Client

zenml.client

Client implementation.

Attributes

AnyResponse = TypeVar('AnyResponse', bound=BaseIdentifiedResponse) module-attribute

ENV_ZENML_ACTIVE_PROJECT_ID = 'ZENML_ACTIVE_PROJECT_ID' module-attribute

ENV_ZENML_ACTIVE_STACK_ID = 'ZENML_ACTIVE_STACK_ID' module-attribute

ENV_ZENML_ENABLE_REPO_INIT_WARNINGS = 'ZENML_ENABLE_REPO_INIT_WARNINGS' module-attribute

ENV_ZENML_REPOSITORY_PATH = 'ZENML_REPOSITORY_PATH' module-attribute

ENV_ZENML_SERVER = 'ZENML_SERVER' module-attribute

F = TypeVar('F', bound=Callable[..., Any]) module-attribute

MetadataType = Union[str, int, float, bool, Dict[Any, Any], List[Any], Set[Any], Tuple[Any, ...], Uri, Path, DType, StorageSize] module-attribute

PAGE_SIZE_DEFAULT: int = handle_int_env_var(ENV_ZENML_PAGINATION_DEFAULT_LIMIT, default=20) module-attribute

PAGINATION_STARTING_PAGE: int = 1 module-attribute

REPOSITORY_DIRECTORY_NAME = '.zen' module-attribute

TEXT_FIELD_MAX_LENGTH = 65535 module-attribute

logger = get_logger(__name__) module-attribute

Classes

APIKeyFilter

Bases: BaseFilter

Filter model for API keys.

Functions

apply_filter(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

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: UUID) -> None

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

APIKeyRequest

Bases: BaseRequest

Request model for API keys.

APIKeyResponse

Bases: BaseIdentifiedResponse[APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources]

Response model for API keys.

Attributes

active: bool property

The active property.

Returns:

Type	Description
bool	the value of the property.

description: str property

The description property.

Returns:

Type	Description
str	the value of the property.

key: Optional[str] property

The key property.

Returns:

Type	Description
Optional[str]	the value of the property.

last_login: Optional[datetime] property

The last_login property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

last_rotated: Optional[datetime] property

The last_rotated property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

retain_period_minutes: int property

The retain_period_minutes property.

Returns:

Type	Description
int	the value of the property.

service_account: ServiceAccountResponse property

The service_account property.

Returns:

Type	Description
ServiceAccountResponse	the value of the property.

Functions

get_hydrated_version() -> APIKeyResponse

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: str) -> None

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()

APIKeyRotateRequest

Bases: BaseRequest

Request model for API key rotation.

APIKeyUpdate

Bases: BaseUpdate

Update model for API keys.

ActionFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all actions.

ActionRequest

Bases: ProjectScopedRequest

Model for creating a new action.

ActionResponse

Bases: ProjectScopedResponse[ActionResponseBody, ActionResponseMetadata, ActionResponseResources]

Response model for actions.

Attributes

auth_window: int property

The auth_window property.

Returns:

Type	Description
int	the value of the property.

configuration: Dict[str, Any] property

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

description: str property

The description property.

Returns:

Type	Description
str	the value of the property.

flavor: str property

The flavor property.

Returns:

Type	Description
str	the value of the property.

plugin_subtype: PluginSubType property

The plugin_subtype property.

Returns:

Type	Description
PluginSubType	the value of the property.

service_account: UserResponse property

The service_account property.

Returns:

Type	Description
UserResponse	the value of the property.

Functions

get_hydrated_version() -> ActionResponse

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: Dict[str, Any]) -> None

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

ActionUpdate

Bases: BaseUpdate

Update model for actions.

Functions

from_response(response: ActionResponse) -> ActionUpdate 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.

Functions

apply_sorting(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

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)

ArtifactResponse

Bases: ProjectScopedResponse[ArtifactResponseBody, ArtifactResponseMetadata, ArtifactResponseResources]

Artifact response model.

Attributes

has_custom_name: bool property

The has_custom_name property.

Returns:

Type	Description
bool	the value of the property.

latest_version_id: Optional[UUID] property

The latest_version_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

latest_version_name: Optional[str] property

The latest_version_name property.

Returns:

Type	Description
Optional[str]	the value of the property.

tags: List[TagResponse] property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

versions: Dict[str, ArtifactVersionResponse] property

Get a list of all versions of this artifact.

Returns:

Type	Description
Dict[str, ArtifactVersionResponse]	A list of all versions of this artifact.

Functions

get_hydrated_version() -> ArtifactResponse

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)

ArtifactType

Bases: StrEnum

All possible types an artifact can have.

ArtifactUpdate

Bases: BaseUpdate

Artifact update model.

ArtifactVersionFilter

Bases: ProjectScopedFilter, TaggableFilter, RunMetadataFilterMixin

Model to enable advanced filtering of artifact versions.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[Union[ColumnElement[bool]]]

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

ArtifactVersionResponse

Bases: ProjectScopedResponse[ArtifactVersionResponseBody, ArtifactVersionResponseMetadata, ArtifactVersionResponseResources]

Response model for artifact versions.

Attributes

artifact: ArtifactResponse property

The artifact property.

Returns:

Type	Description
ArtifactResponse	the value of the property.

artifact_store_id: Optional[UUID] property

The artifact_store_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

data_type: Source property

The data_type property.

Returns:

Type	Description
Source	the value of the property.

materializer: Source property

The materializer property.

Returns:

Type	Description
Source	the value of the property.

name: str property

The name property.

Returns:

Type	Description
str	the value of the property.

producer_pipeline_run_id: Optional[UUID] property

The producer_pipeline_run_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

producer_step_run_id: Optional[UUID] property

The producer_step_run_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

run: PipelineRunResponse property

Get the pipeline run that produced this artifact.

Returns:

Type	Description
PipelineRunResponse	The pipeline run that produced this artifact.

run_metadata: Dict[str, MetadataType] property

The metadata property.

Returns:

Type	Description
Dict[str, MetadataType]	the value of the property.

save_type: ArtifactSaveType property

The save_type property.

Returns:

Type	Description
ArtifactSaveType	the value of the property.

step: StepRunResponse property

Get the step that produced this artifact.

Returns:

Type	Description
StepRunResponse	The step that produced this artifact.

tags: List[TagResponse] property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

type: ArtifactType property

The type property.

Returns:

Type	Description
ArtifactType	the value of the property.

uri: str property

The uri property.

Returns:

Type	Description
str	the value of the property.

version: Union[str, int] property

The version property.

Returns:

Type	Description
Union[str, int]	the value of the property.

visualizations: Optional[List[ArtifactVisualizationResponse]] property

The visualizations property.

Returns:

Type	Description
Optional[List[ArtifactVisualizationResponse]]	the value of the property.

Functions

download_files(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.

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 import (
        download_artifact_files_from_response,
    )

    download_artifact_files_from_response(
        self,
        path=path,
        overwrite=overwrite,
    )

get_hydrated_version() -> ArtifactVersionResponse

Get the hydrated version of this artifact version.

Returns:

Type	Description
ArtifactVersionResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/artifact_version.py

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)

load() -> Any

Materializes (loads) the data stored in this artifact.

Returns:

Type	Description
Any	The materialized data.

Source code in src/zenml/models/v2/core/artifact_version.py

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)

visualize(title: Optional[str] = None) -> None

Visualize the artifact in notebook environments.

Parameters:

Name	Type	Description	Default
title	Optional[str]	Optional title to show before the visualizations.	None

Source code in src/zenml/models/v2/core/artifact_version.py

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)

ArtifactVersionUpdate

Bases: BaseUpdate

Artifact version update model.

AuthorizationException(message: Optional[str] = None, url: Optional[str] = None)

Bases: ZenMLBaseException

Raised when an authorization error occurred while trying to access a ZenML resource .

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)

BaseIdentifiedResponse

Bases: BaseResponse[AnyDatedBody, AnyMetadata, AnyResources], Generic[AnyDatedBody, AnyMetadata, AnyResources]

Base domain model for resources with DB representation.

Attributes

created: datetime property

The created property.

Returns:

Type	Description
datetime	the value of the property.

updated: datetime property

The updated property.

Returns:

Type	Description
datetime	the value of the property.

Functions

get_analytics_metadata() -> Dict[str, Any]

Fetches the analytics metadata for base response models.

Returns:

Type	Description
Dict[str, Any]	The analytics metadata.

Source code in src/zenml/models/v2/base/base.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for base response models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata["entity_id"] = self.id
    return metadata

get_body() -> AnyDatedBody

Fetch the body of the entity.

Returns:

Type	Description
AnyDatedBody	The body field of the response.

Raises:

Type	Description
IllegalOperationError	If the user lacks permission to access the entity represented by this response.

Source code in src/zenml/models/v2/base/base.py

def get_body(self) -> "AnyDatedBody":
    """Fetch the body of the entity.

    Returns:
        The body field of the response.

    Raises:
        IllegalOperationError: If the user lacks permission to access the
            entity represented by this response.
    """
    if self.permission_denied:
        raise IllegalOperationError(
            f"Missing permissions to access {type(self).__name__} with "
            f"ID {self.id}."
        )

    return super().get_body()

get_hydrated_version() -> BaseIdentifiedResponse[AnyDatedBody, AnyMetadata, AnyResources]

Abstract method to fetch the hydrated version of the model.

Raises:

Type	Description
NotImplementedError	in case the method is not implemented.

Source code in src/zenml/models/v2/base/base.py

def get_hydrated_version(
    self,
) -> "BaseIdentifiedResponse[AnyDatedBody, AnyMetadata, AnyResources]":
    """Abstract method to fetch the hydrated version of the model.

    Raises:
        NotImplementedError: in case the method is not implemented.
    """
    raise NotImplementedError(
        "Please implement a `get_hydrated_version` method before "
        "using/hydrating the model."
    )

get_metadata() -> AnyMetadata

Fetch the metadata of the entity.

Returns:

Type	Description
AnyMetadata	The metadata field of the response.

Raises:

Type	Description
IllegalOperationError	If the user lacks permission to access this entity represented by this response.

Source code in src/zenml/models/v2/base/base.py

def get_metadata(self) -> "AnyMetadata":
    """Fetch the metadata of the entity.

    Returns:
        The metadata field of the response.

    Raises:
        IllegalOperationError: If the user lacks permission to access this
            entity represented by this response.
    """
    if self.permission_denied:
        raise IllegalOperationError(
            f"Missing permissions to access {type(self).__name__} with "
            f"ID {self.id}."
        )

    return super().get_metadata()

BaseZenStore(skip_default_registrations: bool = False, **kwargs: Any)

Bases: BaseModel, ZenStoreInterface, ABC

Base class for accessing and persisting ZenML core objects.

Attributes:

Name	Type	Description
config	StoreConfiguration	The configuration of the store.

Create and initialize a store.

Parameters:

Name	Type	Description	Default
skip_default_registrations	bool	If True, the creation of the default stack and user in the store will be skipped.	False
**kwargs	Any	Additional keyword arguments to pass to the Pydantic constructor.	{}

Source code in src/zenml/zen_stores/base_zen_store.py

def __init__(
    self,
    skip_default_registrations: bool = False,
    **kwargs: Any,
) -> None:
    """Create and initialize a store.

    Args:
        skip_default_registrations: If `True`, the creation of the default
            stack and user in the store will be skipped.
        **kwargs: Additional keyword arguments to pass to the Pydantic
            constructor.
    """
    super().__init__(**kwargs)

    self._initialize()

    if not skip_default_registrations:
        logger.debug("Initializing database")
        self._initialize_database()
    else:
        logger.debug("Skipping database initialization")

Attributes

type: StoreType property

The type of the store.

Returns:

Type	Description
StoreType	The type of the store.

url: str property

The URL of the store.

Returns:

Type	Description
str	The URL of the store.

Functions

convert_config(data: Dict[str, Any]) -> Dict[str, Any] classmethod

Method to infer the correct type of the config and convert.

Parameters:

Name	Type	Description	Default
data	Dict[str, Any]	The provided configuration object, can potentially be a generic object	required

Raises:

Type	Description
ValueError	If the provided config object's type does not match any of the current implementations.

Returns:

Type	Description
Dict[str, Any]	The converted configuration object.

Source code in src/zenml/zen_stores/base_zen_store.py

@model_validator(mode="before")
@classmethod
@before_validator_handler
def convert_config(cls, data: Dict[str, Any]) -> Dict[str, Any]:
    """Method to infer the correct type of the config and convert.

    Args:
        data: The provided configuration object, can potentially be a
            generic object

    Raises:
        ValueError: If the provided config object's type does not match
            any of the current implementations.

    Returns:
        The converted configuration object.
    """
    if data["config"].type == StoreType.SQL:
        from zenml.zen_stores.sql_zen_store import SqlZenStoreConfiguration

        data["config"] = SqlZenStoreConfiguration(
            **data["config"].model_dump()
        )

    elif data["config"].type == StoreType.REST:
        from zenml.zen_stores.rest_zen_store import (
            RestZenStoreConfiguration,
        )

        data["config"] = RestZenStoreConfiguration(
            **data["config"].model_dump()
        )
    else:
        raise ValueError(
            f"Unknown type '{data['config'].type}' for the configuration."
        )

    return data

create_store(config: StoreConfiguration, skip_default_registrations: bool = False, **kwargs: Any) -> BaseZenStore staticmethod

Create and initialize a store from a store configuration.

Parameters:

Name	Type	Description	Default
config	StoreConfiguration	The store configuration to use.	required
skip_default_registrations	bool	If True, the creation of the default stack and user in the store will be skipped.	False
**kwargs	Any	Additional keyword arguments to pass to the store class	{}

Returns:

Type	Description
BaseZenStore	The initialized store.

Source code in src/zenml/zen_stores/base_zen_store.py

@staticmethod
def create_store(
    config: StoreConfiguration,
    skip_default_registrations: bool = False,
    **kwargs: Any,
) -> "BaseZenStore":
    """Create and initialize a store from a store configuration.

    Args:
        config: The store configuration to use.
        skip_default_registrations: If `True`, the creation of the default
            stack and user in the store will be skipped.
        **kwargs: Additional keyword arguments to pass to the store class

    Returns:
        The initialized store.
    """
    store_class = BaseZenStore.get_store_class(config.type)
    store = store_class(
        config=config,
        skip_default_registrations=skip_default_registrations,
        **kwargs,
    )

    return store

get_default_store_config(path: str) -> StoreConfiguration staticmethod

Get the default store configuration.

The default store is a SQLite store that saves the DB contents on the local filesystem.

Parameters:

Name	Type	Description	Default
path	str	The local path where the store DB will be stored.	required

Returns:

Type	Description
StoreConfiguration	The default store configuration.

Source code in src/zenml/zen_stores/base_zen_store.py

@staticmethod
def get_default_store_config(path: str) -> StoreConfiguration:
    """Get the default store configuration.

    The default store is a SQLite store that saves the DB contents on the
    local filesystem.

    Args:
        path: The local path where the store DB will be stored.

    Returns:
        The default store configuration.
    """
    from zenml.zen_stores.secrets_stores.sql_secrets_store import (
        SqlSecretsStoreConfiguration,
    )
    from zenml.zen_stores.sql_zen_store import SqlZenStoreConfiguration

    config = SqlZenStoreConfiguration(
        type=StoreType.SQL,
        url=SqlZenStoreConfiguration.get_local_url(path),
        secrets_store=SqlSecretsStoreConfiguration(
            type=SecretsStoreType.SQL,
        ),
    )
    return config

get_external_user(user_id: UUID) -> UserResponse

Get a user by external ID.

Parameters:

Name	Type	Description	Default
user_id	UUID	The external ID of the user.	required

Returns:

Type	Description
UserResponse	The user with the supplied external ID.

Raises:

Type	Description
KeyError	If the user doesn't exist.

Source code in src/zenml/zen_stores/base_zen_store.py

def get_external_user(self, user_id: UUID) -> UserResponse:
    """Get a user by external ID.

    Args:
        user_id: The external ID of the user.

    Returns:
        The user with the supplied external ID.

    Raises:
        KeyError: If the user doesn't exist.
    """
    users = self.list_users(UserFilter(external_user_id=user_id))
    if users.total == 0:
        raise KeyError(f"User with external ID '{user_id}' not found.")
    return users.items[0]

get_store_class(store_type: StoreType) -> Type[BaseZenStore] staticmethod

Returns the class of the given store type.

Parameters:

Name	Type	Description	Default
store_type	StoreType	The type of the store to get the class for.	required

Returns:

Type	Description
Type[BaseZenStore]	The class of the given store type or None if the type is unknown.

Raises:

Type	Description
TypeError	If the store type is unsupported.

Source code in src/zenml/zen_stores/base_zen_store.py

@staticmethod
def get_store_class(store_type: StoreType) -> Type["BaseZenStore"]:
    """Returns the class of the given store type.

    Args:
        store_type: The type of the store to get the class for.

    Returns:
        The class of the given store type or None if the type is unknown.

    Raises:
        TypeError: If the store type is unsupported.
    """
    if store_type == StoreType.SQL:
        if os.environ.get(ENV_ZENML_SERVER):
            from zenml.zen_server.rbac.rbac_sql_zen_store import (
                RBACSqlZenStore,
            )

            return RBACSqlZenStore
        else:
            from zenml.zen_stores.sql_zen_store import SqlZenStore

            return SqlZenStore
    elif store_type == StoreType.REST:
        from zenml.zen_stores.rest_zen_store import RestZenStore

        return RestZenStore
    else:
        raise TypeError(
            f"No store implementation found for store type "
            f"`{store_type.value}`."
        )

get_store_config_class(store_type: StoreType) -> Type[StoreConfiguration] staticmethod

Returns the store config class of the given store type.

Parameters:

Name	Type	Description	Default
store_type	StoreType	The type of the store to get the class for.	required

Returns:

Type	Description
Type[StoreConfiguration]	The config class of the given store type.

Source code in src/zenml/zen_stores/base_zen_store.py

@staticmethod
def get_store_config_class(
    store_type: StoreType,
) -> Type["StoreConfiguration"]:
    """Returns the store config class of the given store type.

    Args:
        store_type: The type of the store to get the class for.

    Returns:
        The config class of the given store type.
    """
    store_class = BaseZenStore.get_store_class(store_type)
    return store_class.CONFIG_TYPE

get_store_info() -> ServerModel

Get information about the store.

Returns:

Type	Description
ServerModel	Information about the store.

Source code in src/zenml/zen_stores/base_zen_store.py

def get_store_info(self) -> ServerModel:
    """Get information about the store.

    Returns:
        Information about the store.
    """
    from zenml.zen_stores.sql_zen_store import SqlZenStore

    server_config = ServerConfiguration.get_server_config()
    deployment_type = server_config.deployment_type
    auth_scheme = server_config.auth_scheme
    metadata = server_config.metadata
    secrets_store_type = SecretsStoreType.NONE
    if isinstance(self, SqlZenStore) and self.config.secrets_store:
        secrets_store_type = self.config.secrets_store.type
    store_info = ServerModel(
        id=GlobalConfiguration().user_id,
        active=True,
        version=zenml.__version__,
        deployment_type=deployment_type,
        database_type=ServerDatabaseType.OTHER,
        debug=IS_DEBUG_ENV,
        secrets_store_type=secrets_store_type,
        auth_scheme=auth_scheme,
        server_url=server_config.server_url or "",
        dashboard_url=server_config.dashboard_url or "",
        analytics_enabled=GlobalConfiguration().analytics_opt_in,
        metadata=metadata,
    )

    # Add ZenML Pro specific store information to the server model, if available.
    if store_info.deployment_type == ServerDeploymentType.CLOUD:
        from zenml.config.server_config import ServerProConfiguration

        pro_config = ServerProConfiguration.get_server_config()

        store_info.pro_api_url = pro_config.api_url
        store_info.pro_dashboard_url = pro_config.dashboard_url
        store_info.pro_organization_id = pro_config.organization_id
        store_info.pro_workspace_id = pro_config.workspace_id
        if pro_config.workspace_name:
            store_info.pro_workspace_name = pro_config.workspace_name
        if pro_config.organization_name:
            store_info.pro_organization_name = pro_config.organization_name

    return store_info

get_store_type(url: str) -> StoreType staticmethod

Returns the store type associated with a URL schema.

Parameters:

Name	Type	Description	Default
url	str	The store URL.	required

Returns:

Type	Description
StoreType	The store type associated with the supplied URL schema.

Raises:

Type	Description
TypeError	If no store type was found to support the supplied URL.

Source code in src/zenml/zen_stores/base_zen_store.py

@staticmethod
def get_store_type(url: str) -> StoreType:
    """Returns the store type associated with a URL schema.

    Args:
        url: The store URL.

    Returns:
        The store type associated with the supplied URL schema.

    Raises:
        TypeError: If no store type was found to support the supplied URL.
    """
    from zenml.zen_stores.rest_zen_store import RestZenStoreConfiguration
    from zenml.zen_stores.sql_zen_store import SqlZenStoreConfiguration

    if SqlZenStoreConfiguration.supports_url_scheme(url):
        return StoreType.SQL
    elif RestZenStoreConfiguration.supports_url_scheme(url):
        return StoreType.REST
    else:
        raise TypeError(f"No store implementation found for URL: {url}.")

is_local_store() -> bool

Check if the store is local or connected to a local ZenML server.

Returns:

Type	Description
bool	True if the store is local, False otherwise.

Source code in src/zenml/zen_stores/base_zen_store.py

def is_local_store(self) -> bool:
    """Check if the store is local or connected to a local ZenML server.

    Returns:
        True if the store is local, False otherwise.
    """
    return self.get_store_info().is_local()

validate_active_config(active_project_id: Optional[UUID] = None, active_stack_id: Optional[UUID] = None, config_name: str = '') -> Tuple[Optional[ProjectResponse], StackResponse]

Validate the active configuration.

Call this method to validate the supplied active project and active stack values.

This method returns a valid project and stack values. If the supplied project and stack are not set or are not valid (e.g. they do not exist or are not accessible), the default project and default stack will be returned in their stead.

Parameters:

Name	Type	Description	Default
active_project_id	Optional[UUID]	The ID of the active project.	None
active_stack_id	Optional[UUID]	The ID of the active stack.	None
config_name	str	The name of the configuration to validate (used in the displayed logs/messages).	''

Returns:

Type	Description
Tuple[Optional[ProjectResponse], StackResponse]	A tuple containing the active project and active stack.

Source code in src/zenml/zen_stores/base_zen_store.py

def validate_active_config(
    self,
    active_project_id: Optional[UUID] = None,
    active_stack_id: Optional[UUID] = None,
    config_name: str = "",
) -> Tuple[Optional[ProjectResponse], StackResponse]:
    """Validate the active configuration.

    Call this method to validate the supplied active project and active
    stack values.

    This method returns a valid project and stack values. If the
    supplied project and stack are not set or are not valid (e.g. they
    do not exist or are not accessible), the default project and default
    stack will be returned in their stead.

    Args:
        active_project_id: The ID of the active project.
        active_stack_id: The ID of the active stack.
        config_name: The name of the configuration to validate (used in the
            displayed logs/messages).

    Returns:
        A tuple containing the active project and active stack.
    """
    active_project: Optional[ProjectResponse] = None

    if active_project_id:
        try:
            active_project = self.get_project(active_project_id)
        except (KeyError, IllegalOperationError):
            active_project_id = None
            logger.warning(
                f"The current {config_name} active project is no longer "
                f"available."
            )

    if active_project is None:
        user = self.get_user()
        if user.default_project_id:
            try:
                active_project = self.get_project(user.default_project_id)
            except (KeyError, IllegalOperationError):
                logger.warning(
                    "The default project %s for the active user is no longer "
                    "available.",
                    user.default_project_id,
                )
            else:
                logger.info(
                    f"Setting the {config_name} active project "
                    f"to '{active_project.name}'."
                )

    if active_project is None:
        try:
            projects = self.list_projects(
                project_filter_model=ProjectFilter()
            )
        except Exception:
            # There was some failure, we force the user to set the active
            # project manually
            logger.warning(
                "An active project is not set. Please set the active "
                "project by running `zenml project set <NAME>`."
            )
        else:
            if len(projects) == 0:
                logger.warning(
                    "No available projects. Please create a project by "
                    "running `zenml project register <NAME> --set`."
                )
            elif len(projects) == 1:
                active_project = projects.items[0]
                logger.info(
                    f"Setting the {config_name} active project "
                    f"to '{active_project.name}'."
                )
            else:
                logger.warning(
                    "Multiple projects are available. Please set the "
                    "active project by running `zenml project set <NAME>`."
                )

    active_stack: StackResponse

    # Sanitize the active stack
    if active_stack_id:
        # Ensure that the active stack is still valid
        try:
            active_stack = self.get_stack(stack_id=active_stack_id)
        except (KeyError, IllegalOperationError):
            logger.warning(
                "The current %s active stack is no longer available. "
                "Resetting the active stack to default.",
                config_name,
            )
            active_stack = self._get_default_stack()

    else:
        logger.warning(
            "Setting the %s active stack to default.",
            config_name,
        )
        active_stack = self._get_default_stack()

    return active_project, active_stack

Client(root: Optional[Path] = None)

ZenML client class.

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

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)

Attributes

active_project: ProjectResponse 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: Stack property

The active stack for this client.

Returns:

Type	Description
Stack	The active stack for this client.

active_stack_model: StackResponse 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: UserResponse property

Get the user that is currently in use.

Returns:

Type	Description
UserResponse	The active user.

config_directory: Optional[Path] 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: Optional[Path] 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: bool 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: BaseZenStore property

Shortcut to return the global zen store.

Returns:

Type	Description
BaseZenStore	The global zen store.

Functions

activate_root(root: Optional[Path] = None) -> 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: Union[str, UUID]) -> None

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: Union[str, UUID], resources: List[TagResource]) -> None

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: bool = True, delete_secrets: bool = False) -> None

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: 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.

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: 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.

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: str, config: Dict[str, Any], source: Source, description: Optional[str] = None, logo_url: Optional[str] = None) -> CodeRepositoryResponse

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: str, configuration: Dict[str, Any], flavor: str, event_source_subtype: PluginSubType, description: str = '') -> EventSourceResponse

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: str, component_type: StackComponentType) -> FlavorResponse

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: 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.

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: 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.

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: str, description: str, display_name: Optional[str] = None) -> ProjectResponse

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: Dict[str, MetadataType], resources: List[RunMetadataResource], stack_component_id: Optional[UUID] = None, publisher_step_id: Optional[UUID] = None) -> 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: str, deployment_id: UUID, description: Optional[str] = None, tags: Optional[List[str]] = None) -> RunTemplateResponse

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: str, values: Dict[str, str], private: bool = False) -> SecretResponse

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: ServiceConfig, service_type: ServiceType, model_version_id: Optional[UUID] = None) -> ServiceResponse

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: str, description: str = '') -> ServiceAccountResponse

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: 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.

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: 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.

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: str, flavor: str, component_type: StackComponentType, configuration: Dict[str, str], labels: Optional[Dict[str, Any]] = None) -> ComponentResponse

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: str, exclusive: bool = False, color: Optional[Union[str, ColorVariants]] = None) -> TagResponse

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: str, event_source_id: UUID, event_filter: Dict[str, Any], action_id: UUID, description: str = '') -> TriggerResponse

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: str, password: Optional[str] = None, is_admin: bool = False) -> UserResponse

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: str) -> UserResponse

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: UUID, only_links: bool) -> None

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: Union[str, UUID], name_id_or_prefix: Union[str, UUID]) -> None

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: 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.

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: Union[str, UUID]) -> None

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: str, project: Optional[Union[str, UUID]] = None) -> 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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: str, project: Optional[Union[str, UUID]] = None) -> 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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: str) -> None

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: UUID) -> None

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: UUID, artifact_version_id: UUID) -> None

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: str) -> None

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: str, private: Optional[bool] = None) -> 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: UUID, project: Optional[Union[str, UUID]] = None) -> 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: Union[str, UUID]) -> None

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: Union[str, UUID]) -> None

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: Union[str, UUID], recursive: bool = False) -> None

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: Union[str, UUID], component_type: StackComponentType) -> None

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: Union[str, UUID]) -> None

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None) -> 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: UUID) -> None

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: str) -> None

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: Union[str, UUID], resources: List[TagResource]) -> None

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: Optional[Path] = None, enable_warnings: bool = False) -> Optional[Path] 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: 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.

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: 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.

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None, hydrate: bool = False) -> ArtifactResponse

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: 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.

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: Union[UUID, str], allow_id_prefix_match: bool = True, hydrate: bool = True) -> OAuthDeviceResponse

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None, hydrate: bool = True) -> PipelineBuildResponse

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: 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.

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None, hydrate: bool = True) -> PipelineDeploymentResponse

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: 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.

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: str, allow_name_prefix_match: bool = True, hydrate: bool = True) -> FlavorResponse

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: str, component_type: StackComponentType) -> FlavorResponse

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: StackComponentType) -> Page[FlavorResponse]

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() -> Optional[Client] 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: 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.

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: 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.

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None, hydrate: bool = True) -> PipelineResponse

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: 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.

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: Optional[Union[UUID, str]], allow_name_prefix_match: bool = True, hydrate: bool = True) -> ProjectResponse

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: UUID, hydrate: bool = True) -> StepRunResponse

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: Union[str, UUID], project: Optional[Union[str, UUID]] = None, hydrate: bool = True) -> RunTemplateResponse

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: 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.

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: 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.

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: 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.

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: 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.

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: Union[str, UUID], allow_name_prefix_match: bool = True, hydrate: bool = True) -> ServiceAccountResponse

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: Union[str, UUID], allow_name_prefix_match: bool = True, load_secrets: bool = False, hydrate: bool = True) -> ServiceConnectorResponse

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: 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.

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: str) -> ServiceConnectorTypeModel

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: bool = True) -> ServerSettingsResponse

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: 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.

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: 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.

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: Union[str, UUID], hydrate: bool = True) -> TagResponse

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: 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.

