        return LocalImageBuilderConfig

    @property
    def implementation_class(self) -> Type[LocalImageBuilder]:
        """Implementation class.

        Returns:
            The implementation class.
        """
        return LocalImageBuilder

config_class property

Config class.

Returns:

Type	Description
Type[LocalImageBuilderConfig]	The config class.

docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

implementation_class property

Implementation class.

Returns:

Type	Description
Type[LocalImageBuilder]	The implementation class.

logo_url property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name property

The flavor name.

Returns:

Type	Description
str	The flavor name.

sdk_docs_url property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

Io

The io module handles file operations for the ZenML package.

It offers a standard interface for reading, writing and manipulating files and directories. It is heavily influenced and inspired by the io module of tfx.

Logger

Logger implementation.

CustomFormatter

Bases: Formatter

Formats logs according to custom specifications.

Source code in src/zenml/logger.py

class CustomFormatter(logging.Formatter):
    """Formats logs according to custom specifications."""

    grey: str = "\x1b[38;21m"
    pink: str = "\x1b[35m"
    green: str = "\x1b[32m"
    yellow: str = "\x1b[33m"
    red: str = "\x1b[31m"
    cyan: str = "\x1b[1;36m"
    bold_red: str = "\x1b[31;1m"
    purple: str = "\x1b[1;35m"
    blue: str = "\x1b[34m"
    reset: str = "\x1b[0m"

    format_template: str = (
        "%(asctime)s - %(name)s - %(levelname)s - %(message)s (%("
        "filename)s:%(lineno)d)"
        if LoggingLevels[ZENML_LOGGING_VERBOSITY] == LoggingLevels.DEBUG
        else "%(message)s"
    )

    COLORS: Dict[LoggingLevels, str] = {
        LoggingLevels.DEBUG: grey,
        LoggingLevels.INFO: purple,
        LoggingLevels.WARN: yellow,
        LoggingLevels.ERROR: red,
        LoggingLevels.CRITICAL: bold_red,
    }

    def format(self, record: logging.LogRecord) -> str:
        """Converts a log record to a (colored) string.

        Args:
            record: LogRecord generated by the code.

        Returns:
            A string formatted according to specifications.
        """
        if ZENML_LOGGING_COLORS_DISABLED:
            # If color formatting is disabled, use the default format without colors
            formatter = logging.Formatter(self.format_template)
            return formatter.format(record)
        else:
            # Use color formatting
            log_fmt = (
                self.COLORS[LoggingLevels(record.levelno)]
                + self.format_template
                + self.reset
            )
            formatter = logging.Formatter(log_fmt)
            formatted_message = formatter.format(record)
            quoted_groups = re.findall("`([^`]*)`", formatted_message)
            for quoted in quoted_groups:
                formatted_message = formatted_message.replace(
                    "`" + quoted + "`",
                    self.reset
                    + self.cyan
                    + quoted
                    + self.COLORS.get(LoggingLevels(record.levelno)),
                )

            # Format URLs
            url_pattern = r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"
            urls = re.findall(url_pattern, formatted_message)
            for url in urls:
                formatted_message = formatted_message.replace(
                    url,
                    self.reset
                    + self.blue
                    + url
                    + self.COLORS.get(LoggingLevels(record.levelno)),
                )
            return formatted_message

format(record)

Converts a log record to a (colored) string.

Parameters:

Name	Type	Description	Default
record	LogRecord	LogRecord generated by the code.	required

Returns:

Type	Description
str	A string formatted according to specifications.

Source code in src/zenml/logger.py

def format(self, record: logging.LogRecord) -> str:
    """Converts a log record to a (colored) string.

    Args:
        record: LogRecord generated by the code.

    Returns:
        A string formatted according to specifications.
    """
    if ZENML_LOGGING_COLORS_DISABLED:
        # If color formatting is disabled, use the default format without colors
        formatter = logging.Formatter(self.format_template)
        return formatter.format(record)
    else:
        # Use color formatting
        log_fmt = (
            self.COLORS[LoggingLevels(record.levelno)]
            + self.format_template
            + self.reset
        )
        formatter = logging.Formatter(log_fmt)
        formatted_message = formatter.format(record)
        quoted_groups = re.findall("`([^`]*)`", formatted_message)
        for quoted in quoted_groups:
            formatted_message = formatted_message.replace(
                "`" + quoted + "`",
                self.reset
                + self.cyan
                + quoted
                + self.COLORS.get(LoggingLevels(record.levelno)),
            )

        # Format URLs
        url_pattern = r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"
        urls = re.findall(url_pattern, formatted_message)
        for url in urls:
            formatted_message = formatted_message.replace(
                url,
                self.reset
                + self.blue
                + url
                + self.COLORS.get(LoggingLevels(record.levelno)),
            )
        return formatted_message

get_console_handler()

Get console handler for logging.

Returns:

Type	Description
Any	A console handler.

Source code in src/zenml/logger.py

def get_console_handler() -> Any:
    """Get console handler for logging.

    Returns:
        A console handler.
    """
    console_handler = logging.StreamHandler(sys.stdout)
    console_handler.setFormatter(get_formatter())
    return console_handler

get_formatter()

Get a configured logging formatter.

Returns:

Type	Description
Formatter	The formatter.

Source code in src/zenml/logger.py

def get_formatter() -> logging.Formatter:
    """Get a configured logging formatter.

    Returns:
        The formatter.
    """
    if log_format := os.environ.get(ENV_ZENML_LOGGING_FORMAT, None):
        return logging.Formatter(fmt=log_format)
    else:
        return CustomFormatter()

get_logger(logger_name)

Main function to get logger name,.

Parameters:

Name	Type	Description	Default
logger_name	str	Name of logger to initialize.	required

Returns:

Type	Description
Logger	A logger object.

Source code in src/zenml/logger.py

def get_logger(logger_name: str) -> logging.Logger:
    """Main function to get logger name,.

    Args:
        logger_name: Name of logger to initialize.

    Returns:
        A logger object.
    """
    logger = logging.getLogger(logger_name)
    logger.setLevel(get_logging_level().value)
    logger.addHandler(get_console_handler())

    logger.propagate = False
    return logger

get_logging_level()

Get logging level from the env variable.

Returns:

Type	Description
LoggingLevels	The logging level.

Raises:

Type	Description
KeyError	If the logging level is not found.

Source code in src/zenml/logger.py

def get_logging_level() -> LoggingLevels:
    """Get logging level from the env variable.

    Returns:
        The logging level.

    Raises:
        KeyError: If the logging level is not found.
    """
    verbosity = ZENML_LOGGING_VERBOSITY.upper()
    if verbosity not in LoggingLevels.__members__:
        raise KeyError(
            f"Verbosity must be one of {list(LoggingLevels.__members__.keys())}"
        )
    return LoggingLevels[verbosity]

init_logging()

Initialize logging with default levels.

Source code in src/zenml/logger.py

def init_logging() -> None:
    """Initialize logging with default levels."""
    # Mute tensorflow cuda warnings
    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
    set_root_verbosity()

    console_handler = logging.StreamHandler(sys.stdout)
    console_handler.setFormatter(get_formatter())
    logging.root.addHandler(console_handler)

    # Enable logs if environment variable SUPPRESS_ZENML_LOGS is not set to True
    suppress_zenml_logs: bool = handle_bool_env_var(
        ENV_ZENML_SUPPRESS_LOGS, True
    )
    if suppress_zenml_logs:
        # suppress logger info messages
        suppressed_logger_names = [
            "urllib3",
            "azure.core.pipeline.policies.http_logging_policy",
            "grpc",
            "requests",
            "kfp",
            "tensorflow",
        ]
        for logger_name in suppressed_logger_names:
            logging.getLogger(logger_name).setLevel(logging.WARNING)

        # disable logger messages
        disabled_logger_names = [
            "rdbms_metadata_access_object",
            "backoff",
            "segment",
        ]
        for logger_name in disabled_logger_names:
            logging.getLogger(logger_name).setLevel(logging.WARNING)
            logging.getLogger(logger_name).disabled = True

set_root_verbosity()

Set the root verbosity.

Source code in src/zenml/logger.py

def set_root_verbosity() -> None:
    """Set the root verbosity."""
    level = get_logging_level()
    if level != LoggingLevels.NOTSET:
        if ENABLE_RICH_TRACEBACK:
            rich_tb_install(show_locals=(level == LoggingLevels.DEBUG))

        logging.root.setLevel(level=level.value)
        get_logger(__name__).debug(
            f"Logging set to level: {logging.getLevelName(level.value)}"
        )
    else:
        logging.disable(sys.maxsize)
        logging.getLogger().disabled = True
        get_logger(__name__).debug("Logging NOTSET")

Logging

Logging utilities.

Login

ZenML login utilities.

Materializers

Initialization of ZenML materializers.

Materializers are used to convert a ZenML artifact into a specific format. They are most often used to handle the input or output of ZenML steps, and can be extended by building on the BaseMaterializer class.

BuiltInContainerMaterializer

Bases: BaseMaterializer

Handle built-in container types (dict, list, set, tuple).

Source code in src/zenml/materializers/built_in_materializer.py

class BuiltInContainerMaterializer(BaseMaterializer):
    """Handle built-in container types (dict, list, set, tuple)."""

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (
        dict,
        list,
        set,
        tuple,
    )

    def __init__(
        self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
    ):
        """Define `self.data_path` and `self.metadata_path`.

        Args:
            uri: The URI where the artifact data is stored.
            artifact_store: The artifact store where the artifact data is stored.
        """
        super().__init__(uri, artifact_store)
        self.data_path = os.path.join(self.uri, DEFAULT_FILENAME)
        self.metadata_path = os.path.join(self.uri, DEFAULT_METADATA_FILENAME)

    def load(self, data_type: Type[Any]) -> Any:
        """Reads a materialized built-in container object.

        If the data was serialized to JSON, deserialize it.

        Otherwise, reconstruct all elements according to the metadata file:
            1. Resolve the data type using `find_type_by_str()`,
            2. Get the materializer via the `default_materializer_registry`,
            3. Initialize the materializer with the desired path,
            4. Use `load()` of that materializer to load the element.

        Args:
            data_type: The type of the data to read.

        Returns:
            The data read.

        Raises:
            RuntimeError: If the data was not found.
        """
        # If the data was not serialized, there must be metadata present.
        if not self.artifact_store.exists(
            self.data_path
        ) and not self.artifact_store.exists(self.metadata_path):
            raise RuntimeError(
                f"Materialization of type {data_type} failed. Expected either"
                f"{self.data_path} or {self.metadata_path} to exist."
            )

        # If the data was serialized as JSON, deserialize it.
        if self.artifact_store.exists(self.data_path):
            outputs = yaml_utils.read_json(self.data_path)

        # Otherwise, use the metadata to reconstruct the data as a list.
        else:
            metadata = yaml_utils.read_json(self.metadata_path)
            outputs = []

            # Backwards compatibility for zenml <= 0.37.0
            if isinstance(metadata, dict):
                for path_, type_str in zip(
                    metadata["paths"], metadata["types"]
                ):
                    type_ = find_type_by_str(type_str)
                    materializer_class = materializer_registry[type_]
                    materializer = materializer_class(uri=path_)
                    element = materializer.load(type_)
                    outputs.append(element)

            # New format for zenml > 0.37.0
            elif isinstance(metadata, list):
                for entry in metadata:
                    path_ = entry["path"]
                    type_ = source_utils.load(entry["type"])
                    materializer_class = source_utils.load(
                        entry["materializer"]
                    )
                    materializer = materializer_class(uri=path_)
                    element = materializer.load(type_)
                    outputs.append(element)

            else:
                raise RuntimeError(f"Unknown metadata format: {metadata}.")

        # Cast the data to the correct type.
        if issubclass(data_type, dict) and not isinstance(outputs, dict):
            keys, values = outputs
            return data_type(zip(keys, values))
        if issubclass(data_type, tuple) and not isinstance(outputs, tuple):
            return data_type(outputs)
        if issubclass(data_type, set) and not isinstance(outputs, set):
            return data_type(outputs)
        return outputs

    def save(self, data: Any) -> None:
        """Materialize a built-in container object.

        If the object can be serialized to JSON, serialize it.

        Otherwise, use the `default_materializer_registry` to find the correct
        materializer for each element and materialize each element into a
        subdirectory.

        Tuples and sets are cast to list before materialization.

        For non-serializable dicts, materialize keys/values as separate lists.

        Args:
            data: The built-in container object to materialize.

        Raises:
            Exception: If any exception occurs, it is raised after cleanup.
        """
        # tuple and set: handle as list.
        if isinstance(data, tuple) or isinstance(data, set):
            data = list(data)

        # If the data is serializable, just write it into a single JSON file.
        if _is_serializable(data):
            yaml_utils.write_json(
                self.data_path,
                data,
                ensure_ascii=not ZENML_MATERIALIZER_ALLOW_NON_ASCII_JSON_DUMPS,
            )
            return

        # non-serializable dict: Handle as non-serializable list of lists.
        if isinstance(data, dict):
            data = [list(data.keys()), list(data.values())]

        # non-serializable list: Materialize each element into a subfolder.
        # Get path, type, and corresponding materializer for each element.
        metadata: List[Dict[str, str]] = []
        materializers: List[BaseMaterializer] = []
        try:
            for i, element in enumerate(data):
                element_path = os.path.join(self.uri, str(i))
                self.artifact_store.mkdir(element_path)
                type_ = type(element)
                materializer_class = materializer_registry[type_]
                materializer = materializer_class(uri=element_path)
                materializers.append(materializer)
                metadata.append(
                    {
                        "path": element_path,
                        "type": source_utils.resolve(type_).import_path,
                        "materializer": source_utils.resolve(
                            materializer_class
                        ).import_path,
                    }
                )
            # Write metadata as JSON.
            yaml_utils.write_json(self.metadata_path, metadata)
            # Materialize each element.
            for element, materializer in zip(data, materializers):
                materializer.validate_save_type_compatibility(type(element))
                materializer.save(element)
        # If an error occurs, delete all created files.
        except Exception as e:
            # Delete metadata
            if self.artifact_store.exists(self.metadata_path):
                self.artifact_store.remove(self.metadata_path)
            # Delete all elements that were already saved.
            for entry in metadata:
                self.artifact_store.rmtree(entry["path"])
            raise e

    # save dict type objects to JSON file with JSON visualization type
    def save_visualizations(self, data: Any) -> Dict[str, "VisualizationType"]:
        """Save visualizations for the given data.

        Args:
            data: The data to save visualizations for.

        Returns:
            A dictionary of visualization URIs and their types.
        """
        # dict/list type objects are always saved as JSON files
        # doesn't work for non-serializable types as they
        # are saved as list of lists in different files
        if _is_serializable(data):
            return {self.data_path.replace("\\", "/"): VisualizationType.JSON}
        return {}

    def extract_metadata(self, data: Any) -> Dict[str, "MetadataType"]:
        """Extract metadata from the given built-in container object.

        Args:
            data: The built-in container object to extract metadata from.

        Returns:
            The extracted metadata as a dictionary.
        """
        if hasattr(data, "__len__"):
            return {"length": len(data)}
        return {}

__init__(uri, artifact_store=None)

Define self.data_path and self.metadata_path.

Parameters:

Name	Type	Description	Default
uri	str	The URI where the artifact data is stored.	required
artifact_store	Optional[BaseArtifactStore]	The artifact store where the artifact data is stored.	None

Source code in src/zenml/materializers/built_in_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Define `self.data_path` and `self.metadata_path`.

    Args:
        uri: The URI where the artifact data is stored.
        artifact_store: The artifact store where the artifact data is stored.
    """
    super().__init__(uri, artifact_store)
    self.data_path = os.path.join(self.uri, DEFAULT_FILENAME)
    self.metadata_path = os.path.join(self.uri, DEFAULT_METADATA_FILENAME)

extract_metadata(data)

Extract metadata from the given built-in container object.

Parameters:

Name	Type	Description	Default
data	Any	The built-in container object to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/materializers/built_in_materializer.py

def extract_metadata(self, data: Any) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given built-in container object.

    Args:
        data: The built-in container object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    if hasattr(data, "__len__"):
        return {"length": len(data)}
    return {}

load(data_type)

Reads a materialized built-in container object.

If the data was serialized to JSON, deserialize it.

Otherwise, reconstruct all elements according to the metadata file: 1. Resolve the data type using find_type_by_str(), 2. Get the materializer via the default_materializer_registry, 3. Initialize the materializer with the desired path, 4. Use load() of that materializer to load the element.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the data to read.	required

Returns:

Type	Description
Any	The data read.

Raises:

Type	Description
RuntimeError	If the data was not found.

Source code in src/zenml/materializers/built_in_materializer.py

def load(self, data_type: Type[Any]) -> Any:
    """Reads a materialized built-in container object.

    If the data was serialized to JSON, deserialize it.

    Otherwise, reconstruct all elements according to the metadata file:
        1. Resolve the data type using `find_type_by_str()`,
        2. Get the materializer via the `default_materializer_registry`,
        3. Initialize the materializer with the desired path,
        4. Use `load()` of that materializer to load the element.

    Args:
        data_type: The type of the data to read.

    Returns:
        The data read.

    Raises:
        RuntimeError: If the data was not found.
    """
    # If the data was not serialized, there must be metadata present.
    if not self.artifact_store.exists(
        self.data_path
    ) and not self.artifact_store.exists(self.metadata_path):
        raise RuntimeError(
            f"Materialization of type {data_type} failed. Expected either"
            f"{self.data_path} or {self.metadata_path} to exist."
        )

    # If the data was serialized as JSON, deserialize it.
    if self.artifact_store.exists(self.data_path):
        outputs = yaml_utils.read_json(self.data_path)

    # Otherwise, use the metadata to reconstruct the data as a list.
    else:
        metadata = yaml_utils.read_json(self.metadata_path)
        outputs = []

        # Backwards compatibility for zenml <= 0.37.0
        if isinstance(metadata, dict):
            for path_, type_str in zip(
                metadata["paths"], metadata["types"]
            ):
                type_ = find_type_by_str(type_str)
                materializer_class = materializer_registry[type_]
                materializer = materializer_class(uri=path_)
                element = materializer.load(type_)
                outputs.append(element)

        # New format for zenml > 0.37.0
        elif isinstance(metadata, list):
            for entry in metadata:
                path_ = entry["path"]
                type_ = source_utils.load(entry["type"])
                materializer_class = source_utils.load(
                    entry["materializer"]
                )
                materializer = materializer_class(uri=path_)
                element = materializer.load(type_)
                outputs.append(element)

        else:
            raise RuntimeError(f"Unknown metadata format: {metadata}.")

    # Cast the data to the correct type.
    if issubclass(data_type, dict) and not isinstance(outputs, dict):
        keys, values = outputs
        return data_type(zip(keys, values))
    if issubclass(data_type, tuple) and not isinstance(outputs, tuple):
        return data_type(outputs)
    if issubclass(data_type, set) and not isinstance(outputs, set):
        return data_type(outputs)
    return outputs

save(data)

Materialize a built-in container object.

If the object can be serialized to JSON, serialize it.

Otherwise, use the default_materializer_registry to find the correct materializer for each element and materialize each element into a subdirectory.

Tuples and sets are cast to list before materialization.