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: UUID, hydrate: bool = True) -> TriggerExecutionResponse

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: Union[str, UUID], allow_name_prefix_match: bool = True, hydrate: bool = True) -> UserResponse

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: Optional[Path] = None) -> 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: str) -> bool 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: Path) -> bool 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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: Optional[str] = None, resource_type: Optional[str] = None, resource_id: Optional[str] = None) -> List[ServiceConnectorResourcesModel]

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: Optional[str] = None, resource_type: Optional[str] = None, auth_method: Optional[str] = None) -> List[ServiceConnectorTypeModel]

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: bool = True, delete_from_artifact_store: bool = False, project: Optional[Union[str, UUID]] = None) -> 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: bool = False, delete_secrets: bool = False) -> None

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: Union[str, UUID], name_id_or_prefix: Union[UUID, str], retain_period_minutes: int = 0, set_key: bool = False) -> APIKeyResponse

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: Union[str, UUID]) -> ProjectResponse

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: str) -> None

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: 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:

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: 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.

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: 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.

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: 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.

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: 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.

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: Union[UUID, str], locked: Optional[bool] = None) -> OAuthDeviceResponse

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: 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.

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: 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.

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: 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.

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: 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.

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: Optional[Union[UUID, str]], new_name: Optional[str] = None, new_display_name: Optional[str] = None, new_description: Optional[str] = None) -> ProjectResponse

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: 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.

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: 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.

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: 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.

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: 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.

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: Union[str, UUID], updated_name: Optional[str] = None, description: Optional[str] = None, active: Optional[bool] = None) -> ServiceAccountResponse

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: 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.

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: 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.

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: 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.

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: Union[str, UUID], name: Optional[str] = None, exclusive: Optional[bool] = None, color: Optional[Union[str, ColorVariants]] = None) -> TagResponse

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: 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.

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: 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.

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: 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.

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.

Attributes

active_project: ProjectResponse 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.

Functions

set_active_project(project: ProjectResponse) -> None

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: StackResponse) -> None

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(*args: Any, **kwargs: Any)

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).

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

Functions

CodeRepositoryFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all code repositories.

CodeRepositoryRequest

Bases: ProjectScopedRequest

Request model for code repositories.

CodeRepositoryResponse

Bases: ProjectScopedResponse[CodeRepositoryResponseBody, CodeRepositoryResponseMetadata, CodeRepositoryResponseResources]

Response model for code repositories.

Attributes

config: Dict[str, Any] property

The config property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

description: Optional[str] property

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

logo_url: Optional[str] property

The logo_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

source: Source property

The source property.

Returns:

Type	Description
Source	the value of the property.

Functions

get_hydrated_version() -> CodeRepositoryResponse

Get the hydrated version of this code repository.

Returns:

Type	Description
CodeRepositoryResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/code_repository.py

def get_hydrated_version(self) -> "CodeRepositoryResponse":
    """Get the hydrated version of this code repository.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_code_repository(self.id)

CodeRepositoryUpdate

Bases: BaseUpdate

Update model for code repositories.

ColorVariants

Bases: StrEnum

All possible color variants for frontend.

ComponentFilter

Bases: UserScopedFilter

Model to enable advanced stack component filtering.

Functions

generate_filter(table: Type[AnySchema]) -> Union[ColumnElement[bool]]

Generate the filter for the query.

Stack components can be scoped by type to narrow the search.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The Table that is being queried from.	required

Returns:

Type	Description
Union[ColumnElement[bool]]	The filter expression for the query.

Source code in src/zenml/models/v2/core/component.py

def generate_filter(
    self, table: Type["AnySchema"]
) -> Union["ColumnElement[bool]"]:
    """Generate the filter for the query.

    Stack components can be scoped by type to narrow the search.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.
    """
    from sqlmodel import and_, or_

    from zenml.zen_stores.schemas import (
        StackComponentSchema,
        StackCompositionSchema,
    )

    base_filter = super().generate_filter(table)
    if self.scope_type:
        type_filter = getattr(table, "type") == self.scope_type
        return and_(base_filter, type_filter)

    if self.stack_id:
        operator = (
            or_ if self.logical_operator == LogicalOperators.OR else and_
        )

        stack_filter = and_(
            StackCompositionSchema.stack_id == self.stack_id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
        )
        base_filter = operator(base_filter, stack_filter)

    return base_filter

set_scope_type(component_type: str) -> None

Set the type of component on which to perform the filtering to scope the response.

Parameters:

Name	Type	Description	Default
component_type	str	The type of component to scope the query to.	required

Source code in src/zenml/models/v2/core/component.py

def set_scope_type(self, component_type: str) -> None:
    """Set the type of component on which to perform the filtering to scope the response.

    Args:
        component_type: The type of component to scope the query to.
    """
    self.scope_type = component_type

ComponentRequest

Bases: ComponentBase, UserScopedRequest

Request model for stack components.

Functions

name_cant_be_a_secret_reference(name: str) -> str classmethod

Validator to ensure that the given name is not a secret reference.

Parameters:

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

Returns:

Type	Description
str	The name if it is not a secret reference.

Raises:

Type	Description
ValueError	If the name is a secret reference.

Source code in src/zenml/models/v2/core/component.py

@field_validator("name")
@classmethod
def name_cant_be_a_secret_reference(cls, name: str) -> str:
    """Validator to ensure that the given name is not a secret reference.

    Args:
        name: The name to validate.

    Returns:
        The name if it is not a secret reference.

    Raises:
        ValueError: If the name is a secret reference.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )
    return name

ComponentResponse

Bases: UserScopedResponse[ComponentResponseBody, ComponentResponseMetadata, ComponentResponseResources]

Response model for stack components.

Attributes

configuration: Dict[str, Any] property

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

connector: Optional[ServiceConnectorResponse] property

The connector property.

Returns:

Type	Description
Optional[ServiceConnectorResponse]	the value of the property.

connector_resource_id: Optional[str] property

The connector_resource_id property.

Returns:

Type	Description
Optional[str]	the value of the property.

flavor: FlavorResponse property

The flavor property.

Returns:

Type	Description
FlavorResponse	the value of the property.

flavor_name: str property

The flavor_name property.

Returns:

Type	Description
str	the value of the property.

integration: Optional[str] property

The integration property.

Returns:

Type	Description
Optional[str]	the value of the property.

labels: Optional[Dict[str, Any]] property

The labels property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

logo_url: Optional[str] property

The logo_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

type: StackComponentType property

The type property.

Returns:

Type	Description
StackComponentType	the value of the property.

Functions

get_analytics_metadata() -> Dict[str, Any]

Add the component labels to analytics metadata.

Returns:

Type	Description
Dict[str, Any]	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/component.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Add the component labels to analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()

    if self.labels is not None:
        metadata.update(
            {
                label[6:]: value
                for label, value in self.labels.items()
                if label.startswith("zenml:")
            }
        )
    metadata["flavor"] = self.flavor_name

    return metadata

get_hydrated_version() -> ComponentResponse

Get the hydrated version of this component.

Returns:

Type	Description
ComponentResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/component.py

def get_hydrated_version(self) -> "ComponentResponse":
    """Get the hydrated version of this component.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_stack_component(self.id)

ComponentUpdate

Bases: BaseUpdate

Update model for stack components.

EntityExistsError(message: Optional[str] = None, url: Optional[str] = None)

Bases: ZenMLBaseException

Raised when trying to register an entity that already exists.

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)

EventSourceFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all EventSourceModels.

EventSourceRequest

Bases: ProjectScopedRequest

BaseModel for all event sources.

EventSourceResponse

Bases: ProjectScopedResponse[EventSourceResponseBody, EventSourceResponseMetadata, EventSourceResponseResources]

Response model for event sources.

Attributes

configuration: Dict[str, Any] property

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

description: str property

The description property.

Returns:

Type	Description
str	the value of the property.

flavor: str property

The flavor property.

Returns:

Type	Description
str	the value of the property.

is_active: bool property

The is_active property.

Returns:

Type	Description
bool	the value of the property.

plugin_subtype: PluginSubType property

The plugin_subtype property.

Returns:

Type	Description
PluginSubType	the value of the property.

Functions

get_hydrated_version() -> EventSourceResponse

Get the hydrated version of this event source.

Returns:

Type	Description
EventSourceResponse	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/event_source.py

def get_hydrated_version(self) -> "EventSourceResponse":
    """Get the hydrated version of this event source.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_event_source(self.id)

set_configuration(configuration: Dict[str, Any]) -> None

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/event_source.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

EventSourceUpdate

Bases: BaseUpdate

Update model for event sources.

Functions

from_response(response: EventSourceResponse) -> EventSourceUpdate classmethod

Create an update model from a response model.

Parameters:

Name	Type	Description	Default
response	EventSourceResponse	The response model to create the update model from.	required

Returns:

Type	Description
EventSourceUpdate	The update model.

Source code in src/zenml/models/v2/core/event_source.py

@classmethod
def from_response(
    cls, response: "EventSourceResponse"
) -> "EventSourceUpdate":
    """Create an update model from a response model.

    Args:
        response: The response model to create the update model from.

    Returns:
        The update model.
    """
    return EventSourceUpdate(
        name=response.name,
        description=response.description,
        configuration=copy.deepcopy(response.configuration),
        is_active=response.is_active,
    )

FileSyncModel

Bases: BaseModel

Pydantic model synchronized with a configuration file.

Use this class as a base Pydantic model that is automatically synchronized with a configuration file on disk.

This class overrides the setattr and getattr magic methods to ensure that the FileSyncModel instance acts as an in-memory cache of the information stored in the associated configuration file.

Functions

config_validator(data: Any, handler: ValidatorFunctionWrapHandler, info: ValidationInfo) -> FileSyncModel classmethod

Wrap model validator to infer the config_file during initialization.

Parameters:

Name	Type	Description	Default
data	Any	The raw data that is provided before the validation.	required
handler	ValidatorFunctionWrapHandler	The actual validation function pydantic would use for the built-in validation function.	required
info	ValidationInfo	The context information during the execution of this validation function.	required

Returns:

Type	Description
FileSyncModel	the actual instance after the validation

Raises:

Type	Description
ValidationError	if you try to validate through a JSON string. You need to provide a config_file path when you create a FileSyncModel.
AssertionError	if the raw input does not include a config_file path for the configuration file.

Source code in src/zenml/utils/filesync_model.py

@model_validator(mode="wrap")
@classmethod
def config_validator(
    cls,
    data: Any,
    handler: ValidatorFunctionWrapHandler,
    info: ValidationInfo,
) -> "FileSyncModel":
    """Wrap model validator to infer the config_file during initialization.

    Args:
        data: The raw data that is provided before the validation.
        handler: The actual validation function pydantic would use for the
            built-in validation function.
        info: The context information during the execution of this
            validation function.

    Returns:
        the actual instance after the validation

    Raises:
        ValidationError: if you try to validate through a JSON string. You
            need to provide a config_file path when you create a
            FileSyncModel.
        AssertionError: if the raw input does not include a config_file
            path for the configuration file.
    """
    # Disable json validation
    if info.mode == "json":
        raise ValidationError(
            "You can not instantiate filesync models using the JSON mode."
        )

    if isinstance(data, dict):
        # Assert that the config file is defined
        assert "config_file" in data, (
            "You have to provide a path for the configuration file."
        )

        config_file = data.pop("config_file")

        # Load the current values and update with new values
        config_dict = {}
        if fileio.exists(config_file):
            config_dict = yaml_utils.read_yaml(config_file)
        config_dict.update(data)

        # Execute the regular validation
        model = handler(config_dict)

        assert isinstance(model, cls)

        # Assign the private attribute and save the config
        model._config_file = config_file
        model.write_config()

    else:
        # If the raw value is not a dict, apply proper validation.
        model = handler(data)

        assert isinstance(model, cls)

    return model

load_config() -> None

Loads the model from the configuration file on disk.

Source code in src/zenml/utils/filesync_model.py

def load_config(self) -> None:
    """Loads the model from the configuration file on disk."""
    if not fileio.exists(self._config_file):
        return

    # don't reload the configuration if the file hasn't
    # been updated since the last load
    file_timestamp = os.path.getmtime(self._config_file)
    if file_timestamp == self._config_file_timestamp:
        return

    if self._config_file_timestamp is not None:
        logger.info(f"Reloading configuration file {self._config_file}")

    # refresh the model from the configuration file values
    config_dict = yaml_utils.read_yaml(self._config_file)
    for key, value in config_dict.items():
        super(FileSyncModel, self).__setattr__(key, value)

    self._config_file_timestamp = file_timestamp

write_config() -> None

Writes the model to the configuration file.

Source code in src/zenml/utils/filesync_model.py

def write_config(self) -> None:
    """Writes the model to the configuration file."""
    yaml_utils.write_yaml(self._config_file, self.model_dump(mode="json"))
    self._config_file_timestamp = os.path.getmtime(self._config_file)

FlavorFilter

Bases: UserScopedFilter

Model to enable advanced stack component flavor filtering.

FlavorResponse

Bases: UserScopedResponse[FlavorResponseBody, FlavorResponseMetadata, FlavorResponseResources]

Response model for stack component flavors.

Attributes

config_schema: Dict[str, Any] property

The config_schema property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

connector_requirements: Optional[ServiceConnectorRequirements] property

Returns the connector requirements for the flavor.

Returns:

Type	Description
Optional[ServiceConnectorRequirements]	The connector requirements for the flavor.

connector_resource_id_attr: Optional[str] property

The connector_resource_id_attr property.

Returns:

Type	Description
Optional[str]	the value of the property.

connector_resource_type: Optional[str] property

The connector_resource_type property.

Returns:

Type	Description
Optional[str]	the value of the property.

connector_type: Optional[str] property

The connector_type property.

Returns:

Type	Description
Optional[str]	the value of the property.

docs_url: Optional[str] property

The docs_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

integration: Optional[str] property

The integration property.

Returns:

Type	Description
Optional[str]	the value of the property.

is_custom: bool property

The is_custom property.

Returns:

Type	Description
bool	the value of the property.

logo_url: Optional[str] property

The logo_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

sdk_docs_url: Optional[str] property

The sdk_docs_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

source: str property

The source property.

Returns:

Type	Description
str	the value of the property.

type: StackComponentType property

The type property.

Returns:

Type	Description
StackComponentType	the value of the property.

Functions

get_hydrated_version() -> FlavorResponse

Get the hydrated version of the flavor.

Returns:

Type	Description
FlavorResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/flavor.py

def get_hydrated_version(self) -> "FlavorResponse":
    """Get the hydrated version of the flavor.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_flavor(self.id)

GlobalConfiguration(**data: Any)

Bases: BaseModel

Stores global configuration options.

Configuration options are read from a config file, but can be overwritten by environment variables. See GlobalConfiguration.__getattribute__ for more details.

Attributes:

Name	Type	Description
user_id	UUID	Unique user id.
user_email	Optional[str]	Email address associated with this client.
user_email_opt_in	Optional[bool]	Whether the user has opted in to email communication.
analytics_opt_in	bool	If a user agreed to sending analytics or not.
version	Optional[str]	Version of ZenML that was last used to create or update the global config.
store	Optional[SerializeAsAny[StoreConfiguration]]	Store configuration.
active_stack_id	Optional[UUID]	The ID of the active stack.
active_project_id	Optional[UUID]	The ID of the active project.

Initializes a GlobalConfiguration using values from the config file.

GlobalConfiguration is a singleton class: only one instance can exist. Calling this constructor multiple times will always yield the same instance.

Parameters:

Name	Type	Description	Default
data	Any	Custom configuration options.	{}

Source code in src/zenml/config/global_config.py

def __init__(self, **data: Any) -> None:
    """Initializes a GlobalConfiguration using values from the config file.

    GlobalConfiguration is a singleton class: only one instance can exist.
    Calling this constructor multiple times will always yield the same
    instance.

    Args:
        data: Custom configuration options.
    """
    config_values = self._read_config()
    config_values.update(data)

    super().__init__(**config_values)

    if not fileio.exists(self._config_file):
        self._write_config()

Attributes

config_directory: str property

Directory where the global configuration file is located.

Returns:

Type	Description
str	The directory where the global configuration file is located.

is_initialized: bool property

Check if the global configuration is initialized.

Returns:

Type	Description
bool	True if the global configuration is initialized.

local_stores_path: str property

Path where local stores information is stored.

Returns:

Type	Description
str	The path where local stores information is stored.

store_configuration: StoreConfiguration property

Get the current store configuration.

Returns:

Type	Description
StoreConfiguration	The store configuration.

zen_store: BaseZenStore property

Initialize and/or return the global zen store.

If the store hasn't been initialized yet, it is initialized when this property is first accessed according to the global store configuration.

Returns:

Type	Description
BaseZenStore	The current zen store.

Functions

get_active_project() -> ProjectResponse

Get a model of the active project for the local client.

Returns:

Type	Description
ProjectResponse	The model of the active project.

Source code in src/zenml/config/global_config.py

def get_active_project(self) -> "ProjectResponse":
    """Get a model of the active project for the local client.

    Returns:
        The model of the active project.
    """
    project_id = self.get_active_project_id()

    if self._active_project is not None:
        return self._active_project

    project = self.zen_store.get_project(
        project_name_or_id=project_id,
    )
    return self.set_active_project(project)

get_active_project_id() -> UUID

Get the ID of the active project.

Returns:

Type	Description
UUID	The ID of the active project.

Raises:

Type	Description
RuntimeError	If the active project is not set.

Source code in src/zenml/config/global_config.py

def get_active_project_id(self) -> UUID:
    """Get the ID of the active project.

    Returns:
        The ID of the active project.

    Raises:
        RuntimeError: If the active project is not set.
    """
    if self.active_project_id is None:
        _ = self.zen_store
        if self.active_project_id is None:
            raise RuntimeError(
                "No project is currently set as active. Please set the "
                "active project using the `zenml project set <NAME>` CLI "
                "command."
            )

    return self.active_project_id

get_active_stack_id() -> UUID

Get the ID of the active stack.

If the active stack doesn't exist yet, the ZenStore is reinitialized.

Returns:

Type	Description
UUID	The active stack ID.

Source code in src/zenml/config/global_config.py

def get_active_stack_id(self) -> UUID:
    """Get the ID of the active stack.

    If the active stack doesn't exist yet, the ZenStore is reinitialized.

    Returns:
        The active stack ID.
    """
    if self.active_stack_id is None:
        _ = self.zen_store
        assert self.active_stack_id is not None

    return self.active_stack_id

get_config_environment_vars() -> Dict[str, str]

Convert the global configuration to environment variables.

Returns:

Type	Description
Dict[str, str]	Environment variables dictionary.

Source code in src/zenml/config/global_config.py

def get_config_environment_vars(self) -> Dict[str, str]:
    """Convert the global configuration to environment variables.

    Returns:
        Environment variables dictionary.
    """
    environment_vars = {}

    for key in type(self).model_fields.keys():
        if key == "store":
            # The store configuration uses its own environment variable
            # naming scheme
            continue

        value = getattr(self, key)
        if value is not None:
            environment_vars[CONFIG_ENV_VAR_PREFIX + key.upper()] = str(
                value
            )

    store_dict = self.store_configuration.model_dump(exclude_none=True)

    # The secrets store and backup secrets store configurations use their
    # own environment variables naming scheme
    secrets_store_dict = store_dict.pop("secrets_store", None) or {}
    backup_secrets_store_dict = (
        store_dict.pop("backup_secrets_store", None) or {}
    )

    for key, value in store_dict.items():
        if key in ["username", "password"]:
            # Never include the username and password in the env vars. Use
            # the API token instead.
            continue

        environment_vars[ENV_ZENML_STORE_PREFIX + key.upper()] = str(value)

    for key, value in secrets_store_dict.items():
        environment_vars[ENV_ZENML_SECRETS_STORE_PREFIX + key.upper()] = (
            str(value)
        )

    for key, value in backup_secrets_store_dict.items():
        environment_vars[
            ENV_ZENML_BACKUP_SECRETS_STORE_PREFIX + key.upper()
        ] = str(value)

    return environment_vars

get_default_store() -> StoreConfiguration

Get the default SQLite store configuration.

Returns:

Type	Description
StoreConfiguration	The default SQLite store configuration.

Source code in src/zenml/config/global_config.py

def get_default_store(self) -> StoreConfiguration:
    """Get the default SQLite store configuration.

    Returns:
        The default SQLite store configuration.
    """
    from zenml.zen_stores.base_zen_store import BaseZenStore

    return BaseZenStore.get_default_store_config(
        path=os.path.join(
            self.local_stores_path,
            DEFAULT_STORE_DIRECTORY_NAME,
        )
    )

get_instance() -> Optional[GlobalConfiguration] classmethod

Return the GlobalConfiguration singleton instance.

Returns:

Type	Description
Optional[GlobalConfiguration]	The GlobalConfiguration singleton instance or None, if the
Optional[GlobalConfiguration]	GlobalConfiguration hasn't been initialized yet.

Source code in src/zenml/config/global_config.py

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

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

set_active_project(project: ProjectResponse) -> ProjectResponse

Set the project for the local client.

Parameters:

Name	Type	Description	Default
project	ProjectResponse	The project to set active.	required

Returns:

Type	Description
ProjectResponse	The project that was set active.

Source code in src/zenml/config/global_config.py

def set_active_project(
    self, project: "ProjectResponse"
) -> "ProjectResponse":
    """Set the project for the local client.

    Args:
        project: The project to set active.

    Returns:
        The project that was set active.
    """
    self.active_project_id = project.id
    self._active_project = project
    # Sanitize the global configuration to reflect the new project
    self._sanitize_config()
    return project

set_active_stack(stack: StackResponse) -> None

Set the active stack for the local client.

Parameters:

Name	Type	Description	Default
stack	StackResponse	The model of the stack to set active.	required

Source code in src/zenml/config/global_config.py

def set_active_stack(self, stack: "StackResponse") -> None:
    """Set the active stack for the local client.

    Args:
        stack: The model of the stack to set active.
    """
    self.active_stack_id = stack.id
    self._active_stack = stack

set_default_store() -> None

Initializes and sets the default store configuration.

Call this method to initialize or revert the store configuration to the default store.

Source code in src/zenml/config/global_config.py

def set_default_store(self) -> None:
    """Initializes and sets the default store configuration.

    Call this method to initialize or revert the store configuration to the
    default store.
    """
    # Apply the environment variables to the default store configuration
    default_store_cfg = self._get_store_configuration(
        baseline=self.get_default_store()
    )
    self._configure_store(default_store_cfg)
    logger.debug("Using the default store for the global config.")

set_store(config: StoreConfiguration, skip_default_registrations: bool = False, **kwargs: Any) -> None

Update the active store configuration.

Call this method to validate and update the active store configuration.

Parameters:

Name	Type	Description	Default
config	StoreConfiguration	The new store configuration to use.	required
skip_default_registrations	bool	If True, the creation of the default stack and user in the store will be skipped.	False
**kwargs	Any	Additional keyword arguments to pass to the store constructor.	{}

Source code in src/zenml/config/global_config.py

def set_store(
    self,
    config: StoreConfiguration,
    skip_default_registrations: bool = False,
    **kwargs: Any,
) -> None:
    """Update the active store configuration.

    Call this method to validate and update the active store configuration.

    Args:
        config: The new store configuration to use.
        skip_default_registrations: If `True`, the creation of the default
            stack and user in the store will be skipped.
        **kwargs: Additional keyword arguments to pass to the store
            constructor.
    """
    # Apply the environment variables to the custom store configuration
    config = self._get_store_configuration(baseline=config)
    self._configure_store(config, skip_default_registrations, **kwargs)
    logger.info("Updated the global store configuration.")

uses_default_store() -> bool

Check if the global configuration uses the default store.

Returns:

Type	Description
bool	True if the global configuration uses the default store.

Source code in src/zenml/config/global_config.py

def uses_default_store(self) -> bool:
    """Check if the global configuration uses the default store.

    Returns:
        `True` if the global configuration uses the default store.
    """
    return self.store_configuration.url == self.get_default_store().url

IllegalOperationError(message: Optional[str] = None, url: Optional[str] = None)

Bases: ZenMLBaseException

Raised when an illegal operation is attempted.

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)

InitializationException(message: Optional[str] = None, url: Optional[str] = None)

Bases: ZenMLBaseException

Raised when an error occurred during initialization of a ZenML repository.

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)

LogicalOperators

Bases: StrEnum

Logical Ops to use to combine filters on list methods.

MetadataTypeEnum

Bases: StrEnum

String Enum of all possible types that metadata can have.

ModelFilter

Bases: ProjectScopedFilter, TaggableFilter

Model to enable advanced filtering of all models.

Functions

apply_sorting(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Apply sorting to the query for Models.

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/model.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query for Models.

    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 (
        ModelSchema,
        ModelVersionSchema,
    )

    sort_by, operand = self.sorting_params

    if sort_by == SORT_BY_LATEST_VERSION_KEY:
        # Subquery to find the latest version per model
        latest_version_subquery = (
            select(
                ModelSchema.id,
                case(
                    (
                        func.max(ModelVersionSchema.created).is_(None),
                        ModelSchema.created,
                    ),
                    else_=func.max(ModelVersionSchema.created),
                ).label("latest_version_created"),
            )
            .outerjoin(
                ModelVersionSchema,
                ModelSchema.id == ModelVersionSchema.model_id,  # type: ignore[arg-type]
            )
            .group_by(col(ModelSchema.id))
            .subquery()
        )

        query = query.add_columns(
            latest_version_subquery.c.latest_version_created,
        ).where(ModelSchema.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(ModelSchema.id),
            )
        else:
            query = query.order_by(
                desc(latest_version_subquery.c.latest_version_created),
                desc(ModelSchema.id),
            )
        return query

    # For other sorting cases, delegate to the parent class
    return super().apply_sorting(query=query, table=table)

ModelRequest

Bases: ProjectScopedRequest

Request model for models.

ModelResponse

Bases: ProjectScopedResponse[ModelResponseBody, ModelResponseMetadata, ModelResponseResources]

Response model for models.

Attributes

audience: Optional[str] property

The audience property.

Returns:

Type	Description
Optional[str]	the value of the property.

description: Optional[str] property

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

ethics: Optional[str] property

The ethics property.

Returns:

Type	Description
Optional[str]	the value of the property.

latest_version_id: Optional[UUID] property

The latest_version_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

latest_version_name: Optional[str] property

The latest_version_name property.

Returns:

Type	Description
Optional[str]	the value of the property.

license: Optional[str] property

The license property.

Returns:

Type	Description
Optional[str]	the value of the property.

limitations: Optional[str] property

The limitations property.

Returns:

Type	Description
Optional[str]	the value of the property.

save_models_to_registry: bool property

The save_models_to_registry property.

Returns:

Type	Description
bool	the value of the property.

tags: List[TagResponse] property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

trade_offs: Optional[str] property

The trade_offs property.

Returns:

Type	Description
Optional[str]	the value of the property.

use_cases: Optional[str] property

The use_cases property.

Returns:

Type	Description
Optional[str]	the value of the property.

versions: List[Model] property

List all versions of the model.

Returns:

Type	Description
List[Model]	The list of all model version.

Functions

get_hydrated_version() -> ModelResponse

Get the hydrated version of this model.

Returns:

Type	Description
ModelResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/model.py

def get_hydrated_version(self) -> "ModelResponse":
    """Get the hydrated version of this model.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_model(self.id)

ModelStages

Bases: StrEnum

All possible stages of a Model Version.

ModelUpdate

Bases: BaseUpdate

Update model for models.

ModelVersionArtifactFilter

Bases: BaseFilter

Model version pipeline run links filter model.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[Union[ColumnElement[bool]]]

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/model_version_artifact.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_, col

    from zenml.zen_stores.schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
        ModelVersionArtifactSchema,
        UserSchema,
    )

    if self.artifact_name:
        value, filter_operator = self._resolve_operator(self.artifact_name)
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        artifact_name_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            filter_.generate_query_conditions(ArtifactSchema),
        )
        custom_filters.append(artifact_name_filter)

    if self.only_data_artifacts:
        data_artifact_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            col(ArtifactVersionSchema.type).not_in(
                ["ServiceArtifact", "ModelArtifact"]
            ),
        )
        custom_filters.append(data_artifact_filter)

    if self.only_model_artifacts:
        model_artifact_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.type == "ModelArtifact",
        )
        custom_filters.append(model_artifact_filter)

    if self.only_deployment_artifacts:
        deployment_artifact_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.type == "ServiceArtifact",
        )
        custom_filters.append(deployment_artifact_filter)

    if self.has_custom_name is not None:
        custom_name_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            ArtifactSchema.has_custom_name == self.has_custom_name,
        )
        custom_filters.append(custom_name_filter)

    if self.user:
        user_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.user_id == UserSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.user,
                table=UserSchema,
                additional_columns=["full_name"],
            ),
        )
        custom_filters.append(user_filter)

    return custom_filters

ModelVersionArtifactResponse

Bases: BaseIdentifiedResponse[ModelVersionArtifactResponseBody, BaseResponseMetadata, ModelVersionArtifactResponseResources]

Response model for links between model versions and artifacts.

Attributes

artifact_version: ArtifactVersionResponse property

The artifact_version property.

Returns:

Type	Description
ArtifactVersionResponse	the value of the property.

model_version: UUID property

The model_version property.

Returns:

Type	Description
UUID	the value of the property.

ModelVersionFilter

Bases: ProjectScopedFilter, TaggableFilter, RunMetadataFilterMixin

Filter model for model versions.

Functions

apply_filter(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Applies the filter to a query.

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.

Raises:

Type	Description
ValueError	if the filter is not scoped to a model.

Source code in src/zenml/models/v2/core/model_version.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.

    Raises:
        ValueError: if the filter is not scoped to a model.
    """
    query = super().apply_filter(query=query, table=table)

    # The model scope must always be set and must be a UUID. If the
    # client sets this to a string, the server will try to resolve it to a
    # model ID.
    #
    # If not set by the client, the server will raise a ValueError.
    #
    # See: SqlZenStore._set_filter_model_id

    if not self.model:
        raise ValueError("Model scope missing from the filter.")

    if not isinstance(self.model, UUID):
        raise ValueError(
            f"Model scope must be a UUID, got {type(self.model)}."
        )

    query = query.where(getattr(table, "model_id") == self.model)

    return query

ModelVersionPipelineRunFilter

Bases: BaseFilter

Model version pipeline run links filter model.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/model_version_pipeline_run.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["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_

    from zenml.zen_stores.schemas import (
        ModelVersionPipelineRunSchema,
        PipelineRunSchema,
        UserSchema,
    )

    if self.pipeline_run_name:
        value, filter_operator = self._resolve_operator(
            self.pipeline_run_name
        )
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        pipeline_run_name_filter = and_(
            ModelVersionPipelineRunSchema.pipeline_run_id
            == PipelineRunSchema.id,
            filter_.generate_query_conditions(PipelineRunSchema),
        )
        custom_filters.append(pipeline_run_name_filter)

    if self.user:
        user_filter = and_(
            ModelVersionPipelineRunSchema.pipeline_run_id
            == PipelineRunSchema.id,
            PipelineRunSchema.user_id == UserSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.user,
                table=UserSchema,
                additional_columns=["full_name"],
            ),
        )
        custom_filters.append(user_filter)

    return custom_filters

ModelVersionPipelineRunResponse

Bases: BaseIdentifiedResponse[ModelVersionPipelineRunResponseBody, BaseResponseMetadata, ModelVersionPipelineRunResponseResources]

Response model for links between model versions and pipeline runs.

Attributes

model_version: UUID property

The model_version property.

Returns:

Type	Description
UUID	the value of the property.

pipeline_run: PipelineRunResponse property

The pipeline_run property.

Returns:

Type	Description
PipelineRunResponse	the value of the property.

ModelVersionRequest

Bases: ProjectScopedRequest

Request model for model versions.

ModelVersionResponse

Bases: ProjectScopedResponse[ModelVersionResponseBody, ModelVersionResponseMetadata, ModelVersionResponseResources]

Response model for model versions.

Attributes

data_artifact_ids: Dict[str, Dict[str, UUID]] property

The data_artifact_ids property.

Returns:

Type	Description
Dict[str, Dict[str, UUID]]	the value of the property.

data_artifacts: Dict[str, Dict[str, ArtifactVersionResponse]] property

Get all data artifacts linked to this model version.

Returns:

Type	Description
Dict[str, Dict[str, ArtifactVersionResponse]]	Dictionary of data artifacts with versions as
Dict[str, Dict[str, ArtifactVersionResponse]]	Dict[str, Dict[str, ArtifactResponse]]

deployment_artifact_ids: Dict[str, Dict[str, UUID]] property

The deployment_artifact_ids property.

Returns:

Type	Description
Dict[str, Dict[str, UUID]]	the value of the property.

deployment_artifacts: Dict[str, Dict[str, ArtifactVersionResponse]] property

Get all deployment artifacts linked to this model version.

Returns:

Type	Description
Dict[str, Dict[str, ArtifactVersionResponse]]	Dictionary of deployment artifacts with versions as
Dict[str, Dict[str, ArtifactVersionResponse]]	Dict[str, Dict[str, ArtifactResponse]]

description: Optional[str] property

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

model: ModelResponse property

The model property.

Returns:

Type	Description
ModelResponse	the value of the property.

model_artifact_ids: Dict[str, Dict[str, UUID]] property

The model_artifact_ids property.

Returns:

Type	Description
Dict[str, Dict[str, UUID]]	the value of the property.

model_artifacts: Dict[str, Dict[str, ArtifactVersionResponse]] property

Get all model artifacts linked to this model version.

Returns:

Type	Description
Dict[str, Dict[str, ArtifactVersionResponse]]	Dictionary of model artifacts with versions as
Dict[str, Dict[str, ArtifactVersionResponse]]	Dict[str, Dict[str, ArtifactResponse]]

number: int property

The number property.

Returns:

Type	Description
int	the value of the property.

pipeline_run_ids: Dict[str, UUID] property

The pipeline_run_ids property.

Returns:

Type	Description
Dict[str, UUID]	the value of the property.

pipeline_runs: Dict[str, PipelineRunResponse] property

Get all pipeline runs linked to this version.

Returns:

Type	Description
Dict[str, PipelineRunResponse]	Dictionary of Pipeline Runs as PipelineRunResponseModel

run_metadata: Dict[str, MetadataType] property

The run_metadata property.

Returns:

Type	Description
Dict[str, MetadataType]	the value of the property.

stage: Optional[str] property

The stage property.

Returns:

Type	Description
Optional[str]	the value of the property.

tags: List[TagResponse] property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

Functions

get_artifact(name: str, version: Optional[str] = None) -> Optional[ArtifactVersionResponse]

Get the artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the artifact to retrieve.	required
version	Optional[str]	The version of the artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of an artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the artifact linked to this model version.

    Args:
        name: The name of the artifact to retrieve.
        version: The version of the artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of an artifact or None
    """
    return self._get_linked_object(name, version)

get_data_artifact(name: str, version: Optional[str] = None) -> Optional[ArtifactVersionResponse]

Get the data artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the data artifact to retrieve.	required
version	Optional[str]	The version of the data artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of the data artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_data_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the data artifact linked to this model version.

    Args:
        name: The name of the data artifact to retrieve.
        version: The version of the data artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the data artifact or None
    """
    return self._get_linked_object(name, version, ArtifactType.DATA)

get_deployment_artifact(name: str, version: Optional[str] = None) -> Optional[ArtifactVersionResponse]

Get the deployment artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the deployment artifact to retrieve.	required
version	Optional[str]	The version of the deployment artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of the deployment artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_deployment_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the deployment artifact linked to this model version.

    Args:
        name: The name of the deployment artifact to retrieve.
        version: The version of the deployment artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the deployment artifact or None
    """
    return self._get_linked_object(name, version, ArtifactType.SERVICE)

get_hydrated_version() -> ModelVersionResponse

Get the hydrated version of this model version.

Returns:

Type	Description
ModelVersionResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/model_version.py

def get_hydrated_version(self) -> "ModelVersionResponse":
    """Get the hydrated version of this model version.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_model_version(self.id)

get_model_artifact(name: str, version: Optional[str] = None) -> Optional[ArtifactVersionResponse]

Get the model artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the model artifact to retrieve.	required
version	Optional[str]	The version of the model artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of the model artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_model_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the model artifact linked to this model version.

    Args:
        name: The name of the model artifact to retrieve.
        version: The version of the model artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the model artifact or None
    """
    return self._get_linked_object(name, version, ArtifactType.MODEL)

get_pipeline_run(name: str) -> PipelineRunResponse

Get pipeline run linked to this version.

Parameters:

Name	Type	Description	Default
name	str	The name of the pipeline run to retrieve.	required

Returns:

Type	Description
PipelineRunResponse	PipelineRun as PipelineRunResponseModel

Source code in src/zenml/models/v2/core/model_version.py

def get_pipeline_run(self, name: str) -> "PipelineRunResponse":
    """Get pipeline run linked to this version.

    Args:
        name: The name of the pipeline run to retrieve.

    Returns:
        PipelineRun as PipelineRunResponseModel
    """
    from zenml.client import Client

    return Client().get_pipeline_run(self.pipeline_run_ids[name])

set_stage(stage: Union[str, ModelStages], force: bool = False) -> None

Sets this Model Version to a desired stage.

Parameters:

Name	Type	Description	Default
stage	Union[str, ModelStages]	the target stage for model version.	required
force	bool	whether to force archiving of current model version in target stage or raise.	False

Raises:

Type	Description
ValueError	if model_stage is not valid.

Source code in src/zenml/models/v2/core/model_version.py

def set_stage(
    self, stage: Union[str, ModelStages], force: bool = False
) -> None:
    """Sets this Model Version to a desired stage.

    Args:
        stage: the target stage for model version.
        force: whether to force archiving of current model version in
            target stage or raise.

    Raises:
        ValueError: if model_stage is not valid.
    """
    from zenml.client import Client

    stage = getattr(stage, "value", stage)
    if stage not in [stage.value for stage in ModelStages]:
        raise ValueError(f"`{stage}` is not a valid model stage.")

    Client().update_model_version(
        model_name_or_id=self.model.id,
        version_name_or_id=self.id,
        stage=stage,
        force=force,
    )

to_model_class(suppress_class_validation_warnings: bool = True) -> Model

Convert response model to Model object.

Parameters:

Name	Type	Description	Default
suppress_class_validation_warnings	bool	internally used to suppress repeated warnings.	True

Returns:

Type	Description
Model	Model object

Source code in src/zenml/models/v2/core/model_version.py

def to_model_class(
    self,
    suppress_class_validation_warnings: bool = True,
) -> "Model":
    """Convert response model to Model object.

    Args:
        suppress_class_validation_warnings: internally used to suppress
            repeated warnings.

    Returns:
        Model object
    """
    from zenml.model.model import Model

    mv = Model(
        name=self.model.name,
        license=self.model.license,
        description=self.description,
        audience=self.model.audience,
        use_cases=self.model.use_cases,
        limitations=self.model.limitations,
        trade_offs=self.model.trade_offs,
        ethics=self.model.ethics,
        tags=[t.name for t in self.tags],
        version=self.name,
        suppress_class_validation_warnings=suppress_class_validation_warnings,
        model_version_id=self.id,
    )

    return mv

ModelVersionUpdate

Bases: BaseUpdate

Update model for model versions.

OAuthDeviceFilter

Bases: UserScopedFilter

Model to enable advanced filtering of OAuth2 devices.

OAuthDeviceResponse

Bases: UserScopedResponse[OAuthDeviceResponseBody, OAuthDeviceResponseMetadata, OAuthDeviceResponseResources]

Response model for OAuth2 devices.

Attributes

city: Optional[str] property

The city property.

Returns:

Type	Description
Optional[str]	the value of the property.

client_id: UUID property

The client_id property.

Returns:

Type	Description
UUID	the value of the property.

country: Optional[str] property

The country property.

Returns:

Type	Description
Optional[str]	the value of the property.

expires: Optional[datetime] property

The expires property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

failed_auth_attempts: int property

The failed_auth_attempts property.

Returns:

Type	Description
int	the value of the property.

hostname: Optional[str] property

The hostname property.

Returns:

Type	Description
Optional[str]	the value of the property.

ip_address: Optional[str] property

The ip_address property.

Returns:

Type	Description
Optional[str]	the value of the property.

last_login: Optional[datetime] property

The last_login property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

os: Optional[str] property

The os property.

Returns:

Type	Description
Optional[str]	the value of the property.

python_version: Optional[str] property

The python_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

region: Optional[str] property

The region property.

Returns:

Type	Description
Optional[str]	the value of the property.

status: OAuthDeviceStatus property

The status property.

Returns:

Type	Description
OAuthDeviceStatus	the value of the property.

trusted_device: bool property

The trusted_device property.

Returns:

Type	Description
bool	the value of the property.

zenml_version: Optional[str] property

The zenml_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

Functions

get_hydrated_version() -> OAuthDeviceResponse

Get the hydrated version of this OAuth2 device.

Returns:

Type	Description
OAuthDeviceResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/device.py

def get_hydrated_version(self) -> "OAuthDeviceResponse":
    """Get the hydrated version of this OAuth2 device.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_authorized_device(self.id)

OAuthDeviceStatus

Bases: StrEnum

The OAuth device status.

OAuthDeviceUpdate

Bases: BaseUpdate

OAuth2 device update model.

Page

Bases: BaseModel, Generic[B]

Return Model for List Models to accommodate pagination.

Attributes

size: int property

Return the item count of the page.

Returns:

Type	Description
int	The amount of items in the page.

PipelineBuildFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all pipeline builds.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_custom_filters(
    self,
    table: Type["AnySchema"],
) -> List["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_

    from zenml.enums import StackComponentType
    from zenml.zen_stores.schemas import (
        PipelineBuildSchema,
        StackComponentSchema,
        StackCompositionSchema,
        StackSchema,
    )

    if self.container_registry_id:
        container_registry_filter = and_(
            PipelineBuildSchema.stack_id == StackSchema.id,
            StackSchema.id == StackCompositionSchema.stack_id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
            StackComponentSchema.type
            == StackComponentType.CONTAINER_REGISTRY.value,
            StackComponentSchema.id == self.container_registry_id,
        )
        custom_filters.append(container_registry_filter)

    return custom_filters

PipelineBuildResponse

Bases: ProjectScopedResponse[PipelineBuildResponseBody, PipelineBuildResponseMetadata, PipelineBuildResponseResources]

Response model for pipeline builds.

Attributes

checksum: Optional[str] property

The checksum property.

Returns:

Type	Description
Optional[str]	the value of the property.

contains_code: bool property

The contains_code property.

Returns:

Type	Description
bool	the value of the property.

duration: Optional[int] property

The duration property.

Returns:

Type	Description
Optional[int]	the value of the property.

images: Dict[str, BuildItem] property

The images property.

Returns:

Type	Description
Dict[str, BuildItem]	the value of the property.

is_local: bool property

The is_local property.

Returns:

Type	Description
bool	the value of the property.

pipeline: Optional[PipelineResponse] property

The pipeline property.

Returns:

Type	Description
Optional[PipelineResponse]	the value of the property.

python_version: Optional[str] property

The python_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

requires_code_download: bool property

Whether the build requires code download.

Returns:

Type	Description
bool	Whether the build requires code download.

stack: Optional[StackResponse] property

The stack property.

Returns:

Type	Description
Optional[StackResponse]	the value of the property.

stack_checksum: Optional[str] property

The stack_checksum property.

Returns:

Type	Description
Optional[str]	the value of the property.

zenml_version: Optional[str] property

The zenml_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

Functions

get_hydrated_version() -> PipelineBuildResponse

Return the hydrated version of this pipeline build.

Returns:

Type	Description
PipelineBuildResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_hydrated_version(self) -> "PipelineBuildResponse":
    """Return the hydrated version of this pipeline build.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_build(self.id)

get_image(component_key: str, step: Optional[str] = None) -> str

Get the image built for a specific key.

Parameters:

Name	Type	Description	Default
component_key	str	The key for which to get the image.	required
step	Optional[str]	The pipeline step for which to get the image. If no image exists for this step, will fall back to the pipeline image for the same key.	None

Returns:

Type	Description
str	The image name or digest.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_image(self, component_key: str, step: Optional[str] = None) -> str:
    """Get the image built for a specific key.

    Args:
        component_key: The key for which to get the image.
        step: The pipeline step for which to get the image. If no image
            exists for this step, will fall back to the pipeline image for
            the same key.

    Returns:
        The image name or digest.
    """
    return self._get_item(component_key=component_key, step=step).image

get_image_key(component_key: str, step: Optional[str] = None) -> str staticmethod

Get the image key.

Parameters:

Name	Type	Description	Default
component_key	str	The component key.	required
step	Optional[str]	The pipeline step for which the image was built.	None

Returns:

Type	Description
str	The image key.

Source code in src/zenml/models/v2/core/pipeline_build.py

@staticmethod
def get_image_key(component_key: str, step: Optional[str] = None) -> str:
    """Get the image key.

    Args:
        component_key: The component key.
        step: The pipeline step for which the image was built.

    Returns:
        The image key.
    """
    if step:
        return f"{step}.{component_key}"
    else:
        return component_key

get_settings_checksum(component_key: str, step: Optional[str] = None) -> Optional[str]

Get the settings checksum for a specific key.

Parameters:

Name	Type	Description	Default
component_key	str	The key for which to get the checksum.	required
step	Optional[str]	The pipeline step for which to get the checksum. If no image exists for this step, will fall back to the pipeline image for the same key.	None

Returns:

Type	Description
Optional[str]	The settings checksum.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_settings_checksum(
    self, component_key: str, step: Optional[str] = None
) -> Optional[str]:
    """Get the settings checksum for a specific key.

    Args:
        component_key: The key for which to get the checksum.
        step: The pipeline step for which to get the checksum. If no
            image exists for this step, will fall back to the pipeline image
            for the same key.

    Returns:
        The settings checksum.
    """
    return self._get_item(
        component_key=component_key, step=step
    ).settings_checksum

to_yaml() -> Dict[str, Any]

Create a yaml representation of the pipeline build.

Create a yaml representation of the pipeline build that can be used to create a PipelineBuildBase instance.

Returns:

Type	Description
Dict[str, Any]	The yaml representation of the pipeline build.

Source code in src/zenml/models/v2/core/pipeline_build.py

def to_yaml(self) -> Dict[str, Any]:
    """Create a yaml representation of the pipeline build.

    Create a yaml representation of the pipeline build that can be used
    to create a PipelineBuildBase instance.

    Returns:
        The yaml representation of the pipeline build.
    """
    # Get the base attributes
    yaml_dict: Dict[str, Any] = json.loads(
        self.model_dump_json(
            exclude={
                "body",
                "metadata",
            }
        )
    )
    images = json.loads(
        self.get_metadata().model_dump_json(
            exclude={
                "pipeline",
                "stack",
                "project",
            }
        )
    )
    yaml_dict.update(images)
    return yaml_dict

PipelineDeploymentFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all pipeline deployments.

PipelineDeploymentResponse

Bases: ProjectScopedResponse[PipelineDeploymentResponseBody, PipelineDeploymentResponseMetadata, PipelineDeploymentResponseResources]

Response model for pipeline deployments.

Attributes

build: Optional[PipelineBuildResponse] property

The build property.

Returns:

Type	Description
Optional[PipelineBuildResponse]	the value of the property.

client_environment: Dict[str, str] property

The client_environment property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

client_version: Optional[str] property

The client_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

code_path: Optional[str] property

The code_path property.

Returns:

Type	Description
Optional[str]	the value of the property.

code_reference: Optional[CodeReferenceResponse] property

The code_reference property.

Returns:

Type	Description
Optional[CodeReferenceResponse]	the value of the property.

pipeline: Optional[PipelineResponse] property

The pipeline property.

Returns:

Type	Description
Optional[PipelineResponse]	the value of the property.

pipeline_configuration: PipelineConfiguration property

The pipeline_configuration property.

Returns:

Type	Description
PipelineConfiguration	the value of the property.

pipeline_spec: Optional[PipelineSpec] property

The pipeline_spec property.

Returns:

Type	Description
Optional[PipelineSpec]	the value of the property.

pipeline_version_hash: Optional[str] property

The pipeline_version_hash property.

Returns:

Type	Description
Optional[str]	the value of the property.

run_name_template: str property

The run_name_template property.

Returns:

Type	Description
str	the value of the property.

schedule: Optional[ScheduleResponse] property

The schedule property.

Returns:

Type	Description
Optional[ScheduleResponse]	the value of the property.

server_version: Optional[str] property

The server_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

stack: Optional[StackResponse] property

The stack property.

Returns:

Type	Description
Optional[StackResponse]	the value of the property.

step_configurations: Dict[str, Step] property

The step_configurations property.

Returns:

Type	Description
Dict[str, Step]	the value of the property.

template_id: Optional[UUID] property

The template_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

Functions

get_hydrated_version() -> PipelineDeploymentResponse

Return the hydrated version of this pipeline deployment.

Returns:

Type	Description
PipelineDeploymentResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

def get_hydrated_version(self) -> "PipelineDeploymentResponse":
    """Return the hydrated version of this pipeline deployment.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_deployment(self.id)

PipelineFilter

Bases: ProjectScopedFilter, TaggableFilter

Pipeline filter model.

Functions

apply_filter(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Applies the filter to a query.

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/pipeline.py

def apply_filter(
    self, query: AnyQuery, table: Type["AnySchema"]
) -> AnyQuery:
    """Applies the filter to a query.

    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, table)

    from sqlmodel import and_, col, func, select

    from zenml.zen_stores.schemas import PipelineRunSchema, PipelineSchema

    if self.latest_run_status:
        latest_pipeline_run_subquery = (
            select(
                PipelineRunSchema.pipeline_id,
                func.max(PipelineRunSchema.created).label("created"),
            )
            .where(col(PipelineRunSchema.pipeline_id).is_not(None))
            .group_by(col(PipelineRunSchema.pipeline_id))
            .subquery()
        )

        query = (
            query.join(
                PipelineRunSchema,
                PipelineSchema.id == PipelineRunSchema.pipeline_id,
            )
            .join(
                latest_pipeline_run_subquery,
                and_(
                    PipelineRunSchema.pipeline_id
                    == latest_pipeline_run_subquery.c.pipeline_id,
                    PipelineRunSchema.created
                    == latest_pipeline_run_subquery.c.created,
                ),
            )
            .where(
                self.generate_custom_query_conditions_for_column(
                    value=self.latest_run_status,
                    table=PipelineRunSchema,
                    column="status",
                )
            )
        )

    return query

apply_sorting(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Apply sorting to the query.

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/pipeline.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query.

    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 PipelineRunSchema, PipelineSchema

    sort_by, operand = self.sorting_params

    if sort_by == SORT_PIPELINES_BY_LATEST_RUN_KEY:
        # Subquery to find the latest run per pipeline
        latest_run_subquery = (
            select(
                PipelineSchema.id,
                case(
                    (
                        func.max(PipelineRunSchema.created).is_(None),
                        PipelineSchema.created,
                    ),
                    else_=func.max(PipelineRunSchema.created),
                ).label("latest_run"),
            )
            .outerjoin(
                PipelineRunSchema,
                PipelineSchema.id == PipelineRunSchema.pipeline_id,  # type: ignore[arg-type]
            )
            .group_by(col(PipelineSchema.id))
            .subquery()
        )

        query = query.add_columns(
            latest_run_subquery.c.latest_run,
        ).where(PipelineSchema.id == latest_run_subquery.c.id)

        if operand == SorterOps.ASCENDING:
            query = query.order_by(
                asc(latest_run_subquery.c.latest_run),
                asc(PipelineSchema.id),
            )
        else:
            query = query.order_by(
                desc(latest_run_subquery.c.latest_run),
                desc(PipelineSchema.id),
            )
        return query
    else:
        return super().apply_sorting(query=query, table=table)

PipelineResponse

Bases: ProjectScopedResponse[PipelineResponseBody, PipelineResponseMetadata, PipelineResponseResources]

Response model for pipelines.

Attributes

last_run: PipelineRunResponse property

Returns the last run of this pipeline.

Returns:

Type	Description
PipelineRunResponse	The last run of this pipeline.

Raises:

Type	Description
RuntimeError	If no runs were found for this pipeline.

last_successful_run: PipelineRunResponse property

Returns the last successful run of this pipeline.

Returns:

Type	Description
PipelineRunResponse	The last successful run of this pipeline.

Raises:

Type	Description
RuntimeError	If no successful runs were found for this pipeline.

latest_run_id: Optional[UUID] property

The latest_run_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

latest_run_status: Optional[ExecutionStatus] property

The latest_run_status property.

Returns:

Type	Description
Optional[ExecutionStatus]	the value of the property.

num_runs: int property

Returns the number of runs of this pipeline.

Returns:

Type	Description
int	The number of runs of this pipeline.

runs: List[PipelineRunResponse] property

Returns the 20 most recent runs of this pipeline in descending order.

Returns:

Type	Description
List[PipelineRunResponse]	The 20 most recent runs of this pipeline in descending order.

tags: List[TagResponse] property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

Functions

get_hydrated_version() -> PipelineResponse

Get the hydrated version of this pipeline.

Returns:

Type	Description
PipelineResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline.py

def get_hydrated_version(self) -> "PipelineResponse":
    """Get the hydrated version of this pipeline.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_pipeline(self.id)

get_runs(**kwargs: Any) -> List[PipelineRunResponse]

Get runs of this pipeline.

Can be used to fetch runs other than self.runs and supports fine-grained filtering and pagination.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Further arguments for filtering or pagination that are passed to client.list_pipeline_runs().	{}

Returns:

Type	Description
List[PipelineRunResponse]	List of runs of this pipeline.

Source code in src/zenml/models/v2/core/pipeline.py

def get_runs(self, **kwargs: Any) -> List["PipelineRunResponse"]:
    """Get runs of this pipeline.

    Can be used to fetch runs other than `self.runs` and supports
    fine-grained filtering and pagination.

    Args:
        **kwargs: Further arguments for filtering or pagination that are
            passed to `client.list_pipeline_runs()`.

    Returns:
        List of runs of this pipeline.
    """
    from zenml.client import Client

    return Client().list_pipeline_runs(pipeline_id=self.id, **kwargs).items

PipelineRunConfiguration

Bases: StrictBaseModel, YAMLSerializationMixin

Class for pipeline run configurations.

PipelineRunFilter

Bases: ProjectScopedFilter, TaggableFilter, RunMetadataFilterMixin

Model to enable advanced filtering of all pipeline runs.

Functions

apply_sorting(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Apply sorting to the query.

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/pipeline_run.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    from sqlmodel import asc, desc

    from zenml.enums import SorterOps
    from zenml.zen_stores.schemas import (
        ModelSchema,
        ModelVersionSchema,
        PipelineDeploymentSchema,
        PipelineRunSchema,
        PipelineSchema,
        StackSchema,
    )

    sort_by, operand = self.sorting_params

    if sort_by == "pipeline":
        query = query.outerjoin(
            PipelineSchema,
            PipelineRunSchema.pipeline_id == PipelineSchema.id,
        )
        column = PipelineSchema.name
    elif sort_by == "stack":
        query = query.outerjoin(
            PipelineDeploymentSchema,
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
        ).outerjoin(
            StackSchema,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
        )
        column = StackSchema.name
    elif sort_by == "model":
        query = query.outerjoin(
            ModelVersionSchema,
            PipelineRunSchema.model_version_id == ModelVersionSchema.id,
        ).outerjoin(
            ModelSchema,
            ModelVersionSchema.model_id == ModelSchema.id,
        )
        column = ModelSchema.name
    elif sort_by == "model_version":
        query = query.outerjoin(
            ModelVersionSchema,
            PipelineRunSchema.model_version_id == ModelVersionSchema.id,
        )
        column = ModelVersionSchema.name
    else:
        return super().apply_sorting(query=query, table=table)

    query = query.add_columns(column)

    if operand == SorterOps.ASCENDING:
        query = query.order_by(asc(column))
    else:
        query = query.order_by(desc(column))

    return query

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/pipeline_run.py

def get_custom_filters(
    self,
    table: Type["AnySchema"],
) -> List["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_, col, or_

    from zenml.zen_stores.schemas import (
        CodeReferenceSchema,
        CodeRepositorySchema,
        ModelSchema,
        ModelVersionSchema,
        PipelineBuildSchema,
        PipelineDeploymentSchema,
        PipelineRunSchema,
        PipelineSchema,
        ScheduleSchema,
        StackComponentSchema,
        StackCompositionSchema,
        StackSchema,
    )

    if self.unlisted is not None:
        if self.unlisted is True:
            unlisted_filter = PipelineRunSchema.pipeline_id.is_(None)  # type: ignore[union-attr]
        else:
            unlisted_filter = PipelineRunSchema.pipeline_id.is_not(None)  # type: ignore[union-attr]
        custom_filters.append(unlisted_filter)

    if self.code_repository_id:
        code_repo_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.code_reference_id
            == CodeReferenceSchema.id,
            CodeReferenceSchema.code_repository_id
            == self.code_repository_id,
        )
        custom_filters.append(code_repo_filter)

    if self.stack_id:
        stack_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            StackSchema.id == self.stack_id,
        )
        custom_filters.append(stack_filter)

    if self.schedule_id:
        schedule_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.schedule_id == ScheduleSchema.id,
            ScheduleSchema.id == self.schedule_id,
        )
        custom_filters.append(schedule_filter)

    if self.build_id:
        pipeline_build_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.build_id == PipelineBuildSchema.id,
            PipelineBuildSchema.id == self.build_id,
        )
        custom_filters.append(pipeline_build_filter)

    if self.template_id:
        run_template_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.template_id == self.template_id,
        )
        custom_filters.append(run_template_filter)

    if self.pipeline:
        pipeline_filter = and_(
            PipelineRunSchema.pipeline_id == PipelineSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.pipeline, table=PipelineSchema
            ),
        )
        custom_filters.append(pipeline_filter)

    if self.stack:
        stack_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.stack,
                table=StackSchema,
            ),
        )
        custom_filters.append(stack_filter)

    if self.code_repository:
        code_repo_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.code_reference_id
            == CodeReferenceSchema.id,
            CodeReferenceSchema.code_repository_id
            == CodeRepositorySchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.code_repository,
                table=CodeRepositorySchema,
            ),
        )
        custom_filters.append(code_repo_filter)

    if self.model:
        model_filter = and_(
            PipelineRunSchema.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.stack_component:
        component_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            StackSchema.id == StackCompositionSchema.stack_id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.stack_component,
                table=StackComponentSchema,
            ),
        )
        custom_filters.append(component_filter)

    if self.pipeline_name:
        pipeline_name_filter = and_(
            PipelineRunSchema.pipeline_id == PipelineSchema.id,
            self.generate_custom_query_conditions_for_column(
                value=self.pipeline_name,
                table=PipelineSchema,
                column="name",
            ),
        )
        custom_filters.append(pipeline_name_filter)

    if self.templatable is not None:
        if self.templatable is True:
            templatable_filter = and_(
                # The following condition is not perfect as it does not
                # consider stacks with custom flavor components or local
                # components, but the best we can do currently with our
                # table columns.
                PipelineRunSchema.deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.build_id
                == PipelineBuildSchema.id,
                col(PipelineBuildSchema.is_local).is_(False),
                col(PipelineBuildSchema.stack_id).is_not(None),
            )
        else:
            templatable_filter = or_(
                col(PipelineRunSchema.deployment_id).is_(None),
                and_(
                    PipelineRunSchema.deployment_id
                    == PipelineDeploymentSchema.id,
                    col(PipelineDeploymentSchema.build_id).is_(None),
                ),
                and_(
                    PipelineRunSchema.deployment_id
                    == PipelineDeploymentSchema.id,
                    PipelineDeploymentSchema.build_id
                    == PipelineBuildSchema.id,
                    or_(
                        col(PipelineBuildSchema.is_local).is_(True),
                        col(PipelineBuildSchema.stack_id).is_(None),
                    ),
                ),
            )

        custom_filters.append(templatable_filter)

    return custom_filters

PipelineRunResponse

Bases: ProjectScopedResponse[PipelineRunResponseBody, PipelineRunResponseMetadata, PipelineRunResponseResources]

Response model for pipeline runs.

Attributes

artifact_versions: List[ArtifactVersionResponse] property

Get all artifact versions that are outputs of steps of this run.

Returns:

Type	Description
List[ArtifactVersionResponse]	All output artifact versions of this run (including cached ones).

build: Optional[PipelineBuildResponse] property

The build property.

Returns:

Type	Description
Optional[PipelineBuildResponse]	the value of the property.

client_environment: Dict[str, str] property

The client_environment property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

code_path: Optional[str] property

The code_path property.

Returns:

Type	Description
Optional[str]	the value of the property.

code_reference: Optional[CodeReferenceResponse] property

The schedule property.