For non-serializable dicts, materialize keys/values as separate lists.

Parameters:

Name	Type	Description	Default
data	Any	The built-in container object to materialize.	required

Raises:

Type	Description
Exception	If any exception occurs, it is raised after cleanup.

Source code in src/zenml/materializers/built_in_materializer.py

def save(self, data: Any) -> None:
    """Materialize a built-in container object.

    If the object can be serialized to JSON, serialize it.

    Otherwise, use the `default_materializer_registry` to find the correct
    materializer for each element and materialize each element into a
    subdirectory.

    Tuples and sets are cast to list before materialization.

    For non-serializable dicts, materialize keys/values as separate lists.

    Args:
        data: The built-in container object to materialize.

    Raises:
        Exception: If any exception occurs, it is raised after cleanup.
    """
    # tuple and set: handle as list.
    if isinstance(data, tuple) or isinstance(data, set):
        data = list(data)

    # If the data is serializable, just write it into a single JSON file.
    if _is_serializable(data):
        yaml_utils.write_json(
            self.data_path,
            data,
            ensure_ascii=not ZENML_MATERIALIZER_ALLOW_NON_ASCII_JSON_DUMPS,
        )
        return

    # non-serializable dict: Handle as non-serializable list of lists.
    if isinstance(data, dict):
        data = [list(data.keys()), list(data.values())]

    # non-serializable list: Materialize each element into a subfolder.
    # Get path, type, and corresponding materializer for each element.
    metadata: List[Dict[str, str]] = []
    materializers: List[BaseMaterializer] = []
    try:
        for i, element in enumerate(data):
            element_path = os.path.join(self.uri, str(i))
            self.artifact_store.mkdir(element_path)
            type_ = type(element)
            materializer_class = materializer_registry[type_]
            materializer = materializer_class(uri=element_path)
            materializers.append(materializer)
            metadata.append(
                {
                    "path": element_path,
                    "type": source_utils.resolve(type_).import_path,
                    "materializer": source_utils.resolve(
                        materializer_class
                    ).import_path,
                }
            )
        # Write metadata as JSON.
        yaml_utils.write_json(self.metadata_path, metadata)
        # Materialize each element.
        for element, materializer in zip(data, materializers):
            materializer.validate_save_type_compatibility(type(element))
            materializer.save(element)
    # If an error occurs, delete all created files.
    except Exception as e:
        # Delete metadata
        if self.artifact_store.exists(self.metadata_path):
            self.artifact_store.remove(self.metadata_path)
        # Delete all elements that were already saved.
        for entry in metadata:
            self.artifact_store.rmtree(entry["path"])
        raise e

save_visualizations(data)

Save visualizations for the given data.

Parameters:

Name	Type	Description	Default
data	Any	The data to save visualizations for.	required

Returns:

Type	Description
Dict[str, VisualizationType]	A dictionary of visualization URIs and their types.

Source code in src/zenml/materializers/built_in_materializer.py

def save_visualizations(self, data: Any) -> Dict[str, "VisualizationType"]:
    """Save visualizations for the given data.

    Args:
        data: The data to save visualizations for.

    Returns:
        A dictionary of visualization URIs and their types.
    """
    # dict/list type objects are always saved as JSON files
    # doesn't work for non-serializable types as they
    # are saved as list of lists in different files
    if _is_serializable(data):
        return {self.data_path.replace("\\", "/"): VisualizationType.JSON}
    return {}

BuiltInMaterializer

Bases: BaseMaterializer

Handle JSON-serializable basic types (bool, float, int, str).

Source code in src/zenml/materializers/built_in_materializer.py

class BuiltInMaterializer(BaseMaterializer):
    """Handle JSON-serializable basic types (`bool`, `float`, `int`, `str`)."""

    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA
    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = BASIC_TYPES

    def __init__(
        self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
    ):
        """Define `self.data_path`.

        Args:
            uri: The URI where the artifact data is stored.
            artifact_store: The artifact store where the artifact data is stored.
        """
        super().__init__(uri, artifact_store)
        self.data_path = os.path.join(self.uri, DEFAULT_FILENAME)

    def load(
        self, data_type: Union[Type[bool], Type[float], Type[int], Type[str]]
    ) -> Any:
        """Reads basic primitive types from JSON.

        Args:
            data_type: The type of the data to read.

        Returns:
            The data read.
        """
        contents = yaml_utils.read_json(self.data_path)
        if type(contents) is not data_type:
            # TODO [ENG-142]: Raise error or try to coerce
            logger.debug(
                f"Contents {contents} was type {type(contents)} but expected "
                f"{data_type}"
            )
        return contents

    def save(self, data: Union[bool, float, int, str]) -> None:
        """Serialize a basic type to JSON.

        Args:
            data: The data to store.
        """
        yaml_utils.write_json(
            self.data_path,
            data,
            ensure_ascii=not ZENML_MATERIALIZER_ALLOW_NON_ASCII_JSON_DUMPS,
        )

    def extract_metadata(
        self, data: Union[bool, float, int, str]
    ) -> Dict[str, "MetadataType"]:
        """Extract metadata from the given built-in container object.

        Args:
            data: The built-in container object to extract metadata from.

        Returns:
            The extracted metadata as a dictionary.
        """
        # For boolean and numbers, add the string representation as metadata.
        # We don't to this for strings because they can be arbitrarily long.
        if isinstance(data, (bool, float, int)):
            return {"string_representation": str(data)}

        return {}

__init__(uri, artifact_store=None)

Define self.data_path.

Parameters:

Name	Type	Description	Default
uri	str	The URI where the artifact data is stored.	required
artifact_store	Optional[BaseArtifactStore]	The artifact store where the artifact data is stored.	None

Source code in src/zenml/materializers/built_in_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Define `self.data_path`.

    Args:
        uri: The URI where the artifact data is stored.
        artifact_store: The artifact store where the artifact data is stored.
    """
    super().__init__(uri, artifact_store)
    self.data_path = os.path.join(self.uri, DEFAULT_FILENAME)

extract_metadata(data)

Extract metadata from the given built-in container object.

Parameters:

Name	Type	Description	Default
data	Union[bool, float, int, str]	The built-in container object to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/materializers/built_in_materializer.py

def extract_metadata(
    self, data: Union[bool, float, int, str]
) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given built-in container object.

    Args:
        data: The built-in container object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    # For boolean and numbers, add the string representation as metadata.
    # We don't to this for strings because they can be arbitrarily long.
    if isinstance(data, (bool, float, int)):
        return {"string_representation": str(data)}

    return {}

load(data_type)

Reads basic primitive types from JSON.

Parameters:

Name	Type	Description	Default
data_type	Union[Type[bool], Type[float], Type[int], Type[str]]	The type of the data to read.	required

Returns:

Type	Description
Any	The data read.

Source code in src/zenml/materializers/built_in_materializer.py

def load(
    self, data_type: Union[Type[bool], Type[float], Type[int], Type[str]]
) -> Any:
    """Reads basic primitive types from JSON.

    Args:
        data_type: The type of the data to read.

    Returns:
        The data read.
    """
    contents = yaml_utils.read_json(self.data_path)
    if type(contents) is not data_type:
        # TODO [ENG-142]: Raise error or try to coerce
        logger.debug(
            f"Contents {contents} was type {type(contents)} but expected "
            f"{data_type}"
        )
    return contents

save(data)

Serialize a basic type to JSON.

Parameters:

Name	Type	Description	Default
data	Union[bool, float, int, str]	The data to store.	required

Source code in src/zenml/materializers/built_in_materializer.py

def save(self, data: Union[bool, float, int, str]) -> None:
    """Serialize a basic type to JSON.

    Args:
        data: The data to store.
    """
    yaml_utils.write_json(
        self.data_path,
        data,
        ensure_ascii=not ZENML_MATERIALIZER_ALLOW_NON_ASCII_JSON_DUMPS,
    )

BytesMaterializer

Bases: BaseMaterializer

Handle bytes data type, which is not JSON serializable.

Source code in src/zenml/materializers/built_in_materializer.py

class BytesMaterializer(BaseMaterializer):
    """Handle `bytes` data type, which is not JSON serializable."""

    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA
    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (bytes,)

    def __init__(
        self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
    ):
        """Define `self.data_path`.

        Args:
            uri: The URI where the artifact data is stored.
            artifact_store: The artifact store where the artifact data is stored.
        """
        super().__init__(uri, artifact_store)
        self.data_path = os.path.join(self.uri, DEFAULT_BYTES_FILENAME)

    def load(self, data_type: Type[Any]) -> Any:
        """Reads a bytes object from file.

        Args:
            data_type: The type of the data to read.

        Returns:
            The data read.
        """
        with self.artifact_store.open(self.data_path, "rb") as file_:
            return file_.read()

    def save(self, data: Any) -> None:
        """Save a bytes object to file.

        Args:
            data: The data to store.
        """
        with self.artifact_store.open(self.data_path, "wb") as file_:
            file_.write(data)

__init__(uri, artifact_store=None)

Define self.data_path.

Parameters:

Name	Type	Description	Default
uri	str	The URI where the artifact data is stored.	required
artifact_store	Optional[BaseArtifactStore]	The artifact store where the artifact data is stored.	None

Source code in src/zenml/materializers/built_in_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Define `self.data_path`.

    Args:
        uri: The URI where the artifact data is stored.
        artifact_store: The artifact store where the artifact data is stored.
    """
    super().__init__(uri, artifact_store)
    self.data_path = os.path.join(self.uri, DEFAULT_BYTES_FILENAME)

load(data_type)

Reads a bytes object from file.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the data to read.	required

Returns:

Type	Description
Any	The data read.

Source code in src/zenml/materializers/built_in_materializer.py

def load(self, data_type: Type[Any]) -> Any:
    """Reads a bytes object from file.

    Args:
        data_type: The type of the data to read.

    Returns:
        The data read.
    """
    with self.artifact_store.open(self.data_path, "rb") as file_:
        return file_.read()

save(data)

Save a bytes object to file.

Parameters:

Name	Type	Description	Default
data	Any	The data to store.	required

Source code in src/zenml/materializers/built_in_materializer.py

def save(self, data: Any) -> None:
    """Save a bytes object to file.

    Args:
        data: The data to store.
    """
    with self.artifact_store.open(self.data_path, "wb") as file_:
        file_.write(data)

CloudpickleMaterializer

Bases: BaseMaterializer

Materializer using cloudpickle.

This materializer can materialize (almost) any object, but does so in a non-reproducble way since artifacts cannot be loaded from other Python versions. It is recommended to use this materializer only as a last resort.

That is also why it has SKIP_REGISTRATION set to True and is currently only used as a fallback materializer inside the materializer registry.

Source code in src/zenml/materializers/cloudpickle_materializer.py

class CloudpickleMaterializer(BaseMaterializer):
    """Materializer using cloudpickle.

    This materializer can materialize (almost) any object, but does so in a
    non-reproducble way since artifacts cannot be loaded from other Python
    versions. It is recommended to use this materializer only as a last resort.

    That is also why it has `SKIP_REGISTRATION` set to True and is currently
    only used as a fallback materializer inside the materializer registry.
    """

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (object,)
    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA
    SKIP_REGISTRATION: ClassVar[bool] = True

    def load(self, data_type: Type[Any]) -> Any:
        """Reads an artifact from a cloudpickle file.

        Args:
            data_type: The data type of the artifact.

        Returns:
            The loaded artifact data.
        """
        # validate python version
        source_python_version = self._load_python_version()
        current_python_version = Environment().python_version()
        if source_python_version != current_python_version:
            logger.warning(
                f"Your artifact was materialized under Python version "
                f"'{source_python_version}' but you are currently using "
                f"'{current_python_version}'. This might cause unexpected "
                "behavior since pickle is not reproducible across Python "
                "versions. Attempting to load anyway..."
            )

        # load data
        filepath = os.path.join(self.uri, DEFAULT_FILENAME)
        with self.artifact_store.open(filepath, "rb") as fid:
            data = cloudpickle.load(fid)
        return data

    def _load_python_version(self) -> str:
        """Loads the Python version that was used to materialize the artifact.

        Returns:
            The Python version that was used to materialize the artifact.
        """
        filepath = os.path.join(self.uri, DEFAULT_PYTHON_VERSION_FILENAME)
        if os.path.exists(filepath):
            return read_file_contents_as_string(filepath)
        return "unknown"

    def save(self, data: Any) -> None:
        """Saves an artifact to a cloudpickle file.

        Args:
            data: The data to save.
        """
        # Log a warning if this materializer was not explicitly specified for
        # the given data type.
        if type(self) is CloudpickleMaterializer:
            logger.warning(
                f"No materializer is registered for type `{type(data)}`, so "
                "the default Pickle materializer was used. Pickle is not "
                "production ready and should only be used for prototyping as "
                "the artifacts cannot be loaded when running with a different "
                "Python version. Please consider implementing a custom "
                f"materializer for type `{type(data)}` according to the "
                "instructions at https://docs.zenml.io/how-to/handle-data-artifacts/handle-custom-data-types"
            )

        # save python version for validation on loading
        self._save_python_version()

        # save data
        filepath = os.path.join(self.uri, DEFAULT_FILENAME)
        with self.artifact_store.open(filepath, "wb") as fid:
            cloudpickle.dump(data, fid)

    def _save_python_version(self) -> None:
        """Saves the Python version used to materialize the artifact."""
        filepath = os.path.join(self.uri, DEFAULT_PYTHON_VERSION_FILENAME)
        current_python_version = Environment().python_version()
        write_file_contents_as_string(filepath, current_python_version)

load(data_type)

Reads an artifact from a cloudpickle file.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The data type of the artifact.	required

Returns:

Type	Description
Any	The loaded artifact data.

Source code in src/zenml/materializers/cloudpickle_materializer.py

def load(self, data_type: Type[Any]) -> Any:
    """Reads an artifact from a cloudpickle file.

    Args:
        data_type: The data type of the artifact.

    Returns:
        The loaded artifact data.
    """
    # validate python version
    source_python_version = self._load_python_version()
    current_python_version = Environment().python_version()
    if source_python_version != current_python_version:
        logger.warning(
            f"Your artifact was materialized under Python version "
            f"'{source_python_version}' but you are currently using "
            f"'{current_python_version}'. This might cause unexpected "
            "behavior since pickle is not reproducible across Python "
            "versions. Attempting to load anyway..."
        )

    # load data
    filepath = os.path.join(self.uri, DEFAULT_FILENAME)
    with self.artifact_store.open(filepath, "rb") as fid:
        data = cloudpickle.load(fid)
    return data

save(data)

Saves an artifact to a cloudpickle file.

Parameters:

Name	Type	Description	Default
data	Any	The data to save.	required

Source code in src/zenml/materializers/cloudpickle_materializer.py

def save(self, data: Any) -> None:
    """Saves an artifact to a cloudpickle file.

    Args:
        data: The data to save.
    """
    # Log a warning if this materializer was not explicitly specified for
    # the given data type.
    if type(self) is CloudpickleMaterializer:
        logger.warning(
            f"No materializer is registered for type `{type(data)}`, so "
            "the default Pickle materializer was used. Pickle is not "
            "production ready and should only be used for prototyping as "
            "the artifacts cannot be loaded when running with a different "
            "Python version. Please consider implementing a custom "
            f"materializer for type `{type(data)}` according to the "
            "instructions at https://docs.zenml.io/how-to/handle-data-artifacts/handle-custom-data-types"
        )

    # save python version for validation on loading
    self._save_python_version()

    # save data
    filepath = os.path.join(self.uri, DEFAULT_FILENAME)
    with self.artifact_store.open(filepath, "wb") as fid:
        cloudpickle.dump(data, fid)

PydanticMaterializer

Bases: BaseMaterializer

Handle Pydantic BaseModel objects.

Source code in src/zenml/materializers/pydantic_materializer.py

class PydanticMaterializer(BaseMaterializer):
    """Handle Pydantic BaseModel objects."""

    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA
    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (BaseModel,)

    def load(self, data_type: Type[BaseModel]) -> Any:
        """Reads BaseModel from JSON.

        Args:
            data_type: The type of the data to read.

        Returns:
            The data read.
        """
        data_path = os.path.join(self.uri, DEFAULT_FILENAME)
        contents = yaml_utils.read_json(data_path)
        return data_type.model_validate_json(contents)

    def save(self, data: BaseModel) -> None:
        """Serialize a BaseModel to JSON.

        Args:
            data: The data to store.
        """
        data_path = os.path.join(self.uri, DEFAULT_FILENAME)
        yaml_utils.write_json(data_path, data.model_dump_json())

    def extract_metadata(self, data: BaseModel) -> Dict[str, "MetadataType"]:
        """Extract metadata from the given BaseModel object.

        Args:
            data: The BaseModel object to extract metadata from.

        Returns:
            The extracted metadata as a dictionary.
        """
        return {"schema": data.schema()}

extract_metadata(data)

Extract metadata from the given BaseModel object.

Parameters:

Name	Type	Description	Default
data	BaseModel	The BaseModel object to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/materializers/pydantic_materializer.py

def extract_metadata(self, data: BaseModel) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given BaseModel object.

    Args:
        data: The BaseModel object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    return {"schema": data.schema()}

load(data_type)

Reads BaseModel from JSON.

Parameters:

Name	Type	Description	Default
data_type	Type[BaseModel]	The type of the data to read.	required

Returns:

Type	Description
Any	The data read.

Source code in src/zenml/materializers/pydantic_materializer.py

def load(self, data_type: Type[BaseModel]) -> Any:
    """Reads BaseModel from JSON.

    Args:
        data_type: The type of the data to read.

    Returns:
        The data read.
    """
    data_path = os.path.join(self.uri, DEFAULT_FILENAME)
    contents = yaml_utils.read_json(data_path)
    return data_type.model_validate_json(contents)

save(data)

Serialize a BaseModel to JSON.

Parameters:

Name	Type	Description	Default
data	BaseModel	The data to store.	required

Source code in src/zenml/materializers/pydantic_materializer.py

def save(self, data: BaseModel) -> None:
    """Serialize a BaseModel to JSON.

    Args:
        data: The data to store.
    """
    data_path = os.path.join(self.uri, DEFAULT_FILENAME)
    yaml_utils.write_json(data_path, data.model_dump_json())

ServiceMaterializer

Bases: BaseMaterializer

Materializer to read/write service instances.

Source code in src/zenml/materializers/service_materializer.py

class ServiceMaterializer(BaseMaterializer):
    """Materializer to read/write service instances."""

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (BaseService,)
    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.SERVICE

    def load(self, data_type: Type[Any]) -> BaseService:
        """Creates and returns a service.

        This service is instantiated from the serialized service configuration
        and last known status information saved as artifact.

        Args:
            data_type: The type of the data to read.

        Returns:
            A ZenML service instance.
        """
        filepath = os.path.join(self.uri, SERVICE_CONFIG_FILENAME)
        with self.artifact_store.open(filepath, "r") as f:
            service_id = f.read().strip()

        service = Client().get_service(name_id_or_prefix=uuid.UUID(service_id))
        return BaseDeploymentService.from_model(service)

    def save(self, service: BaseService) -> None:
        """Writes a ZenML service.

        The configuration and last known status of the input service instance
        are serialized and saved as an artifact.