Returns:

Type	Description
Optional[CodeReferenceResponse]	the value of the property.

config: PipelineConfiguration property

The config property.

Returns:

Type	Description
PipelineConfiguration	the value of the property.

deployment_id: Optional[UUID] property

The deployment_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

end_time: Optional[datetime] property

The end_time property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

is_templatable: bool property

The is_templatable property.

Returns:

Type	Description
bool	the value of the property.

logs: Optional[LogsResponse] property

The logs property.

Returns:

Type	Description
Optional[LogsResponse]	the value of the property.

model_version: Optional[ModelVersionResponse] property

The model_version property.

Returns:

Type	Description
Optional[ModelVersionResponse]	the value of the property.

model_version_id: Optional[UUID] property

The model_version_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

orchestrator_environment: Dict[str, str] property

The orchestrator_environment property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

orchestrator_run_id: Optional[str] property

The orchestrator_run_id property.

Returns:

Type	Description
Optional[str]	the value of the property.

pipeline: Optional[PipelineResponse] property

The pipeline property.

Returns:

Type	Description
Optional[PipelineResponse]	the value of the property.

produced_artifact_versions: List[ArtifactVersionResponse] property

Get all artifact versions produced during this pipeline run.

Returns:

Type	Description
List[ArtifactVersionResponse]	A list of all artifact versions produced during this pipeline run.

run_metadata: Dict[str, MetadataType] property

The run_metadata property.

Returns:

Type	Description
Dict[str, MetadataType]	the value of the property.

schedule: Optional[ScheduleResponse] property

The schedule property.

Returns:

Type	Description
Optional[ScheduleResponse]	the value of the property.

stack: Optional[StackResponse] property

The stack property.

Returns:

Type	Description
Optional[StackResponse]	the value of the property.

start_time: Optional[datetime] property

The start_time property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

status: ExecutionStatus property

The status property.

Returns:

Type	Description
ExecutionStatus	the value of the property.

step_substitutions: Dict[str, Dict[str, str]] property

The step_substitutions property.

Returns:

Type	Description
Dict[str, Dict[str, str]]	the value of the property.

steps: Dict[str, StepRunResponse] property

The steps property.

Returns:

Type	Description
Dict[str, StepRunResponse]	the value of the property.

tags: List[TagResponse] property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

template_id: Optional[UUID] property

The template_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

trigger_execution: Optional[TriggerExecutionResponse] property

The trigger_execution property.

Returns:

Type	Description
Optional[TriggerExecutionResponse]	the value of the property.

Functions

get_hydrated_version() -> PipelineRunResponse

Get the hydrated version of this pipeline run.

Returns:

Type	Description
PipelineRunResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline_run.py

def get_hydrated_version(self) -> "PipelineRunResponse":
    """Get the hydrated version of this pipeline run.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_run(self.id)

refresh_run_status() -> PipelineRunResponse

Method to refresh the status of a run if it is initializing/running.

Returns:

Type	Description
PipelineRunResponse	The updated pipeline.

Raises:

Type	Description
ValueError	If the stack of the run response is None.

Source code in src/zenml/models/v2/core/pipeline_run.py

def refresh_run_status(self) -> "PipelineRunResponse":
    """Method to refresh the status of a run if it is initializing/running.

    Returns:
        The updated pipeline.

    Raises:
        ValueError: If the stack of the run response is None.
    """
    if self.status in [
        ExecutionStatus.INITIALIZING,
        ExecutionStatus.RUNNING,
    ]:
        # Check if the stack still accessible
        if self.stack is None:
            raise ValueError(
                "The stack that this pipeline run response was executed on"
                "has been deleted."
            )

        # Create the orchestrator instance
        from zenml.enums import StackComponentType
        from zenml.orchestrators.base_orchestrator import BaseOrchestrator
        from zenml.stack.stack_component import StackComponent

        # Check if the stack still accessible
        orchestrator_list = self.stack.components.get(
            StackComponentType.ORCHESTRATOR, []
        )
        if len(orchestrator_list) == 0:
            raise ValueError(
                "The orchestrator that this pipeline run response was "
                "executed with has been deleted."
            )

        orchestrator = cast(
            BaseOrchestrator,
            StackComponent.from_model(
                component_model=orchestrator_list[0]
            ),
        )

        # Fetch the status
        status = orchestrator.fetch_status(run=self)

        # If it is different from the current status, update it
        if status != self.status:
            from zenml.client import Client
            from zenml.models import PipelineRunUpdate

            client = Client()
            return client.zen_store.update_run(
                run_id=self.id,
                run_update=PipelineRunUpdate(status=status),
            )

    return self

PluginSubType

Bases: StrEnum

All possible types of Plugins.

PluginType

Bases: StrEnum

All possible types of Plugins.

ProjectFilter

Bases: BaseFilter

Model to enable advanced filtering of all projects.

ProjectRequest

Bases: BaseRequest

Request model for projects.

ProjectResponse

Bases: BaseIdentifiedResponse[ProjectResponseBody, ProjectResponseMetadata, ProjectResponseResources]

Response model for projects.

Attributes

description: str property

The description property.

Returns:

Type	Description
str	the value of the property.

display_name: str property

The display_name property.

Returns:

Type	Description
str	the value of the property.

Functions

get_hydrated_version() -> ProjectResponse

Get the hydrated version of this project.

Returns:

Type	Description
ProjectResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/project.py

def get_hydrated_version(self) -> "ProjectResponse":
    """Get the hydrated version of this project.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_project(self.id)

ProjectUpdate

Bases: BaseUpdate

Update model for projects.

RunMetadataRequest

Bases: ProjectScopedRequest

Request model for run metadata.

Functions

validate_values_keys() -> RunMetadataRequest

Validates if the keys in the metadata are properly defined.

Returns:

Type	Description
RunMetadataRequest	self

Raises:

Type	Description
ValueError	if one of the key in the metadata contains :

Source code in src/zenml/models/v2/core/run_metadata.py

@model_validator(mode="after")
def validate_values_keys(self) -> "RunMetadataRequest":
    """Validates if the keys in the metadata are properly defined.

    Returns:
        self

    Raises:
        ValueError: if one of the key in the metadata contains `:`
    """
    invalid_keys = [key for key in self.values.keys() if ":" in key]
    if invalid_keys:
        raise ValueError(
            "You can not use colons (`:`) in the key names when you "
            "are creating metadata for your ZenML objects. Please change "
            f"the following keys: {invalid_keys}"
        )
    return self

RunMetadataResource

Bases: BaseModel

Utility class to help identify resources to tag metadata to.

RunTemplateFilter

Bases: ProjectScopedFilter, TaggableFilter

Model for filtering of run templates.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/run_template.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["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_, col

    from zenml.zen_stores.schemas import (
        CodeReferenceSchema,
        PipelineDeploymentSchema,
        PipelineSchema,
        RunTemplateSchema,
        StackSchema,
    )

    if self.hidden is not None:
        custom_filters.append(
            col(RunTemplateSchema.hidden).is_(self.hidden)
        )

    if self.code_repository_id:
        code_repo_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.code_reference_id
            == CodeReferenceSchema.id,
            CodeReferenceSchema.code_repository_id
            == self.code_repository_id,
        )
        custom_filters.append(code_repo_filter)

    if self.stack_id:
        stack_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == self.stack_id,
        )
        custom_filters.append(stack_filter)

    if self.build_id:
        build_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.build_id == self.build_id,
        )
        custom_filters.append(build_filter)

    if self.pipeline_id:
        pipeline_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.pipeline_id == self.pipeline_id,
        )
        custom_filters.append(pipeline_filter)

    if self.pipeline:
        pipeline_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.pipeline_id == PipelineSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.pipeline,
                table=PipelineSchema,
            ),
        )
        custom_filters.append(pipeline_filter)

    if self.stack:
        stack_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.stack,
                table=StackSchema,
            ),
        )
        custom_filters.append(stack_filter)

    return custom_filters

RunTemplateRequest

Bases: ProjectScopedRequest

Request model for run templates.

RunTemplateResponse

Bases: ProjectScopedResponse[RunTemplateResponseBody, RunTemplateResponseMetadata, RunTemplateResponseResources]

Response model for run templates.

Attributes

build: Optional[PipelineBuildResponse] property

The build property.

Returns:

Type	Description
Optional[PipelineBuildResponse]	the value of the property.

code_reference: Optional[CodeReferenceResponse] property

The code_reference property.

Returns:

Type	Description
Optional[CodeReferenceResponse]	the value of the property.

config_schema: Optional[Dict[str, Any]] property

The config_schema property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

config_template: Optional[Dict[str, Any]] property

The config_template property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

description: Optional[str] property

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

hidden: bool property

The hidden property.

Returns:

Type	Description
bool	the value of the property.

latest_run_id: Optional[UUID] property

The latest_run_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

latest_run_status: Optional[ExecutionStatus] property

The latest_run_status property.

Returns:

Type	Description
Optional[ExecutionStatus]	the value of the property.

pipeline: Optional[PipelineResponse] property

The pipeline property.

Returns:

Type	Description
Optional[PipelineResponse]	the value of the property.

pipeline_spec: Optional[PipelineSpec] property

The pipeline_spec property.

Returns:

Type	Description
Optional[PipelineSpec]	the value of the property.

runnable: bool property

The runnable property.

Returns:

Type	Description
bool	the value of the property.

source_deployment: Optional[PipelineDeploymentResponse] property

The source_deployment property.

Returns:

Type	Description
Optional[PipelineDeploymentResponse]	the value of the property.

tags: List[TagResponse] property

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

Functions

get_hydrated_version() -> RunTemplateResponse

Return the hydrated version of this run template.

Returns:

Type	Description
RunTemplateResponse	The hydrated run template.

Source code in src/zenml/models/v2/core/run_template.py

def get_hydrated_version(self) -> "RunTemplateResponse":
    """Return the hydrated version of this run template.

    Returns:
        The hydrated run template.
    """
    from zenml.client import Client

    return Client().zen_store.get_run_template(
        template_id=self.id, hydrate=True
    )

RunTemplateUpdate

Bases: BaseUpdate

Run template update model.

ScheduleFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all Users.

ScheduleResponse

Bases: ProjectScopedResponse[ScheduleResponseBody, ScheduleResponseMetadata, ScheduleResponseResources]

Response model for schedules.

Attributes

active: bool property

The active property.

Returns:

Type	Description
bool	the value of the property.

catchup: bool property

The catchup property.

Returns:

Type	Description
bool	the value of the property.

cron_expression: Optional[str] property

The cron_expression property.

Returns:

Type	Description
Optional[str]	the value of the property.

end_time: Optional[datetime] property

The end_time property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

interval_second: Optional[timedelta] property

The interval_second property.

Returns:

Type	Description
Optional[timedelta]	the value of the property.

orchestrator_id: Optional[UUID] property

The orchestrator_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

pipeline_id: Optional[UUID] property

The pipeline_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

run_metadata: Dict[str, MetadataType] property

The run_metadata property.

Returns:

Type	Description
Dict[str, MetadataType]	the value of the property.

run_once_start_time: Optional[datetime] property

The run_once_start_time property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

start_time: Optional[datetime] property

The start_time property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

utc_end_time: Optional[str] property

Optional ISO-formatted string of the UTC end time.

Returns:

Type	Description
Optional[str]	Optional ISO-formatted string of the UTC end time.

utc_start_time: Optional[str] property

Optional ISO-formatted string of the UTC start time.

Returns:

Type	Description
Optional[str]	Optional ISO-formatted string of the UTC start time.

Functions

get_hydrated_version() -> ScheduleResponse

Get the hydrated version of this schedule.

Returns:

Type	Description
ScheduleResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/schedule.py

def get_hydrated_version(self) -> "ScheduleResponse":
    """Get the hydrated version of this schedule.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_schedule(self.id)

SecretFilter

Bases: UserScopedFilter

Model to enable advanced secret filtering.

Functions

apply_filter(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Applies the filter to a query.

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/secret.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    # The secret user scoping works a bit differently than the other
    # scoped filters. We have to filter out all private secrets that are
    # not owned by the current user.
    if not self.scope_user:
        return super().apply_filter(query=query, table=table)

    scope_user = self.scope_user

    # First we apply the inherited filters without the user scoping
    # applied.
    self.scope_user = None
    query = super().apply_filter(query=query, table=table)
    self.scope_user = scope_user

    # Then we apply the user scoping filter.
    if self.scope_user:
        from sqlmodel import and_, or_

        query = query.where(
            or_(
                and_(
                    getattr(table, "user_id") == self.scope_user,
                    getattr(table, "private") == True,  # noqa: E712
                ),
                getattr(table, "private") == False,  # noqa: E712
            )
        )

    else:
        query = query.where(getattr(table, "private") == False)  # noqa: E712

    return query

SecretRequest

Bases: UserScopedRequest

Request model for secrets.

Attributes

secret_values: Dict[str, str] property

A dictionary with all un-obfuscated values stored in this secret.

The values are returned as strings, not SecretStr. If a value is None, it is not included in the returned dictionary. This is to enable the use of None values in the update model to indicate that a secret value should be deleted.

Returns:

Type	Description
Dict[str, str]	A dictionary containing the secret's values.

SecretResponse

Bases: UserScopedResponse[SecretResponseBody, SecretResponseMetadata, SecretResponseResources]

Response model for secrets.

Attributes

has_missing_values: bool property

Returns True if the secret has missing values (i.e. None).

Values can be missing from a secret for example if the user retrieves a secret but does not have the permission to view the secret values.

Returns:

Type	Description
bool	True if the secret has any values set to None.

private: bool property

The private property.

Returns:

Type	Description
bool	the value of the property.

secret_values: Dict[str, str] property

A dictionary with all un-obfuscated values stored in this secret.

The values are returned as strings, not SecretStr. If a value is None, it is not included in the returned dictionary. This is to enable the use of None values in the update model to indicate that a secret value should be deleted.

Returns:

Type	Description
Dict[str, str]	A dictionary containing the secret's values.

values: Dict[str, Optional[SecretStr]] property

The values property.

Returns:

Type	Description
Dict[str, Optional[SecretStr]]	the value of the property.

Functions

add_secret(key: str, value: str) -> None

Adds a secret value to the secret.

Parameters:

Name	Type	Description	Default
key	str	The key of the secret value.	required
value	str	The secret value.	required

Source code in src/zenml/models/v2/core/secret.py

def add_secret(self, key: str, value: str) -> None:
    """Adds a secret value to the secret.

    Args:
        key: The key of the secret value.
        value: The secret value.
    """
    self.get_body().values[key] = SecretStr(value)

get_hydrated_version() -> SecretResponse

Get the hydrated version of this secret.

Returns:

Type	Description
SecretResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/secret.py

def get_hydrated_version(self) -> "SecretResponse":
    """Get the hydrated version of this secret.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_secret(self.id)

remove_secret(key: str) -> None

Removes a secret value from the secret.

Parameters:

Name	Type	Description	Default
key	str	The key of the secret value.	required

Source code in src/zenml/models/v2/core/secret.py

def remove_secret(self, key: str) -> None:
    """Removes a secret value from the secret.

    Args:
        key: The key of the secret value.
    """
    del self.get_body().values[key]

remove_secrets() -> None

Removes all secret values from the secret but keep the keys.

Source code in src/zenml/models/v2/core/secret.py

def remove_secrets(self) -> None:
    """Removes all secret values from the secret but keep the keys."""
    self.get_body().values = {k: None for k in self.values.keys()}

set_secrets(values: Dict[str, str]) -> None

Sets the secret values of the secret.

Parameters:

Name	Type	Description	Default
values	Dict[str, str]	The secret values to set.	required

Source code in src/zenml/models/v2/core/secret.py

def set_secrets(self, values: Dict[str, str]) -> None:
    """Sets the secret values of the secret.

    Args:
        values: The secret values to set.
    """
    self.get_body().values = {k: SecretStr(v) for k, v in values.items()}

SecretUpdate

Bases: BaseUpdate

Update model for secrets.

Functions

get_secret_values_update() -> Dict[str, Optional[str]]

Returns a dictionary with the secret values to update.

Returns:

Type	Description
Dict[str, Optional[str]]	A dictionary with the secret values to update.

Source code in src/zenml/models/v2/core/secret.py

def get_secret_values_update(self) -> Dict[str, Optional[str]]:
    """Returns a dictionary with the secret values to update.

    Returns:
        A dictionary with the secret values to update.
    """
    if self.values is not None:
        return {
            k: v.get_secret_value() if v is not None else None
            for k, v in self.values.items()
        }

    return {}

ServerSettingsResponse

Bases: BaseResponse[ServerSettingsResponseBody, ServerSettingsResponseMetadata, ServerSettingsResponseResources]

Response model for server settings.

Attributes

active: bool property

The active property.

Returns:

Type	Description
bool	the value of the property.

display_announcements: Optional[bool] property

The display_announcements property.

Returns:

Type	Description
Optional[bool]	the value of the property.

display_updates: Optional[bool] property

The display_updates property.

Returns:

Type	Description
Optional[bool]	the value of the property.

enable_analytics: bool property

The enable_analytics property.

Returns:

Type	Description
bool	the value of the property.

last_user_activity: datetime property

The last_user_activity property.

Returns:

Type	Description
datetime	the value of the property.

logo_url: Optional[str] property

The logo_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

server_id: UUID property

The server_id property.

Returns:

Type	Description
UUID	the value of the property.

server_name: str property

The server_name property.

Returns:

Type	Description
str	the value of the property.

updated: datetime property

The updated property.

Returns:

Type	Description
datetime	the value of the property.

Functions

get_hydrated_version() -> ServerSettingsResponse

Get the hydrated version of the server settings.

Returns:

Type	Description
ServerSettingsResponse	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/server_settings.py

def get_hydrated_version(self) -> "ServerSettingsResponse":
    """Get the hydrated version of the server settings.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_server_settings(hydrate=True)

ServerSettingsUpdate

Bases: BaseUpdate

Model for updating server settings.

ServiceAccountFilter

Bases: BaseFilter

Model to enable advanced filtering of service accounts.

Functions

apply_filter(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Override to filter out user accounts from the query.

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/service_account.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to filter out user accounts from the query.

    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)
    query = query.where(
        getattr(table, "is_service_account") == True  # noqa: E712
    )

    return query

ServiceAccountRequest

Bases: BaseRequest

Request model for service accounts.

ServiceAccountResponse

Bases: BaseIdentifiedResponse[ServiceAccountResponseBody, ServiceAccountResponseMetadata, ServiceAccountResponseResources]

Response model for service accounts.

Attributes

active: bool property

The active property.

Returns:

Type	Description
bool	the value of the property.

description: str property

The description property.

Returns:

Type	Description
str	the value of the property.

Functions

get_hydrated_version() -> ServiceAccountResponse

Get the hydrated version of this service account.

Returns:

Type	Description
ServiceAccountResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/service_account.py

def get_hydrated_version(self) -> "ServiceAccountResponse":
    """Get the hydrated version of this service account.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_service_account(self.id)

to_user_model() -> UserResponse

Converts the service account to a user model.

For now, a lot of code still relies on the active user and resource owners being a UserResponse object, which is a superset of the ServiceAccountResponse object. We need this method to convert the service account to a user.

Returns:

Type	Description
UserResponse	The user model.

Source code in src/zenml/models/v2/core/service_account.py

def to_user_model(self) -> "UserResponse":
    """Converts the service account to a user model.

    For now, a lot of code still relies on the active user and resource
    owners being a UserResponse object, which is a superset of the
    ServiceAccountResponse object. We need this method to convert the
    service account to a user.

    Returns:
        The user model.
    """
    from zenml.models.v2.core.user import (
        UserResponse,
        UserResponseBody,
        UserResponseMetadata,
    )

    return UserResponse(
        id=self.id,
        name=self.name,
        body=UserResponseBody(
            active=self.active,
            is_service_account=True,
            email_opted_in=False,
            created=self.created,
            updated=self.updated,
            is_admin=False,
        ),
        metadata=UserResponseMetadata(
            description=self.description,
        ),
    )

ServiceAccountUpdate

Bases: BaseUpdate

Update model for service accounts.

ServiceConfig(**data: Any)

Bases: BaseTypedModel

Generic service configuration.

Concrete service classes should extend this class and add additional attributes that they want to see reflected and used in the service configuration.

Attributes:

Name	Type	Description
name	str	name for the service instance
description	str	description of the service
pipeline_name	str	name of the pipeline that spun up the service
pipeline_step_name	str	name of the pipeline step that spun up the service
run_name	str	name of the pipeline run that spun up the service.

Initialize the service configuration.

Parameters:

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

Raises:

Type	Description
ValueError	if neither 'name' nor 'model_name' is set.

Source code in src/zenml/services/service.py

def __init__(self, **data: Any):
    """Initialize the service configuration.

    Args:
        **data: keyword arguments.

    Raises:
        ValueError: if neither 'name' nor 'model_name' is set.
    """
    super().__init__(**data)
    if self.name or self.model_name:
        self.service_name = data.get(
            "service_name",
            f"{ZENM_ENDPOINT_PREFIX}{self.name or self.model_name}",
        )
    else:
        raise ValueError("Either 'name' or 'model_name' must be set.")

Functions

get_service_labels() -> Dict[str, str]

Get the service labels.

Returns:

Type	Description
Dict[str, str]	a dictionary of service labels.

Source code in src/zenml/services/service.py

def get_service_labels(self) -> Dict[str, str]:
    """Get the service labels.

    Returns:
        a dictionary of service labels.
    """
    labels = {}
    for k, v in self.model_dump().items():
        label = f"zenml_{k}".upper()
        labels[label] = str(v)
    return labels

ServiceConnector(**kwargs: Any)

Bases: BaseModel

Base service connector class.

Service connectors are standalone components that can be used to link ZenML to external resources. They are responsible for validating and storing authentication configuration and sensitive credentials and for providing authentication services to other ZenML components. Service connectors are built on top of the (otherwise opaque) ZenML secrets and secrets store mechanisms and add secret auto-configuration, secret discovery and secret schema validation capabilities.

The implementation independent service connector abstraction is made possible through the use of generic "resource types" and "resource IDs". These constitute the "contract" between connectors and the consumers of the authentication services that they provide. In a nutshell, a connector instance advertises what resource(s) it can be used to gain access to, whereas a consumer may run a query to search for compatible connectors by specifying the resource(s) that they need to access and then use a matching connector instance to connect to said resource(s).

The resource types and authentication methods supported by a connector are declared in the connector type specification. The role of this specification is two-fold:

• it declares a schema for the configuration that needs to be provided to configure the connector. This can be used to validate the configuration without having to instantiate the connector itself (e.g. in the CLI and dashboard), which also makes it possible to configure connectors and store their configuration without having to instantiate them.
• it provides a way for ZenML to keep a registry of available connector implementations and configured connector instances. Users who want to connect ZenML to external resources via connectors can use this registry to discover what types of connectors are available and what types of resources they can be configured to access. Consumers can also use the registry to find connector instances that are compatible with the types of resources that they need to access.

Initialize a new service connector instance.

Parameters:

Name	Type	Description	Default
kwargs	Any	Additional keyword arguments to pass to the base class constructor.	{}

Source code in src/zenml/service_connectors/service_connector.py

def __init__(self, **kwargs: Any) -> None:
    """Initialize a new service connector instance.

    Args:
        kwargs: Additional keyword arguments to pass to the base class
            constructor.
    """
    super().__init__(**kwargs)

    # Convert the resource ID to its canonical form. For resource types
    # that don't support multiple instances:
    # - if a resource ID is not provided, we use the default resource ID for
    # the resource type
    # - if a resource ID is provided, we verify that it matches the default
    # resource ID for the resource type
    if self.resource_type:
        try:
            self.resource_id = self._validate_resource_id(
                self.resource_type, self.resource_id
            )
        except AuthorizationException as e:
            error = (
                f"Authorization error validating resource ID "
                f"{self.resource_id} for resource type "
                f"{self.resource_type}: {e}"
            )
            # Log an exception if debug logging is enabled
            if logger.isEnabledFor(logging.DEBUG):
                logger.exception(error)
            else:
                logger.warning(error)

            self.resource_id = None

Attributes

supported_resource_types: List[str] property

The resource types supported by this connector instance.

Returns:

Type	Description
List[str]	A list with the resource types supported by this connector instance.

type: ServiceConnectorTypeModel property

Get the connector type specification.

Returns:

Type	Description
ServiceConnectorTypeModel	The connector type specification.

Functions

auto_configure(auth_method: Optional[str] = None, resource_type: Optional[str] = None, resource_id: Optional[str] = None, **kwargs: Any) -> Optional[ServiceConnector] classmethod

Auto-configure a connector instance.

Instantiate a connector with a configuration extracted from the authentication configuration available in the environment (e.g. environment variables or local client/SDK configuration files).

Parameters:

Name	Type	Description	Default
auth_method	Optional[str]	The particular authentication method to use. If omitted and if the connector implementation cannot decide which authentication method to use, it may raise an exception.	None
resource_type	Optional[str]	The type of resource to configure. If not specified, the method returns a connector instance configured to access any of the supported resource types (multi-type connector) or configured to use a default resource type. If the connector doesn't support multi-type configurations or if it cannot decide which resource type to use, it may raise an exception.	None
resource_id	Optional[str]	The ID of the resource instance to configure. The connector implementation may choose to either require or ignore this parameter if it does not support or detect a resource type that supports multiple instances.	None
kwargs	Any	Additional implementation specific keyword arguments to use.	{}

Returns:

Type	Description
Optional[ServiceConnector]	A connector instance configured with authentication credentials
Optional[ServiceConnector]	automatically extracted from the environment or None if
Optional[ServiceConnector]	auto-configuration is not supported.

Raises:

Type	Description
ValueError	If the connector does not support the requested authentication method or resource type.
AuthorizationException	If the connector's authentication credentials have expired.

Source code in src/zenml/service_connectors/service_connector.py

@classmethod
def auto_configure(
    cls,
    auth_method: Optional[str] = None,
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
    **kwargs: Any,
) -> Optional["ServiceConnector"]:
    """Auto-configure a connector instance.

    Instantiate a connector with a configuration extracted from the
    authentication configuration available in the environment (e.g.
    environment variables or local client/SDK configuration files).

    Args:
        auth_method: The particular authentication method to use. If
            omitted and if the connector implementation cannot decide which
            authentication method to use, it may raise an exception.
        resource_type: The type of resource to configure. If not specified,
            the method returns a connector instance configured to access any
            of the supported resource types (multi-type connector) or
            configured to use a default resource type. If the connector
            doesn't support multi-type configurations or if it cannot decide
            which resource type to use, it may raise an exception.
        resource_id: The ID of the resource instance to configure. The
            connector implementation may choose to either require or ignore
            this parameter if it does not support or detect a resource type
            that supports multiple instances.
        kwargs: Additional implementation specific keyword arguments to use.

    Returns:
        A connector instance configured with authentication credentials
        automatically extracted from the environment or None if
        auto-configuration is not supported.

    Raises:
        ValueError: If the connector does not support the requested
            authentication method or resource type.
        AuthorizationException: If the connector's authentication
            credentials have expired.
    """
    spec = cls.get_type()

    if not spec.supports_auto_configuration:
        return None

    if auth_method and auth_method not in spec.auth_method_dict:
        raise ValueError(
            f"connector type {spec.name} does not support authentication "
            f"method: '{auth_method}'"
        )

    if resource_type and resource_type not in spec.resource_type_dict:
        raise ValueError(
            f"connector type {spec.name} does not support resource type: "
            f"'{resource_type}'"
        )

    connector = cls._auto_configure(
        auth_method=auth_method,
        resource_type=resource_type,
        resource_id=resource_id,
        **kwargs,
    )

    if connector.has_expired():
        raise AuthorizationException(
            "the connector's auto-configured authentication credentials "
            "have expired."
        )

    connector._verify(
        resource_type=connector.resource_type,
        resource_id=connector.resource_id,
    )
    return connector

configure_local_client(**kwargs: Any) -> None

Configure a local client to authenticate and connect to a resource.

This method uses the connector's configuration to configure a local client or SDK installed on the localhost so that it can authenticate and connect to the resource that the connector is configured to access.

The connector has to be fully configured for this method to succeed (i.e. the connector's configuration must be valid, a resource type must be specified and the resource ID must be specified if the resource type supports multiple instances). This method should only be called on a connector client retrieved by calling get_connector_client on the main service connector.

Parameters:

Name	Type	Description	Default
kwargs	Any	Additional implementation specific keyword arguments to use to configure the client.	{}

Raises:

Type	Description
AuthorizationException	If the connector's authentication credentials have expired.

Source code in src/zenml/service_connectors/service_connector.py

def configure_local_client(
    self,
    **kwargs: Any,
) -> None:
    """Configure a local client to authenticate and connect to a resource.

    This method uses the connector's configuration to configure a local
    client or SDK installed on the localhost so that it can authenticate
    and connect to the resource that the connector is configured to access.

    The connector has to be fully configured for this method to succeed
    (i.e. the connector's configuration must be valid, a resource type must
    be specified and the resource ID must be specified if the resource type
    supports multiple instances). This method should only be called on a
    connector client retrieved by calling `get_connector_client` on the
    main service connector.

    Args:
        kwargs: Additional implementation specific keyword arguments to use
            to configure the client.

    Raises:
        AuthorizationException: If the connector's authentication
            credentials have expired.
    """
    resource_type, resource_id = self.validate_runtime_args(
        resource_type=self.resource_type,
        resource_id=self.resource_id,
        require_resource_type=True,
        require_resource_id=True,
    )

    if self.has_expired():
        raise AuthorizationException(
            "the connector's authentication credentials have expired."
        )

    self._verify(
        resource_type=resource_type,
        resource_id=resource_id,
    )

    self._configure_local_client(
        **kwargs,
    )

connect(verify: bool = True, **kwargs: Any) -> Any

Authenticate and connect to a resource.

Initialize and return an implementation specific object representing an authenticated service client, connection or session that can be used to access the resource that the connector is configured to access.

The connector has to be fully configured for this method to succeed (i.e. the connector's configuration must be valid, a resource type and a resource ID must be configured). This method should only be called on a connector client retrieved by calling get_connector_client on the main service connector.

Parameters:

Name	Type	Description	Default
verify	bool	Whether to verify that the connector can access the configured resource before connecting to it.	True
kwargs	Any	Additional implementation specific keyword arguments to use to configure the client.	{}

Returns:

Type	Description
Any	An implementation specific object representing the authenticated
Any	service client, connection or session.