        Args:
            service: A ZenML service instance.
        """
        filepath = os.path.join(self.uri, SERVICE_CONFIG_FILENAME)
        with self.artifact_store.open(filepath, "w") as f:
            f.write(str(service.uuid))

    def extract_metadata(
        self, service: BaseService
    ) -> Dict[str, "MetadataType"]:
        """Extract metadata from the given service.

        Args:
            service: The service to extract metadata from.

        Returns:
            The extracted metadata as a dictionary.
        """
        from zenml.metadata.metadata_types import Uri

        if prediction_url := service.get_prediction_url() or None:
            return {"uri": Uri(prediction_url)}
        return {}

extract_metadata(service)

Extract metadata from the given service.

Parameters:

Name	Type	Description	Default
service	BaseService	The service to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/materializers/service_materializer.py

def extract_metadata(
    self, service: BaseService
) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given service.

    Args:
        service: The service to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    from zenml.metadata.metadata_types import Uri

    if prediction_url := service.get_prediction_url() or None:
        return {"uri": Uri(prediction_url)}
    return {}

load(data_type)

Creates and returns a service.

This service is instantiated from the serialized service configuration and last known status information saved as artifact.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the data to read.	required

Returns:

Type	Description
BaseService	A ZenML service instance.

Source code in src/zenml/materializers/service_materializer.py

def load(self, data_type: Type[Any]) -> BaseService:
    """Creates and returns a service.

    This service is instantiated from the serialized service configuration
    and last known status information saved as artifact.

    Args:
        data_type: The type of the data to read.

    Returns:
        A ZenML service instance.
    """
    filepath = os.path.join(self.uri, SERVICE_CONFIG_FILENAME)
    with self.artifact_store.open(filepath, "r") as f:
        service_id = f.read().strip()

    service = Client().get_service(name_id_or_prefix=uuid.UUID(service_id))
    return BaseDeploymentService.from_model(service)

save(service)

Writes a ZenML service.

The configuration and last known status of the input service instance are serialized and saved as an artifact.

Parameters:

Name	Type	Description	Default
service	BaseService	A ZenML service instance.	required

Source code in src/zenml/materializers/service_materializer.py

def save(self, service: BaseService) -> None:
    """Writes a ZenML service.

    The configuration and last known status of the input service instance
    are serialized and saved as an artifact.

    Args:
        service: A ZenML service instance.
    """
    filepath = os.path.join(self.uri, SERVICE_CONFIG_FILENAME)
    with self.artifact_store.open(filepath, "w") as f:
        f.write(str(service.uuid))

StructuredStringMaterializer

Bases: BaseMaterializer

Materializer for HTML or Markdown strings.

Source code in src/zenml/materializers/structured_string_materializer.py

class StructuredStringMaterializer(BaseMaterializer):
    """Materializer for HTML or Markdown strings."""

    ASSOCIATED_TYPES = (CSVString, HTMLString, MarkdownString, JSONString)
    ASSOCIATED_ARTIFACT_TYPE = ArtifactType.DATA_ANALYSIS

    def load(self, data_type: Type[STRUCTURED_STRINGS]) -> STRUCTURED_STRINGS:
        """Loads the data from the HTML or Markdown file.

        Args:
            data_type: The type of the data to read.

        Returns:
            The loaded data.
        """
        with self.artifact_store.open(self._get_filepath(data_type), "r") as f:
            return data_type(f.read())

    def save(self, data: STRUCTURED_STRINGS) -> None:
        """Save data as an HTML or Markdown file.

        Args:
            data: The data to save as an HTML or Markdown file.
        """
        with self.artifact_store.open(
            self._get_filepath(type(data)), "w"
        ) as f:
            f.write(data)

    def save_visualizations(
        self, data: STRUCTURED_STRINGS
    ) -> Dict[str, VisualizationType]:
        """Save visualizations for the given data.

        Args:
            data: The data to save visualizations for.

        Returns:
            A dictionary of visualization URIs and their types.
        """
        filepath = self._get_filepath(type(data))
        filepath = filepath.replace("\\", "/")
        visualization_type = self._get_visualization_type(type(data))
        return {filepath: visualization_type}

    def _get_filepath(self, data_type: Type[STRUCTURED_STRINGS]) -> str:
        """Get the file path for the given data type.

        Args:
            data_type: The type of the data.

        Returns:
            The file path for the given data type.

        Raises:
            ValueError: If the data type is not supported.
        """
        if issubclass(data_type, CSVString):
            filename = CSV_FILENAME
        elif issubclass(data_type, HTMLString):
            filename = HTML_FILENAME
        elif issubclass(data_type, MarkdownString):
            filename = MARKDOWN_FILENAME
        elif issubclass(data_type, JSONString):
            filename = JSON_FILENAME
        else:
            raise ValueError(
                f"Data type {data_type} is not supported by this materializer."
            )
        return os.path.join(self.uri, filename)

    def _get_visualization_type(
        self, data_type: Type[STRUCTURED_STRINGS]
    ) -> VisualizationType:
        """Get the visualization type for the given data type.

        Args:
            data_type: The type of the data.

        Returns:
            The visualization type for the given data type.

        Raises:
            ValueError: If the data type is not supported.
        """
        if issubclass(data_type, CSVString):
            return VisualizationType.CSV
        elif issubclass(data_type, HTMLString):
            return VisualizationType.HTML
        elif issubclass(data_type, MarkdownString):
            return VisualizationType.MARKDOWN
        elif issubclass(data_type, JSONString):
            return VisualizationType.JSON
        else:
            raise ValueError(
                f"Data type {data_type} is not supported by this materializer."
            )

load(data_type)

Loads the data from the HTML or Markdown file.

Parameters:

Name	Type	Description	Default
data_type	Type[STRUCTURED_STRINGS]	The type of the data to read.	required

Returns:

Type	Description
STRUCTURED_STRINGS	The loaded data.

Source code in src/zenml/materializers/structured_string_materializer.py

def load(self, data_type: Type[STRUCTURED_STRINGS]) -> STRUCTURED_STRINGS:
    """Loads the data from the HTML or Markdown file.

    Args:
        data_type: The type of the data to read.

    Returns:
        The loaded data.
    """
    with self.artifact_store.open(self._get_filepath(data_type), "r") as f:
        return data_type(f.read())

save(data)

Save data as an HTML or Markdown file.

Parameters:

Name	Type	Description	Default
data	STRUCTURED_STRINGS	The data to save as an HTML or Markdown file.	required

Source code in src/zenml/materializers/structured_string_materializer.py

def save(self, data: STRUCTURED_STRINGS) -> None:
    """Save data as an HTML or Markdown file.

    Args:
        data: The data to save as an HTML or Markdown file.
    """
    with self.artifact_store.open(
        self._get_filepath(type(data)), "w"
    ) as f:
        f.write(data)

save_visualizations(data)

Save visualizations for the given data.

Parameters:

Name	Type	Description	Default
data	STRUCTURED_STRINGS	The data to save visualizations for.	required

Returns:

Type	Description
Dict[str, VisualizationType]	A dictionary of visualization URIs and their types.

Source code in src/zenml/materializers/structured_string_materializer.py

def save_visualizations(
    self, data: STRUCTURED_STRINGS
) -> Dict[str, VisualizationType]:
    """Save visualizations for the given data.

    Args:
        data: The data to save visualizations for.

    Returns:
        A dictionary of visualization URIs and their types.
    """
    filepath = self._get_filepath(type(data))
    filepath = filepath.replace("\\", "/")
    visualization_type = self._get_visualization_type(type(data))
    return {filepath: visualization_type}

UUIDMaterializer

Bases: BaseMaterializer

Materializer to handle UUID objects.

Source code in src/zenml/materializers/uuid_materializer.py

class UUIDMaterializer(BaseMaterializer):
    """Materializer to handle UUID objects."""

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (uuid.UUID,)
    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA

    def __init__(
        self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
    ):
        """Define `self.data_path`.

        Args:
            uri: The URI where the artifact data is stored.
            artifact_store: The artifact store where the artifact data is stored.
        """
        super().__init__(uri, artifact_store)
        self.data_path = os.path.join(self.uri, DEFAULT_FILENAME)

    def load(self, _: Type[uuid.UUID]) -> uuid.UUID:
        """Read UUID from artifact store.

        Args:
            _: The type of the data to be loaded.

        Returns:
            The loaded UUID.
        """
        with self.artifact_store.open(self.data_path, "r") as f:
            uuid_str = f.read().strip()
        return uuid.UUID(uuid_str)

    def save(self, data: uuid.UUID) -> None:
        """Write UUID to artifact store.

        Args:
            data: The UUID to be saved.
        """
        with self.artifact_store.open(self.data_path, "w") as f:
            f.write(str(data))

    def extract_metadata(self, data: uuid.UUID) -> Dict[str, MetadataType]:
        """Extract metadata from the UUID.

        Args:
            data: The UUID to extract metadata from.

        Returns:
            A dictionary of metadata extracted from the UUID.
        """
        return {
            "string_representation": str(data),
        }

__init__(uri, artifact_store=None)

Define self.data_path.

Parameters:

Name	Type	Description	Default
uri	str	The URI where the artifact data is stored.	required
artifact_store	Optional[BaseArtifactStore]	The artifact store where the artifact data is stored.	None

Source code in src/zenml/materializers/uuid_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Define `self.data_path`.

    Args:
        uri: The URI where the artifact data is stored.
        artifact_store: The artifact store where the artifact data is stored.
    """
    super().__init__(uri, artifact_store)
    self.data_path = os.path.join(self.uri, DEFAULT_FILENAME)

extract_metadata(data)

Extract metadata from the UUID.

Parameters:

Name	Type	Description	Default
data	UUID	The UUID to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	A dictionary of metadata extracted from the UUID.

Source code in src/zenml/materializers/uuid_materializer.py

def extract_metadata(self, data: uuid.UUID) -> Dict[str, MetadataType]:
    """Extract metadata from the UUID.

    Args:
        data: The UUID to extract metadata from.

    Returns:
        A dictionary of metadata extracted from the UUID.
    """
    return {
        "string_representation": str(data),
    }

load(_)

Read UUID from artifact store.

Parameters:

Name	Type	Description	Default
_	Type[UUID]	The type of the data to be loaded.	required

Returns:

Type	Description
UUID	The loaded UUID.

Source code in src/zenml/materializers/uuid_materializer.py

def load(self, _: Type[uuid.UUID]) -> uuid.UUID:
    """Read UUID from artifact store.

    Args:
        _: The type of the data to be loaded.

    Returns:
        The loaded UUID.
    """
    with self.artifact_store.open(self.data_path, "r") as f:
        uuid_str = f.read().strip()
    return uuid.UUID(uuid_str)

save(data)

Write UUID to artifact store.

Parameters:

Name	Type	Description	Default
data	UUID	The UUID to be saved.	required

Source code in src/zenml/materializers/uuid_materializer.py

def save(self, data: uuid.UUID) -> None:
    """Write UUID to artifact store.

    Args:
        data: The UUID to be saved.
    """
    with self.artifact_store.open(self.data_path, "w") as f:
        f.write(str(data))

Metadata

Initialization of ZenML metadata.

ZenML metadata is any additional, dynamic information that is associated with your pipeline runs and artifacts at runtime.

Model Deployers

Model deployers are stack components responsible for online model serving.

Online serving is the process of hosting and loading machine-learning models as part of a managed web service and providing access to the models through an API endpoint like HTTP or GRPC. Once deployed, you can send inference requests to the model through the web service's API and receive fast, low-latency responses.

Add a model deployer to your ZenML stack to be able to implement continuous model deployment pipelines that train models and continuously deploy them to a model prediction web service.

When present in a stack, the model deployer also acts as a registry for models that are served with ZenML. You can use the model deployer to list all models that are currently deployed for online inference or filtered according to a particular pipeline run or step, or to suspend, resume or delete an external model server managed through ZenML.

BaseModelDeployer

Bases: StackComponent, ABC

Base class for all ZenML model deployers.

The model deployer serves three major purposes:

1. It contains all the stack related configuration attributes required to interact with the remote model serving tool, service or platform (e.g. hostnames, URLs, references to credentials, other client related configuration parameters).

2. It implements the continuous deployment logic necessary to deploy models in a way that updates an existing model server that is already serving a previous version of the same model instead of creating a new model server for every new model version (see the deploy_model abstract method). This functionality can be consumed directly from ZenML pipeline steps, but it can also be used outside the pipeline to deploy ad hoc models. It is also usually coupled with a standard model deployer step, implemented by each integration, that hides the details of the deployment process away from the user.

3. It acts as a ZenML BaseService registry, where every BaseService instance is used as an internal representation of a remote model server (see the find_model_server abstract method). To achieve this, it must be able to re-create the configuration of a BaseService from information that is persisted externally, alongside or even part of the remote model server configuration itself. For example, for model servers that are implemented as Kubernetes resources, the BaseService instances can be serialized and saved as Kubernetes resource annotations. This allows the model deployer to keep track of all externally running model servers and to re-create their corresponding BaseService instance representations at any given time. The model deployer also defines methods that implement basic life-cycle management on remote model servers outside the coverage of a pipeline (see stop_model_server, start_model_server and delete_model_server).

Source code in src/zenml/model_deployers/base_model_deployer.py

class BaseModelDeployer(StackComponent, ABC):
    """Base class for all ZenML model deployers.

    The model deployer serves three major purposes:

    1. It contains all the stack related configuration attributes required to
    interact with the remote model serving tool, service or platform (e.g.
    hostnames, URLs, references to credentials, other client related
    configuration parameters).

    2. It implements the continuous deployment logic necessary to deploy models
    in a way that updates an existing model server that is already serving a
    previous version of the same model instead of creating a new model server
    for every new model version (see the `deploy_model` abstract method).
    This functionality can be consumed directly from ZenML pipeline steps, but
    it can also be used outside the pipeline to deploy ad hoc models. It is
    also usually coupled with a standard model deployer step, implemented by
    each integration, that hides the details of the deployment process away from
    the user.

    3. It acts as a ZenML BaseService registry, where every BaseService instance
    is used as an internal representation of a remote model server (see the
    `find_model_server` abstract method). To achieve this, it must be able to
    re-create the configuration of a BaseService from information that is
    persisted externally, alongside or even part of the remote model server
    configuration itself. For example, for model servers that are implemented as
    Kubernetes resources, the BaseService instances can be serialized and saved
    as Kubernetes resource annotations. This allows the model deployer to keep
    track of all externally running model servers and to re-create their
    corresponding BaseService instance representations at any given time.
    The model deployer also defines methods that implement basic life-cycle
    management on remote model servers outside the coverage of a pipeline
    (see `stop_model_server`, `start_model_server` and `delete_model_server`).
    """

    NAME: ClassVar[str]
    FLAVOR: ClassVar[Type["BaseModelDeployerFlavor"]]

    @property
    def config(self) -> BaseModelDeployerConfig:
        """Returns the `BaseModelDeployerConfig` config.

        Returns:
            The configuration.
        """
        return cast(BaseModelDeployerConfig, self._config)

    @classmethod
    def get_active_model_deployer(cls) -> "BaseModelDeployer":
        """Get the model deployer registered in the active stack.

        Returns:
            The model deployer registered in the active stack.

        Raises:
            TypeError: if a model deployer is not part of the
                active stack.
        """
        flavor: BaseModelDeployerFlavor = cls.FLAVOR()
        client = Client()
        model_deployer = client.active_stack.model_deployer
        if not model_deployer or not isinstance(model_deployer, cls):
            raise TypeError(
                f"The active stack needs to have a {cls.NAME} model "
                f"deployer component registered to be able deploy models "
                f"with {cls.NAME}. You can create a new stack with "
                f"a {cls.NAME} model deployer component or update your "
                f"active stack to add this component, e.g.:\n\n"
                f"  `zenml model-deployer register {flavor.name} "
                f"--flavor={flavor.name} ...`\n"
                f"  `zenml stack register <STACK-NAME> -d {flavor.name} ...`\n"
                f"  or:\n"
                f"  `zenml stack update -d {flavor.name}`\n\n"
            )

        return model_deployer

    def deploy_model(
        self,
        config: ServiceConfig,
        service_type: ServiceType,
        replace: bool = False,
        continuous_deployment_mode: bool = False,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    ) -> BaseService:
        """Deploy a model.

        the deploy_model method is the main entry point for deploying models
        using the model deployer. It is used to deploy a model to a model server
        instance that is running on a remote serving platform or service. The
        method is responsible for detecting if there is an existing model server
        instance running serving one or more previous versions of the same model
        and deploying the model to the serving platform or updating the existing
        model server instance to include the new model version. The method
        returns a Service object that is a representation of the external model
        server instance. The Service object must implement basic operational
        state tracking and lifecycle management operations for the model server
        (e.g. start, stop, etc.).

        Args:
            config: Custom Service configuration parameters for the model
                deployer. Can include the pipeline name, the run id, the step
                name, the model name, the model uri, the model type etc.
            replace: If True, it will replace any existing model server instances
                that serve the same model. If False, it does not replace any
                existing model server instance.
            continuous_deployment_mode: If True, it will replace any existing
                model server instances that serve the same model, regardless of
                the configuration. If False, it will only replace existing model
                server instances that serve the same model if the configuration
                is exactly the same.
            timeout: The maximum time in seconds to wait for the model server
                to start serving the model.
            service_type: The type of the service to deploy. If not provided,
                the default service type of the model deployer will be used.

        Raises:
            RuntimeError: if the model deployment fails.

        Returns:
            The deployment Service object.
        """
        # Instantiate the client
        client = Client()
        if not continuous_deployment_mode:
            # Find existing model server
            services = self.find_model_server(
                config=config.model_dump(),
                service_type=service_type,
            )
            if len(services) > 0:
                logger.info(
                    f"Existing model server found for {config.name or config.model_name} with the exact same configuration. Returning the existing service named {services[0].config.service_name}."
                )
                return services[0]
        else:
            # Find existing model server
            services = self.find_model_server(
                pipeline_name=config.pipeline_name,
                pipeline_step_name=config.pipeline_step_name,
                model_name=config.model_name,
                service_type=service_type,
            )
            if len(services) > 0:
                logger.info(
                    f"Existing model server found for {config.pipeline_name} and {config.pipeline_step_name}, since continuous deployment mode is enabled, replacing the existing service named {services[0].config.service_name}."
                )
                service = services[0]
                self.delete_model_server(service.uuid)
        logger.info(
            f"Deploying model server for {config.model_name} with the following configuration: {config.model_dump()}"
        )
        service_response = client.create_service(
            config=config,
            service_type=service_type,
            model_version_id=get_model_version_id_if_exists(
                config.model_name, config.model_version
            ),
        )
        try:
            service = self.perform_deploy_model(
                id=service_response.id,
                config=config,
                timeout=timeout,
            )
        except Exception as e:
            client.delete_service(service_response.id)
            raise RuntimeError(
                f"Failed to deploy model server for {config.model_name}: {e}"
            ) from e
        # Update the service in store
        client.update_service(
            id=service.uuid,
            name=service.config.service_name,
            service_source=service.model_dump().get("type"),
            admin_state=service.admin_state,
            status=service.status.model_dump(),
            endpoint=service.endpoint.model_dump()
            if service.endpoint
            else None,
            # labels=service.config.get_service_labels()  # TODO: fix labels in services and config
            prediction_url=service.get_prediction_url(),
            health_check_url=service.get_healthcheck_url(),
        )
        return service

    @abstractmethod
    def perform_deploy_model(
        self,
        id: UUID,
        config: ServiceConfig,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    ) -> BaseService:
        """Abstract method to deploy a model.

        Concrete model deployer subclasses must implement the following
        functionality in this method:
        - Detect if there is an existing model server instance running serving
        one or more previous versions of the same model
        - Deploy the model to the serving platform or update the existing model
        server instance to include the new model version
        - Return a Service object that is a representation of the external model
        server instance. The Service must implement basic operational state
        tracking and lifecycle management operations for the model server (e.g.
        start, stop, etc.)

        Args:
            id: UUID of the service that was originally used to deploy the model.
            config: Custom Service configuration parameters for the model
                deployer. Can include the pipeline name, the run id, the step
                name, the model name, the model uri, the model type etc.
            timeout: The maximum time in seconds to wait for the model server
                to start serving the model.

        Returns:
            The deployment Service object.
        """

    @staticmethod
    @abstractmethod
    def get_model_server_info(
        service: BaseService,
    ) -> Dict[str, Optional[str]]:
        """Give implementation specific way to extract relevant model server properties for the user.

        Args:
            service: Integration-specific service instance

        Returns:
            A dictionary containing the relevant model server properties.
        """

    def find_model_server(
        self,
        config: Optional[Dict[str, Any]] = None,
        running: Optional[bool] = None,
        service_uuid: Optional[UUID] = None,
        pipeline_name: Optional[str] = None,
        pipeline_step_name: Optional[str] = None,
        service_name: Optional[str] = None,
        model_name: Optional[str] = None,
        model_version: Optional[str] = None,
        service_type: Optional[ServiceType] = None,
        type: Optional[str] = None,
        flavor: Optional[str] = None,
        pipeline_run_id: Optional[str] = None,
    ) -> List[BaseService]:
        """Abstract method to find one or more a model servers that match the given criteria.

        Args:
            running: If true, only running services will be returned.
            service_uuid: The UUID of the service that was originally used
                to deploy the model.
            pipeline_step_name: The name of the pipeline step that was originally used
                to deploy the model.
            pipeline_name: The name of the pipeline that was originally used to deploy
                the model from the model registry.
            model_name: The name of the model that was originally used to deploy
                the model from the model registry.
            model_version: The version of the model that was originally used to
                deploy the model from the model registry.
            service_type: The type of the service to find.
            type: The type of the service to find.
            flavor: The flavor of the service to find.
            pipeline_run_id: The UUID of the pipeline run that was originally used
                to deploy the model.
            config: Custom Service configuration parameters for the model
                deployer. Can include the pipeline name, the run id, the step
                name, the model name, the model uri, the model type etc.
            service_name: The name of the service to find.

        Returns:
            One or more Service objects representing model servers that match
            the input search criteria.
        """
        client = Client()
        service_responses = client.list_services(
            sort_by="desc:created",
            id=service_uuid,
            running=running,
            service_name=service_name,
            pipeline_name=pipeline_name,
            pipeline_step_name=pipeline_step_name,
            model_version_id=get_model_version_id_if_exists(
                model_name, model_version
            ),
            pipeline_run_id=pipeline_run_id,
            config=config,
            type=type or service_type.type if service_type else None,
            flavor=flavor or service_type.flavor if service_type else None,
            hydrate=True,
        )
        services = []
        for service_response in service_responses.items:
            if not service_response.service_source:
                client.delete_service(service_response.id)
                continue
            service = BaseDeploymentService.from_model(service_response)
            service.update_status()
            if service.status.model_dump() != service_response.status:
                client.update_service(
                    id=service.uuid,
                    admin_state=service.admin_state,
                    status=service.status.model_dump(),
                    endpoint=service.endpoint.model_dump()
                    if service.endpoint
                    else None,
                )
            if running and not service.is_running:
                logger.warning(
                    f"Service {service.uuid} is in an unexpected state. "
                    f"Expected running={running}, but found running={service.is_running}."
                )
                continue
            services.append(service)
        return services

    @abstractmethod
    def perform_stop_model(
        self,
        service: BaseService,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
        force: bool = False,
    ) -> BaseService:
        """Abstract method to stop a model server.

        This operation should be reversible. A stopped model server should still
        show up in the list of model servers returned by `find_model_server` and
        it should be possible to start it again by calling `start_model_server`.

        Args:
            service: The service to stop.
            timeout: timeout in seconds to wait for the service to stop. If
                set to 0, the method will return immediately after
                deprovisioning the service, without waiting for it to stop.
            force: if True, force the service to stop.
        """

    def stop_model_server(
        self,
        uuid: UUID,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
        force: bool = False,
    ) -> None:
        """Abstract method to stop a model server.

        This operation should be reversible. A stopped model server should still
        show up in the list of model servers returned by `find_model_server` and
        it should be possible to start it again by calling `start_model_server`.

        Args:
            uuid: UUID of the model server to stop.
            timeout: timeout in seconds to wait for the service to stop. If
                set to 0, the method will return immediately after
                deprovisioning the service, without waiting for it to stop.
            force: if True, force the service to stop.

        Raises:
            RuntimeError: if the model server is not found.
        """
        client = Client()
        try:
            service = self.find_model_server(service_uuid=uuid)[0]
            updated_service = self.perform_stop_model(service, timeout, force)
            client.update_service(
                id=updated_service.uuid,
                admin_state=updated_service.admin_state,
                status=updated_service.status.model_dump(),
                endpoint=updated_service.endpoint.model_dump()
                if updated_service.endpoint
                else None,
            )
        except Exception as e:
            raise RuntimeError(
                f"Failed to stop model server with UUID {uuid}: {e}"
            ) from e

    @abstractmethod
    def perform_start_model(
        self,
        service: BaseService,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    ) -> BaseService:
        """Abstract method to start a model server.

        Args:
            service: The service to start.
            timeout: timeout in seconds to wait for the service to start. If
                set to 0, the method will return immediately after
                provisioning the service, without waiting for it to become
                active.
        """

    def start_model_server(
        self,
        uuid: UUID,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    ) -> None:
        """Abstract method to start a model server.

        Args:
            uuid: UUID of the model server to start.
            timeout: timeout in seconds to wait for the service to start. If
                set to 0, the method will return immediately after
                provisioning the service, without waiting for it to become
                active.

        Raises:
            RuntimeError: if the model server is not found.
        """
        client = Client()
        try:
            service = self.find_model_server(service_uuid=uuid)[0]
            updated_service = self.perform_start_model(service, timeout)
            client.update_service(
                id=updated_service.uuid,
                admin_state=updated_service.admin_state,
                status=updated_service.status.model_dump(),
                endpoint=updated_service.endpoint.model_dump()
                if updated_service.endpoint
                else None,
            )
        except Exception as e:
            raise RuntimeError(
                f"Failed to start model server with UUID {uuid}: {e}"
            ) from e

    @abstractmethod
    def perform_delete_model(
        self,
        service: BaseService,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
        force: bool = False,
    ) -> None:
        """Abstract method to delete a model server.

        This operation is irreversible. A deleted model server must no longer
        show up in the list of model servers returned by `find_model_server`.

        Args:
            service: The service to delete.
            timeout: timeout in seconds to wait for the service to stop. If
                set to 0, the method will return immediately after
                deprovisioning the service, without waiting for it to stop.
            force: if True, force the service to stop.
        """

    def delete_model_server(
        self,
        uuid: UUID,
        timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
        force: bool = False,
    ) -> None:
        """Abstract method to delete a model server.

        This operation is irreversible. A deleted model server must no longer
        show up in the list of model servers returned by `find_model_server`.

        Args:
            uuid: UUID of the model server to stop.
            timeout: timeout in seconds to wait for the service to stop. If
                set to 0, the method will return immediately after
                deprovisioning the service, without waiting for it to stop.
            force: if True, force the service to stop.

        Raises:
            RuntimeError: if the model server is not found.
        """
        client = Client()
        try:
            service = self.find_model_server(service_uuid=uuid)[0]
            self.perform_delete_model(service, timeout, force)
            client.delete_service(uuid)
        except Exception as e:
            raise RuntimeError(
                f"Failed to delete model server with UUID {uuid}: {e}"
            ) from e

    def get_model_server_logs(
        self,
        uuid: UUID,
        follow: bool = False,
        tail: Optional[int] = None,
    ) -> Generator[str, bool, None]:
        """Get the logs of a model server.

        Args:
            uuid: UUID of the model server to get the logs of.
            follow: if True, the logs will be streamed as they are written
            tail: only retrieve the last NUM lines of log output.

        Returns:
            A generator that yields the logs of the model server.

        Raises:
            RuntimeError: if the model server is not found.
        """
        services = self.find_model_server(service_uuid=uuid)
        if len(services) == 0:
            raise RuntimeError(f"No model server found with UUID {uuid}")
        return services[0].get_logs(follow=follow, tail=tail)

    def load_service(
        self,
        service_id: UUID,
    ) -> BaseService:
        """Load a service from a URI.

        Args:
            service_id: The ID of the service to load.

        Returns:
            The loaded service.
        """
        client = Client()
        service = client.get_service(service_id)
        return BaseDeploymentService.from_model(service)

config property

Returns the BaseModelDeployerConfig config.

Returns:

Type	Description
BaseModelDeployerConfig	The configuration.

delete_model_server(uuid, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False)

Abstract method to delete a model server.

This operation is irreversible. A deleted model server must no longer show up in the list of model servers returned by find_model_server.

Parameters:

Name	Type	Description	Default
uuid	UUID	UUID of the model server to stop.	required
timeout	int	timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
force	bool	if True, force the service to stop.	False

Raises:

Type	Description
RuntimeError	if the model server is not found.

Source code in src/zenml/model_deployers/base_model_deployer.py

def delete_model_server(
    self,
    uuid: UUID,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    force: bool = False,
) -> None:
    """Abstract method to delete a model server.

    This operation is irreversible. A deleted model server must no longer
    show up in the list of model servers returned by `find_model_server`.

    Args:
        uuid: UUID of the model server to stop.
        timeout: timeout in seconds to wait for the service to stop. If
            set to 0, the method will return immediately after
            deprovisioning the service, without waiting for it to stop.
        force: if True, force the service to stop.

    Raises:
        RuntimeError: if the model server is not found.
    """
    client = Client()
    try:
        service = self.find_model_server(service_uuid=uuid)[0]
        self.perform_delete_model(service, timeout, force)
        client.delete_service(uuid)
    except Exception as e:
        raise RuntimeError(
            f"Failed to delete model server with UUID {uuid}: {e}"
        ) from e

deploy_model(config, service_type, replace=False, continuous_deployment_mode=False, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT)

Deploy a model.

the deploy_model method is the main entry point for deploying models using the model deployer. It is used to deploy a model to a model server instance that is running on a remote serving platform or service. The method is responsible for detecting if there is an existing model server instance running serving one or more previous versions of the same model and deploying the model to the serving platform or updating the existing model server instance to include the new model version. The method returns a Service object that is a representation of the external model server instance. The Service object must implement basic operational state tracking and lifecycle management operations for the model server (e.g. start, stop, etc.).

Parameters:

Name	Type	Description	Default
config	ServiceConfig	Custom Service configuration parameters for the model deployer. Can include the pipeline name, the run id, the step name, the model name, the model uri, the model type etc.	required
replace	bool	If True, it will replace any existing model server instances that serve the same model. If False, it does not replace any existing model server instance.	False
continuous_deployment_mode	bool	If True, it will replace any existing model server instances that serve the same model, regardless of the configuration. If False, it will only replace existing model server instances that serve the same model if the configuration is exactly the same.	False
timeout	int	The maximum time in seconds to wait for the model server to start serving the model.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
service_type	ServiceType	The type of the service to deploy. If not provided, the default service type of the model deployer will be used.	required

Raises:

Type	Description
RuntimeError	if the model deployment fails.

Returns:

Type	Description
BaseService	The deployment Service object.

Source code in src/zenml/model_deployers/base_model_deployer.py

def deploy_model(
    self,
    config: ServiceConfig,
    service_type: ServiceType,
    replace: bool = False,
    continuous_deployment_mode: bool = False,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
) -> BaseService:
    """Deploy a model.

    the deploy_model method is the main entry point for deploying models
    using the model deployer. It is used to deploy a model to a model server
    instance that is running on a remote serving platform or service. The
    method is responsible for detecting if there is an existing model server
    instance running serving one or more previous versions of the same model
    and deploying the model to the serving platform or updating the existing
    model server instance to include the new model version. The method
    returns a Service object that is a representation of the external model
    server instance. The Service object must implement basic operational
    state tracking and lifecycle management operations for the model server
    (e.g. start, stop, etc.).

    Args:
        config: Custom Service configuration parameters for the model
            deployer. Can include the pipeline name, the run id, the step
            name, the model name, the model uri, the model type etc.
        replace: If True, it will replace any existing model server instances
            that serve the same model. If False, it does not replace any
            existing model server instance.
        continuous_deployment_mode: If True, it will replace any existing
            model server instances that serve the same model, regardless of
            the configuration. If False, it will only replace existing model
            server instances that serve the same model if the configuration
            is exactly the same.
        timeout: The maximum time in seconds to wait for the model server
            to start serving the model.
        service_type: The type of the service to deploy. If not provided,
            the default service type of the model deployer will be used.

    Raises:
        RuntimeError: if the model deployment fails.

    Returns:
        The deployment Service object.
    """
    # Instantiate the client
    client = Client()
    if not continuous_deployment_mode:
        # Find existing model server
        services = self.find_model_server(
            config=config.model_dump(),
            service_type=service_type,
        )
        if len(services) > 0:
            logger.info(
                f"Existing model server found for {config.name or config.model_name} with the exact same configuration. Returning the existing service named {services[0].config.service_name}."
            )
            return services[0]
    else:
        # Find existing model server
        services = self.find_model_server(
            pipeline_name=config.pipeline_name,
            pipeline_step_name=config.pipeline_step_name,
            model_name=config.model_name,
            service_type=service_type,
        )
        if len(services) > 0:
            logger.info(
                f"Existing model server found for {config.pipeline_name} and {config.pipeline_step_name}, since continuous deployment mode is enabled, replacing the existing service named {services[0].config.service_name}."
            )
            service = services[0]
            self.delete_model_server(service.uuid)
    logger.info(
        f"Deploying model server for {config.model_name} with the following configuration: {config.model_dump()}"
    )
    service_response = client.create_service(
        config=config,
        service_type=service_type,
        model_version_id=get_model_version_id_if_exists(
            config.model_name, config.model_version
        ),
    )
    try:
        service = self.perform_deploy_model(
            id=service_response.id,
            config=config,
            timeout=timeout,
        )
    except Exception as e:
        client.delete_service(service_response.id)
        raise RuntimeError(
            f"Failed to deploy model server for {config.model_name}: {e}"
        ) from e
    # Update the service in store
    client.update_service(
        id=service.uuid,
        name=service.config.service_name,
        service_source=service.model_dump().get("type"),
        admin_state=service.admin_state,
        status=service.status.model_dump(),
        endpoint=service.endpoint.model_dump()
        if service.endpoint
        else None,
        # labels=service.config.get_service_labels()  # TODO: fix labels in services and config
        prediction_url=service.get_prediction_url(),
        health_check_url=service.get_healthcheck_url(),
    )
    return service

find_model_server(config=None, running=None, service_uuid=None, pipeline_name=None, pipeline_step_name=None, service_name=None, model_name=None, model_version=None, service_type=None, type=None, flavor=None, pipeline_run_id=None)

Abstract method to find one or more a model servers that match the given criteria.

Parameters:

Name	Type	Description	Default
running	Optional[bool]	If true, only running services will be returned.	None
service_uuid	Optional[UUID]	The UUID of the service that was originally used to deploy the model.	None
pipeline_step_name	Optional[str]	The name of the pipeline step that was originally used to deploy the model.	None
pipeline_name	Optional[str]	The name of the pipeline that was originally used to deploy the model from the model registry.	None
model_name	Optional[str]	The name of the model that was originally used to deploy the model from the model registry.	None
model_version	Optional[str]	The version of the model that was originally used to deploy the model from the model registry.	None
service_type	Optional[ServiceType]	The type of the service to find.	None
type	Optional[str]	The type of the service to find.	None
flavor	Optional[str]	The flavor of the service to find.	None
pipeline_run_id	Optional[str]	The UUID of the pipeline run that was originally used to deploy the model.	None
config	Optional[Dict[str, Any]]	Custom Service configuration parameters for the model deployer. Can include the pipeline name, the run id, the step name, the model name, the model uri, the model type etc.	None
service_name	Optional[str]	The name of the service to find.	None

Returns:

Type	Description
List[BaseService]	One or more Service objects representing model servers that match
List[BaseService]	the input search criteria.

Source code in src/zenml/model_deployers/base_model_deployer.py

def find_model_server(
    self,
    config: Optional[Dict[str, Any]] = None,
    running: Optional[bool] = None,
    service_uuid: Optional[UUID] = None,
    pipeline_name: Optional[str] = None,
    pipeline_step_name: Optional[str] = None,
    service_name: Optional[str] = None,
    model_name: Optional[str] = None,
    model_version: Optional[str] = None,
    service_type: Optional[ServiceType] = None,
    type: Optional[str] = None,
    flavor: Optional[str] = None,
    pipeline_run_id: Optional[str] = None,
) -> List[BaseService]:
    """Abstract method to find one or more a model servers that match the given criteria.

    Args:
        running: If true, only running services will be returned.
        service_uuid: The UUID of the service that was originally used
            to deploy the model.
        pipeline_step_name: The name of the pipeline step that was originally used
            to deploy the model.
        pipeline_name: The name of the pipeline that was originally used to deploy
            the model from the model registry.
        model_name: The name of the model that was originally used to deploy
            the model from the model registry.
        model_version: The version of the model that was originally used to
            deploy the model from the model registry.
        service_type: The type of the service to find.
        type: The type of the service to find.
        flavor: The flavor of the service to find.
        pipeline_run_id: The UUID of the pipeline run that was originally used
            to deploy the model.
        config: Custom Service configuration parameters for the model
            deployer. Can include the pipeline name, the run id, the step
            name, the model name, the model uri, the model type etc.
        service_name: The name of the service to find.

    Returns:
        One or more Service objects representing model servers that match
        the input search criteria.
    """
    client = Client()
    service_responses = client.list_services(
        sort_by="desc:created",
        id=service_uuid,
        running=running,
        service_name=service_name,
        pipeline_name=pipeline_name,
        pipeline_step_name=pipeline_step_name,
        model_version_id=get_model_version_id_if_exists(
            model_name, model_version
        ),
        pipeline_run_id=pipeline_run_id,
        config=config,
        type=type or service_type.type if service_type else None,
        flavor=flavor or service_type.flavor if service_type else None,
        hydrate=True,
    )
    services = []
    for service_response in service_responses.items:
        if not service_response.service_source:
            client.delete_service(service_response.id)
            continue
        service = BaseDeploymentService.from_model(service_response)
        service.update_status()
        if service.status.model_dump() != service_response.status:
            client.update_service(
                id=service.uuid,
                admin_state=service.admin_state,
                status=service.status.model_dump(),
                endpoint=service.endpoint.model_dump()
                if service.endpoint
                else None,
            )
        if running and not service.is_running:
            logger.warning(
                f"Service {service.uuid} is in an unexpected state. "
                f"Expected running={running}, but found running={service.is_running}."
            )
            continue
        services.append(service)
    return services

get_active_model_deployer() classmethod

Get the model deployer registered in the active stack.