Raises:

Type	Description
AuthorizationException	If the connector's authentication credentials have expired.

Source code in src/zenml/service_connectors/service_connector.py

def connect(
    self,
    verify: bool = True,
    **kwargs: Any,
) -> Any:
    """Authenticate and connect to a resource.

    Initialize and return an implementation specific object representing an
    authenticated service client, connection or session that can be used
    to access the resource that the connector is configured to access.

    The connector has to be fully configured for this method to succeed
    (i.e. the connector's configuration must be valid, a resource type and
    a resource ID must be configured). This method should only be called on
    a connector client retrieved by calling `get_connector_client` on the
    main service connector.

    Args:
        verify: Whether to verify that the connector can access the
            configured resource before connecting to it.
        kwargs: Additional implementation specific keyword arguments to use
            to configure the client.

    Returns:
        An implementation specific object representing the authenticated
        service client, connection or session.

    Raises:
        AuthorizationException: If the connector's authentication
            credentials have expired.
    """
    if verify:
        resource_type, resource_id = self.validate_runtime_args(
            resource_type=self.resource_type,
            resource_id=self.resource_id,
            require_resource_type=True,
            require_resource_id=True,
        )

        if self.has_expired():
            raise AuthorizationException(
                "the connector's authentication credentials have expired."
            )

        self._verify(
            resource_type=resource_type,
            resource_id=resource_id,
        )

    return self._connect_to_resource(
        **kwargs,
    )

from_model(model: Union[ServiceConnectorRequest, ServiceConnectorResponse]) -> ServiceConnector classmethod

Creates a service connector instance from a service connector model.

Parameters:

Name	Type	Description	Default
model	Union[ServiceConnectorRequest, ServiceConnectorResponse]	The service connector model.	required

Returns:

Type	Description
ServiceConnector	The created service connector instance.

Raises:

Type	Description
ValueError	If the connector configuration is invalid.

Source code in src/zenml/service_connectors/service_connector.py

@classmethod
def from_model(
    cls,
    model: Union["ServiceConnectorRequest", "ServiceConnectorResponse"],
) -> "ServiceConnector":
    """Creates a service connector instance from a service connector model.

    Args:
        model: The service connector model.

    Returns:
        The created service connector instance.

    Raises:
        ValueError: If the connector configuration is invalid.
    """
    # Validate the connector configuration model
    spec = cls.get_type()

    # Multiple resource types in the model means that the connector
    # instance is configured to access any of the supported resource
    # types (a multi-type connector). We represent that here by setting the
    # resource type to None.
    resource_type: Optional[str] = None
    if len(model.resource_types) == 1:
        resource_type = model.resource_types[0]

    expiration_seconds: Optional[int] = None
    try:
        method_spec, _ = spec.find_resource_specifications(
            model.auth_method,
            resource_type,
        )
        expiration_seconds = method_spec.validate_expiration(
            model.expiration_seconds
        )
    except (KeyError, ValueError) as e:
        raise ValueError(
            f"connector configuration is not valid: {e}"
        ) from e

    # Unpack the authentication configuration
    config = model.configuration.copy()
    if isinstance(model, ServiceConnectorResponse) and model.secret_id:
        try:
            secret = Client().get_secret(model.secret_id)
        except KeyError as e:
            raise ValueError(
                f"could not fetch secret with ID '{model.secret_id}' "
                f"referenced in the connector configuration: {e}"
            ) from e

        if secret.has_missing_values:
            raise ValueError(
                f"secret with ID '{model.secret_id}' referenced in the "
                "connector configuration has missing values. This can "
                "happen for example if your user lacks the permissions "
                "required to access the secret."
            )

        config.update(secret.secret_values)

    if model.secrets:
        config.update(
            {
                k: v.get_secret_value()
                for k, v in model.secrets.items()
                if v
            }
        )

    if method_spec.config_class is None:
        raise ValueError(
            f"the implementation of the {model.name} connector type is "
            "not available in the environment. Please check that you "
            "have installed the required dependencies."
        )

    # Validate the authentication configuration
    try:
        auth_config = method_spec.config_class(**config)
    except ValidationError as e:
        raise ValueError(
            f"connector configuration is not valid: {e}"
        ) from e

    assert isinstance(auth_config, AuthenticationConfig)

    connector = cls(
        auth_method=model.auth_method,
        resource_type=resource_type,
        resource_id=model.resource_id,
        config=auth_config,
        expires_at=model.expires_at,
        expires_skew_tolerance=model.expires_skew_tolerance,
        expiration_seconds=expiration_seconds,
    )
    if isinstance(model, ServiceConnectorResponse):
        connector.id = model.id
        connector.name = model.name

    return connector

get_connector_client(resource_type: Optional[str] = None, resource_id: Optional[str] = None) -> ServiceConnector

Get a connector client that can be used to connect to a resource.

The connector client can be used by consumers to connect to a resource (i.e. make calls to connect and configure_local_client).

The returned connector may be the same as the original connector or it may a different instance configured with different credentials or even of a different connector type.

Parameters:

Name	Type	Description	Default
resource_type	Optional[str]	The type of the resource to connect to.	None
resource_id	Optional[str]	The ID of a particular resource to connect to.	None

Returns:

Type	Description
ServiceConnector	A service connector client that can be used to connect to the
ServiceConnector	resource.

Raises:

Type	Description
AuthorizationException	If authentication failed.

Source code in src/zenml/service_connectors/service_connector.py

def get_connector_client(
    self,
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
) -> "ServiceConnector":
    """Get a connector client that can be used to connect to a resource.

    The connector client can be used by consumers to connect to a resource
    (i.e. make calls to `connect` and `configure_local_client`).

    The returned connector may be the same as the original connector
    or it may a different instance configured with different credentials or
    even of a different connector type.

    Args:
        resource_type: The type of the resource to connect to.
        resource_id: The ID of a particular resource to connect to.

    Returns:
        A service connector client that can be used to connect to the
        resource.

    Raises:
        AuthorizationException: If authentication failed.
    """
    resource_type, resource_id = self.validate_runtime_args(
        resource_type=resource_type,
        resource_id=resource_id,
        require_resource_type=True,
        require_resource_id=True,
    )

    if self.has_expired():
        raise AuthorizationException(
            "the connector's authentication credentials have expired."
        )

    # Verify if the connector allows access to the requested resource type
    # and instance.
    self._verify(
        resource_type=resource_type,
        resource_id=resource_id,
    )

    assert resource_type is not None
    assert resource_id is not None

    connector_client = self._get_connector_client(
        resource_type=resource_type,
        resource_id=resource_id,
    )
    # Transfer the expiration skew tolerance to the connector client
    # if an expiration time is set for the connector client credentials.
    if connector_client.expires_at is not None:
        connector_client.expires_skew_tolerance = (
            self.expires_skew_tolerance
        )

    if connector_client.has_expired():
        raise AuthorizationException(
            "the connector's authentication credentials have expired."
        )

    connector_client._verify(
        resource_type=resource_type,
        resource_id=connector_client.resource_id,
    )

    return connector_client

get_type() -> ServiceConnectorTypeModel classmethod

Get the connector type specification.

Returns:

Type	Description
ServiceConnectorTypeModel	The connector type specification.

Source code in src/zenml/service_connectors/service_connector.py

@classmethod
def get_type(cls) -> ServiceConnectorTypeModel:
    """Get the connector type specification.

    Returns:
        The connector type specification.
    """
    if cls._TYPE is not None:
        return cls._TYPE

    connector_type = cls._get_connector_type()
    connector_type.set_connector_class(cls)
    cls._TYPE = connector_type
    return cls._TYPE

has_expired() -> bool

Check if the connector authentication credentials have expired.

Verify that the authentication credentials associated with the connector have not expired by checking the expiration time against the current time.

Returns:

Type	Description
bool	True if the connector has expired, False otherwise.

Source code in src/zenml/service_connectors/service_connector.py

def has_expired(self) -> bool:
    """Check if the connector authentication credentials have expired.

    Verify that the authentication credentials associated with the connector
    have not expired by checking the expiration time against the current
    time.

    Returns:
        True if the connector has expired, False otherwise.
    """
    if not self.expires_at:
        return False

    expires_at = self.expires_at.replace(tzinfo=timezone.utc)
    # Subtract some time to account for clock skew or other delays.
    expires_at = expires_at - timedelta(
        seconds=self.expires_skew_tolerance
        if self.expires_skew_tolerance is not None
        else SERVICE_CONNECTOR_SKEW_TOLERANCE_SECONDS
    )
    now = utc_now(tz_aware=expires_at)
    delta = expires_at - now
    result = delta < timedelta(seconds=0)

    logger.debug(
        f"Checking if connector {self.name} has expired.\n"
        f"Expires at: {self.expires_at}\n"
        f"Expires at (+skew): {expires_at}\n"
        f"Current UTC time: {now}\n"
        f"Delta: {delta}\n"
        f"Result: {result}\n"
    )

    return result

to_model(name: Optional[str] = None, description: str = '', labels: Optional[Dict[str, str]] = None) -> ServiceConnectorRequest

Convert the connector instance to a service connector model.

Parameters:

Name	Type	Description	Default
name	Optional[str]	The name of the connector.	None
description	str	The description of the connector.	''
labels	Optional[Dict[str, str]]	The labels of the connector.	None

Returns:

Type	Description
ServiceConnectorRequest	The service connector model corresponding to the connector
ServiceConnectorRequest	instance.

Raises:

Type	Description
ValueError	If the connector configuration is not valid.

Source code in src/zenml/service_connectors/service_connector.py

def to_model(
    self,
    name: Optional[str] = None,
    description: str = "",
    labels: Optional[Dict[str, str]] = None,
) -> "ServiceConnectorRequest":
    """Convert the connector instance to a service connector model.

    Args:
        name: The name of the connector.
        description: The description of the connector.
        labels: The labels of the connector.

    Returns:
        The service connector model corresponding to the connector
        instance.

    Raises:
        ValueError: If the connector configuration is not valid.
    """
    spec = self.get_type()

    name = name or self.name
    if name is None:
        raise ValueError(
            "connector configuration is not valid: name must be set"
        )

    model = ServiceConnectorRequest(
        connector_type=spec.connector_type,
        name=name,
        description=description,
        auth_method=self.auth_method,
        expires_at=self.expires_at,
        expires_skew_tolerance=self.expires_skew_tolerance,
        expiration_seconds=self.expiration_seconds,
        labels=labels or {},
    )

    # Validate the connector configuration.
    model.validate_and_configure_resources(
        connector_type=spec,
        resource_types=self.resource_type,
        resource_id=self.resource_id,
        configuration=self.config.non_secret_values,
        secrets=self.config.secret_values,  # type: ignore[arg-type]
    )

    return model

to_response_model(user: Optional[UserResponse] = None, name: Optional[str] = None, id: Optional[UUID] = None, description: str = '', labels: Optional[Dict[str, str]] = None) -> ServiceConnectorResponse

Convert the connector instance to a service connector response model.

Parameters:

Name	Type	Description	Default
user	Optional[UserResponse]	The user that created the connector.	None
name	Optional[str]	The name of the connector.	None
id	Optional[UUID]	The ID of the connector.	None
description	str	The description of the connector.	''
labels	Optional[Dict[str, str]]	The labels of the connector.	None

Returns:

Type	Description
ServiceConnectorResponse	The service connector response model corresponding to the connector
ServiceConnectorResponse	instance.

Raises:

Type	Description
ValueError	If the connector configuration is not valid.

Source code in src/zenml/service_connectors/service_connector.py

def to_response_model(
    self,
    user: Optional[UserResponse] = None,
    name: Optional[str] = None,
    id: Optional[UUID] = None,
    description: str = "",
    labels: Optional[Dict[str, str]] = None,
) -> "ServiceConnectorResponse":
    """Convert the connector instance to a service connector response model.

    Args:
        user: The user that created the connector.
        name: The name of the connector.
        id: The ID of the connector.
        description: The description of the connector.
        labels: The labels of the connector.

    Returns:
        The service connector response model corresponding to the connector
        instance.

    Raises:
        ValueError: If the connector configuration is not valid.
    """
    spec = self.get_type()

    name = name or self.name
    id = id or self.id
    if name is None or id is None:
        raise ValueError(
            "connector configuration is not valid: name and ID must be set"
        )

    now = utc_now()
    model = ServiceConnectorResponse(
        id=id,
        name=name,
        body=ServiceConnectorResponseBody(
            user=user,
            created=now,
            updated=now,
            description=description,
            connector_type=self.get_type(),
            auth_method=self.auth_method,
            expires_at=self.expires_at,
            expires_skew_tolerance=self.expires_skew_tolerance,
        ),
        metadata=ServiceConnectorResponseMetadata(
            expiration_seconds=self.expiration_seconds,
            labels=labels or {},
        ),
    )

    # Validate the connector configuration.
    model.validate_and_configure_resources(
        connector_type=spec,
        resource_types=self.resource_type,
        resource_id=self.resource_id,
        configuration=self.config.non_secret_values,
        secrets=self.config.secret_values,  # type: ignore[arg-type]
    )

    return model

validate_runtime_args(resource_type: Optional[str], resource_id: Optional[str] = None, require_resource_type: bool = False, require_resource_id: bool = False, **kwargs: Any) -> Tuple[Optional[str], Optional[str]]

Validate the runtime arguments against the connector configuration.

Validate that the supplied runtime arguments are compatible with the connector configuration and its specification. This includes validating that the resource type and resource ID are compatible with the connector configuration and its capabilities.

Parameters:

Name	Type	Description	Default
resource_type	Optional[str]	The type of the resource supplied at runtime by the connector's consumer. Must be the same as the resource type that the connector is configured to access, unless the connector is configured to access any resource type.	required
resource_id	Optional[str]	The ID of the resource requested by the connector's consumer. Can be different than the resource ID that the connector is configured to access, e.g. if it is not in the canonical form.	None
require_resource_type	bool	Whether the resource type is required.	False
require_resource_id	bool	Whether the resource ID is required.	False
kwargs	Any	Additional runtime arguments.	{}

Returns:

Type	Description
Tuple[Optional[str], Optional[str]]	The validated resource type and resource ID.

Raises:

Type	Description
ValueError	If the runtime arguments are not valid.

Source code in src/zenml/service_connectors/service_connector.py

def validate_runtime_args(
    self,
    resource_type: Optional[str],
    resource_id: Optional[str] = None,
    require_resource_type: bool = False,
    require_resource_id: bool = False,
    **kwargs: Any,
) -> Tuple[Optional[str], Optional[str]]:
    """Validate the runtime arguments against the connector configuration.

    Validate that the supplied runtime arguments are compatible with the
    connector configuration and its specification. This includes validating
    that the resource type and resource ID are compatible with the connector
    configuration and its capabilities.

    Args:
        resource_type: The type of the resource supplied at runtime by the
            connector's consumer. Must be the same as the resource type that
            the connector is configured to access, unless the connector is
            configured to access any resource type.
        resource_id: The ID of the resource requested by the connector's
            consumer. Can be different than the resource ID that the
            connector is configured to access, e.g. if it is not in the
            canonical form.
        require_resource_type: Whether the resource type is required.
        require_resource_id: Whether the resource ID is required.
        kwargs: Additional runtime arguments.

    Returns:
        The validated resource type and resource ID.

    Raises:
        ValueError: If the runtime arguments are not valid.
    """
    if (
        self.resource_type
        and resource_type
        and (self.resource_type != resource_type)
    ):
        raise ValueError(
            f"the connector is configured to provide access to a "
            f"'{self.resource_type}' resource type, but a different "
            f"resource type was requested: '{resource_type}'."
        )

    resource_type = resource_type or self.resource_type
    resource_id = resource_id or self.resource_id

    if require_resource_type and not resource_type:
        raise ValueError(
            "the connector is configured to provide access to multiple "
            "resource types. A resource type must be specified when "
            "requesting access to a resource."
        )

    spec = self.get_type()

    try:
        # Get the resource specification corresponding to the
        # connector configuration.
        _, resource_spec = spec.find_resource_specifications(
            self.auth_method,
            resource_type,
        )
    except (KeyError, ValueError) as e:
        raise ValueError(
            f"connector configuration is not valid: {e}"
        ) from e

    if not resource_type or not resource_spec:
        if resource_id:
            raise ValueError(
                "the connector is configured to provide access to multiple "
                "resource types, but only a resource name was specified. A "
                "resource type must also be specified when "
                "requesting access to a resource."
            )

        return resource_type, resource_id

    # Validate and convert the resource ID to its canonical form.
    # A default resource ID is returned for resource types that do not
    # support instances, if no resource ID is specified.
    resource_id = self._validate_resource_id(
        resource_type=resource_type,
        resource_id=resource_id,
    )

    if resource_id:
        if self.resource_id and self.resource_id != resource_id:
            raise ValueError(
                f"the connector is configured to provide access to a "
                f"single {resource_spec.name} resource with a "
                f"resource name of '{self.resource_id}', but a "
                f"different resource name was requested: "
                f"'{resource_id}'."
            )

    else:
        if not self.resource_id and require_resource_id:
            raise ValueError(
                f"the connector is configured to provide access to "
                f"multiple {resource_spec.name} resources. A resource name "
                "must be specified when requesting access to a resource."
            )

    return resource_type, resource_id

verify(resource_type: Optional[str] = None, resource_id: Optional[str] = None, list_resources: bool = True) -> ServiceConnectorResourcesModel

Verify and optionally list all the resources that the connector can access.

This method uses the connector's configuration to verify that it can authenticate and access the indicated resource(s).

If list_resources is set, the list of resources that the connector can access, scoped to the supplied resource type and resource ID is included in the response, otherwise the connector only verifies that it can globally authenticate and doesn't verify or return resource information (i.e. the resource_ids fields in the response are empty).

Parameters:

Name	Type	Description	Default
resource_type	Optional[str]	The type of the resource to verify. If the connector instance is already configured with a resource type, this argument must be the same as the one configured if supplied.	None
resource_id	Optional[str]	The ID of a particular resource instance to check whether the connector can access. 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.	None
list_resources	bool	Whether to list the resources that the connector can access.	True

Returns:

Type	Description
ServiceConnectorResourcesModel	A list of resources that the connector can access.

Raises:

Type	Description
ValueError	If the arguments or the connector configuration are not valid.

Source code in src/zenml/service_connectors/service_connector.py

def verify(
    self,
    resource_type: Optional[str] = None,
    resource_id: Optional[str] = None,
    list_resources: bool = True,
) -> ServiceConnectorResourcesModel:
    """Verify and optionally list all the resources that the connector can access.

    This method uses the connector's configuration to verify that it can
    authenticate and access the indicated resource(s).

    If `list_resources` is set, the list of resources that the connector can
    access, scoped to the supplied resource type and resource ID is included
    in the response, otherwise the connector only verifies that it can
    globally authenticate and doesn't verify or return resource information
    (i.e. the `resource_ids` fields in the response are empty).

    Args:
        resource_type: The type of the resource to verify. If the connector
            instance is already configured with a resource type, this
            argument must be the same as the one configured if supplied.
        resource_id: The ID of a particular resource instance to check
            whether the connector can access. 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.
        list_resources: Whether to list the resources that the connector can
            access.

    Returns:
        A list of resources that the connector can access.

    Raises:
        ValueError: If the arguments or the connector configuration are
            not valid.
    """
    spec = self.get_type()

    resources = ServiceConnectorResourcesModel(
        connector_type=spec,
        id=self.id,
        name=self.name,
    )

    name_msg = f" '{self.name}'" if self.name else ""

    resource_types = self.supported_resource_types
    if resource_type:
        if resource_type not in resource_types:
            raise ValueError(
                f"connector{name_msg} does not support resource type: "
                f"'{resource_type}'. Supported resource types are: "
                f"{', '.join(resource_types)}"
            )
        resource_types = [resource_type]

    # Pre-populate the list of resources with entries corresponding to the
    # supported resource types and scoped to the supplied resource type
    resources.resources = [
        ServiceConnectorTypedResourcesModel(
            resource_type=rt,
        )
        for rt in resource_types
    ]

    if self.has_expired():
        error = "the connector's authentication credentials have expired."
        # Log the error in the resources object
        resources.set_error(error)
        return resources

    verify_resource_types: List[Optional[str]] = []
    verify_resource_id = None
    if not list_resources and not resource_id:
        # If we're not listing resources, we're only verifying that the
        # connector can authenticate globally (i.e. without supplying a
        # resource type or resource ID). This is the same as if no resource
        # type or resource ID was supplied. The only exception is when the
        # verification scope is already set to a single resource ID, in
        # which case we still perform the verification on that resource ID.
        verify_resource_types = [None]
        verify_resource_id = None
    else:
        if len(resource_types) > 1:
            # This forces the connector to verify that it can authenticate
            # globally (i.e. without supplying a resource type or resource
            # ID) before listing individual resources in the case of
            # multi-type service connectors.
            verify_resource_types = [None]

        verify_resource_types.extend(resource_types)
        if len(verify_resource_types) == 1:
            verify_resource_id = resource_id

    # Here we go through each resource type and attempt to verify and list
    # the resources that the connector can access for that resource type.
    # This list may start with a `None` resource type, which indicates that
    # the connector should verify that it can authenticate globally.
    for resource_type in verify_resource_types:
        try:
            resource_type, resource_id = self.validate_runtime_args(
                resource_type=resource_type,
                resource_id=verify_resource_id,
                require_resource_type=False,
                require_resource_id=False,
            )

            resource_ids = self._verify(
                resource_type=resource_type,
                resource_id=resource_id,
            )
        except ValueError as exc:
            raise ValueError(
                f"The connector configuration is incomplete or invalid: "
                f"{exc}",
            )
        except AuthorizationException as exc:
            error = f"connector{name_msg} authorization failure: {exc}"
            # Log an exception if debug logging is enabled
            if logger.isEnabledFor(logging.DEBUG):
                logger.exception(error)
            else:
                logger.warning(error)

            # Log the error in the resources object
            resources.set_error(error, resource_type=resource_type)
            if resource_type:
                continue
            else:
                # We stop on a global failure
                break
        except Exception as exc:
            error = (
                f"connector{name_msg} verification failed with "
                f"unexpected error: {exc}"
            )
            # Log an exception if debug logging is enabled
            if logger.isEnabledFor(logging.DEBUG):
                logger.exception(error)
            else:
                logger.warning(error)
            error = (
                "an unexpected error occurred while verifying the "
                "connector."
            )
            # Log the error in the resources object
            resources.set_error(error, resource_type=resource_type)
            if resource_type:
                continue
            else:
                # We stop on a global failure
                break

        if not resource_type:
            # If a resource type is not provided as argument, we don't
            # expect any resources to be listed
            continue

        resource_type_spec = spec.resource_type_dict[resource_type]

        if resource_id:
            # A single resource was requested, so we expect a single
            # resource to be listed
            if [resource_id] != resource_ids:
                logger.error(
                    f"a different resource ID '{resource_ids}' was "
                    f"returned than the one requested: {resource_ids}. "
                    f"This is likely a bug in the {self.__class__} "
                    "connector implementation."
                )
            resources.set_resource_ids(resource_type, [resource_id])
        elif not resource_ids:
            # If no resources were listed, signal this as an error that the
            # connector cannot access any resources.
            error = (
                f"connector{name_msg} didn't list any "
                f"{resource_type_spec.name} resources. This is likely "
                "caused by the connector credentials not being valid or "
                "not having sufficient permissions to list or access "
                "resources of this type. Please check the connector "
                "configuration and its credentials and try again."
            )
            logger.debug(error)
            resources.set_error(error, resource_type=resource_type)
        else:
            resources.set_resource_ids(resource_type, resource_ids)

    return resources

ServiceConnectorFilter

Bases: UserScopedFilter

Model to enable advanced filtering of service connectors.

Functions

validate_labels() -> ServiceConnectorFilter

Parse the labels string into a label dictionary and vice-versa.

Returns:

Type	Description
ServiceConnectorFilter	The validated values.

Source code in src/zenml/models/v2/core/service_connector.py

@model_validator(mode="after")
def validate_labels(self) -> "ServiceConnectorFilter":
    """Parse the labels string into a label dictionary and vice-versa.

    Returns:
        The validated values.
    """
    if self.labels_str is not None:
        try:
            self.labels = json.loads(self.labels_str)
        except json.JSONDecodeError:
            # Interpret as comma-separated values instead
            self.labels = {
                label.split("=", 1)[0]: label.split("=", 1)[1]
                if "=" in label
                else None
                for label in self.labels_str.split(",")
            }
    elif self.labels is not None:
        self.labels_str = json.dumps(self.labels)

    return self

ServiceConnectorRequest

Bases: UserScopedRequest

Request model for service connectors.

Attributes

emojified_connector_type: str property

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

emojified_resource_types: List[str] property

Get the emojified connector type.

Returns:

Type	Description
List[str]	The emojified connector type.

type: str property

Get the connector type.

Returns:

Type	Description
str	The connector type.

Functions

get_analytics_metadata() -> Dict[str, Any]

Format the resource types in the analytics metadata.

Returns:

Type	Description
Dict[str, Any]	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/service_connector.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Format the resource types in the analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    if len(self.resource_types) == 1:
        metadata["resource_types"] = self.resource_types[0]
    else:
        metadata["resource_types"] = ", ".join(self.resource_types)
    metadata["connector_type"] = self.type
    return metadata

validate_and_configure_resources(connector_type: ServiceConnectorTypeModel, resource_types: Optional[Union[str, List[str]]] = None, resource_id: Optional[str] = None, configuration: Optional[Dict[str, Any]] = None, secrets: Optional[Dict[str, Optional[SecretStr]]] = None) -> None

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
connector_type	ServiceConnectorTypeModel	The connector type specification used to validate the connector configuration.	required
resource_types	Optional[Union[str, List[str]]]	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	None
resource_id	Optional[str]	Uniquely identifies a specific resource instance that the connector instance can be used to access.	None
configuration	Optional[Dict[str, Any]]	The connector configuration.	None
secrets	Optional[Dict[str, Optional[SecretStr]]]	The connector secrets.	None

Source code in src/zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

ServiceConnectorResourcesModel

Bases: BaseModel

Service connector resources list.

Lists the resource types and resource instances that a service connector can provide access to.

Attributes

emojified_connector_type: str property

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

resource_types: List[str] property

Get the resource types.

Returns:

Type	Description
List[str]	The resource types.

resources_dict: Dict[str, ServiceConnectorTypedResourcesModel] property

Get the resources as a dictionary indexed by resource type.

Returns:

Type	Description
Dict[str, ServiceConnectorTypedResourcesModel]	The resources as a dictionary indexed by resource type.

type: str property

Get the connector type.

Returns:

Type	Description
str	The connector type.

Functions

from_connector_model(connector_model: ServiceConnectorResponse, resource_type: Optional[str] = None) -> ServiceConnectorResourcesModel classmethod

Initialize a resource model from a connector model.

Parameters:

Name	Type	Description	Default
connector_model	ServiceConnectorResponse	The connector model.	required
resource_type	Optional[str]	The resource type to set on the resource model. If omitted, the resource type is set according to the connector model.	None

Returns:

Type	Description
ServiceConnectorResourcesModel	A resource list model instance.

Source code in src/zenml/models/v2/misc/service_connector_type.py

@classmethod
def from_connector_model(
    cls,
    connector_model: "ServiceConnectorResponse",
    resource_type: Optional[str] = None,
) -> "ServiceConnectorResourcesModel":
    """Initialize a resource model from a connector model.

    Args:
        connector_model: The connector model.
        resource_type: The resource type to set on the resource model. If
            omitted, the resource type is set according to the connector
            model.

    Returns:
        A resource list model instance.
    """
    resources = cls(
        id=connector_model.id,
        name=connector_model.name,
        connector_type=connector_model.type,
    )

    resource_types = resource_type or connector_model.resource_types
    for resource_type in resource_types:
        resources.resources.append(
            ServiceConnectorTypedResourcesModel(
                resource_type=resource_type,
                resource_ids=[connector_model.resource_id]
                if connector_model.resource_id
                else None,
            )
        )

    return resources

get_default_resource_id() -> Optional[str]

Get the default resource ID, if included in the resource list.

The default resource ID is a resource ID supplied by the connector implementation only for resource types that do not support multiple instances.

Returns:

Type	Description
Optional[str]	The default resource ID, or None if no resource ID is set.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def get_default_resource_id(self) -> Optional[str]:
    """Get the default resource ID, if included in the resource list.

    The default resource ID is a resource ID supplied by the connector
    implementation only for resource types that do not support multiple
    instances.

    Returns:
        The default resource ID, or None if no resource ID is set.
    """
    if len(self.resources) != 1:
        # multi-type connectors do not have a default resource ID
        return None

    if isinstance(self.connector_type, str):
        # can't determine default resource ID for unknown connector types
        return None

    resource_type_spec = self.connector_type.resource_type_dict[
        self.resources[0].resource_type
    ]
    if resource_type_spec.supports_instances:
        # resource types that support multiple instances do not have a
        # default resource ID
        return None

    resource_ids = self.resources[0].resource_ids

    if not resource_ids or len(resource_ids) != 1:
        return None

    return resource_ids[0]

get_emojified_resource_types(resource_type: Optional[str] = None) -> List[str]

Get the emojified resource type.

Parameters:

Name	Type	Description	Default
resource_type	Optional[str]	The resource type to get the emojified resource type for. If omitted, the emojified resource type for all resource types is returned.	None

Returns:

Type	Description
List[str]	The list of emojified resource types.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def get_emojified_resource_types(
    self, resource_type: Optional[str] = None
) -> List[str]:
    """Get the emojified resource type.

    Args:
        resource_type: The resource type to get the emojified resource type
            for. If omitted, the emojified resource type for all resource
            types is returned.


    Returns:
        The list of emojified resource types.
    """
    if not isinstance(self.connector_type, str):
        if resource_type:
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
            ]
        return [
            self.connector_type.resource_type_dict[
                resource_type
            ].emojified_resource_type
            for resource_type in self.resources_dict.keys()
        ]
    if resource_type:
        return [resource_type]
    return list(self.resources_dict.keys())

set_error(error: str, resource_type: Optional[str] = None) -> None

Set a global error message or an error for a single resource type.