Returns:

Type	Description
BaseModelDeployer	The model deployer registered in the active stack.

Raises:

Type	Description
TypeError	if a model deployer is not part of the active stack.

Source code in src/zenml/model_deployers/base_model_deployer.py

@classmethod
def get_active_model_deployer(cls) -> "BaseModelDeployer":
    """Get the model deployer registered in the active stack.

    Returns:
        The model deployer registered in the active stack.

    Raises:
        TypeError: if a model deployer is not part of the
            active stack.
    """
    flavor: BaseModelDeployerFlavor = cls.FLAVOR()
    client = Client()
    model_deployer = client.active_stack.model_deployer
    if not model_deployer or not isinstance(model_deployer, cls):
        raise TypeError(
            f"The active stack needs to have a {cls.NAME} model "
            f"deployer component registered to be able deploy models "
            f"with {cls.NAME}. You can create a new stack with "
            f"a {cls.NAME} model deployer component or update your "
            f"active stack to add this component, e.g.:\n\n"
            f"  `zenml model-deployer register {flavor.name} "
            f"--flavor={flavor.name} ...`\n"
            f"  `zenml stack register <STACK-NAME> -d {flavor.name} ...`\n"
            f"  or:\n"
            f"  `zenml stack update -d {flavor.name}`\n\n"
        )

    return model_deployer

get_model_server_info(service) abstractmethod staticmethod

Give implementation specific way to extract relevant model server properties for the user.

Parameters:

Name	Type	Description	Default
service	BaseService	Integration-specific service instance	required

Returns:

Type	Description
Dict[str, Optional[str]]	A dictionary containing the relevant model server properties.

Source code in src/zenml/model_deployers/base_model_deployer.py

@staticmethod
@abstractmethod
def get_model_server_info(
    service: BaseService,
) -> Dict[str, Optional[str]]:
    """Give implementation specific way to extract relevant model server properties for the user.

    Args:
        service: Integration-specific service instance

    Returns:
        A dictionary containing the relevant model server properties.
    """

get_model_server_logs(uuid, follow=False, tail=None)

Get the logs of a model server.

Parameters:

Name	Type	Description	Default
uuid	UUID	UUID of the model server to get the logs of.	required
follow	bool	if True, the logs will be streamed as they are written	False
tail	Optional[int]	only retrieve the last NUM lines of log output.	None

Returns:

Type	Description
None	A generator that yields the logs of the model server.

Raises:

Type	Description
RuntimeError	if the model server is not found.

Source code in src/zenml/model_deployers/base_model_deployer.py

def get_model_server_logs(
    self,
    uuid: UUID,
    follow: bool = False,
    tail: Optional[int] = None,
) -> Generator[str, bool, None]:
    """Get the logs of a model server.

    Args:
        uuid: UUID of the model server to get the logs of.
        follow: if True, the logs will be streamed as they are written
        tail: only retrieve the last NUM lines of log output.

    Returns:
        A generator that yields the logs of the model server.

    Raises:
        RuntimeError: if the model server is not found.
    """
    services = self.find_model_server(service_uuid=uuid)
    if len(services) == 0:
        raise RuntimeError(f"No model server found with UUID {uuid}")
    return services[0].get_logs(follow=follow, tail=tail)

load_service(service_id)

Load a service from a URI.

Parameters:

Name	Type	Description	Default
service_id	UUID	The ID of the service to load.	required

Returns:

Type	Description
BaseService	The loaded service.

Source code in src/zenml/model_deployers/base_model_deployer.py

def load_service(
    self,
    service_id: UUID,
) -> BaseService:
    """Load a service from a URI.

    Args:
        service_id: The ID of the service to load.

    Returns:
        The loaded service.
    """
    client = Client()
    service = client.get_service(service_id)
    return BaseDeploymentService.from_model(service)

perform_delete_model(service, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False) abstractmethod

Abstract method to delete a model server.

This operation is irreversible. A deleted model server must no longer show up in the list of model servers returned by find_model_server.

Parameters:

Name	Type	Description	Default
service	BaseService	The service to delete.	required
timeout	int	timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
force	bool	if True, force the service to stop.	False

Source code in src/zenml/model_deployers/base_model_deployer.py

@abstractmethod
def perform_delete_model(
    self,
    service: BaseService,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    force: bool = False,
) -> None:
    """Abstract method to delete a model server.

    This operation is irreversible. A deleted model server must no longer
    show up in the list of model servers returned by `find_model_server`.

    Args:
        service: The service to delete.
        timeout: timeout in seconds to wait for the service to stop. If
            set to 0, the method will return immediately after
            deprovisioning the service, without waiting for it to stop.
        force: if True, force the service to stop.
    """

perform_deploy_model(id, config, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT) abstractmethod

Abstract method to deploy a model.

Concrete model deployer subclasses must implement the following functionality in this method: - Detect if there is an existing model server instance running serving one or more previous versions of the same model - Deploy the model to the serving platform or update the existing model server instance to include the new model version - Return a Service object that is a representation of the external model server instance. The Service must implement basic operational state tracking and lifecycle management operations for the model server (e.g. start, stop, etc.)

Parameters:

Name	Type	Description	Default
id	UUID	UUID of the service that was originally used to deploy the model.	required
config	ServiceConfig	Custom Service configuration parameters for the model deployer. Can include the pipeline name, the run id, the step name, the model name, the model uri, the model type etc.	required
timeout	int	The maximum time in seconds to wait for the model server to start serving the model.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT

Returns:

Type	Description
BaseService	The deployment Service object.

Source code in src/zenml/model_deployers/base_model_deployer.py

@abstractmethod
def perform_deploy_model(
    self,
    id: UUID,
    config: ServiceConfig,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
) -> BaseService:
    """Abstract method to deploy a model.

    Concrete model deployer subclasses must implement the following
    functionality in this method:
    - Detect if there is an existing model server instance running serving
    one or more previous versions of the same model
    - Deploy the model to the serving platform or update the existing model
    server instance to include the new model version
    - Return a Service object that is a representation of the external model
    server instance. The Service must implement basic operational state
    tracking and lifecycle management operations for the model server (e.g.
    start, stop, etc.)

    Args:
        id: UUID of the service that was originally used to deploy the model.
        config: Custom Service configuration parameters for the model
            deployer. Can include the pipeline name, the run id, the step
            name, the model name, the model uri, the model type etc.
        timeout: The maximum time in seconds to wait for the model server
            to start serving the model.

    Returns:
        The deployment Service object.
    """

perform_start_model(service, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT) abstractmethod

Abstract method to start a model server.

Parameters:

Name	Type	Description	Default
service	BaseService	The service to start.	required
timeout	int	timeout in seconds to wait for the service to start. If set to 0, the method will return immediately after provisioning the service, without waiting for it to become active.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT

Source code in src/zenml/model_deployers/base_model_deployer.py

@abstractmethod
def perform_start_model(
    self,
    service: BaseService,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
) -> BaseService:
    """Abstract method to start a model server.

    Args:
        service: The service to start.
        timeout: timeout in seconds to wait for the service to start. If
            set to 0, the method will return immediately after
            provisioning the service, without waiting for it to become
            active.
    """

perform_stop_model(service, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False) abstractmethod

Abstract method to stop a model server.

This operation should be reversible. A stopped model server should still show up in the list of model servers returned by find_model_server and it should be possible to start it again by calling start_model_server.

Parameters:

Name	Type	Description	Default
service	BaseService	The service to stop.	required
timeout	int	timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
force	bool	if True, force the service to stop.	False

Source code in src/zenml/model_deployers/base_model_deployer.py

@abstractmethod
def perform_stop_model(
    self,
    service: BaseService,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    force: bool = False,
) -> BaseService:
    """Abstract method to stop a model server.

    This operation should be reversible. A stopped model server should still
    show up in the list of model servers returned by `find_model_server` and
    it should be possible to start it again by calling `start_model_server`.

    Args:
        service: The service to stop.
        timeout: timeout in seconds to wait for the service to stop. If
            set to 0, the method will return immediately after
            deprovisioning the service, without waiting for it to stop.
        force: if True, force the service to stop.
    """

start_model_server(uuid, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT)

Abstract method to start a model server.

Parameters:

Name	Type	Description	Default
uuid	UUID	UUID of the model server to start.	required
timeout	int	timeout in seconds to wait for the service to start. If set to 0, the method will return immediately after provisioning the service, without waiting for it to become active.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT

Raises:

Type	Description
RuntimeError	if the model server is not found.

Source code in src/zenml/model_deployers/base_model_deployer.py

def start_model_server(
    self,
    uuid: UUID,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
) -> None:
    """Abstract method to start a model server.

    Args:
        uuid: UUID of the model server to start.
        timeout: timeout in seconds to wait for the service to start. If
            set to 0, the method will return immediately after
            provisioning the service, without waiting for it to become
            active.

    Raises:
        RuntimeError: if the model server is not found.
    """
    client = Client()
    try:
        service = self.find_model_server(service_uuid=uuid)[0]
        updated_service = self.perform_start_model(service, timeout)
        client.update_service(
            id=updated_service.uuid,
            admin_state=updated_service.admin_state,
            status=updated_service.status.model_dump(),
            endpoint=updated_service.endpoint.model_dump()
            if updated_service.endpoint
            else None,
        )
    except Exception as e:
        raise RuntimeError(
            f"Failed to start model server with UUID {uuid}: {e}"
        ) from e

stop_model_server(uuid, timeout=DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT, force=False)

Abstract method to stop a model server.

This operation should be reversible. A stopped model server should still show up in the list of model servers returned by find_model_server and it should be possible to start it again by calling start_model_server.

Parameters:

Name	Type	Description	Default
uuid	UUID	UUID of the model server to stop.	required
timeout	int	timeout in seconds to wait for the service to stop. If set to 0, the method will return immediately after deprovisioning the service, without waiting for it to stop.	DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT
force	bool	if True, force the service to stop.	False

Raises:

Type	Description
RuntimeError	if the model server is not found.

Source code in src/zenml/model_deployers/base_model_deployer.py

def stop_model_server(
    self,
    uuid: UUID,
    timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,
    force: bool = False,
) -> None:
    """Abstract method to stop a model server.

    This operation should be reversible. A stopped model server should still
    show up in the list of model servers returned by `find_model_server` and
    it should be possible to start it again by calling `start_model_server`.

    Args:
        uuid: UUID of the model server to stop.
        timeout: timeout in seconds to wait for the service to stop. If
            set to 0, the method will return immediately after
            deprovisioning the service, without waiting for it to stop.
        force: if True, force the service to stop.

    Raises:
        RuntimeError: if the model server is not found.
    """
    client = Client()
    try:
        service = self.find_model_server(service_uuid=uuid)[0]
        updated_service = self.perform_stop_model(service, timeout, force)
        client.update_service(
            id=updated_service.uuid,
            admin_state=updated_service.admin_state,
            status=updated_service.status.model_dump(),
            endpoint=updated_service.endpoint.model_dump()
            if updated_service.endpoint
            else None,
        )
    except Exception as e:
        raise RuntimeError(
            f"Failed to stop model server with UUID {uuid}: {e}"
        ) from e

BaseModelDeployerFlavor

Bases: Flavor

Base class for model deployer flavors.

Source code in src/zenml/model_deployers/base_model_deployer.py

class BaseModelDeployerFlavor(Flavor):
    """Base class for model deployer flavors."""

    @property
    def type(self) -> StackComponentType:
        """Returns the flavor type.

        Returns:
            The flavor type.
        """
        return StackComponentType.MODEL_DEPLOYER

    @property
    def config_class(self) -> Type[BaseModelDeployerConfig]:
        """Returns `BaseModelDeployerConfig` config class.

        Returns:
                The config class.
        """
        return BaseModelDeployerConfig

    @property
    @abstractmethod
    def implementation_class(self) -> Type[BaseModelDeployer]:
        """The class that implements the model deployer."""

config_class property

Returns BaseModelDeployerConfig config class.

Returns:

Type	Description
Type[BaseModelDeployerConfig]	The config class.

implementation_class abstractmethod property

The class that implements the model deployer.

type property

Returns the flavor type.

Returns:

Type	Description
StackComponentType	The flavor type.

Model Registries

Initialization of the MLflow Service.

Model registries are centralized repositories that facilitate the collaboration and management of machine learning models. They provide functionalities such as version control, metadata tracking, and storage of model artifacts, enabling data scientists to efficiently share and keep track of their models within a team or organization.

BaseModelRegistry

Bases: StackComponent, ABC

Base class for all ZenML model registries.

Source code in src/zenml/model_registries/base_model_registry.py

class BaseModelRegistry(StackComponent, ABC):
    """Base class for all ZenML model registries."""

    @property
    def config(self) -> BaseModelRegistryConfig:
        """Returns the config of the model registries.

        Returns:
            The config of the model registries.
        """
        return cast(BaseModelRegistryConfig, self._config)

    # ---------
    # Model Registration Methods
    # ---------

    @abstractmethod
    def register_model(
        self,
        name: str,
        description: Optional[str] = None,
        metadata: Optional[Dict[str, str]] = None,
    ) -> RegisteredModel:
        """Registers a model in the model registry.

        Args:
            name: The name of the registered model.
            description: The description of the registered model.
            metadata: The metadata associated with the registered model.

        Returns:
            The registered model.

        Raises:
            zenml.exceptions.EntityExistsError: If a model with the same name already exists.
            RuntimeError: If registration fails.
        """

    @abstractmethod
    def delete_model(
        self,
        name: str,
    ) -> None:
        """Deletes a registered model from the model registry.

        Args:
            name: The name of the registered model.

        Raises:
            KeyError: If the model does not exist.
            RuntimeError: If deletion fails.
        """

    @abstractmethod
    def update_model(
        self,
        name: str,
        description: Optional[str] = None,
        metadata: Optional[Dict[str, str]] = None,
        remove_metadata: Optional[List[str]] = None,
    ) -> RegisteredModel:
        """Updates a registered model in the model registry.

        Args:
            name: The name of the registered model.
            description: The description of the registered model.
            metadata: The metadata associated with the registered model.
            remove_metadata: The metadata to remove from the registered model.

        Raises:
            KeyError: If the model does not exist.
            RuntimeError: If update fails.
        """

    @abstractmethod
    def get_model(self, name: str) -> RegisteredModel:
        """Gets a registered model from the model registry.

        Args:
            name: The name of the registered model.

        Returns:
            The registered model.

        Raises:
            zenml.exceptions.EntityExistsError: If the model does not exist.
            RuntimeError: If retrieval fails.
        """

    @abstractmethod
    def list_models(
        self,
        name: Optional[str] = None,
        metadata: Optional[Dict[str, str]] = None,
    ) -> List[RegisteredModel]:
        """Lists all registered models in the model registry.

        Args:
            name: The name of the registered model.
            metadata: The metadata associated with the registered model.

        Returns:
            A list of registered models.
        """

    # ---------
    # Model Version Methods
    # ---------

    @abstractmethod
    def register_model_version(
        self,
        name: str,
        version: Optional[str] = None,
        model_source_uri: Optional[str] = None,
        description: Optional[str] = None,
        metadata: Optional[ModelRegistryModelMetadata] = None,
        **kwargs: Any,
    ) -> RegistryModelVersion:
        """Registers a model version in the model registry.

        Args:
            name: The name of the registered model.
            model_source_uri: The source URI of the model.
            version: The version of the model version.
            description: The description of the model version.
            metadata: The metadata associated with the model
                version.
            **kwargs: Additional keyword arguments.

        Returns:
            The registered model version.

        Raises:
            RuntimeError: If registration fails.
        """

    @abstractmethod
    def delete_model_version(
        self,
        name: str,
        version: str,
    ) -> None:
        """Deletes a model version from the model registry.

        Args:
            name: The name of the registered model.
            version: The version of the model version to delete.

        Raises:
            KeyError: If the model version does not exist.
            RuntimeError: If deletion fails.
        """

    @abstractmethod
    def update_model_version(
        self,
        name: str,
        version: str,
        description: Optional[str] = None,
        metadata: Optional[ModelRegistryModelMetadata] = None,
        remove_metadata: Optional[List[str]] = None,
        stage: Optional[ModelVersionStage] = None,
    ) -> RegistryModelVersion:
        """Updates a model version in the model registry.

        Args:
            name: The name of the registered model.
            version: The version of the model version to update.
            description: The description of the model version.
            metadata: Metadata associated with this model version.
            remove_metadata: The metadata to remove from the model version.
            stage: The stage of the model version.

        Returns:
            The updated model version.

        Raises:
            KeyError: If the model version does not exist.
            RuntimeError: If update fails.
        """

    @abstractmethod
    def list_model_versions(
        self,
        name: Optional[str] = None,
        model_source_uri: Optional[str] = None,
        metadata: Optional[ModelRegistryModelMetadata] = None,
        stage: Optional[ModelVersionStage] = None,
        count: Optional[int] = None,
        created_after: Optional[datetime] = None,
        created_before: Optional[datetime] = None,
        order_by_date: Optional[str] = None,
        **kwargs: Any,
    ) -> Optional[List[RegistryModelVersion]]:
        """Lists all model versions for a registered model.

        Args:
            name: The name of the registered model.
            model_source_uri: The model source URI of the registered model.
            metadata: Metadata associated with this model version.
            stage: The stage of the model version.
            count: The number of model versions to return.
            created_after: The timestamp after which to list model versions.
            created_before: The timestamp before which to list model versions.
            order_by_date: Whether to sort by creation time, this can
                be "asc" or "desc".
            kwargs: Additional keyword arguments.

        Returns:
            A list of model versions.
        """

    def get_latest_model_version(
        self,
        name: str,
        stage: Optional[ModelVersionStage] = None,
    ) -> Optional[RegistryModelVersion]:
        """Gets the latest model version for a registered model.

        This method is used to get the latest model version for a registered
        model. If no stage is provided, the latest model version across all
        stages is returned. If a stage is provided, the latest model version
        for that stage is returned.

        Args:
            name: The name of the registered model.
            stage: The stage of the model version.

        Returns:
            The latest model version.
        """
        model_versions = self.list_model_versions(
            name=name, stage=stage, order_by_date="desc", count=1
        )
        if model_versions:
            return model_versions[0]
        return None

    @abstractmethod
    def get_model_version(
        self, name: str, version: str
    ) -> RegistryModelVersion:
        """Gets a model version for a registered model.

        Args:
            name: The name of the registered model.
            version: The version of the model version to get.

        Returns:
            The model version.

        Raises:
            KeyError: If the model version does not exist.
            RuntimeError: If retrieval fails.
        """

    @abstractmethod
    def load_model_version(
        self,
        name: str,
        version: str,
        **kwargs: Any,
    ) -> Any:
        """Loads a model version from the model registry.

        Args:
            name: The name of the registered model.
            version: The version of the model version to load.
            **kwargs: Additional keyword arguments.

        Returns:
            The loaded model version.

        Raises:
            KeyError: If the model version does not exist.
            RuntimeError: If loading fails.
        """

    @abstractmethod
    def get_model_uri_artifact_store(
        self,
        model_version: RegistryModelVersion,
    ) -> str:
        """Gets the URI artifact store for a model version.

        This method retrieves the URI of the artifact store for a specific model
        version. Its purpose is to ensure that the URI is in the correct format
        for the specific artifact store being used. This is essential for the
        model serving component, which relies on the URI to serve the model
        version. In some cases, the URI may be stored in a different format by
        certain model registry integrations. This method allows us to obtain the
        URI in the correct format, regardless of the integration being used.

        Note: In some cases the URI artifact store may not be available to the
        user, the method should save the target model in one of the other
        artifact stores supported by ZenML and return the URI of that artifact
        store.

        Args:
            model_version: The model version for which to get the URI artifact
                store.

        Returns:
            The URI artifact store for the model version.
        """

config property

Returns the config of the model registries.

Returns:

Type	Description
BaseModelRegistryConfig	The config of the model registries.

delete_model(name) abstractmethod

Deletes a registered model from the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required

Raises:

Type	Description
KeyError	If the model does not exist.
RuntimeError	If deletion fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def delete_model(
    self,
    name: str,
) -> None:
    """Deletes a registered model from the model registry.

    Args:
        name: The name of the registered model.

    Raises:
        KeyError: If the model does not exist.
        RuntimeError: If deletion fails.
    """

delete_model_version(name, version) abstractmethod

Deletes a model version from the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
version	str	The version of the model version to delete.	required

Raises:

Type	Description
KeyError	If the model version does not exist.
RuntimeError	If deletion fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def delete_model_version(
    self,
    name: str,
    version: str,
) -> None:
    """Deletes a model version from the model registry.

    Args:
        name: The name of the registered model.
        version: The version of the model version to delete.

    Raises:
        KeyError: If the model version does not exist.
        RuntimeError: If deletion fails.
    """

get_latest_model_version(name, stage=None)

Gets the latest model version for a registered model.

This method is used to get the latest model version for a registered model. If no stage is provided, the latest model version across all stages is returned. If a stage is provided, the latest model version for that stage is returned.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
stage	Optional[ModelVersionStage]	The stage of the model version.	None

Returns:

Type	Description
Optional[RegistryModelVersion]	The latest model version.

Source code in src/zenml/model_registries/base_model_registry.py

def get_latest_model_version(
    self,
    name: str,
    stage: Optional[ModelVersionStage] = None,
) -> Optional[RegistryModelVersion]:
    """Gets the latest model version for a registered model.

    This method is used to get the latest model version for a registered
    model. If no stage is provided, the latest model version across all
    stages is returned. If a stage is provided, the latest model version
    for that stage is returned.

    Args:
        name: The name of the registered model.
        stage: The stage of the model version.

    Returns:
        The latest model version.
    """
    model_versions = self.list_model_versions(
        name=name, stage=stage, order_by_date="desc", count=1
    )
    if model_versions:
        return model_versions[0]
    return None

get_model(name) abstractmethod

Gets a registered model from the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required

Returns:

Type	Description
RegisteredModel	The registered model.

Raises:

Type	Description
EntityExistsError	If the model does not exist.
RuntimeError	If retrieval fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def get_model(self, name: str) -> RegisteredModel:
    """Gets a registered model from the model registry.

    Args:
        name: The name of the registered model.

    Returns:
        The registered model.

    Raises:
        zenml.exceptions.EntityExistsError: If the model does not exist.
        RuntimeError: If retrieval fails.
    """

get_model_uri_artifact_store(model_version) abstractmethod

Gets the URI artifact store for a model version.

This method retrieves the URI of the artifact store for a specific model version. Its purpose is to ensure that the URI is in the correct format for the specific artifact store being used. This is essential for the model serving component, which relies on the URI to serve the model version. In some cases, the URI may be stored in a different format by certain model registry integrations. This method allows us to obtain the URI in the correct format, regardless of the integration being used.

Note: In some cases the URI artifact store may not be available to the user, the method should save the target model in one of the other artifact stores supported by ZenML and return the URI of that artifact store.

Parameters:

Name	Type	Description	Default
model_version	RegistryModelVersion	The model version for which to get the URI artifact store.	required

Returns:

Type	Description
str	The URI artifact store for the model version.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def get_model_uri_artifact_store(
    self,
    model_version: RegistryModelVersion,
) -> str:
    """Gets the URI artifact store for a model version.

    This method retrieves the URI of the artifact store for a specific model
    version. Its purpose is to ensure that the URI is in the correct format
    for the specific artifact store being used. This is essential for the
    model serving component, which relies on the URI to serve the model
    version. In some cases, the URI may be stored in a different format by
    certain model registry integrations. This method allows us to obtain the
    URI in the correct format, regardless of the integration being used.

    Note: In some cases the URI artifact store may not be available to the
    user, the method should save the target model in one of the other
    artifact stores supported by ZenML and return the URI of that artifact
    store.

    Args:
        model_version: The model version for which to get the URI artifact
            store.

    Returns:
        The URI artifact store for the model version.
    """

get_model_version(name, version) abstractmethod

Gets a model version for a registered model.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
version	str	The version of the model version to get.	required

Returns:

Type	Description
RegistryModelVersion	The model version.

Raises:

Type	Description
KeyError	If the model version does not exist.
RuntimeError	If retrieval fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def get_model_version(
    self, name: str, version: str
) -> RegistryModelVersion:
    """Gets a model version for a registered model.

    Args:
        name: The name of the registered model.
        version: The version of the model version to get.

    Returns:
        The model version.

    Raises:
        KeyError: If the model version does not exist.
        RuntimeError: If retrieval fails.
    """

list_model_versions(name=None, model_source_uri=None, metadata=None, stage=None, count=None, created_after=None, created_before=None, order_by_date=None, **kwargs) abstractmethod

Lists all model versions for a registered model.

Parameters:

Name	Type	Description	Default
name	Optional[str]	The name of the registered model.	None
model_source_uri	Optional[str]	The model source URI of the registered model.	None
metadata	Optional[ModelRegistryModelMetadata]	Metadata associated with this model version.	None
stage	Optional[ModelVersionStage]	The stage of the model version.	None
count	Optional[int]	The number of model versions to return.	None
created_after	Optional[datetime]	The timestamp after which to list model versions.	None
created_before	Optional[datetime]	The timestamp before which to list model versions.	None
order_by_date	Optional[str]	Whether to sort by creation time, this can be "asc" or "desc".	None
kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
Optional[List[RegistryModelVersion]]	A list of model versions.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def list_model_versions(
    self,
    name: Optional[str] = None,
    model_source_uri: Optional[str] = None,
    metadata: Optional[ModelRegistryModelMetadata] = None,
    stage: Optional[ModelVersionStage] = None,
    count: Optional[int] = None,
    created_after: Optional[datetime] = None,
    created_before: Optional[datetime] = None,
    order_by_date: Optional[str] = None,
    **kwargs: Any,
) -> Optional[List[RegistryModelVersion]]:
    """Lists all model versions for a registered model.

    Args:
        name: The name of the registered model.
        model_source_uri: The model source URI of the registered model.
        metadata: Metadata associated with this model version.
        stage: The stage of the model version.
        count: The number of model versions to return.
        created_after: The timestamp after which to list model versions.
        created_before: The timestamp before which to list model versions.
        order_by_date: Whether to sort by creation time, this can
            be "asc" or "desc".
        kwargs: Additional keyword arguments.

    Returns:
        A list of model versions.
    """

list_models(name=None, metadata=None) abstractmethod

Lists all registered models in the model registry.

Parameters:

Name	Type	Description	Default
name	Optional[str]	The name of the registered model.	None
metadata	Optional[Dict[str, str]]	The metadata associated with the registered model.	None

Returns:

Type	Description
List[RegisteredModel]	A list of registered models.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def list_models(
    self,
    name: Optional[str] = None,
    metadata: Optional[Dict[str, str]] = None,
) -> List[RegisteredModel]:
    """Lists all registered models in the model registry.

    Args:
        name: The name of the registered model.
        metadata: The metadata associated with the registered model.

    Returns:
        A list of registered models.
    """

load_model_version(name, version, **kwargs) abstractmethod

Loads a model version from the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
version	str	The version of the model version to load.	required
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
Any	The loaded model version.

Raises:

Type	Description
KeyError	If the model version does not exist.
RuntimeError	If loading fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def load_model_version(
    self,
    name: str,
    version: str,
    **kwargs: Any,
) -> Any:
    """Loads a model version from the model registry.

    Args:
        name: The name of the registered model.
        version: The version of the model version to load.
        **kwargs: Additional keyword arguments.

    Returns:
        The loaded model version.

    Raises:
        KeyError: If the model version does not exist.
        RuntimeError: If loading fails.
    """

register_model(name, description=None, metadata=None) abstractmethod

Registers a model in the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
description	Optional[str]	The description of the registered model.	None
metadata	Optional[Dict[str, str]]	The metadata associated with the registered model.	None

Returns:

Type	Description
RegisteredModel	The registered model.

Raises:

Type	Description
EntityExistsError	If a model with the same name already exists.
RuntimeError	If registration fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def register_model(
    self,
    name: str,
    description: Optional[str] = None,
    metadata: Optional[Dict[str, str]] = None,
) -> RegisteredModel:
    """Registers a model in the model registry.

    Args:
        name: The name of the registered model.
        description: The description of the registered model.
        metadata: The metadata associated with the registered model.

    Returns:
        The registered model.

    Raises:
        zenml.exceptions.EntityExistsError: If a model with the same name already exists.
        RuntimeError: If registration fails.
    """

register_model_version(name, version=None, model_source_uri=None, description=None, metadata=None, **kwargs) abstractmethod

Registers a model version in the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
model_source_uri	Optional[str]	The source URI of the model.	None
version	Optional[str]	The version of the model version.	None
description	Optional[str]	The description of the model version.	None
metadata	Optional[ModelRegistryModelMetadata]	The metadata associated with the model version.	None
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
RegistryModelVersion	The registered model version.

Raises:

Type	Description
RuntimeError	If registration fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def register_model_version(
    self,
    name: str,
    version: Optional[str] = None,
    model_source_uri: Optional[str] = None,
    description: Optional[str] = None,
    metadata: Optional[ModelRegistryModelMetadata] = None,
    **kwargs: Any,
) -> RegistryModelVersion:
    """Registers a model version in the model registry.

    Args:
        name: The name of the registered model.
        model_source_uri: The source URI of the model.
        version: The version of the model version.
        description: The description of the model version.
        metadata: The metadata associated with the model
            version.
        **kwargs: Additional keyword arguments.

    Returns:
        The registered model version.

    Raises:
        RuntimeError: If registration fails.
    """

update_model(name, description=None, metadata=None, remove_metadata=None) abstractmethod

Updates a registered model in the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
description	Optional[str]	The description of the registered model.	None
metadata	Optional[Dict[str, str]]	The metadata associated with the registered model.	None
remove_metadata	Optional[List[str]]	The metadata to remove from the registered model.	None

Raises:

Type	Description
KeyError	If the model does not exist.
RuntimeError	If update fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def update_model(
    self,
    name: str,
    description: Optional[str] = None,
    metadata: Optional[Dict[str, str]] = None,
    remove_metadata: Optional[List[str]] = None,
) -> RegisteredModel:
    """Updates a registered model in the model registry.

    Args:
        name: The name of the registered model.
        description: The description of the registered model.
        metadata: The metadata associated with the registered model.
        remove_metadata: The metadata to remove from the registered model.

    Raises:
        KeyError: If the model does not exist.
        RuntimeError: If update fails.
    """

update_model_version(name, version, description=None, metadata=None, remove_metadata=None, stage=None) abstractmethod

Updates a model version in the model registry.

Parameters:

Name	Type	Description	Default
name	str	The name of the registered model.	required
version	str	The version of the model version to update.	required
description	Optional[str]	The description of the model version.	None
metadata	Optional[ModelRegistryModelMetadata]	Metadata associated with this model version.	None
remove_metadata	Optional[List[str]]	The metadata to remove from the model version.	None
stage	Optional[ModelVersionStage]	The stage of the model version.	None

Returns:

Type	Description
RegistryModelVersion	The updated model version.

Raises:

Type	Description
KeyError	If the model version does not exist.
RuntimeError	If update fails.

Source code in src/zenml/model_registries/base_model_registry.py

@abstractmethod
def update_model_version(
    self,
    name: str,
    version: str,
    description: Optional[str] = None,
    metadata: Optional[ModelRegistryModelMetadata] = None,
    remove_metadata: Optional[List[str]] = None,
    stage: Optional[ModelVersionStage] = None,
) -> RegistryModelVersion:
    """Updates a model version in the model registry.

    Args:
        name: The name of the registered model.
        version: The version of the model version to update.
        description: The description of the model version.
        metadata: Metadata associated with this model version.
        remove_metadata: The metadata to remove from the model version.
        stage: The stage of the model version.

    Returns:
        The updated model version.

    Raises:
        KeyError: If the model version does not exist.
        RuntimeError: If update fails.
    """

BaseModelRegistryConfig

Bases: StackComponentConfig

Base config for model registries.

Source code in src/zenml/model_registries/base_model_registry.py

class BaseModelRegistryConfig(StackComponentConfig):
    """Base config for model registries."""

BaseModelRegistryFlavor

Bases: Flavor

Base class for all ZenML model registry flavors.

Source code in src/zenml/model_registries/base_model_registry.py

class BaseModelRegistryFlavor(Flavor):
    """Base class for all ZenML model registry flavors."""

    @property
    def type(self) -> StackComponentType:
        """Type of the flavor.

        Returns:
            StackComponentType: The type of the flavor.
        """
        return StackComponentType.MODEL_REGISTRY

    @property
    def config_class(self) -> Type[BaseModelRegistryConfig]:
        """Config class for this flavor.

        Returns:
            The config class for this flavor.
        """
        return BaseModelRegistryConfig

    @property
    @abstractmethod
    def implementation_class(self) -> Type[StackComponent]:
        """Returns the implementation class for this flavor.

        Returns:
            The implementation class for this flavor.
        """
        return BaseModelRegistry

config_class property

Config class for this flavor.

Returns:

Type	Description
Type[BaseModelRegistryConfig]	The config class for this flavor.

implementation_class abstractmethod property

Returns the implementation class for this flavor.

Returns:

Type	Description
Type[StackComponent]	The implementation class for this flavor.

type property

Type of the flavor.

Returns:

Name	Type	Description
StackComponentType	StackComponentType	The type of the flavor.

Model

Concepts related to the Model Control Plane feature.

Models

Pydantic models for the various concepts in ZenML.

APIKey

Bases: BaseModel

Encoded model for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKey(BaseModel):
    """Encoded model for API keys."""

    id: UUID
    key: str

    @classmethod
    def decode_api_key(cls, encoded_key: str) -> "APIKey":
        """Decodes an API key from a base64 string.

        Args:
            encoded_key: The encoded API key.

        Returns:
            The decoded API key.

        Raises:
            ValueError: If the key is not valid.
        """
        if encoded_key.startswith(ZENML_API_KEY_PREFIX):
            encoded_key = encoded_key[len(ZENML_API_KEY_PREFIX) :]
        try:
            json_key = b64_decode(encoded_key)
            return cls.model_validate_json(json_key)
        except Exception:
            raise ValueError("Invalid API key.")

    def encode(self) -> str:
        """Encodes the API key in a base64 string that includes the key ID and prefix.

        Returns:
            The encoded API key.
        """
        encoded_key = b64_encode(self.model_dump_json())
        return f"{ZENML_API_KEY_PREFIX}{encoded_key}"

decode_api_key(encoded_key) classmethod

Decodes an API key from a base64 string.

Parameters:

Name	Type	Description	Default
encoded_key	str	The encoded API key.	required

Returns:

Type	Description
APIKey	The decoded API key.

Raises:

Type	Description
ValueError	If the key is not valid.

Source code in src/zenml/models/v2/core/api_key.py

@classmethod
def decode_api_key(cls, encoded_key: str) -> "APIKey":
    """Decodes an API key from a base64 string.

    Args:
        encoded_key: The encoded API key.

    Returns:
        The decoded API key.

    Raises:
        ValueError: If the key is not valid.
    """
    if encoded_key.startswith(ZENML_API_KEY_PREFIX):
        encoded_key = encoded_key[len(ZENML_API_KEY_PREFIX) :]
    try:
        json_key = b64_decode(encoded_key)
        return cls.model_validate_json(json_key)
    except Exception:
        raise ValueError("Invalid API key.")

encode()

Encodes the API key in a base64 string that includes the key ID and prefix.

Returns:

Type	Description
str	The encoded API key.

Source code in src/zenml/models/v2/core/api_key.py

def encode(self) -> str:
    """Encodes the API key in a base64 string that includes the key ID and prefix.

    Returns:
        The encoded API key.
    """
    encoded_key = b64_encode(self.model_dump_json())
    return f"{ZENML_API_KEY_PREFIX}{encoded_key}"

APIKeyFilter

Bases: BaseFilter

Filter model for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyFilter(BaseFilter):
    """Filter model for API keys."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "service_account",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.CLI_EXCLUDE_FIELDS,
        "service_account",
    ]

    service_account: Optional[UUID] = Field(
        default=None,
        description="The service account to scope this query to.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Name of the API key",
    )
    description: Optional[str] = Field(
        default=None,
        title="Filter by the API key description.",
    )
    active: Optional[Union[bool, str]] = Field(
        default=None,
        title="Whether the API key is active.",
        union_mode="left_to_right",
    )
    last_login: Optional[Union[datetime, str]] = Field(
        default=None,
        title="Time when the API key was last used to log in.",
        union_mode="left_to_right",
    )
    last_rotated: Optional[Union[datetime, str]] = Field(
        default=None,
        title="Time when the API key was last rotated.",
        union_mode="left_to_right",
    )

    def set_service_account(self, service_account_id: UUID) -> None:
        """Set the service account by which to scope this query.

        Args:
            service_account_id: The service account ID.
        """
        self.service_account = service_account_id

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Override to apply the service account scope as an additional filter.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)

        if self.service_account:
            scope_filter = (
                getattr(table, "service_account_id") == self.service_account
            )
            query = query.where(scope_filter)

        return query

apply_filter(query, table)

Override to apply the service account scope as an additional filter.

Parameters:

Name	Type	Description	Default
query	AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
AnyQuery	The query with filter applied.

Source code in src/zenml/models/v2/core/api_key.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to apply the service account scope as an additional filter.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)

    if self.service_account:
        scope_filter = (
            getattr(table, "service_account_id") == self.service_account
        )
        query = query.where(scope_filter)

    return query

set_service_account(service_account_id)

Set the service account by which to scope this query.

Parameters:

Name	Type	Description	Default
service_account_id	UUID	The service account ID.	required

Source code in src/zenml/models/v2/core/api_key.py

def set_service_account(self, service_account_id: UUID) -> None:
    """Set the service account by which to scope this query.

    Args:
        service_account_id: The service account ID.
    """
    self.service_account = service_account_id

APIKeyInternalResponse

Bases: APIKeyResponse

Response model for API keys used internally.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyInternalResponse(APIKeyResponse):
    """Response model for API keys used internally."""

    previous_key: Optional[str] = Field(
        default=None,
        title="The previous API key. Only set if the key was rotated.",
    )

    def verify_key(
        self,
        key: str,
    ) -> bool:
        """Verifies a given key against the stored (hashed) key(s).