Parameters:

Name	Type	Description	Default
error	str	The error message.	required
resource_type	Optional[str]	The resource type to set the error message for. If omitted, or if there is only one resource type involved, the error message is (also) set globally.	None

Raises:

Type	Description
KeyError	If the resource type is not found in the resources list.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def set_error(
    self, error: str, resource_type: Optional[str] = None
) -> None:
    """Set a global error message or an error for a single resource type.

    Args:
        error: The error message.
        resource_type: The resource type to set the error message for. If
            omitted, or if there is only one resource type involved, the
            error message is (also) set globally.

    Raises:
        KeyError: If the resource type is not found in the resources list.
    """
    if resource_type:
        resource = self.resources_dict.get(resource_type)
        if not resource:
            raise KeyError(
                f"resource type '{resource_type}' not found in "
                "service connector resources list"
            )
        resource.error = error
        resource.resource_ids = None
        if len(self.resources) == 1:
            # If there is only one resource type involved, set the global
            # error message as well.
            self.error = error
    else:
        self.error = error
        for resource in self.resources:
            resource.error = error
            resource.resource_ids = None

set_resource_ids(resource_type: str, resource_ids: List[str]) -> None

Set the resource IDs for a resource type.

Parameters:

Name	Type	Description	Default
resource_type	str	The resource type to set the resource IDs for.	required
resource_ids	List[str]	The resource IDs to set.	required

Raises:

Type	Description
KeyError	If the resource type is not found in the resources list.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def set_resource_ids(
    self, resource_type: str, resource_ids: List[str]
) -> None:
    """Set the resource IDs for a resource type.

    Args:
        resource_type: The resource type to set the resource IDs for.
        resource_ids: The resource IDs to set.

    Raises:
        KeyError: If the resource type is not found in the resources list.
    """
    resource = self.resources_dict.get(resource_type)
    if not resource:
        raise KeyError(
            f"resource type '{resource_type}' not found in "
            "service connector resources list"
        )
    resource.resource_ids = resource_ids
    resource.error = None

ServiceConnectorResponse

Bases: UserScopedResponse[ServiceConnectorResponseBody, ServiceConnectorResponseMetadata, ServiceConnectorResponseResources]

Response model for service connectors.

Attributes

auth_method: str property

The auth_method property.

Returns:

Type	Description
str	the value of the property.

configuration: Dict[str, Any] property

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

connector_type: Union[str, ServiceConnectorTypeModel] property

The connector_type property.

Returns:

Type	Description
Union[str, ServiceConnectorTypeModel]	the value of the property.

description: str property

The description property.

Returns:

Type	Description
str	the value of the property.

emojified_connector_type: str property

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

emojified_resource_types: List[str] property

Get the emojified connector type.

Returns:

Type	Description
List[str]	The emojified connector type.

expiration_seconds: Optional[int] property

The expiration_seconds property.

Returns:

Type	Description
Optional[int]	the value of the property.

expires_at: Optional[datetime] property

The expires_at property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

expires_skew_tolerance: Optional[int] property

The expires_skew_tolerance property.

Returns:

Type	Description
Optional[int]	the value of the property.

full_configuration: Dict[str, str] property

Get the full connector configuration, including secrets.

Returns:

Type	Description
Dict[str, str]	The full connector configuration, including secrets.

is_multi_instance: bool property

Checks if the connector is multi-instance.

A multi-instance connector is configured to access multiple instances of the configured resource type.

Returns:

Type	Description
bool	True if the connector is multi-instance, False otherwise.

is_multi_type: bool property

Checks if the connector is multi-type.

A multi-type connector can be used to access multiple types of resources.

Returns:

Type	Description
bool	True if the connector is multi-type, False otherwise.

is_single_instance: bool property

Checks if the connector is single-instance.

A single-instance connector is configured to access only a single instance of the configured resource type or does not support multiple resource instances.

Returns:

Type	Description
bool	True if the connector is single-instance, False otherwise.

labels: Dict[str, str] property

The labels property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

resource_id: Optional[str] property

The resource_id property.

Returns:

Type	Description
Optional[str]	the value of the property.

resource_types: List[str] property

The resource_types property.

Returns:

Type	Description
List[str]	the value of the property.

secret_id: Optional[UUID] property

The secret_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

secrets: Dict[str, Optional[SecretStr]] property

The secrets property.

Returns:

Type	Description
Dict[str, Optional[SecretStr]]	the value of the property.

supports_instances: bool property

The supports_instances property.

Returns:

Type	Description
bool	the value of the property.

type: str property

Get the connector type.

Returns:

Type	Description
str	The connector type.

Functions

get_analytics_metadata() -> Dict[str, Any]

Add the service connector labels to analytics metadata.

Returns:

Type	Description
Dict[str, Any]	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/service_connector.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Add the service connector labels to analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()

    metadata.update(
        {
            label[6:]: value
            for label, value in self.labels.items()
            if label.startswith("zenml:")
        }
    )
    return metadata

get_hydrated_version() -> ServiceConnectorResponse

Get the hydrated version of this service connector.

Returns:

Type	Description
ServiceConnectorResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/service_connector.py

def get_hydrated_version(self) -> "ServiceConnectorResponse":
    """Get the hydrated version of this service connector.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_service_connector(self.id)

set_connector_type(value: Union[str, ServiceConnectorTypeModel]) -> None

Auxiliary method to set the connector type.

Parameters:

Name	Type	Description	Default
value	Union[str, ServiceConnectorTypeModel]	the new value for the connector type.	required

Source code in src/zenml/models/v2/core/service_connector.py

def set_connector_type(
    self, value: Union[str, "ServiceConnectorTypeModel"]
) -> None:
    """Auxiliary method to set the connector type.

    Args:
        value: the new value for the connector type.
    """
    self.get_body().connector_type = value

validate_and_configure_resources(connector_type: ServiceConnectorTypeModel, resource_types: Optional[Union[str, List[str]]] = None, resource_id: Optional[str] = None, configuration: Optional[Dict[str, Any]] = None, secrets: Optional[Dict[str, Optional[SecretStr]]] = None) -> None

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
connector_type	ServiceConnectorTypeModel	The connector type specification used to validate the connector configuration.	required
resource_types	Optional[Union[str, List[str]]]	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	None
resource_id	Optional[str]	Uniquely identifies a specific resource instance that the connector instance can be used to access.	None
configuration	Optional[Dict[str, Any]]	The connector configuration.	None
secrets	Optional[Dict[str, Optional[SecretStr]]]	The connector secrets.	None

Source code in src/zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

ServiceConnectorTypeModel

Bases: BaseModel

Service connector type specification.

Describes the types of resources to which the service connector can be used to gain access and the authentication methods that are supported by the service connector.

The connector type, resource types, resource IDs and authentication methods can all be used as search criteria to lookup and filter service connector instances that are compatible with the requirements of a consumer (e.g. a stack component).

Attributes

auth_method_dict: Dict[str, AuthenticationMethodModel] property

Returns a map of authentication methods to authentication method specifications.

Returns:

Type	Description
Dict[str, AuthenticationMethodModel]	A map of authentication methods to authentication method
Dict[str, AuthenticationMethodModel]	specifications.

connector_class: Optional[Type[ServiceConnector]] property

Get the service connector class.

Returns:

Type	Description
Optional[Type[ServiceConnector]]	The service connector class.

emojified_connector_type: str property

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

emojified_resource_types: List[str] property

Get the emojified connector types.

Returns:

Type	Description
List[str]	The emojified connector types.

resource_type_dict: Dict[str, ResourceTypeModel] property

Returns a map of resource types to resource type specifications.

Returns:

Type	Description
Dict[str, ResourceTypeModel]	A map of resource types to resource type specifications.

Functions

find_resource_specifications(auth_method: str, resource_type: Optional[str] = None) -> Tuple[AuthenticationMethodModel, Optional[ResourceTypeModel]]

Find the specifications for a configurable resource.

Validate the supplied connector configuration parameters against the connector specification and return the matching authentication method specification and resource specification.

Parameters:

Name	Type	Description	Default
auth_method	str	The name of the authentication method.	required
resource_type	Optional[str]	The type of resource being configured.	None

Returns:

Type	Description
AuthenticationMethodModel	The authentication method specification and resource specification
Optional[ResourceTypeModel]	for the specified authentication method and resource type.

Raises:

Type	Description
KeyError	If the authentication method is not supported by the connector for the specified resource type and ID.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def find_resource_specifications(
    self,
    auth_method: str,
    resource_type: Optional[str] = None,
) -> Tuple[AuthenticationMethodModel, Optional[ResourceTypeModel]]:
    """Find the specifications for a configurable resource.

    Validate the supplied connector configuration parameters against the
    connector specification and return the matching authentication method
    specification and resource specification.

    Args:
        auth_method: The name of the authentication method.
        resource_type: The type of resource being configured.

    Returns:
        The authentication method specification and resource specification
        for the specified authentication method and resource type.

    Raises:
        KeyError: If the authentication method is not supported by the
            connector for the specified resource type and ID.
    """
    # Verify the authentication method
    auth_method_dict = self.auth_method_dict
    if auth_method in auth_method_dict:
        # A match was found for the authentication method
        auth_method_spec = auth_method_dict[auth_method]
    else:
        # No match was found for the authentication method
        raise KeyError(
            f"connector type '{self.connector_type}' does not support the "
            f"'{auth_method}' authentication method. Supported "
            f"authentication methods are: {list(auth_method_dict.keys())}."
        )

    if resource_type is None:
        # No resource type was specified, so no resource type
        # specification can be returned.
        return auth_method_spec, None

    # Verify the resource type
    resource_type_dict = self.resource_type_dict
    if resource_type in resource_type_dict:
        resource_type_spec = resource_type_dict[resource_type]
    else:
        raise KeyError(
            f"connector type '{self.connector_type}' does not support "
            f"resource type '{resource_type}'. Supported resource types "
            f"are: {list(resource_type_dict.keys())}."
        )

    if auth_method not in resource_type_spec.auth_methods:
        raise KeyError(
            f"the '{self.connector_type}' connector type does not support "
            f"the '{auth_method}' authentication method for the "
            f"'{resource_type}' resource type. Supported authentication "
            f"methods are: {resource_type_spec.auth_methods}."
        )

    return auth_method_spec, resource_type_spec

set_connector_class(connector_class: Type[ServiceConnector]) -> None

Set the service connector class.

Parameters:

Name	Type	Description	Default
connector_class	Type[ServiceConnector]	The service connector class.	required

Source code in src/zenml/models/v2/misc/service_connector_type.py

def set_connector_class(
    self, connector_class: Type["ServiceConnector"]
) -> None:
    """Set the service connector class.

    Args:
        connector_class: The service connector class.
    """
    self._connector_class = connector_class

validate_auth_methods(values: List[AuthenticationMethodModel]) -> List[AuthenticationMethodModel] classmethod

Validate that the authentication methods are unique.

Parameters:

Name	Type	Description	Default
values	List[AuthenticationMethodModel]	The list of authentication methods.	required

Returns:

Type	Description
List[AuthenticationMethodModel]	The list of authentication methods.

Raises:

Type	Description
ValueError	If two or more authentication method specifications share the same authentication method value.

Source code in src/zenml/models/v2/misc/service_connector_type.py

@field_validator("auth_methods")
@classmethod
def validate_auth_methods(
    cls, values: List[AuthenticationMethodModel]
) -> List[AuthenticationMethodModel]:
    """Validate that the authentication methods are unique.

    Args:
        values: The list of authentication methods.

    Returns:
        The list of authentication methods.

    Raises:
        ValueError: If two or more authentication method specifications
            share the same authentication method value.
    """
    # Gather all auth methods from the list of auth method
    # specifications.
    auth_methods = [a.auth_method for a in values]
    if len(auth_methods) != len(set(auth_methods)):
        raise ValueError(
            "Two or more authentication method specifications must not "
            "share the same authentication method value."
        )

    return values

validate_resource_types(values: List[ResourceTypeModel]) -> List[ResourceTypeModel] classmethod

Validate that the resource types are unique.

Parameters:

Name	Type	Description	Default
values	List[ResourceTypeModel]	The list of resource types.	required

Returns:

Type	Description
List[ResourceTypeModel]	The list of resource types.

Raises:

Type	Description
ValueError	If two or more resource type specifications list the same resource type.

Source code in src/zenml/models/v2/misc/service_connector_type.py

@field_validator("resource_types")
@classmethod
def validate_resource_types(
    cls, values: List[ResourceTypeModel]
) -> List[ResourceTypeModel]:
    """Validate that the resource types are unique.

    Args:
        values: The list of resource types.

    Returns:
        The list of resource types.

    Raises:
        ValueError: If two or more resource type specifications list the
            same resource type.
    """
    # Gather all resource types from the list of resource type
    # specifications.
    resource_types = [r.resource_type for r in values]
    if len(resource_types) != len(set(resource_types)):
        raise ValueError(
            "Two or more resource type specifications must not list "
            "the same resource type."
        )

    return values

ServiceConnectorUpdate

Bases: BaseUpdate

Model used for service connector updates.

Most fields in the update model are optional and will not be updated if omitted. However, the following fields are "special" and leaving them out will also cause the corresponding value to be removed from the service connector in the database:

• the resource_id field
• the expiration_seconds field

In addition to the above exceptions, the following rules apply:

• the configuration and secrets fields together represent a full valid configuration update, not just a partial update. If either is set (i.e. not None) in the update, their values are merged together and will replace the existing configuration and secrets values.
• the labels field is also a full labels update: if set (i.e. not None), all existing labels are removed and replaced by the new labels in the update.

NOTE: the attributes here override the ones in the base class, so they have a None default value.

Attributes

type: Optional[str] property

Get the connector type.

Returns:

Type	Description
Optional[str]	The connector type.

Functions

convert_to_request() -> ServiceConnectorRequest

Method to generate a service connector request object from self.

For certain operations, the service connector update model need to adhere to the limitations set by the request model. In order to use update models in such situations, we need to be able to convert an update model into a request model.

Returns:

Type	Description
ServiceConnectorRequest	The equivalent request model

Raises:

Type	Description
RuntimeError	if the model can not be converted to a request model.

Source code in src/zenml/models/v2/core/service_connector.py

def convert_to_request(self) -> "ServiceConnectorRequest":
    """Method to generate a service connector request object from self.

    For certain operations, the service connector update model need to
    adhere to the limitations set by the request model. In order to use
    update models in such situations, we need to be able to convert an
    update model into a request model.

    Returns:
        The equivalent request model

    Raises:
        RuntimeError: if the model can not be converted to a request model.
    """
    try:
        return ServiceConnectorRequest.model_validate(self.model_dump())
    except ValidationError as e:
        raise RuntimeError(
            "The service connector update model can not be converted into "
            f"an equivalent request model: {e}"
        )

get_analytics_metadata() -> Dict[str, Any]

Format the resource types in the analytics metadata.

Returns:

Type	Description
Dict[str, Any]	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/service_connector.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Format the resource types in the analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()

    if self.resource_types is not None:
        if len(self.resource_types) == 1:
            metadata["resource_types"] = self.resource_types[0]
        else:
            metadata["resource_types"] = ", ".join(self.resource_types)

    if self.connector_type is not None:
        metadata["connector_type"] = self.type

    return metadata

validate_and_configure_resources(connector_type: ServiceConnectorTypeModel, resource_types: Optional[Union[str, List[str]]] = None, resource_id: Optional[str] = None, configuration: Optional[Dict[str, Any]] = None, secrets: Optional[Dict[str, Optional[SecretStr]]] = None) -> None

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
connector_type	ServiceConnectorTypeModel	The connector type specification used to validate the connector configuration.	required
resource_types	Optional[Union[str, List[str]]]	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	None
resource_id	Optional[str]	Uniquely identifies a specific resource instance that the connector instance can be used to access.	None
configuration	Optional[Dict[str, Any]]	The connector configuration.	None
secrets	Optional[Dict[str, Optional[SecretStr]]]	The connector secrets.	None

Source code in src/zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

ServiceFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of services.

Functions

generate_filter(table: Type[AnySchema]) -> Union[ColumnElement[bool]]

Generate the filter for the query.

Services can be scoped by type to narrow the search.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The Table that is being queried from.	required

Returns:

Type	Description
Union[ColumnElement[bool]]	The filter expression for the query.

Source code in src/zenml/models/v2/core/service.py

def generate_filter(
    self, table: Type["AnySchema"]
) -> Union["ColumnElement[bool]"]:
    """Generate the filter for the query.

    Services can be scoped by type to narrow the search.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.
    """
    from sqlmodel import and_

    base_filter = super().generate_filter(table)

    if self.type:
        type_filter = getattr(table, "type") == self.type
        base_filter = and_(base_filter, type_filter)

    if self.flavor:
        flavor_filter = getattr(table, "flavor") == self.flavor
        base_filter = and_(base_filter, flavor_filter)

    if self.pipeline_name:
        pipeline_name_filter = (
            getattr(table, "pipeline_name") == self.pipeline_name
        )
        base_filter = and_(base_filter, pipeline_name_filter)

    if self.pipeline_step_name:
        pipeline_step_name_filter = (
            getattr(table, "pipeline_step_name") == self.pipeline_step_name
        )
        base_filter = and_(base_filter, pipeline_step_name_filter)

    return base_filter

set_flavor(flavor: str) -> None

Set the flavor of the service.

Parameters:

Name	Type	Description	Default
flavor	str	The flavor of the service.	required

Source code in src/zenml/models/v2/core/service.py

def set_flavor(self, flavor: str) -> None:
    """Set the flavor of the service.

    Args:
        flavor: The flavor of the service.
    """
    self.flavor = flavor

set_type(type: str) -> None

Set the type of the service.

Parameters:

Name	Type	Description	Default
type	str	The type of the service.	required

Source code in src/zenml/models/v2/core/service.py

def set_type(self, type: str) -> None:
    """Set the type of the service.

    Args:
        type: The type of the service.
    """
    self.type = type

ServiceRequest

Bases: ProjectScopedRequest

Request model for services.

ServiceResponse

Bases: ProjectScopedResponse[ServiceResponseBody, ServiceResponseMetadata, ServiceResponseResources]

Response model for services.

Attributes

admin_state: Optional[ServiceState] property

The admin_state property.

Returns:

Type	Description
Optional[ServiceState]	the value of the property.

config: Dict[str, Any] property

The config property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

created: datetime property

The created property.

Returns:

Type	Description
datetime	the value of the property.

endpoint: Optional[Dict[str, Any]] property

The endpoint property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

health_check_url: Optional[str] property

The health_check_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

labels: Optional[Dict[str, str]] property

The labels property.

Returns:

Type	Description
Optional[Dict[str, str]]	the value of the property.

model_version: Optional[ModelVersionResponse] property

The model_version property.

Returns:

Type	Description
Optional[ModelVersionResponse]	the value of the property.

pipeline_run: Optional[PipelineRunResponse] property

The pipeline_run property.

Returns:

Type	Description
Optional[PipelineRunResponse]	the value of the property.

prediction_url: Optional[str] property

The prediction_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

service_source: Optional[str] property

The service_source property.

Returns:

Type	Description
Optional[str]	the value of the property.

service_type: ServiceType property

The service_type property.

Returns:

Type	Description
ServiceType	the value of the property.

state: Optional[ServiceState] property

The state property.

Returns:

Type	Description
Optional[ServiceState]	the value of the property.

status: Optional[Dict[str, Any]] property

The status property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

updated: datetime property

The updated property.

Returns:

Type	Description
datetime	the value of the property.

Functions

get_hydrated_version() -> ServiceResponse

Get the hydrated version of this artifact.

Returns:

Type	Description
ServiceResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/service.py

def get_hydrated_version(self) -> "ServiceResponse":
    """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_service(self.id)

ServiceState

Bases: StrEnum

Possible states for the service and service endpoint.

ServiceType

Bases: BaseModel

Service type descriptor.

Attributes:

Name	Type	Description
type	str	service type
flavor	str	service flavor
name	str	name of the service type
description	str	description of the service type
logo_url	str	logo of the service type

ServiceUpdate

Bases: BaseUpdate

Update model for stack components.

SorterOps

Bases: StrEnum

Ops for all filters for string values on list methods.

Source

Bases: BaseModel

Source specification.

A source specifies a module name as well as an optional attribute of that module. These values can be used to import the module and get the value of the attribute inside the module.

Example

The source Source(module="zenml.config.source", attribute="Source") references the class that this docstring is describing. This class is defined in the zenml.config.source module and the name of the attribute is the class name Source.

Attributes:

Name	Type	Description
module	str	The module name.
attribute	Optional[str]	Optional name of the attribute inside the module.
type	SourceType	The type of the source.

Attributes

import_path: str property

The import path of the source.

Returns:

Type	Description
str	The import path of the source.

is_internal: bool property

If the source is internal (=from the zenml package).

Returns:

Type	Description
bool	True if the source is internal, False otherwise

is_module_source: bool property

If the source is a module source.

Returns:

Type	Description
bool	If the source is a module source.

Functions

from_import_path(import_path: str, is_module_path: bool = False) -> Source classmethod

Creates a source from an import path.

Parameters:

Name	Type	Description	Default
import_path	str	The import path.	required
is_module_path	bool	If the import path points to a module or not.	False

Raises:

Type	Description
ValueError	If the import path is empty.

Returns:

Type	Description
Source	The source.

Source code in src/zenml/config/source.py

@classmethod
def from_import_path(
    cls, import_path: str, is_module_path: bool = False
) -> "Source":
    """Creates a source from an import path.

    Args:
        import_path: The import path.
        is_module_path: If the import path points to a module or not.

    Raises:
        ValueError: If the import path is empty.

    Returns:
        The source.
    """
    if not import_path:
        raise ValueError(
            "Invalid empty import path. The import path needs to refer "
            "to a Python module and an optional attribute of that module."
        )

    # Remove internal version pins for backwards compatibility
    if "@" in import_path:
        import_path = import_path.split("@", 1)[0]

    if is_module_path or "." not in import_path:
        module = import_path
        attribute = None
    else:
        module, attribute = import_path.rsplit(".", maxsplit=1)

    return Source(
        module=module, attribute=attribute, type=SourceType.UNKNOWN
    )

model_dump(**kwargs: Any) -> Dict[str, Any]

Dump the source as a dictionary.

Parameters:

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

Returns:

Type	Description
Dict[str, Any]	The source as a dictionary.

Source code in src/zenml/config/source.py

def model_dump(self, **kwargs: Any) -> Dict[str, Any]:
    """Dump the source as a dictionary.

    Args:
        **kwargs: Additional keyword arguments.

    Returns:
        The source as a dictionary.
    """
    return super().model_dump(serialize_as_any=True, **kwargs)

model_dump_json(**kwargs: Any) -> str

Dump the source as a JSON string.

Parameters:

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

Returns:

Type	Description
str	The source as a JSON string.

Source code in src/zenml/config/source.py

def model_dump_json(self, **kwargs: Any) -> str:
    """Dump the source as a JSON string.

    Args:
        **kwargs: Additional keyword arguments.

    Returns:
        The source as a JSON string.
    """
    return super().model_dump_json(serialize_as_any=True, **kwargs)

Stack(id: UUID, name: str, *, orchestrator: BaseOrchestrator, artifact_store: BaseArtifactStore, container_registry: Optional[BaseContainerRegistry] = None, step_operator: Optional[BaseStepOperator] = None, feature_store: Optional[BaseFeatureStore] = None, model_deployer: Optional[BaseModelDeployer] = None, experiment_tracker: Optional[BaseExperimentTracker] = None, alerter: Optional[BaseAlerter] = None, annotator: Optional[BaseAnnotator] = None, data_validator: Optional[BaseDataValidator] = None, image_builder: Optional[BaseImageBuilder] = None, model_registry: Optional[BaseModelRegistry] = None)

ZenML stack class.

A ZenML stack is a collection of multiple stack components that are required to run ZenML pipelines. Some of these components (orchestrator, and artifact store) are required to run any kind of pipeline, other components like the container registry are only required if other stack components depend on them.

Initializes and validates a stack instance.

Parameters:

Name	Type	Description	Default
id	UUID	Unique ID of the stack.	required
name	str	Name of the stack.	required
orchestrator	BaseOrchestrator	Orchestrator component of the stack.	required
artifact_store	BaseArtifactStore	Artifact store component of the stack.	required
container_registry	Optional[BaseContainerRegistry]	Container registry component of the stack.	None
step_operator	Optional[BaseStepOperator]	Step operator component of the stack.	None
feature_store	Optional[BaseFeatureStore]	Feature store component of the stack.	None
model_deployer	Optional[BaseModelDeployer]	Model deployer component of the stack.	None
experiment_tracker	Optional[BaseExperimentTracker]	Experiment tracker component of the stack.	None
alerter	Optional[BaseAlerter]	Alerter component of the stack.	None
annotator	Optional[BaseAnnotator]	Annotator component of the stack.	None
data_validator	Optional[BaseDataValidator]	Data validator component of the stack.	None
image_builder	Optional[BaseImageBuilder]	Image builder component of the stack.	None
model_registry	Optional[BaseModelRegistry]	Model registry component of the stack.	None

Source code in src/zenml/stack/stack.py

def __init__(
    self,
    id: UUID,
    name: str,
    *,
    orchestrator: "BaseOrchestrator",
    artifact_store: "BaseArtifactStore",
    container_registry: Optional["BaseContainerRegistry"] = None,
    step_operator: Optional["BaseStepOperator"] = None,
    feature_store: Optional["BaseFeatureStore"] = None,
    model_deployer: Optional["BaseModelDeployer"] = None,
    experiment_tracker: Optional["BaseExperimentTracker"] = None,
    alerter: Optional["BaseAlerter"] = None,
    annotator: Optional["BaseAnnotator"] = None,
    data_validator: Optional["BaseDataValidator"] = None,
    image_builder: Optional["BaseImageBuilder"] = None,
    model_registry: Optional["BaseModelRegistry"] = None,
):
    """Initializes and validates a stack instance.

    Args:
        id: Unique ID of the stack.
        name: Name of the stack.
        orchestrator: Orchestrator component of the stack.
        artifact_store: Artifact store component of the stack.
        container_registry: Container registry component of the stack.
        step_operator: Step operator component of the stack.
        feature_store: Feature store component of the stack.
        model_deployer: Model deployer component of the stack.
        experiment_tracker: Experiment tracker component of the stack.
        alerter: Alerter component of the stack.
        annotator: Annotator component of the stack.
        data_validator: Data validator component of the stack.
        image_builder: Image builder component of the stack.
        model_registry: Model registry component of the stack.
    """
    self._id = id
    self._name = name
    self._orchestrator = orchestrator
    self._artifact_store = artifact_store
    self._container_registry = container_registry
    self._step_operator = step_operator
    self._feature_store = feature_store
    self._model_deployer = model_deployer
    self._experiment_tracker = experiment_tracker
    self._alerter = alerter
    self._annotator = annotator
    self._data_validator = data_validator
    self._model_registry = model_registry
    self._image_builder = image_builder

Attributes

alerter: Optional[BaseAlerter] property

The alerter of the stack.

Returns:

Type	Description
Optional[BaseAlerter]	The alerter of the stack.

annotator: Optional[BaseAnnotator] property

The annotator of the stack.

Returns:

Type	Description
Optional[BaseAnnotator]	The annotator of the stack.

apt_packages: List[str] property

List of APT package requirements for the stack.

Returns:

Type	Description
List[str]	A list of APT package requirements for the stack.

artifact_store: BaseArtifactStore property

The artifact store of the stack.

Returns:

Type	Description
BaseArtifactStore	The artifact store of the stack.

components: Dict[StackComponentType, StackComponent] property

All components of the stack.

Returns:

Type	Description
Dict[StackComponentType, StackComponent]	A dictionary of all components of the stack.

container_registry: Optional[BaseContainerRegistry] property

The container registry of the stack.

Returns:

Type	Description
Optional[BaseContainerRegistry]	The container registry of the stack or None if the stack does not
Optional[BaseContainerRegistry]	have a container registry.

data_validator: Optional[BaseDataValidator] property

The data validator of the stack.

Returns:

Type	Description
Optional[BaseDataValidator]	The data validator of the stack.

experiment_tracker: Optional[BaseExperimentTracker] property

The experiment tracker of the stack.

Returns:

Type	Description
Optional[BaseExperimentTracker]	The experiment tracker of the stack.

feature_store: Optional[BaseFeatureStore] property

The feature store of the stack.

Returns:

Type	Description
Optional[BaseFeatureStore]	The feature store of the stack.

id: UUID property

The ID of the stack.

Returns:

Type	Description
UUID	The ID of the stack.

image_builder: Optional[BaseImageBuilder] property

The image builder of the stack.

Returns:

Type	Description
Optional[BaseImageBuilder]	The image builder of the stack.

model_deployer: Optional[BaseModelDeployer] property

The model deployer of the stack.

Returns:

Type	Description
Optional[BaseModelDeployer]	The model deployer of the stack.

model_registry: Optional[BaseModelRegistry] property

The model registry of the stack.

Returns:

Type	Description
Optional[BaseModelRegistry]	The model registry of the stack.

name: str property

The name of the stack.

Returns:

Name	Type	Description
str	str	The name of the stack.

orchestrator: BaseOrchestrator property

The orchestrator of the stack.

Returns:

Type	Description
BaseOrchestrator	The orchestrator of the stack.

required_secrets: Set[secret_utils.SecretReference] property

All required secrets for this stack.

Returns:

Type	Description
Set[SecretReference]	The required secrets of this stack.

requires_remote_server: bool property

If the stack requires a remote ZenServer to run.

This is the case if any code is getting executed remotely. This is the case for both remote orchestrators as well as remote step operators.

Returns:

Type	Description
bool	If the stack requires a remote ZenServer to run.

setting_classes: Dict[str, Type[BaseSettings]] property

Setting classes of all components of this stack.

Returns:

Type	Description
Dict[str, Type[BaseSettings]]	All setting classes and their respective keys.

step_operator: Optional[BaseStepOperator] property

The step operator of the stack.

Returns:

Type	Description
Optional[BaseStepOperator]	The step operator of the stack.

Functions

check_local_paths() -> bool

Checks if the stack has local paths.

Returns:

Type	Description
bool	True if the stack has local paths, False otherwise.

Raises:

Type	Description
ValueError	If the stack has local paths that do not conform to the convention that all local path must be relative to the local stores directory.

Source code in src/zenml/stack/stack.py

def check_local_paths(self) -> bool:
    """Checks if the stack has local paths.

    Returns:
        True if the stack has local paths, False otherwise.

    Raises:
        ValueError: If the stack has local paths that do not conform to
            the convention that all local path must be relative to the
            local stores directory.
    """
    from zenml.config.global_config import GlobalConfiguration

    local_stores_path = GlobalConfiguration().local_stores_path

    # go through all stack components and identify those that advertise
    # a local path where they persist information that they need to be
    # available when running pipelines.
    has_local_paths = False
    for stack_comp in self.components.values():
        local_path = stack_comp.local_path
        if not local_path:
            continue
        # double-check this convention, just in case it wasn't respected
        # as documented in `StackComponent.local_path`
        if not local_path.startswith(local_stores_path):
            raise ValueError(
                f"Local path {local_path} for component "
                f"{stack_comp.name} is not in the local stores "
                f"directory ({local_stores_path})."
            )
        has_local_paths = True

    return has_local_paths

cleanup_step_run(info: StepRunInfo, step_failed: bool) -> None

Cleans up resources after the step run is finished.

Parameters:

Name	Type	Description	Default
info	StepRunInfo	Info about the step that was executed.	required
step_failed	bool	Whether the step failed.	required

Source code in src/zenml/stack/stack.py

def cleanup_step_run(self, info: "StepRunInfo", step_failed: bool) -> None:
    """Cleans up resources after the step run is finished.

    Args:
        info: Info about the step that was executed.
        step_failed: Whether the step failed.
    """
    for component in self._get_active_components_for_step(
        info.config
    ).values():
        component.cleanup_step_run(info=info, step_failed=step_failed)

deploy_pipeline(deployment: PipelineDeploymentResponse, placeholder_run: Optional[PipelineRunResponse] = None) -> None

Deploys a pipeline on this stack.

Parameters:

Name	Type	Description	Default
deployment	PipelineDeploymentResponse	The pipeline deployment.	required
placeholder_run	Optional[PipelineRunResponse]	An optional placeholder run for the deployment.	None

Source code in src/zenml/stack/stack.py

def deploy_pipeline(
    self,
    deployment: "PipelineDeploymentResponse",
    placeholder_run: Optional["PipelineRunResponse"] = None,
) -> None:
    """Deploys a pipeline on this stack.

    Args:
        deployment: The pipeline deployment.
        placeholder_run: An optional placeholder run for the deployment.
    """
    self.orchestrator.run(
        deployment=deployment, stack=self, placeholder_run=placeholder_run
    )

dict() -> Dict[str, str]

Converts the stack into a dictionary.

Returns:

Type	Description
Dict[str, str]	A dictionary containing the stack components.

Source code in src/zenml/stack/stack.py

def dict(self) -> Dict[str, str]:
    """Converts the stack into a dictionary.

    Returns:
        A dictionary containing the stack components.
    """
    component_dict = {
        component_type.value: json.dumps(
            component.config.model_dump(mode="json"), sort_keys=True
        )
        for component_type, component in self.components.items()
    }
    component_dict.update({"name": self.name})
    return component_dict

from_components(id: UUID, name: str, components: Dict[StackComponentType, StackComponent]) -> Stack classmethod

Creates a stack instance from a dict of stack components.

noqa: DAR402

Parameters:

Name	Type	Description	Default
id	UUID	Unique ID of the stack.	required
name	str	The name of the stack.	required
components	Dict[StackComponentType, StackComponent]	The components of the stack.	required

Returns:

Type	Description
Stack	A stack instance consisting of the given components.

Raises:

Type	Description
TypeError	If a required component is missing or a component doesn't inherit from the expected base class.

Source code in src/zenml/stack/stack.py

@classmethod
def from_components(
    cls,
    id: UUID,
    name: str,
    components: Dict[StackComponentType, "StackComponent"],
) -> "Stack":
    """Creates a stack instance from a dict of stack components.

    # noqa: DAR402

    Args:
        id: Unique ID of the stack.
        name: The name of the stack.
        components: The components of the stack.

    Returns:
        A stack instance consisting of the given components.

    Raises:
        TypeError: If a required component is missing or a component
            doesn't inherit from the expected base class.
    """
    from zenml.alerter import BaseAlerter
    from zenml.annotators import BaseAnnotator
    from zenml.artifact_stores import BaseArtifactStore
    from zenml.container_registries import BaseContainerRegistry
    from zenml.data_validators import BaseDataValidator
    from zenml.experiment_trackers import BaseExperimentTracker
    from zenml.feature_stores import BaseFeatureStore
    from zenml.image_builders import BaseImageBuilder
    from zenml.model_deployers import BaseModelDeployer
    from zenml.model_registries import BaseModelRegistry
    from zenml.orchestrators import BaseOrchestrator
    from zenml.step_operators import BaseStepOperator

    def _raise_type_error(
        component: Optional["StackComponent"], expected_class: Type[Any]
    ) -> NoReturn:
        """Raises a TypeError that the component has an unexpected type.

        Args:
            component: The component that has an unexpected type.
            expected_class: The expected type of the component.

        Raises:
            TypeError: If the component has an unexpected type.
        """
        raise TypeError(
            f"Unable to create stack: Wrong stack component type "
            f"`{component.__class__.__name__}` (expected: subclass "
            f"of `{expected_class.__name__}`)"
        )

    orchestrator = components.get(StackComponentType.ORCHESTRATOR)
    if not isinstance(orchestrator, BaseOrchestrator):
        _raise_type_error(orchestrator, BaseOrchestrator)

    artifact_store = components.get(StackComponentType.ARTIFACT_STORE)
    if not isinstance(artifact_store, BaseArtifactStore):
        _raise_type_error(artifact_store, BaseArtifactStore)

    container_registry = components.get(
        StackComponentType.CONTAINER_REGISTRY
    )
    if container_registry is not None and not isinstance(
        container_registry, BaseContainerRegistry
    ):
        _raise_type_error(container_registry, BaseContainerRegistry)

    step_operator = components.get(StackComponentType.STEP_OPERATOR)
    if step_operator is not None and not isinstance(
        step_operator, BaseStepOperator
    ):
        _raise_type_error(step_operator, BaseStepOperator)

    feature_store = components.get(StackComponentType.FEATURE_STORE)
    if feature_store is not None and not isinstance(
        feature_store, BaseFeatureStore
    ):
        _raise_type_error(feature_store, BaseFeatureStore)

    model_deployer = components.get(StackComponentType.MODEL_DEPLOYER)
    if model_deployer is not None and not isinstance(
        model_deployer, BaseModelDeployer
    ):
        _raise_type_error(model_deployer, BaseModelDeployer)

    experiment_tracker = components.get(
        StackComponentType.EXPERIMENT_TRACKER
    )
    if experiment_tracker is not None and not isinstance(
        experiment_tracker, BaseExperimentTracker
    ):
        _raise_type_error(experiment_tracker, BaseExperimentTracker)

    alerter = components.get(StackComponentType.ALERTER)
    if alerter is not None and not isinstance(alerter, BaseAlerter):
        _raise_type_error(alerter, BaseAlerter)

    annotator = components.get(StackComponentType.ANNOTATOR)
    if annotator is not None and not isinstance(annotator, BaseAnnotator):
        _raise_type_error(annotator, BaseAnnotator)

    data_validator = components.get(StackComponentType.DATA_VALIDATOR)
    if data_validator is not None and not isinstance(
        data_validator, BaseDataValidator
    ):
        _raise_type_error(data_validator, BaseDataValidator)

    image_builder = components.get(StackComponentType.IMAGE_BUILDER)
    if image_builder is not None and not isinstance(
        image_builder, BaseImageBuilder
    ):
        _raise_type_error(image_builder, BaseImageBuilder)

    model_registry = components.get(StackComponentType.MODEL_REGISTRY)
    if model_registry is not None and not isinstance(
        model_registry, BaseModelRegistry
    ):
        _raise_type_error(model_registry, BaseModelRegistry)

    return Stack(
        id=id,
        name=name,
        orchestrator=orchestrator,
        artifact_store=artifact_store,
        container_registry=container_registry,
        step_operator=step_operator,
        feature_store=feature_store,
        model_deployer=model_deployer,
        experiment_tracker=experiment_tracker,
        alerter=alerter,
        annotator=annotator,
        data_validator=data_validator,
        image_builder=image_builder,
        model_registry=model_registry,
    )

from_model(stack_model: StackResponse) -> Stack classmethod

Creates a Stack instance from a StackModel.

Parameters:

Name	Type	Description	Default
stack_model	StackResponse	The StackModel to create the Stack from.	required

Returns:

Type	Description
Stack	The created Stack instance.

Source code in src/zenml/stack/stack.py

@classmethod
def from_model(cls, stack_model: "StackResponse") -> "Stack":
    """Creates a Stack instance from a StackModel.

    Args:
        stack_model: The StackModel to create the Stack from.

    Returns:
        The created Stack instance.
    """
    global _STACK_CACHE
    key = (stack_model.id, stack_model.updated)
    if key in _STACK_CACHE:
        return _STACK_CACHE[key]

    from zenml.stack import StackComponent

    # Run a hydrated list call once to avoid one request per component
    component_models = pagination_utils.depaginate(
        Client().list_stack_components,
        stack_id=stack_model.id,
        hydrate=True,
    )

    stack_components = {
        model.type: StackComponent.from_model(model)
        for model in component_models
    }
    stack = Stack.from_components(
        id=stack_model.id,
        name=stack_model.name,
        components=stack_components,
    )
    _STACK_CACHE[key] = stack

    client = Client()
    if stack_model.id == client.active_stack_model.id:
        if stack_model.updated > client.active_stack_model.updated:
            if client._config:
                client._config.set_active_stack(stack_model)
            else:
                GlobalConfiguration().set_active_stack(stack_model)

    return stack

get_docker_builds(deployment: PipelineDeploymentBase) -> List[BuildConfiguration]

Gets the Docker builds required for the stack.

Parameters:

Name	Type	Description	Default
deployment	PipelineDeploymentBase	The pipeline deployment for which to get the builds.	required

Returns:

Type	Description
List[BuildConfiguration]	The required Docker builds.

Source code in src/zenml/stack/stack.py

def get_docker_builds(
    self, deployment: "PipelineDeploymentBase"
) -> List["BuildConfiguration"]:
    """Gets the Docker builds required for the stack.

    Args:
        deployment: The pipeline deployment for which to get the builds.

    Returns:
        The required Docker builds.
    """
    return list(
        itertools.chain.from_iterable(
            component.get_docker_builds(deployment=deployment)
            for component in self.components.values()
        )
    )

get_pipeline_run_metadata(run_id: UUID) -> Dict[UUID, Dict[str, MetadataType]]

Get general component-specific metadata for a pipeline run.

Parameters:

Name	Type	Description	Default
run_id	UUID	ID of the pipeline run.	required

Returns:

Type	Description
Dict[UUID, Dict[str, MetadataType]]	A dictionary mapping component IDs to the metadata they created.

Source code in src/zenml/stack/stack.py

def get_pipeline_run_metadata(
    self, run_id: UUID
) -> Dict[UUID, Dict[str, MetadataType]]:
    """Get general component-specific metadata for a pipeline run.

    Args:
        run_id: ID of the pipeline run.

    Returns:
        A dictionary mapping component IDs to the metadata they created.
    """
    pipeline_run_metadata: Dict[UUID, Dict[str, MetadataType]] = {}
    for component in self.components.values():
        try:
            component_metadata = component.get_pipeline_run_metadata(
                run_id=run_id
            )
            if component_metadata:
                pipeline_run_metadata[component.id] = component_metadata
        except Exception as e:
            logger.warning(
                f"Extracting pipeline run metadata failed for component "
                f"'{component.name}' of type '{component.type}': {e}"
            )
    return pipeline_run_metadata

get_step_run_metadata(info: StepRunInfo) -> Dict[UUID, Dict[str, MetadataType]]

Get component-specific metadata for a step run.

Parameters:

Name	Type	Description	Default
info	StepRunInfo	Info about the step that was executed.	required

Returns:

Type	Description
Dict[UUID, Dict[str, MetadataType]]	A dictionary mapping component IDs to the metadata they created.

Source code in src/zenml/stack/stack.py

def get_step_run_metadata(
    self, info: "StepRunInfo"
) -> Dict[UUID, Dict[str, MetadataType]]:
    """Get component-specific metadata for a step run.

    Args:
        info: Info about the step that was executed.

    Returns:
        A dictionary mapping component IDs to the metadata they created.
    """
    step_run_metadata: Dict[UUID, Dict[str, MetadataType]] = {}
    for component in self._get_active_components_for_step(
        info.config
    ).values():
        try:
            component_metadata = component.get_step_run_metadata(info=info)
            if component_metadata:
                step_run_metadata[component.id] = component_metadata
        except Exception as e:
            logger.warning(
                f"Extracting step run metadata failed for component "
                f"'{component.name}' of type '{component.type}': {e}"
            )
    return step_run_metadata

prepare_pipeline_deployment(deployment: PipelineDeploymentResponse) -> None

Prepares the stack for a pipeline deployment.

This method is called before a pipeline is deployed.

Parameters:

Name	Type	Description	Default
deployment	PipelineDeploymentResponse	The pipeline deployment	required

Raises:

Type	Description
RuntimeError	If trying to deploy a pipeline that requires a remote ZenML server with a local one.

Source code in src/zenml/stack/stack.py

def prepare_pipeline_deployment(
    self, deployment: "PipelineDeploymentResponse"
) -> None:
    """Prepares the stack for a pipeline deployment.

    This method is called before a pipeline is deployed.

    Args:
        deployment: The pipeline deployment

    Raises:
        RuntimeError: If trying to deploy a pipeline that requires a remote
            ZenML server with a local one.
    """
    self.validate(fail_if_secrets_missing=True)

    if self.requires_remote_server and Client().zen_store.is_local_store():
        raise RuntimeError(
            "Stacks with remote components such as remote orchestrators "
            "and step operators require a remote "
            "ZenML server. To run a pipeline with this stack you need to "
            "connect to a remote ZenML server first. Check out "
            "https://docs.zenml.io/getting-started/deploying-zenml "
            "for more information on how to deploy ZenML."
        )

    for component in self.components.values():
        component.prepare_pipeline_deployment(
            deployment=deployment, stack=self
        )

prepare_step_run(info: StepRunInfo) -> None

Prepares running a step.

Parameters:

Name	Type	Description	Default
info	StepRunInfo	Info about the step that will be executed.	required

Source code in src/zenml/stack/stack.py

def prepare_step_run(self, info: "StepRunInfo") -> None:
    """Prepares running a step.

    Args:
        info: Info about the step that will be executed.
    """
    for component in self._get_active_components_for_step(
        info.config
    ).values():
        component.prepare_step_run(info=info)

requirements(exclude_components: Optional[AbstractSet[StackComponentType]] = None) -> Set[str]

Set of PyPI requirements for the stack.

This method combines the requirements of all stack components (except the ones specified in exclude_components).

Parameters:

Name	Type	Description	Default
exclude_components	Optional[AbstractSet[StackComponentType]]	Set of component types for which the requirements should not be included in the output.	None

Returns:

Type	Description
Set[str]	Set of PyPI requirements.

Source code in src/zenml/stack/stack.py

def requirements(
    self,
    exclude_components: Optional[AbstractSet[StackComponentType]] = None,
) -> Set[str]:
    """Set of PyPI requirements for the stack.

    This method combines the requirements of all stack components (except
    the ones specified in `exclude_components`).

    Args:
        exclude_components: Set of component types for which the
            requirements should not be included in the output.

    Returns:
        Set of PyPI requirements.
    """
    exclude_components = exclude_components or set()
    requirements = [
        component.requirements
        for component in self.components.values()
        if component.type not in exclude_components
    ]
    return set.union(*requirements) if requirements else set()

validate(fail_if_secrets_missing: bool = False) -> None

Checks whether the stack configuration is valid.

To check if a stack configuration is valid, the following criteria must be met: - the stack must have an image builder if other components require it - the StackValidator of each stack component has to validate the stack to make sure all the components are compatible with each other - the required secrets of all components need to exist

Parameters:

Name	Type	Description	Default
fail_if_secrets_missing	bool	If this is True, an error will be raised if a secret for a component is missing. Otherwise, only a warning will be logged.	False

Source code in src/zenml/stack/stack.py

def validate(
    self,
    fail_if_secrets_missing: bool = False,
) -> None:
    """Checks whether the stack configuration is valid.

    To check if a stack configuration is valid, the following criteria must
    be met:
    - the stack must have an image builder if other components require it
    - the `StackValidator` of each stack component has to validate the
        stack to make sure all the components are compatible with each other
    - the required secrets of all components need to exist

    Args:
        fail_if_secrets_missing: If this is `True`, an error will be raised
            if a secret for a component is missing. Otherwise, only a
            warning will be logged.
    """
    if handle_bool_env_var(ENV_ZENML_SKIP_STACK_VALIDATION, default=False):
        logger.debug("Skipping stack validation.")
        return

    self.validate_image_builder()
    for component in self.components.values():
        if component.validator:
            component.validator.validate(stack=self)

    self._validate_secrets(raise_exception=fail_if_secrets_missing)

validate_image_builder() -> None

Validates that the stack has an image builder if required.

If the stack requires an image builder, but none is specified, a local image builder will be created and assigned to the stack to ensure backwards compatibility.

Source code in src/zenml/stack/stack.py

def validate_image_builder(self) -> None:
    """Validates that the stack has an image builder if required.

    If the stack requires an image builder, but none is specified, a
    local image builder will be created and assigned to the stack to
    ensure backwards compatibility.
    """
    requires_image_builder = (
        self.orchestrator.flavor != "local"
        or self.step_operator
        or (self.model_deployer and self.model_deployer.flavor != "mlflow")
    )
    skip_default_image_builder = handle_bool_env_var(
        ENV_ZENML_SKIP_IMAGE_BUILDER_DEFAULT, default=False
    )
    if (
        requires_image_builder
        and not skip_default_image_builder
        and not self.image_builder
    ):
        from uuid import uuid4

        from zenml.image_builders import (
            LocalImageBuilder,
            LocalImageBuilderConfig,
            LocalImageBuilderFlavor,
        )

        flavor = LocalImageBuilderFlavor()

        now = utc_now()
        image_builder = LocalImageBuilder(
            id=uuid4(),
            name="temporary_default",
            flavor=flavor.name,
            type=flavor.type,
            config=LocalImageBuilderConfig(),
            user=Client().active_user.id,
            created=now,
            updated=now,
        )

        self._image_builder = image_builder

StackComponentType

Bases: StrEnum

All possible types a StackComponent can have.

Attributes

plural: str property

Returns the plural of the enum value.

Returns:

Type	Description
str	The plural of the enum value.

StackFilter

Bases: UserScopedFilter

Model to enable advanced stack filtering.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/stack.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from zenml.zen_stores.schemas import (
        StackComponentSchema,
        StackCompositionSchema,
        StackSchema,
    )

    if self.component_id:
        component_id_filter = and_(
            StackCompositionSchema.stack_id == StackSchema.id,
            StackCompositionSchema.component_id == self.component_id,
        )
        custom_filters.append(component_id_filter)

    if self.component:
        component_filter = and_(
            StackCompositionSchema.stack_id == StackSchema.id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.component,
                table=StackComponentSchema,
            ),
        )
        custom_filters.append(component_filter)

    return custom_filters

StackRequest

Bases: UserScopedRequest

Request model for stack creation.

Attributes

is_valid: bool property

Check if the stack is valid.

Returns:

Type	Description
bool	True if the stack is valid, False otherwise.

StackResponse

Bases: UserScopedResponse[StackResponseBody, StackResponseMetadata, StackResponseResources]

Response model for stacks.

Attributes

components: Dict[StackComponentType, List[ComponentResponse]] property

The components property.

Returns:

Type	Description
Dict[StackComponentType, List[ComponentResponse]]	the value of the property.

description: Optional[str] property

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

is_valid: bool property

Check if the stack is valid.

Returns:

Type	Description
bool	True if the stack is valid, False otherwise.

labels: Optional[Dict[str, Any]] property

The labels property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

stack_spec_path: Optional[str] property

The stack_spec_path property.

Returns:

Type	Description
Optional[str]	the value of the property.

Functions

get_analytics_metadata() -> Dict[str, Any]

Add the stack components to the stack analytics metadata.

Returns:

Type	Description
Dict[str, Any]	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/stack.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Add the stack components to the stack analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata.update(
        {ct: c[0].flavor_name for ct, c in self.components.items()}
    )

    if self.labels is not None:
        metadata.update(
            {
                label[6:]: value
                for label, value in self.labels.items()
                if label.startswith("zenml:")
            }
        )
    return metadata

get_hydrated_version() -> StackResponse

Get the hydrated version of this stack.

Returns:

Type	Description
StackResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/stack.py

def get_hydrated_version(self) -> "StackResponse":
    """Get the hydrated version of this stack.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_stack(self.id)

to_yaml() -> Dict[str, Any]

Create yaml representation of the Stack Model.

Returns:

Type	Description
Dict[str, Any]	The yaml representation of the Stack Model.

Source code in src/zenml/models/v2/core/stack.py

def to_yaml(self) -> Dict[str, Any]:
    """Create yaml representation of the Stack Model.

    Returns:
        The yaml representation of the Stack Model.
    """
    component_data = {}
    for component_type, components_list in self.components.items():
        component = components_list[0]
        component_dict = dict(
            name=component.name,
            type=str(component.type),
            flavor=component.flavor_name,
        )
        configuration = json.loads(
            component.get_metadata().model_dump_json(
                include={"configuration"}
            )
        )
        component_dict.update(configuration)

        component_data[component_type.value] = component_dict

    # write zenml version and stack dict to YAML
    yaml_data = {
        "stack_name": self.name,
        "components": component_data,
    }

    return yaml_data

StackUpdate

Bases: BaseUpdate

Update model for stacks.

StepRunFilter

Bases: ProjectScopedFilter, RunMetadataFilterMixin

Model to enable advanced filtering of step runs.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/step_run.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["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_

    from zenml.zen_stores.schemas import (
        ModelSchema,
        ModelVersionSchema,
        StepRunSchema,
    )

    if self.model:
        model_filter = and_(
            StepRunSchema.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)

    return custom_filters

StepRunResponse

Bases: ProjectScopedResponse[StepRunResponseBody, StepRunResponseMetadata, StepRunResponseResources]

Response model for step runs.

Attributes

cache_key: Optional[str] property

The cache_key property.

Returns:

Type	Description
Optional[str]	the value of the property.

code_hash: Optional[str] property

The code_hash property.

Returns:

Type	Description
Optional[str]	the value of the property.

config: StepConfiguration property

The config property.

Returns:

Type	Description
StepConfiguration	the value of the property.

deployment_id: UUID property

The deployment_id property.

Returns:

Type	Description
UUID	the value of the property.

docstring: Optional[str] property

The docstring property.

Returns:

Type	Description
Optional[str]	the value of the property.

end_time: Optional[datetime] property

The end_time property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

input: ArtifactVersionResponse property

Returns the input artifact that was used to run this step.

Returns:

Type	Description
ArtifactVersionResponse	The input artifact.

Raises:

Type	Description
ValueError	If there were zero or multiple inputs to this step.

inputs: Dict[str, StepRunInputResponse] property

The inputs property.

Returns:

Type	Description
Dict[str, StepRunInputResponse]	the value of the property.

logs: Optional[LogsResponse] property

The logs property.

Returns:

Type	Description
Optional[LogsResponse]	the value of the property.

model_version: Optional[ModelVersionResponse] property

The model_version property.

Returns:

Type	Description
Optional[ModelVersionResponse]	the value of the property.

model_version_id: Optional[UUID] property

The model_version_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

original_step_run_id: Optional[UUID] property

The original_step_run_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

output: ArtifactVersionResponse property

Returns the output artifact that was written by this step.

Returns:

Type	Description
ArtifactVersionResponse	The output artifact.

Raises:

Type	Description
ValueError	If there were zero or multiple step outputs.

outputs: Dict[str, List[ArtifactVersionResponse]] property

The outputs property.

Returns:

Type	Description
Dict[str, List[ArtifactVersionResponse]]	the value of the property.

parent_step_ids: List[UUID] property

The parent_step_ids property.

Returns:

Type	Description
List[UUID]	the value of the property.

pipeline_run_id: UUID property

The pipeline_run_id property.

Returns:

Type	Description
UUID	the value of the property.

run_metadata: Dict[str, MetadataType] property

The run_metadata property.

Returns:

Type	Description
Dict[str, MetadataType]	the value of the property.

source_code: Optional[str] property

The source_code property.

Returns:

Type	Description
Optional[str]	the value of the property.

spec: StepSpec property

The spec property.

Returns:

Type	Description
StepSpec	the value of the property.

start_time: Optional[datetime] property

The start_time property.

Returns:

Type	Description
Optional[datetime]	the value of the property.

status: ExecutionStatus property

The status property.

Returns:

Type	Description
ExecutionStatus	the value of the property.

Functions

get_hydrated_version() -> StepRunResponse

Get the hydrated version of this step run.

Returns:

Type	Description
StepRunResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/step_run.py

def get_hydrated_version(self) -> "StepRunResponse":
    """Get the hydrated version of this step run.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_run_step(self.id)

StepRunUpdate

Bases: BaseUpdate

Update model for step runs.

StoreType

Bases: StrEnum

Zen Store Backend Types.

TagFilter

Bases: UserScopedFilter

Model to enable advanced filtering of all tags.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/tag.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["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 exists, select

    from zenml.zen_stores.schemas import (
        TagResourceSchema,
        TagSchema,
    )

    if self.resource_type:
        # Filter for tags that have at least one association with the specified resource type
        resource_type_filter = exists(
            select(TagResourceSchema).where(
                TagResourceSchema.tag_id == TagSchema.id,
                TagResourceSchema.resource_type
                == self.resource_type.value,
            )
        )
        custom_filters.append(resource_type_filter)

    return custom_filters

TagRequest

Bases: UserScopedRequest

Request model for tags.

Functions

validate_name_not_uuid(value: str) -> str classmethod

Validates that the tag name is not a UUID.

Parameters:

Name	Type	Description	Default
value	str	The tag name to validate.	required

Returns:

Type	Description
str	The validated tag name.

Raises:

Type	Description
ValueError	If the tag name can be converted to a UUID.

Source code in src/zenml/models/v2/core/tag.py

@field_validator("name")
@classmethod
def validate_name_not_uuid(cls, value: str) -> str:
    """Validates that the tag name is not a UUID.

    Args:
        value: The tag name to validate.

    Returns:
        The validated tag name.

    Raises:
        ValueError: If the tag name can be converted
            to a UUID.
    """
    if is_valid_uuid(value):
        raise ValueError(
            "Tag names cannot be UUIDs or strings that "
            "can be converted to UUIDs."
        )
    return value

TagResource

Bases: BaseModel

Utility class to help identify resources to tag.

TagResourceRequest

Bases: BaseRequest

Request model for links between tags and resources.

TagResponse

Bases: UserScopedResponse[TagResponseBody, TagResponseMetadata, TagResponseResources]

Response model for tags.

Attributes

color: ColorVariants property

The color property.

Returns:

Type	Description
ColorVariants	the value of the property.

exclusive: bool property

The exclusive property.

Returns:

Type	Description
bool	the value of the property.

tagged_count: int property

The tagged_count property.

Returns:

Type	Description
int	the value of the property.

Functions

get_hydrated_version() -> TagResponse

Get the hydrated version of this tag.

Returns:

Type	Description
TagResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/tag.py

def get_hydrated_version(self) -> "TagResponse":
    """Get the hydrated version of this tag.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_tag(self.id)

TagUpdate

Bases: BaseUpdate

Update model for tags.

Functions

validate_name_not_uuid(value: Optional[str]) -> Optional[str] classmethod

Validates that the tag name is not a UUID.

Parameters:

Name	Type	Description	Default
value	Optional[str]	The tag name to validate.	required

Returns:

Type	Description
Optional[str]	The validated tag name.

Raises:

Type	Description
ValueError	If the tag name can be converted to a UUID.

Source code in src/zenml/models/v2/core/tag.py

@field_validator("name")
@classmethod
def validate_name_not_uuid(cls, value: Optional[str]) -> Optional[str]:
    """Validates that the tag name is not a UUID.

    Args:
        value: The tag name to validate.

    Returns:
        The validated tag name.

    Raises:
        ValueError: If the tag name can be converted to a UUID.
    """
    if value is not None and is_valid_uuid(value):
        raise ValueError(
            "Tag names cannot be UUIDs or strings that "
            "can be converted to UUIDs."
        )
    return value

TaggableResourceTypes

Bases: StrEnum

All possible resource types for tagging.

TriggerExecutionFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all trigger executions.

TriggerExecutionResponse

Bases: BaseIdentifiedResponse[TriggerExecutionResponseBody, TriggerExecutionResponseMetadata, TriggerExecutionResponseResources]

Response model for trigger executions.

Attributes

event_metadata: Dict[str, Any] property

The event_metadata property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

trigger: TriggerResponse property

The trigger property.

Returns:

Type	Description
TriggerResponse	the value of the property.

Functions

get_hydrated_version() -> TriggerExecutionResponse

Get the hydrated version of this trigger execution.

Returns:

Type	Description
TriggerExecutionResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/trigger_execution.py

def get_hydrated_version(self) -> "TriggerExecutionResponse":
    """Get the hydrated version of this trigger execution.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_trigger_execution(self.id)

TriggerFilter

Bases: ProjectScopedFilter

Model to enable advanced filtering of all triggers.

Functions

get_custom_filters(table: Type[AnySchema]) -> List[ColumnElement[bool]]

Get custom filters.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
List[ColumnElement[bool]]	A list of custom filters.

Source code in src/zenml/models/v2/core/trigger.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    from sqlmodel import and_

    from zenml.zen_stores.schemas import (
        ActionSchema,
        EventSourceSchema,
        TriggerSchema,
    )

    custom_filters = super().get_custom_filters(table)

    if self.event_source_flavor:
        event_source_flavor_filter = and_(
            EventSourceSchema.id == TriggerSchema.event_source_id,
            EventSourceSchema.flavor == self.event_source_flavor,
        )
        custom_filters.append(event_source_flavor_filter)

    if self.event_source_subtype:
        event_source_subtype_filter = and_(
            EventSourceSchema.id == TriggerSchema.event_source_id,
            EventSourceSchema.plugin_subtype == self.event_source_subtype,
        )
        custom_filters.append(event_source_subtype_filter)

    if self.action_flavor:
        action_flavor_filter = and_(
            ActionSchema.id == TriggerSchema.action_id,
            ActionSchema.flavor == self.action_flavor,
        )
        custom_filters.append(action_flavor_filter)

    if self.action_subtype:
        action_subtype_filter = and_(
            ActionSchema.id == TriggerSchema.action_id,
            ActionSchema.plugin_subtype == self.action_subtype,
        )
        custom_filters.append(action_subtype_filter)

    return custom_filters

TriggerRequest

Bases: ProjectScopedRequest

Model for creating a new trigger.