        Args:
            key: Input key to be verified.

        Returns:
            True if the keys match.
        """
        # even when the hashed key is not set, we still want to execute
        # the hash verification to protect against response discrepancy
        # attacks (https://cwe.mitre.org/data/definitions/204.html)
        key_hash: Optional[str] = None
        context = CryptContext(schemes=["bcrypt"], deprecated="auto")
        if self.key is not None and self.active:
            key_hash = self.key
        result = context.verify(key, key_hash)

        # same for the previous key, if set and if it's still valid
        key_hash = None
        if (
            self.previous_key is not None
            and self.last_rotated is not None
            and self.active
            and self.retain_period_minutes > 0
        ):
            # check if the previous key is still valid
            if utc_now(
                tz_aware=self.last_rotated
            ) - self.last_rotated < timedelta(
                minutes=self.retain_period_minutes
            ):
                key_hash = self.previous_key
        previous_result = context.verify(key, key_hash)

        return result or previous_result

verify_key(key)

Verifies a given key against the stored (hashed) key(s).

Parameters:

Name	Type	Description	Default
key	str	Input key to be verified.	required

Returns:

Type	Description
bool	True if the keys match.

Source code in src/zenml/models/v2/core/api_key.py

def verify_key(
    self,
    key: str,
) -> bool:
    """Verifies a given key against the stored (hashed) key(s).

    Args:
        key: Input key to be verified.

    Returns:
        True if the keys match.
    """
    # even when the hashed key is not set, we still want to execute
    # the hash verification to protect against response discrepancy
    # attacks (https://cwe.mitre.org/data/definitions/204.html)
    key_hash: Optional[str] = None
    context = CryptContext(schemes=["bcrypt"], deprecated="auto")
    if self.key is not None and self.active:
        key_hash = self.key
    result = context.verify(key, key_hash)

    # same for the previous key, if set and if it's still valid
    key_hash = None
    if (
        self.previous_key is not None
        and self.last_rotated is not None
        and self.active
        and self.retain_period_minutes > 0
    ):
        # check if the previous key is still valid
        if utc_now(
            tz_aware=self.last_rotated
        ) - self.last_rotated < timedelta(
            minutes=self.retain_period_minutes
        ):
            key_hash = self.previous_key
    previous_result = context.verify(key, key_hash)

    return result or previous_result

APIKeyInternalUpdate

Bases: APIKeyUpdate

Update model for API keys used internally.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyInternalUpdate(APIKeyUpdate):
    """Update model for API keys used internally."""

    update_last_login: bool = Field(
        default=False,
        title="Whether to update the last login timestamp.",
    )

APIKeyRequest

Bases: BaseRequest

Request model for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyRequest(BaseRequest):
    """Request model for API keys."""

    name: str = Field(
        title="The name of the API Key.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    description: Optional[str] = Field(
        default=None,
        title="The description of the API Key.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

APIKeyResponse

Bases: BaseIdentifiedResponse[APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources]

Response model for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyResponse(
    BaseIdentifiedResponse[
        APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources
    ]
):
    """Response model for API keys."""

    name: str = Field(
        title="The name of the API Key.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    _warn_on_response_updates = False

    def get_hydrated_version(self) -> "APIKeyResponse":
        """Get the hydrated version of this API key.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_api_key(
            service_account_id=self.service_account.id,
            api_key_name_or_id=self.id,
        )

    # Helper functions
    def set_key(self, key: str) -> None:
        """Sets the API key and encodes it.

        Args:
            key: The API key value to be set.
        """
        self.get_body().key = APIKey(id=self.id, key=key).encode()

    # Body and metadata properties
    @property
    def key(self) -> Optional[str]:
        """The `key` property.

        Returns:
            the value of the property.
        """
        return self.get_body().key

    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def service_account(self) -> "ServiceAccountResponse":
        """The `service_account` property.

        Returns:
            the value of the property.
        """
        return self.get_body().service_account

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def retain_period_minutes(self) -> int:
        """The `retain_period_minutes` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().retain_period_minutes

    @property
    def last_login(self) -> Optional[datetime]:
        """The `last_login` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().last_login

    @property
    def last_rotated(self) -> Optional[datetime]:
        """The `last_rotated` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().last_rotated

active property

The active property.

Returns:

Type	Description
bool	the value of the property.

description property

The description property.

Returns:

Type	Description
str	the value of the property.

key property

The key property.

Returns:

Type	Description
Optional[str]	the value of the property.

last_login property

The last_login property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

last_rotated property

The last_rotated property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

retain_period_minutes property

The retain_period_minutes property.

Returns:

Type	Description
int	the value of the property.

service_account property

The service_account property.

Returns:

Type	Description
ServiceAccountResponse	the value of the property.

get_hydrated_version()

Get the hydrated version of this API key.

Returns:

Type	Description
APIKeyResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/api_key.py

def get_hydrated_version(self) -> "APIKeyResponse":
    """Get the hydrated version of this API key.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_api_key(
        service_account_id=self.service_account.id,
        api_key_name_or_id=self.id,
    )

set_key(key)

Sets the API key and encodes it.

Parameters:

Name	Type	Description	Default
key	str	The API key value to be set.	required

Source code in src/zenml/models/v2/core/api_key.py

def set_key(self, key: str) -> None:
    """Sets the API key and encodes it.

    Args:
        key: The API key value to be set.
    """
    self.get_body().key = APIKey(id=self.id, key=key).encode()

APIKeyResponseBody

Bases: BaseDatedResponseBody

Response body for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyResponseBody(BaseDatedResponseBody):
    """Response body for API keys."""

    key: Optional[str] = Field(
        default=None,
        title="The API key. Only set immediately after creation or rotation.",
    )
    active: bool = Field(
        default=True,
        title="Whether the API key is active.",
    )
    service_account: "ServiceAccountResponse" = Field(
        title="The service account associated with this API key."
    )

APIKeyResponseMetadata

Bases: BaseResponseMetadata

Response metadata for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyResponseMetadata(BaseResponseMetadata):
    """Response metadata for API keys."""

    description: str = Field(
        default="",
        title="The description of the API Key.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    retain_period_minutes: int = Field(
        title="Number of minutes for which the previous key is still valid "
        "after it has been rotated.",
    )
    last_login: Optional[datetime] = Field(
        default=None, title="Time when the API key was last used to log in."
    )
    last_rotated: Optional[datetime] = Field(
        default=None, title="Time when the API key was last rotated."
    )

APIKeyRotateRequest

Bases: BaseRequest

Request model for API key rotation.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyRotateRequest(BaseRequest):
    """Request model for API key rotation."""

    retain_period_minutes: int = Field(
        default=0,
        title="Number of minutes for which the previous key is still valid "
        "after it has been rotated.",
    )

APIKeyUpdate

Bases: BaseUpdate

Update model for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyUpdate(BaseUpdate):
    """Update model for API keys."""

    name: Optional[str] = Field(
        title="The name of the API Key.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="The description of the API Key.",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    active: Optional[bool] = Field(
        title="Whether the API key is active.",
        default=None,
    )

ActionFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all actions.

Source code in src/zenml/models/v2/core/action.py

class ActionFilter(ProjectScopedFilter):
    """Model to enable advanced filtering of all actions."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the action.",
    )
    flavor: Optional[str] = Field(
        default=None,
        title="The flavor of the action.",
    )
    plugin_subtype: Optional[str] = Field(
        default=None,
        title="The subtype of the action.",
    )

ActionFlavorResponse

Bases: BasePluginFlavorResponse[ActionFlavorResponseBody, ActionFlavorResponseMetadata, ActionFlavorResponseResources]

Response model for Action Flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponse(
    BasePluginFlavorResponse[
        ActionFlavorResponseBody,
        ActionFlavorResponseMetadata,
        ActionFlavorResponseResources,
    ]
):
    """Response model for Action Flavors."""

    # Body and metadata properties
    @property
    def config_schema(self) -> Dict[str, Any]:
        """The `source_config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config_schema

config_schema property

The source_config_schema property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

ActionFlavorResponseBody

Bases: BasePluginResponseBody

Response body for action flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseBody(BasePluginResponseBody):
    """Response body for action flavors."""

ActionFlavorResponseMetadata

Bases: BasePluginResponseMetadata

Response metadata for action flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseMetadata(BasePluginResponseMetadata):
    """Response metadata for action flavors."""

    config_schema: Dict[str, Any]

ActionFlavorResponseResources

Bases: BasePluginResponseResources

Response resources for action flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseResources(BasePluginResponseResources):
    """Response resources for action flavors."""

ActionRequest

Bases: ProjectScopedRequest

Model for creating a new action.

Source code in src/zenml/models/v2/core/action.py

class ActionRequest(ProjectScopedRequest):
    """Model for creating a new action."""

    name: str = Field(
        title="The name of the action.", max_length=STR_FIELD_MAX_LENGTH
    )
    description: str = Field(
        default="",
        title="The description of the action",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    flavor: str = Field(
        title="The flavor of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The subtype of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Dict[str, Any] = Field(
        title="The configuration for the action.",
    )
    service_account_id: UUID = Field(
        title="The service account that is used to execute the action.",
    )
    auth_window: Optional[int] = Field(
        default=None,
        title="The time window in minutes for which the service account is "
        "authorized to execute the action. Set this to 0 to authorize the "
        "service account indefinitely (not recommended). If not set, a "
        "default value defined for each individual action type is used.",
    )

ActionResponse

Bases: ProjectScopedResponse[ActionResponseBody, ActionResponseMetadata, ActionResponseResources]

Response model for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionResponse(
    ProjectScopedResponse[
        ActionResponseBody, ActionResponseMetadata, ActionResponseResources
    ]
):
    """Response model for actions."""

    name: str = Field(
        title="The name of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ActionResponse":
        """Get the hydrated version of this action.

        Returns:
            An instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_action(self.id)

    # Body and metadata properties
    @property
    def flavor(self) -> str:
        """The `flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().flavor

    @property
    def plugin_subtype(self) -> PluginSubType:
        """The `plugin_subtype` property.

        Returns:
            the value of the property.
        """
        return self.get_body().plugin_subtype

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def auth_window(self) -> int:
        """The `auth_window` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().auth_window

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    def set_configuration(self, configuration: Dict[str, Any]) -> None:
        """Set the `configuration` property.

        Args:
            configuration: The value to set.
        """
        self.get_metadata().configuration = configuration

    # Resource properties
    @property
    def service_account(self) -> "UserResponse":
        """The `service_account` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().service_account

auth_window property

The auth_window property.

Returns:

Type	Description
int	the value of the property.

configuration property

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

description property

The description property.

Returns:

Type	Description
str	the value of the property.

flavor property

The flavor property.

Returns:

Type	Description
str	the value of the property.

plugin_subtype property

The plugin_subtype property.

Returns:

Type	Description
PluginSubType	the value of the property.

service_account property

The service_account property.

Returns:

Type	Description
UserResponse	the value of the property.

get_hydrated_version()

Get the hydrated version of this action.

Returns:

Type	Description
ActionResponse	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/action.py

def get_hydrated_version(self) -> "ActionResponse":
    """Get the hydrated version of this action.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_action(self.id)

set_configuration(configuration)

Set the configuration property.

Parameters:

Name	Type	Description	Default
configuration	Dict[str, Any]	The value to set.	required

Source code in src/zenml/models/v2/core/action.py

def set_configuration(self, configuration: Dict[str, Any]) -> None:
    """Set the `configuration` property.

    Args:
        configuration: The value to set.
    """
    self.get_metadata().configuration = configuration

ActionResponseBody

Bases: ProjectScopedResponseBody

Response body for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionResponseBody(ProjectScopedResponseBody):
    """Response body for actions."""

    flavor: str = Field(
        title="The flavor of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The subtype of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

ActionResponseMetadata

Bases: ProjectScopedResponseMetadata

Response metadata for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionResponseMetadata(ProjectScopedResponseMetadata):
    """Response metadata for actions."""

    description: str = Field(
        default="",
        title="The description of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Dict[str, Any] = Field(
        title="The configuration for the action.",
    )
    auth_window: int = Field(
        title="The time window in minutes for which the service account is "
        "authorized to execute the action."
    )

ActionResponseResources

Bases: ProjectScopedResponseResources

Class for all resource models associated with the action entity.

Source code in src/zenml/models/v2/core/action.py

class ActionResponseResources(ProjectScopedResponseResources):
    """Class for all resource models associated with the action entity."""

    service_account: UserResponse = Field(
        title="The service account that is used to execute the action.",
    )

ActionUpdate

Bases: BaseUpdate

Update model for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionUpdate(BaseUpdate):
    """Update model for actions."""

    name: Optional[str] = Field(
        default=None,
        title="The new name for the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The new description for the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The configuration for the action.",
    )
    service_account_id: Optional[UUID] = Field(
        default=None,
        title="The service account that is used to execute the action.",
    )
    auth_window: Optional[int] = Field(
        default=None,
        title="The time window in minutes for which the service account is "
        "authorized to execute the action. Set this to 0 to authorize the "
        "service account indefinitely (not recommended). If not set, a "
        "default value defined for each individual action type is used.",
    )

    @classmethod
    def from_response(cls, response: "ActionResponse") -> "ActionUpdate":
        """Create an update model from a response model.

        Args:
            response: The response model to create the update model from.

        Returns:
            The update model.
        """
        return ActionUpdate(
            configuration=copy.deepcopy(response.configuration),
        )

from_response(response) classmethod

Create an update model from a response model.

Parameters:

Name	Type	Description	Default
response	ActionResponse	The response model to create the update model from.	required

Returns:

Type	Description
ActionUpdate	The update model.

Source code in src/zenml/models/v2/core/action.py

@classmethod
def from_response(cls, response: "ActionResponse") -> "ActionUpdate":
    """Create an update model from a response model.

    Args:
        response: The response model to create the update model from.

    Returns:
        The update model.
    """
    return ActionUpdate(
        configuration=copy.deepcopy(response.configuration),
    )

ArtifactFilter

Bases: ProjectScopedFilter, TaggableFilter

Model to enable advanced filtering of artifacts.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactFilter(ProjectScopedFilter, TaggableFilter):
    """Model to enable advanced filtering of artifacts."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *ProjectScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
    ]

    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *ProjectScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
        SORT_BY_LATEST_VERSION_KEY,
    ]

    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *ProjectScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
    ]

    name: Optional[str] = None
    has_custom_name: Optional[bool] = None

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query for Artifacts.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        from sqlmodel import asc, case, col, desc, func, select

        from zenml.enums import SorterOps
        from zenml.zen_stores.schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
        )

        sort_by, operand = self.sorting_params

        if sort_by == SORT_BY_LATEST_VERSION_KEY:
            # Subquery to find the latest version per artifact
            latest_version_subquery = (
                select(
                    ArtifactSchema.id,
                    case(
                        (
                            func.max(ArtifactVersionSchema.created).is_(None),
                            ArtifactSchema.created,
                        ),
                        else_=func.max(ArtifactVersionSchema.created),
                    ).label("latest_version_created"),
                )
                .outerjoin(
                    ArtifactVersionSchema,
                    ArtifactSchema.id == ArtifactVersionSchema.artifact_id,  # type: ignore[arg-type]
                )
                .group_by(col(ArtifactSchema.id))
                .subquery()
            )

            query = query.add_columns(
                latest_version_subquery.c.latest_version_created,
            ).where(ArtifactSchema.id == latest_version_subquery.c.id)

            # Apply sorting based on the operand
            if operand == SorterOps.ASCENDING:
                query = query.order_by(
                    asc(latest_version_subquery.c.latest_version_created),
                    asc(ArtifactSchema.id),
                )
            else:
                query = query.order_by(
                    desc(latest_version_subquery.c.latest_version_created),
                    desc(ArtifactSchema.id),
                )
            return query

        # For other sorting cases, delegate to the parent class
        return super().apply_sorting(query=query, table=table)

apply_sorting(query, table)

Apply sorting to the query for Artifacts.

Parameters:

Name	Type	Description	Default
query	AnyQuery	The query to which to apply the sorting.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
AnyQuery	The query with sorting applied.

Source code in src/zenml/models/v2/core/artifact.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query for Artifacts.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    from sqlmodel import asc, case, col, desc, func, select

    from zenml.enums import SorterOps
    from zenml.zen_stores.schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
    )

    sort_by, operand = self.sorting_params

    if sort_by == SORT_BY_LATEST_VERSION_KEY:
        # Subquery to find the latest version per artifact
        latest_version_subquery = (
            select(
                ArtifactSchema.id,
                case(
                    (
                        func.max(ArtifactVersionSchema.created).is_(None),
                        ArtifactSchema.created,
                    ),
                    else_=func.max(ArtifactVersionSchema.created),
                ).label("latest_version_created"),
            )
            .outerjoin(
                ArtifactVersionSchema,
                ArtifactSchema.id == ArtifactVersionSchema.artifact_id,  # type: ignore[arg-type]
            )
            .group_by(col(ArtifactSchema.id))
            .subquery()
        )

        query = query.add_columns(
            latest_version_subquery.c.latest_version_created,
        ).where(ArtifactSchema.id == latest_version_subquery.c.id)

        # Apply sorting based on the operand
        if operand == SorterOps.ASCENDING:
            query = query.order_by(
                asc(latest_version_subquery.c.latest_version_created),
                asc(ArtifactSchema.id),
            )
        else:
            query = query.order_by(
                desc(latest_version_subquery.c.latest_version_created),
                desc(ArtifactSchema.id),
            )
        return query

    # For other sorting cases, delegate to the parent class
    return super().apply_sorting(query=query, table=table)

ArtifactRequest

Bases: ProjectScopedRequest

Artifact request model.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactRequest(ProjectScopedRequest):
    """Artifact request model."""

    name: str = Field(
        title="Name of the artifact.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )
    tags: Optional[List[str]] = Field(
        title="Artifact tags.",
        description="Should be a list of plain strings, e.g., ['tag1', 'tag2']",
        default=None,
    )

ArtifactResponse

Bases: ProjectScopedResponse[ArtifactResponseBody, ArtifactResponseMetadata, ArtifactResponseResources]

Artifact response model.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactResponse(
    ProjectScopedResponse[
        ArtifactResponseBody,
        ArtifactResponseMetadata,
        ArtifactResponseResources,
    ]
):
    """Artifact response model."""

    def get_hydrated_version(self) -> "ArtifactResponse":
        """Get the hydrated version of this artifact.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact(self.id)

    name: str = Field(
        title="Name of the output in the parent step.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    # Body and metadata properties
    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def latest_version_name(self) -> Optional[str]:
        """The `latest_version_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_name

    @property
    def latest_version_id(self) -> Optional[UUID]:
        """The `latest_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_id

    @property
    def has_custom_name(self) -> bool:
        """The `has_custom_name` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().has_custom_name

    # Helper methods
    @property
    def versions(self) -> Dict[str, "ArtifactVersionResponse"]:
        """Get a list of all versions of this artifact.

        Returns:
            A list of all versions of this artifact.
        """
        from zenml.client import Client

        responses = Client().list_artifact_versions(artifact=self.name)
        return {str(response.version): response for response in responses}

has_custom_name property

The has_custom_name property.

Returns:

Type	Description
bool	the value of the property.

latest_version_id property

The latest_version_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

latest_version_name property

The latest_version_name property.

Returns:

Type	Description
Optional[str]	the value of the property.

tags property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

versions property

Get a list of all versions of this artifact.

Returns:

Type	Description
Dict[str, ArtifactVersionResponse]	A list of all versions of this artifact.

get_hydrated_version()

Get the hydrated version of this artifact.

Returns:

Type	Description
ArtifactResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/artifact.py

def get_hydrated_version(self) -> "ArtifactResponse":
    """Get the hydrated version of this artifact.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_artifact(self.id)

ArtifactResponseBody

Bases: ProjectScopedResponseBody

Response body for artifacts.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactResponseBody(ProjectScopedResponseBody):
    """Response body for artifacts."""

    tags: List[TagResponse] = Field(
        title="Tags associated with the model",
    )
    latest_version_name: Optional[str] = None
    latest_version_id: Optional[UUID] = None

ArtifactResponseMetadata

Bases: ProjectScopedResponseMetadata

Response metadata for artifacts.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactResponseMetadata(ProjectScopedResponseMetadata):
    """Response metadata for artifacts."""

    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )

ArtifactUpdate

Bases: BaseUpdate

Artifact update model.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactUpdate(BaseUpdate):
    """Artifact update model."""

    name: Optional[str] = None
    add_tags: Optional[List[str]] = None
    remove_tags: Optional[List[str]] = None
    has_custom_name: Optional[bool] = None

ArtifactVersionFilter

Bases: ProjectScopedFilter, TaggableFilter, RunMetadataFilterMixin

Model to enable advanced filtering of artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionFilter(
    ProjectScopedFilter, TaggableFilter, RunMetadataFilterMixin
):
    """Model to enable advanced filtering of artifact versions."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *ProjectScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
        *RunMetadataFilterMixin.FILTER_EXCLUDE_FIELDS,
        "artifact_id",
        "artifact",
        "only_unused",
        "has_custom_name",
        "model",
        "pipeline_run",
        "model_version_id",
    ]
    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *ProjectScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
        *RunMetadataFilterMixin.CUSTOM_SORTING_OPTIONS,
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *ProjectScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
        *RunMetadataFilterMixin.CLI_EXCLUDE_FIELDS,
        "artifact_id",
    ]
    API_MULTI_INPUT_PARAMS: ClassVar[List[str]] = [
        *ProjectScopedFilter.API_MULTI_INPUT_PARAMS,
        *TaggableFilter.API_MULTI_INPUT_PARAMS,
        *RunMetadataFilterMixin.API_MULTI_INPUT_PARAMS,
    ]

    artifact: Optional[Union[UUID, str]] = Field(
        default=None,
        description="The name or ID of the artifact which the search is scoped "
        "to. This field must always be set and is always applied in addition "
        "to the other filters, regardless of the value of the "
        "logical_operator field.",
        union_mode="left_to_right",
    )
    artifact_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="[Deprecated] Use 'artifact' instead. ID of the artifact to which this version belongs.",
        union_mode="left_to_right",
    )
    version: Optional[str] = Field(
        default=None,
        description="Version of the artifact",
    )
    version_number: Optional[Union[int, str]] = Field(
        default=None,
        description="Version of the artifact if it is an integer",
        union_mode="left_to_right",
    )
    uri: Optional[str] = Field(
        default=None,
        description="Uri of the artifact",
    )
    materializer: Optional[str] = Field(
        default=None,
        description="Materializer used to produce the artifact",
    )
    type: Optional[str] = Field(
        default=None,
        description="Type of the artifact",
    )
    data_type: Optional[str] = Field(
        default=None,
        description="Datatype of the artifact",
    )
    artifact_store_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Artifact store for this artifact",
        union_mode="left_to_right",
    )
    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="ID of the model version that is associated with this "
        "artifact version.",
        union_mode="left_to_right",
    )
    only_unused: Optional[bool] = Field(
        default=False, description="Filter only for unused artifacts"
    )
    has_custom_name: Optional[bool] = Field(
        default=None,
        description="Filter only artifacts with/without custom names.",
    )
    model: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the model that is associated with this "
        "artifact version.",
    )
    pipeline_run: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of a pipeline run that is associated with this "
        "artifact version.",
    )

    model_config = ConfigDict(protected_namespaces=())

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List[Union["ColumnElement[bool]"]]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_, or_, select

        from zenml.zen_stores.schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
            ModelSchema,
            ModelVersionArtifactSchema,
            ModelVersionSchema,
            PipelineRunSchema,
            StepRunInputArtifactSchema,
            StepRunOutputArtifactSchema,
            StepRunSchema,
        )

        if self.artifact:
            value, operator = self._resolve_operator(self.artifact)
            artifact_filter = and_(
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.artifact, table=ArtifactSchema
                ),
            )
            custom_filters.append(artifact_filter)

        if self.only_unused:
            unused_filter = and_(
                ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                    select(StepRunOutputArtifactSchema.artifact_id)
                ),
                ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                    select(StepRunInputArtifactSchema.artifact_id)
                ),
            )
            custom_filters.append(unused_filter)

        if self.model_version_id:
            value, operator = self._resolve_operator(self.model_version_id)

            model_version_filter = and_(
                ArtifactVersionSchema.id
                == ModelVersionArtifactSchema.artifact_version_id,
                ModelVersionArtifactSchema.model_version_id
                == ModelVersionSchema.id,
                FilterGenerator(ModelVersionSchema)
                .define_filter(column="id", value=value, operator=operator)
                .generate_query_conditions(ModelVersionSchema),
            )
            custom_filters.append(model_version_filter)

        if self.has_custom_name is not None:
            custom_name_filter = and_(
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                ArtifactSchema.has_custom_name == self.has_custom_name,
            )
            custom_filters.append(custom_name_filter)

        if self.model:
            model_filter = and_(
                ArtifactVersionSchema.id
                == ModelVersionArtifactSchema.artifact_version_id,
                ModelVersionArtifactSchema.model_version_id
                == ModelVersionSchema.id,
                ModelVersionSchema.model_id == ModelSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.model, table=ModelSchema
                ),
            )
            custom_filters.append(model_filter)

        if self.pipeline_run:
            pipeline_run_filter = and_(
                or_(
                    and_(
                        ArtifactVersionSchema.id
                        == StepRunOutputArtifactSchema.artifact_id,
                        StepRunOutputArtifactSchema.step_id
                        == StepRunSchema.id,
                    ),
                    and_(
                        ArtifactVersionSchema.id
                        == StepRunInputArtifactSchema.artifact_id,
                        StepRunInputArtifactSchema.step_id == StepRunSchema.id,
                    ),
                ),
                StepRunSchema.pipeline_run_id == PipelineRunSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.pipeline_run, table=PipelineRunSchema
                ),
            )
            custom_filters.append(pipeline_run_filter)

        return custom_filters

    @model_validator(mode="after")
    def _migrate_artifact_id(self) -> "ArtifactVersionFilter":
        """Migrate value from the deprecated artifact_id attribute.

        Returns:
            The filter with migrated value.
        """
        # Handle deprecated artifact_id field
        if self.artifact_id is not None:
            logger.warning(
                "The 'ArtifactVersionFilter.artifact_id' field is deprecated "
                "and will be removed in a future version. Please use "
                "'ArtifactVersionFilter.artifact' instead."
            )
            self.artifact = self.artifact or self.artifact_id

        return self

get_custom_filters(table)

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[Union[ColumnElement[bool]]]	A list of custom filters.

Source code in src/zenml/models/v2/core/artifact_version.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List[Union["ColumnElement[bool]"]]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_, or_, select

    from zenml.zen_stores.schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
        ModelSchema,
        ModelVersionArtifactSchema,
        ModelVersionSchema,
        PipelineRunSchema,
        StepRunInputArtifactSchema,
        StepRunOutputArtifactSchema,
        StepRunSchema,
    )

    if self.artifact:
        value, operator = self._resolve_operator(self.artifact)
        artifact_filter = and_(
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.artifact, table=ArtifactSchema
            ),
        )
        custom_filters.append(artifact_filter)

    if self.only_unused:
        unused_filter = and_(
            ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                select(StepRunOutputArtifactSchema.artifact_id)
            ),
            ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                select(StepRunInputArtifactSchema.artifact_id)
            ),
        )
        custom_filters.append(unused_filter)

    if self.model_version_id:
        value, operator = self._resolve_operator(self.model_version_id)

        model_version_filter = and_(
            ArtifactVersionSchema.id
            == ModelVersionArtifactSchema.artifact_version_id,
            ModelVersionArtifactSchema.model_version_id
            == ModelVersionSchema.id,
            FilterGenerator(ModelVersionSchema)
            .define_filter(column="id", value=value, operator=operator)
            .generate_query_conditions(ModelVersionSchema),
        )
        custom_filters.append(model_version_filter)

    if self.has_custom_name is not None:
        custom_name_filter = and_(
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            ArtifactSchema.has_custom_name == self.has_custom_name,
        )
        custom_filters.append(custom_name_filter)

    if self.model:
        model_filter = and_(
            ArtifactVersionSchema.id
            == ModelVersionArtifactSchema.artifact_version_id,
            ModelVersionArtifactSchema.model_version_id
            == ModelVersionSchema.id,
            ModelVersionSchema.model_id == ModelSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.model, table=ModelSchema
            ),
        )
        custom_filters.append(model_filter)

    if self.pipeline_run:
        pipeline_run_filter = and_(
            or_(
                and_(
                    ArtifactVersionSchema.id
                    == StepRunOutputArtifactSchema.artifact_id,
                    StepRunOutputArtifactSchema.step_id
                    == StepRunSchema.id,
                ),
                and_(
                    ArtifactVersionSchema.id
                    == StepRunInputArtifactSchema.artifact_id,
                    StepRunInputArtifactSchema.step_id == StepRunSchema.id,
                ),
            ),
            StepRunSchema.pipeline_run_id == PipelineRunSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.pipeline_run, table=PipelineRunSchema
            ),
        )
        custom_filters.append(pipeline_run_filter)

    return custom_filters

ArtifactVersionRequest

Bases: ProjectScopedRequest

Request model for artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionRequest(ProjectScopedRequest):
    """Request model for artifact versions."""

    artifact_id: Optional[UUID] = Field(
        default=None,
        title="ID of the artifact to which this version belongs.",
    )
    artifact_name: Optional[str] = Field(
        default=None,
        title="Name of the artifact to which this version belongs.",
    )
    version: Optional[Union[int, str]] = Field(
        default=None, title="Version of the artifact."
    )
    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )
    type: ArtifactType = Field(title="Type of the artifact.")
    artifact_store_id: Optional[UUID] = Field(
        title="ID of the artifact store in which this artifact is stored.",
        default=None,
    )
    uri: str = Field(
        title="URI of the artifact.", max_length=TEXT_FIELD_MAX_LENGTH
    )
    materializer: SourceWithValidator = Field(
        title="Materializer class to use for this artifact.",
    )
    data_type: SourceWithValidator = Field(
        title="Data type of the artifact.",
    )
    tags: Optional[List[str]] = Field(
        title="Tags of the artifact.",
        description="Should be a list of plain strings, e.g., ['tag1', 'tag2']",
        default=None,
    )
    visualizations: Optional[List["ArtifactVisualizationRequest"]] = Field(
        default=None, title="Visualizations of the artifact."
    )
    save_type: ArtifactSaveType = Field(
        title="The save type of the artifact version.",
    )
    metadata: Optional[Dict[str, MetadataType]] = Field(
        default=None, title="Metadata of the artifact version."
    )

    @field_validator("version")
    @classmethod
    def str_field_max_length_check(cls, value: Any) -> Any:
        """Checks if the length of the value exceeds the maximum str length.

        Args:
            value: the value set in the field

        Returns:
            the value itself.

        Raises:
            AssertionError: if the length of the field is longer than the
                maximum threshold.
        """
        assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
            "The length of the value for this field can not "
            f"exceed {STR_FIELD_MAX_LENGTH}"
        )
        return value

    @model_validator(mode="after")
    def _validate_request(self) -> "ArtifactVersionRequest":
        """Validate the request values.

        Raises:
            ValueError: If the request is invalid.

        Returns:
            The validated request.
        """
        if self.artifact_id and self.artifact_name:
            raise ValueError(
                "Only one of artifact_name and artifact_id can be set."
            )

        if not (self.artifact_id or self.artifact_name):
            raise ValueError(
                "Either artifact_name or artifact_id must be set."
            )

        return self

str_field_max_length_check(value) classmethod

Checks if the length of the value exceeds the maximum str length.

Parameters:

Name	Type	Description	Default
value	Any	the value set in the field	required

Returns:

Type	Description
Any	the value itself.

Raises:

Type	Description
AssertionError	if the length of the field is longer than the maximum threshold.

Source code in src/zenml/models/v2/core/artifact_version.py

@field_validator("version")
@classmethod
def str_field_max_length_check(cls, value: Any) -> Any:
    """Checks if the length of the value exceeds the maximum str length.

    Args:
        value: the value set in the field

    Returns:
        the value itself.

    Raises:
        AssertionError: if the length of the field is longer than the
            maximum threshold.
    """
    assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
        "The length of the value for this field can not "
        f"exceed {STR_FIELD_MAX_LENGTH}"
    )
    return value

ArtifactVersionResponse

Bases: ProjectScopedResponse[ArtifactVersionResponseBody, ArtifactVersionResponseMetadata, ArtifactVersionResponseResources]

Response model for artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponse(
    ProjectScopedResponse[
        ArtifactVersionResponseBody,
        ArtifactVersionResponseMetadata,
        ArtifactVersionResponseResources,
    ]
):
    """Response model for artifact versions."""

    def get_hydrated_version(self) -> "ArtifactVersionResponse":
        """Get the hydrated version of this artifact version.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact_version(self.id)

    # Body and metadata properties
    @property
    def artifact(self) -> "ArtifactResponse":
        """The `artifact` property.

        Returns:
            the value of the property.
        """
        return self.get_body().artifact

    @property
    def version(self) -> Union[str, int]:
        """The `version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().version

    @property
    def uri(self) -> str:
        """The `uri` property.

        Returns:
            the value of the property.
        """
        return self.get_body().uri

    @property
    def type(self) -> ArtifactType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def producer_pipeline_run_id(self) -> Optional[UUID]:
        """The `producer_pipeline_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().producer_pipeline_run_id

    @property
    def save_type(self) -> ArtifactSaveType:
        """The `save_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().save_type

    @property
    def artifact_store_id(self) -> Optional[UUID]:
        """The `artifact_store_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().artifact_store_id

    @property
    def producer_step_run_id(self) -> Optional[UUID]:
        """The `producer_step_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().producer_step_run_id

    @property
    def visualizations(
        self,
    ) -> Optional[List["ArtifactVisualizationResponse"]]:
        """The `visualizations` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().visualizations

    @property
    def run_metadata(self) -> Dict[str, MetadataType]:
        """The `metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    @property
    def materializer(self) -> Source:
        """The `materializer` property.

        Returns:
            the value of the property.
        """
        return self.get_body().materializer

    @property
    def data_type(self) -> Source:
        """The `data_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().data_type

    # Helper methods
    @property
    def name(self) -> str:
        """The `name` property.

        Returns:
            the value of the property.
        """
        return self.artifact.name

    @property
    def step(self) -> "StepRunResponse":
        """Get the step that produced this artifact.

        Returns:
            The step that produced this artifact.
        """
        from zenml.artifacts.utils import get_producer_step_of_artifact

        return get_producer_step_of_artifact(self)

    @property
    def run(self) -> "PipelineRunResponse":
        """Get the pipeline run that produced this artifact.

        Returns:
            The pipeline run that produced this artifact.
        """
        from zenml.client import Client

        return Client().get_pipeline_run(self.step.pipeline_run_id)

    def load(self) -> Any:
        """Materializes (loads) the data stored in this artifact.

        Returns:
            The materialized data.
        """
        from zenml.artifacts.utils import load_artifact_from_response

        return load_artifact_from_response(self)

    def download_files(self, path: str, overwrite: bool = False) -> None:
        """Downloads data for an artifact with no materializing.

        Any artifacts will be saved as a zip file to the given path.

        Args:
            path: The path to save the binary data to.
            overwrite: Whether to overwrite the file if it already exists.

        Raises:
            ValueError: If the path does not end with '.zip'.
        """
        if not path.endswith(".zip"):
            raise ValueError(
                "The path should end with '.zip' to save the binary data."
            )
        from zenml.artifacts.utils import (
            download_artifact_files_from_response,
        )

        download_artifact_files_from_response(
            self,
            path=path,
            overwrite=overwrite,
        )

    def visualize(self, title: Optional[str] = None) -> None:
        """Visualize the artifact in notebook environments.

        Args:
            title: Optional title to show before the visualizations.
        """
        from zenml.utils.visualization_utils import visualize_artifact

        visualize_artifact(self, title=title)

artifact property

The artifact property.

Returns:

Type	Description
ArtifactResponse	the value of the property.

artifact_store_id property

The artifact_store_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

data_type property

The data_type property.

Returns:

Type	Description
Source	the value of the property.

materializer property

The materializer property.

Returns:

Type	Description
Source	the value of the property.

name property

The name property.

Returns:

Type	Description
str	the value of the property.

producer_pipeline_run_id property

The producer_pipeline_run_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

producer_step_run_id property

The producer_step_run_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

run property

Get the pipeline run that produced this artifact.

Returns:

Type	Description
PipelineRunResponse	The pipeline run that produced this artifact.

run_metadata property

The metadata property.

Returns:

Type	Description
Dict[str, MetadataType]	the value of the property.

save_type property

The save_type property.

Returns:

Type	Description
ArtifactSaveType	the value of the property.

step property

Get the step that produced this artifact.

Returns:

Type	Description
StepRunResponse	The step that produced this artifact.

tags property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

type property

The type property.

Returns:

Type	Description
ArtifactType	the value of the property.

uri property

The uri property.

Returns:

Type	Description
str	the value of the property.

version property

The version property.

Returns:

Type	Description
Union[str, int]	the value of the property.

visualizations property

The visualizations property.

Returns:

Type	Description
Optional[List[ArtifactVisualizationResponse]]	the value of the property.

download_files(path, overwrite=False)

Downloads data for an artifact with no materializing.

Any artifacts will be saved as a zip file to the given path.

Parameters:

Name	Type	Description	Default
path	str	The path to save the binary data to.	required
overwrite	bool	Whether to overwrite the file if it already exists.	False

Raises:

Type	Description
ValueError	If the path does not end with '.zip'.

Source code in src/zenml/models/v2/core/artifact_version.py

def download_files(self, path: str, overwrite: bool = False) -> None:
    """Downloads data for an artifact with no materializing.

    Any artifacts will be saved as a zip file to the given path.

    Args:
        path: The path to save the binary data to.
        overwrite: Whether to overwrite the file if it already exists.

    Raises:
        ValueError: If the path does not end with '.zip'.
    """
    if not path.endswith(".zip"):
        raise ValueError(
            "The path should end with '.zip' to save the binary data."
        )
    from zenml.artifacts.utils