TriggerResponse

Bases: ProjectScopedResponse[TriggerResponseBody, TriggerResponseMetadata, TriggerResponseResources]

Response model for models.

Attributes

action: ActionResponse property

The action property.

Returns:

Type	Description
ActionResponse	the value of the property.

action_flavor: str property

The action_flavor property.

Returns:

Type	Description
str	the value of the property.

action_subtype: str property

The action_subtype property.

Returns:

Type	Description
str	the value of the property.

description: str property

The description property.

Returns:

Type	Description
str	the value of the property.

event_filter: Optional[Dict[str, Any]] property

The event_filter property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

event_source: Optional[EventSourceResponse] property

The event_source property.

Returns:

Type	Description
Optional[EventSourceResponse]	the value of the property.

event_source_flavor: Optional[str] property

The event_source_flavor property.

Returns:

Type	Description
Optional[str]	the value of the property.

event_source_subtype: Optional[str] property

The event_source_subtype property.

Returns:

Type	Description
Optional[str]	the value of the property.

executions: Page[TriggerExecutionResponse] property

The event_source property.

Returns:

Type	Description
Page[TriggerExecutionResponse]	the value of the property.

is_active: bool property

The is_active property.

Returns:

Type	Description
bool	the value of the property.

Functions

get_hydrated_version() -> TriggerResponse

Get the hydrated version of this trigger.

Returns:

Type	Description
TriggerResponse	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/trigger.py

def get_hydrated_version(self) -> "TriggerResponse":
    """Get the hydrated version of this trigger.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_trigger(self.id)

TriggerUpdate

Bases: BaseUpdate

Update model for triggers.

UserFilter

Bases: BaseFilter

Model to enable advanced filtering of all Users.

Functions

apply_filter(query: AnyQuery, table: Type[AnySchema]) -> AnyQuery

Override to filter out service accounts from the query.

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/user.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to filter out service accounts from the query.

    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)
    query = query.where(
        getattr(table, "is_service_account") != True  # noqa: E712
    )

    return query

UserRequest

Bases: UserBase, BaseRequest

Request model for users.

UserResponse

Bases: BaseIdentifiedResponse[UserResponseBody, UserResponseMetadata, UserResponseResources]

Response model for user and service accounts.

This returns the activation_token that is required for the user-invitation-flow of the frontend. The email is returned optionally as well for use by the analytics on the client-side.

Attributes

activation_token: Optional[str] property

The activation_token property.

Returns:

Type	Description
Optional[str]	the value of the property.

active: bool property

The active property.

Returns:

Type	Description
bool	the value of the property.

default_project_id: Optional[UUID] property

The default_project_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

email: Optional[str] property

The email property.

Returns:

Type	Description
Optional[str]	the value of the property.

email_opted_in: Optional[bool] property

The email_opted_in property.

Returns:

Type	Description
Optional[bool]	the value of the property.

external_user_id: Optional[UUID] property

The external_user_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

full_name: str property

The full_name property.

Returns:

Type	Description
str	the value of the property.

is_admin: bool property

The is_admin property.

Returns:

Type	Description
bool	Whether the user is an admin.

is_service_account: bool property

The is_service_account property.

Returns:

Type	Description
bool	the value of the property.

user_metadata: Dict[str, Any] property

The user_metadata property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

Functions

get_hydrated_version() -> UserResponse

Get the hydrated version of this user.

Returns:

Type	Description
UserResponse	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/user.py

def get_hydrated_version(self) -> "UserResponse":
    """Get the hydrated version of this user.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_user(self.id)

UserUpdate

Bases: UserBase, BaseUpdate

Update model for users.

Functions

create_copy(exclude: AbstractSet[str]) -> UserUpdate

Create a copy of the current instance.

Parameters:

Name	Type	Description	Default
exclude	AbstractSet[str]	Fields to exclude from the copy.	required

Returns:

Type	Description
UserUpdate	A copy of the current instance.

Source code in src/zenml/models/v2/core/user.py

def create_copy(self, exclude: AbstractSet[str]) -> "UserUpdate":
    """Create a copy of the current instance.

    Args:
        exclude: Fields to exclude from the copy.

    Returns:
        A copy of the current instance.
    """
    return UserUpdate(
        **self.model_dump(
            exclude=set(exclude),
            exclude_unset=True,
        )
    )

user_email_updates() -> UserUpdate

Validate that the UserUpdateModel conforms to the email-opt-in-flow.

Returns:

Type	Description
UserUpdate	The validated values.

Raises:

Type	Description
ValueError	If the email was not provided when the email_opted_in field was set to True.

Source code in src/zenml/models/v2/core/user.py

@model_validator(mode="after")
def user_email_updates(self) -> "UserUpdate":
    """Validate that the UserUpdateModel conforms to the email-opt-in-flow.

    Returns:
        The validated values.

    Raises:
        ValueError: If the email was not provided when the email_opted_in
            field was set to True.
    """
    # When someone sets the email, or updates the email and hasn't
    #  before explicitly opted out, they are opted in
    if self.email is not None:
        if self.email_opted_in is None:
            self.email_opted_in = True

    # It should not be possible to do opt in without an email
    if self.email_opted_in is True:
        if self.email is None:
            raise ValueError(
                "Please provide an email, when you are opting-in with "
                "your email."
            )
    return self

ValidationError(message: Optional[str] = None, url: Optional[str] = None)

Bases: ZenMLBaseException

Raised when the Model passed to the ZenStore.

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)

ZenKeyError(message: str)

Bases: KeyError

Specialized key error which allows error messages with line breaks.

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

Functions

Functions

_fail_for_sql_zen_store(method: F) -> F

Decorator for methods that are not allowed with a SQLZenStore.

Parameters:

Name	Type	Description	Default
method	F	The method to decorate.	required

Returns:

Type	Description
F	The decorated method.

Source code in src/zenml/client.py

def _fail_for_sql_zen_store(method: F) -> F:
    """Decorator for methods that are not allowed with a SQLZenStore.

    Args:
        method: The method to decorate.

    Returns:
        The decorated method.
    """

    @functools.wraps(method)
    def wrapper(self: "Client", *args: Any, **kwargs: Any) -> Any:
        # No isinstance check to avoid importing ZenStore implementations
        if self.zen_store.__class__.__name__ == "SqlZenStore":
            raise TypeError(
                "This method is not allowed when not connected "
                "to a ZenML Server through the API interface."
            )
        return method(self, *args, **kwargs)

    return cast(F, wrapper)

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()

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

depaginate(list_method: Callable[..., Page[AnyResponse]], **kwargs: Any) -> List[AnyResponse]

Depaginate the results from a client or store method that returns pages.

Parameters:

Name	Type	Description	Default
list_method	Callable[..., Page[AnyResponse]]	The list method to depaginate.	required
**kwargs	Any	Arguments for the list method.	{}

Returns:

Type	Description
List[AnyResponse]	A list of the corresponding Response Models.

Source code in src/zenml/utils/pagination_utils.py

def depaginate(
    list_method: Callable[..., Page[AnyResponse]], **kwargs: Any
) -> List[AnyResponse]:
    """Depaginate the results from a client or store method that returns pages.

    Args:
        list_method: The list method to depaginate.
        **kwargs: Arguments for the list method.

    Returns:
        A list of the corresponding Response Models.
    """
    page = list_method(**kwargs)
    items = list(page.items)
    while page.index < page.total_pages:
        kwargs["page"] = page.index + 1
        page = list_method(**kwargs)
        items += list(page.items)

    return items

dict_to_bytes(dict_: Dict[str, Any]) -> bytes

Converts a dictionary to bytes.

Parameters:

Name	Type	Description	Default
dict_	Dict[str, Any]	The dictionary to convert.	required

Returns:

Type	Description
bytes	The dictionary as bytes.

Source code in src/zenml/utils/dict_utils.py

def dict_to_bytes(dict_: Dict[str, Any]) -> bytes:
    """Converts a dictionary to bytes.

    Args:
        dict_: The dictionary to convert.

    Returns:
        The dictionary as bytes.
    """
    return base64.b64encode(
        json.dumps(
            dict_,
            sort_keys=False,
            default=pydantic_encoder,
        ).encode("utf-8")
    )

evaluate_all_lazy_load_args_in_client_methods(cls: Type[Client]) -> Type[Client]

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()

get_logger(logger_name: str) -> logging.Logger

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

handle_bool_env_var(var: str, default: bool = False) -> bool

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

is_valid_uuid(value: Any, version: int = 4) -> bool

Checks if a string is a valid UUID.

Parameters:

Name	Type	Description	Default
value	Any	String to check.	required
version	int	Version of UUID to check for.	4

Returns:

Type	Description
bool	True if string is a valid UUID, False otherwise.

Source code in src/zenml/utils/uuid_utils.py

def is_valid_uuid(value: Any, version: int = 4) -> bool:
    """Checks if a string is a valid UUID.

    Args:
        value: String to check.
        version: Version of UUID to check for.

    Returns:
        True if string is a valid UUID, False otherwise.
    """
    if isinstance(value, UUID):
        return True
    if isinstance(value, str):
        try:
            UUID(value, version=version)
            return True
        except ValueError:
            return False
    return False

Modules

fileio

Functionality for reading, writing and managing files.

Classes

Functions

convert_to_str(path: PathType) -> str

Converts a "PathType" to a str using UTF-8.

Parameters:

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

Returns:

Type	Description
str	The path as a string.

Source code in src/zenml/io/fileio.py

def convert_to_str(path: "PathType") -> str:
    """Converts a "PathType" to a str using UTF-8.

    Args:
        path: The path to convert.

    Returns:
        The path as a string.
    """
    if isinstance(path, str):
        return path
    else:
        return path.decode("utf-8")

copy(src: PathType, dst: PathType, overwrite: bool = False) -> None

Copy a file from the source to the destination.

Parameters:

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

Raises:

Type	Description
FileExistsError	If a file already exists at the destination and overwrite is not set to True.

Source code in src/zenml/io/fileio.py

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

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

    Raises:
        FileExistsError: If a file already exists at the destination and
            `overwrite` is not set to `True`.
    """
    src_fs = _get_filesystem(src)
    dst_fs = _get_filesystem(dst)
    if src_fs is dst_fs:
        src_fs.copyfile(src, dst, overwrite=overwrite)
    else:
        if not overwrite and exists(dst):
            raise FileExistsError(
                f"Destination file '{convert_to_str(dst)}' already exists "
                f"and `overwrite` is false."
            )
        with open(src, mode="rb") as f:
            contents = f.read()

        with open(dst, mode="wb") as f:
            f.write(contents)

exists(path: PathType) -> bool

Check whether a given path exists.

Parameters:

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

Returns:

Type	Description
bool	True if the given path exists, False otherwise.

Source code in src/zenml/io/fileio.py

def exists(path: "PathType") -> bool:
    """Check whether a given path exists.

    Args:
        path: The path to check.

    Returns:
        `True` if the given path exists, `False` otherwise.
    """
    return _get_filesystem(path).exists(path)

glob(pattern: PathType) -> List[PathType]

Find all files matching the given pattern.

Parameters:

Name	Type	Description	Default
pattern	PathType	The pattern to match.	required

Returns:

Type	Description
List[PathType]	A list of paths matching the pattern.

Source code in src/zenml/io/fileio.py

def glob(pattern: "PathType") -> List["PathType"]:
    """Find all files matching the given pattern.

    Args:
        pattern: The pattern to match.

    Returns:
        A list of paths matching the pattern.
    """
    return _get_filesystem(pattern).glob(pattern)

isdir(path: PathType) -> bool

Check whether the given path is a directory.

Parameters:

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

Returns:

Type	Description
bool	True if the given path is a directory, False otherwise.

Source code in src/zenml/io/fileio.py

def isdir(path: "PathType") -> bool:
    """Check whether the given path is a directory.

    Args:
        path: The path to check.

    Returns:
        `True` if the given path is a directory, `False` otherwise.
    """
    return _get_filesystem(path).isdir(path)

listdir(path: str, only_file_names: bool = True) -> List[str]

Lists all files in a directory.

Parameters:

Name	Type	Description	Default
path	str	The path to the directory.	required
only_file_names	bool	If True, only return the file names, not the full path.	True

Returns:

Type	Description
List[str]	A list of files in the directory.

Source code in src/zenml/io/fileio.py

def listdir(path: str, only_file_names: bool = True) -> List[str]:
    """Lists all files in a directory.

    Args:
        path: The path to the directory.
        only_file_names: If True, only return the file names, not the full path.

    Returns:
        A list of files in the directory.
    """
    try:
        return [
            os.path.join(path, convert_to_str(f))
            if not only_file_names
            else convert_to_str(f)
            for f in _get_filesystem(path).listdir(path)
        ]
    except IOError:
        logger.debug(f"Dir {path} not found.")
        return []

makedirs(path: PathType) -> None

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

Parameters:

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

Source code in src/zenml/io/fileio.py

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

    Args:
        path: The path to the directory.
    """
    _get_filesystem(path).makedirs(path)

mkdir(path: PathType) -> None

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

Parameters:

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

Source code in src/zenml/io/fileio.py

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

    Args:
        path: The path to the directory.
    """
    _get_filesystem(path).mkdir(path)

open(path: PathType, mode: str = 'r') -> Any

Opens a file.

Parameters:

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

Returns:

Type	Description
Any	The opened file.

Source code in src/zenml/io/fileio.py

def open(path: "PathType", mode: str = "r") -> Any:  # noqa
    """Opens a file.

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

    Returns:
        The opened file.
    """
    return _get_filesystem(path).open(path, mode=mode)

remove(path: PathType) -> None

Remove the file at the given path. Dangerous operation.

Parameters:

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

Raises:

Type	Description
FileNotFoundError	If the file does not exist.

Source code in src/zenml/io/fileio.py

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

    Args:
        path: The path to the file to remove.

    Raises:
        FileNotFoundError: If the file does not exist.
    """
    if not exists(path):
        raise FileNotFoundError(f"{convert_to_str(path)} does not exist!")
    _get_filesystem(path).remove(path)

rename(src: PathType, dst: PathType, overwrite: bool = False) -> None

Rename a file.

Parameters:

Name	Type	Description	Default
src	PathType	The path of the file to rename.	required
dst	PathType	The path to rename the source file to.	required
overwrite	bool	If a file already exists at the destination, this method will overwrite it if overwrite=True and raise a FileExistsError otherwise.	False

Raises:

Type	Description
NotImplementedError	If the source and destination file systems are not the same.

Source code in src/zenml/io/fileio.py

def rename(src: "PathType", dst: "PathType", overwrite: bool = False) -> None:
    """Rename a file.

    Args:
        src: The path of the file to rename.
        dst: The path to rename the source file to.
        overwrite: If a file already exists at the destination, this
            method will overwrite it if overwrite=`True` and
            raise a FileExistsError otherwise.

    Raises:
        NotImplementedError: If the source and destination file systems are not
            the same.
    """
    src_fs = _get_filesystem(src)
    dst_fs = _get_filesystem(dst)
    if src_fs is dst_fs:
        src_fs.rename(src, dst, overwrite=overwrite)
    else:
        raise NotImplementedError(
            f"Renaming from {convert_to_str(src)} to {convert_to_str(dst)} "
            f"using different file systems plugins is currently not supported."
        )

rmtree(dir_path: str) -> None

Deletes a directory recursively. Dangerous operation.

Parameters:

Name	Type	Description	Default
dir_path	str	The path to the directory to delete.	required

Raises:

Type	Description
TypeError	If the path is not pointing to a directory.

Source code in src/zenml/io/fileio.py

def rmtree(dir_path: str) -> None:
    """Deletes a directory recursively. Dangerous operation.

    Args:
        dir_path: The path to the directory to delete.

    Raises:
        TypeError: If the path is not pointing to a directory.
    """
    if not isdir(dir_path):
        raise TypeError(f"Path '{dir_path}' is not a directory.")

    _get_filesystem(dir_path).rmtree(dir_path)

size(path: PathType) -> Optional[int]

Get the size of a file or directory 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 or directory in bytes or None if the responsible
Optional[int]	file system does not implement the size method.

Source code in src/zenml/io/fileio.py

def size(path: "PathType") -> Optional[int]:
    """Get the size of a file or directory in bytes.

    Args:
        path: The path to the file.

    Returns:
        The size of the file or directory in bytes or `None` if the responsible
        file system does not implement the `size` method.
    """
    file_system = _get_filesystem(path)

    # If the file system does not implement the `size` method, return `None`.
    if file_system.size == BaseFilesystem.size:
        logger.warning(
            "Cannot get size of file or directory '%s' since the responsible "
            "file system `%s` does not implement the `size` method.",
            path,
            file_system.__name__,
        )
        return None

    # If the path does not exist, return 0.
    if not exists(path):
        return 0

    # If the path is a file, return its size.
    if not file_system.isdir(path):
        return file_system.size(path)

    # If the path is a directory, recursively sum the sizes of everything in it.
    files = file_system.listdir(path)
    file_sizes = [size(os.path.join(str(path), str(file))) for file in files]
    return sum(
        [file_size for file_size in file_sizes if file_size is not None]
    )

stat(path: PathType) -> Any

Get the stat descriptor for a given file path.

Parameters:

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

Returns:

Type	Description
Any	The stat descriptor.

Source code in src/zenml/io/fileio.py

def stat(path: "PathType") -> Any:
    """Get the stat descriptor for a given file path.

    Args:
        path: The path to the file.

    Returns:
        The stat descriptor.
    """
    return _get_filesystem(path).stat(path)

walk(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.

Parameters:

Name	Type	Description	Default
top	PathType	The path of directory to walk.	required
topdown	bool	Whether to walk directories topdown or bottom-up.	True
onerror	Optional[Callable[..., None]]	Callable that gets called if an error occurs.	None

Returns:

Type	Description
Iterable[Tuple[PathType, List[PathType], List[PathType]]]	An Iterable of Tuples, each of which contain the path of the current
Iterable[Tuple[PathType, List[PathType], List[PathType]]]	directory path, a list of directories inside the current directory
Iterable[Tuple[PathType, List[PathType], List[PathType]]]	and a list of files inside the current directory.

Source code in src/zenml/io/fileio.py

def walk(
    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 of directory to walk.
        topdown: Whether to walk directories topdown or bottom-up.
        onerror: Callable that gets called if an error occurs.

    Returns:
        An Iterable of Tuples, each of which contain the path of the current
        directory path, a list of directories inside the current directory
        and a list of files inside the current directory.
    """
    return _get_filesystem(top).walk(top, topdown=topdown, onerror=onerror)

Modules

io_utils

Various utility functions for the io module.

Functions

copy_dir(source_dir: str, destination_dir: str, overwrite: bool = False) -> None

Copies dir from source to destination.

Parameters:

Name	Type	Description	Default
source_dir	str	Path to copy from.	required
destination_dir	str	Path to copy to.	required
overwrite	bool	Boolean. If false, function throws an error before overwrite.	False

Source code in src/zenml/utils/io_utils.py

def copy_dir(
    source_dir: str, destination_dir: str, overwrite: bool = False
) -> None:
    """Copies dir from source to destination.

    Args:
        source_dir: Path to copy from.
        destination_dir: Path to copy to.
        overwrite: Boolean. If false, function throws an error before overwrite.
    """
    for source_file in listdir(source_dir):
        source_path = os.path.join(source_dir, convert_to_str(source_file))
        destination_path = os.path.join(
            destination_dir, convert_to_str(source_file)
        )
        if isdir(source_path):
            if source_path == destination_dir:
                # if the destination is a subdirectory of the source, we skip
                # copying it to avoid an infinite loop.
                continue
            copy_dir(source_path, destination_path, overwrite)
        else:
            create_dir_recursive_if_not_exists(
                os.path.dirname(destination_path)
            )
            copy(str(source_path), str(destination_path), overwrite)

create_dir_if_not_exists(dir_path: str) -> None

Creates directory if it does not exist.

Parameters:

Name	Type	Description	Default
dir_path	str	Local path in filesystem.	required

Source code in src/zenml/utils/io_utils.py

def create_dir_if_not_exists(dir_path: str) -> None:
    """Creates directory if it does not exist.

    Args:
        dir_path: Local path in filesystem.
    """
    if not isdir(dir_path):
        mkdir(dir_path)

create_dir_recursive_if_not_exists(dir_path: str) -> None

Creates directory recursively if it does not exist.

Parameters:

Name	Type	Description	Default
dir_path	str	Local path in filesystem.	required

Source code in src/zenml/utils/io_utils.py

def create_dir_recursive_if_not_exists(dir_path: str) -> None:
    """Creates directory recursively if it does not exist.

    Args:
        dir_path: Local path in filesystem.
    """
    if not isdir(dir_path):
        makedirs(dir_path)

create_file_if_not_exists(file_path: str, file_contents: str = '{}') -> None

Creates file if it does not exist.

Parameters:

Name	Type	Description	Default
file_path	str	Local path in filesystem.	required
file_contents	str	Contents of file.	'{}'

Source code in src/zenml/utils/io_utils.py

def create_file_if_not_exists(
    file_path: str, file_contents: str = "{}"
) -> None:
    """Creates file if it does not exist.

    Args:
        file_path: Local path in filesystem.
        file_contents: Contents of file.
    """
    full_path = Path(file_path)
    if not exists(file_path):
        create_dir_recursive_if_not_exists(str(full_path.parent))
        with open(str(full_path), "w") as f:
            f.write(file_contents)

find_files(dir_path: PathType, pattern: str) -> Iterable[str]

Find files in a directory that match pattern.

Parameters:

Name	Type	Description	Default
dir_path	PathType	The path to directory.	required
pattern	str	pattern like *.png.	required

Yields:

Type	Description
Iterable[str]	All matching filenames in the directory.

Source code in src/zenml/utils/io_utils.py

def find_files(dir_path: "PathType", pattern: str) -> Iterable[str]:
    """Find files in a directory that match pattern.

    Args:
        dir_path: The path to directory.
        pattern: pattern like *.png.

    Yields:
        All matching filenames in the directory.
    """
    for root, _, files in walk(dir_path):
        for basename in files:
            if fnmatch.fnmatch(convert_to_str(basename), pattern):
                filename = os.path.join(
                    convert_to_str(root), convert_to_str(basename)
                )
                yield filename

get_global_config_directory() -> str

Gets the global config directory for ZenML.

Returns:

Type	Description
str	The global config directory for ZenML.

Source code in src/zenml/utils/io_utils.py

def get_global_config_directory() -> str:
    """Gets the global config directory for ZenML.

    Returns:
        The global config directory for ZenML.
    """
    env_var_path = os.getenv(ENV_ZENML_CONFIG_PATH)
    if env_var_path:
        return str(Path(env_var_path).resolve())
    return click.get_app_dir(APP_NAME)

get_grandparent(dir_path: str) -> str

Get grandparent of dir.

Parameters:

Name	Type	Description	Default
dir_path	str	The path to directory.	required

Returns:

Type	Description
str	The input paths parents parent.

Raises:

Type	Description
ValueError	If dir_path does not exist.

Source code in src/zenml/utils/io_utils.py

def get_grandparent(dir_path: str) -> str:
    """Get grandparent of dir.

    Args:
        dir_path: The path to directory.

    Returns:
        The input paths parents parent.

    Raises:
        ValueError: If dir_path does not exist.
    """
    if not os.path.exists(dir_path):
        raise ValueError(f"Path '{dir_path}' does not exist.")
    return Path(dir_path).parent.parent.stem

get_parent(dir_path: str) -> str

Get parent of dir.

Parameters:

Name	Type	Description	Default
dir_path	str	The path to directory.	required

Returns:

Type	Description
str	Parent (stem) of the dir as a string.

Raises:

Type	Description
ValueError	If dir_path does not exist.

Source code in src/zenml/utils/io_utils.py

def get_parent(dir_path: str) -> str:
    """Get parent of dir.

    Args:
        dir_path: The path to directory.

    Returns:
        Parent (stem) of the dir as a string.

    Raises:
        ValueError: If dir_path does not exist.
    """
    if not os.path.exists(dir_path):
        raise ValueError(f"Path '{dir_path}' does not exist.")
    return Path(dir_path).parent.stem

is_path_within_directory(path: str, directory: str) -> bool

Checks if a path is contained within a given directory.

This utility function verifies that a path (absolute or relative) resolves to a location that is within the specified directory. This is useful for security checks such as preventing path traversal attacks when extracting archives (CVE-2007-4559) or whenever path containment needs to be verified.

Parameters:

Name	Type	Description	Default
path	str	The path to check (can be relative or absolute).	required
directory	str	The directory that should contain the path.	required

Returns:

Type	Description
bool	Boolean indicating whether the path is contained within the directory (True)
bool	or not (False).

Source code in src/zenml/utils/io_utils.py

def is_path_within_directory(path: str, directory: str) -> bool:
    """Checks if a path is contained within a given directory.

    This utility function verifies that a path (absolute or relative) resolves
    to a location that is within the specified directory. This is useful for
    security checks such as preventing path traversal attacks when extracting
    archives (CVE-2007-4559) or whenever path containment needs to be verified.

    Args:
        path: The path to check (can be relative or absolute).
        directory: The directory that should contain the path.

    Returns:
        Boolean indicating whether the path is contained within the directory (True)
        or not (False).
    """
    # Convert to absolute path, ensuring it's normalized
    abs_path = os.path.abspath(os.path.join(directory, path))
    # Check if the path is within the target directory
    dir_abs = os.path.abspath(directory)
    return abs_path.startswith(dir_abs + os.sep) or abs_path == dir_abs

is_remote(path: str) -> bool

Returns True if path exists remotely.

Parameters:

Name	Type	Description	Default
path	str	Any path as a string.	required

Returns:

Type	Description
bool	True if remote path, else False.

Source code in src/zenml/utils/io_utils.py

def is_remote(path: str) -> bool:
    """Returns True if path exists remotely.

    Args:
        path: Any path as a string.

    Returns:
        True if remote path, else False.
    """
    return any(path.startswith(prefix) for prefix in REMOTE_FS_PREFIX)

is_root(path: str) -> bool

Returns true if path has no parent in local filesystem.

Parameters:

Name	Type	Description	Default
path	str	Local path in filesystem.	required

Returns:

Type	Description
bool	True if root, else False.

Source code in src/zenml/utils/io_utils.py

def is_root(path: str) -> bool:
    """Returns true if path has no parent in local filesystem.

    Args:
        path: Local path in filesystem.

    Returns:
        True if root, else False.
    """
    return Path(path).parent == Path(path)

move(source: str, destination: str, overwrite: bool = False) -> None

Moves dir or file from source to destination. Can be used to rename.

Parameters:

Name	Type	Description	Default
source	str	Local path to copy from.	required
destination	str	Local path to copy to.	required
overwrite	bool	boolean, if false, then throws an error before overwrite.	False

Source code in src/zenml/utils/io_utils.py

def move(source: str, destination: str, overwrite: bool = False) -> None:
    """Moves dir or file from source to destination. Can be used to rename.

    Args:
        source: Local path to copy from.
        destination: Local path to copy to.
        overwrite: boolean, if false, then throws an error before overwrite.
    """
    rename(source, destination, overwrite)

read_file_contents_as_string(file_path: str) -> str

Reads contents of file.

Parameters:

Name	Type	Description	Default
file_path	str	Path to file.	required

Returns:

Type	Description
str	Contents of file.

Raises:

Type	Description
FileNotFoundError	If file does not exist.

Source code in src/zenml/utils/io_utils.py

def read_file_contents_as_string(file_path: str) -> str:
    """Reads contents of file.

    Args:
        file_path: Path to file.

    Returns:
        Contents of file.

    Raises:
        FileNotFoundError: If file does not exist.
    """
    if not exists(file_path):
        raise FileNotFoundError(f"{file_path} does not exist!")
    with open(file_path) as f:
        return f.read()  # type: ignore[no-any-return]

resolve_relative_path(path: str) -> str

Takes relative path and resolves it absolutely.

Parameters:

Name	Type	Description	Default
path	str	Local path in filesystem.	required

Returns:

Type	Description
str	Resolved path.

Source code in src/zenml/utils/io_utils.py

def resolve_relative_path(path: str) -> str:
    """Takes relative path and resolves it absolutely.

    Args:
        path: Local path in filesystem.

    Returns:
        Resolved path.
    """
    if is_remote(path):
        return path
    return str(Path(path).resolve())

write_file_contents_as_string(file_path: str, content: str) -> None

Writes contents of file.

Parameters:

Name	Type	Description	Default
file_path	str	Path to file.	required
content	str	Contents of file.	required

Raises:

Type	Description
ValueError	If content is not of type str.

Source code in src/zenml/utils/io_utils.py

def write_file_contents_as_string(file_path: str, content: str) -> None:
    """Writes contents of file.

    Args:
        file_path: Path to file.
        content: Contents of file.

    Raises:
        ValueError: If content is not of type str.
    """
    if not isinstance(content, str):
        raise ValueError(f"Content must be of type str, got {type(content)}")
    with open(file_path, "w") as f:
        f.write(content)

source_utils

Utilities for loading/resolving objects.

Classes

Functions

get_implicit_source_root() -> str

Get the implicit source root (the parent directory of the main module).

Raises:

Type	Description
RuntimeError	If the main module file can't be found.

Returns:

Type	Description
str	The implicit source root.

Source code in src/zenml/utils/source_utils.py

def get_implicit_source_root() -> str:
    """Get the implicit source root (the parent directory of the main module).

    Raises:
        RuntimeError: If the main module file can't be found.

    Returns:
        The implicit source root.
    """
    main_module = sys.modules.get("__main__")
    if main_module is None:
        raise RuntimeError(
            "Unable to determine source root because the main module could not "
            "be found."
        )

    if not hasattr(main_module, "__file__") or not main_module.__file__:
        raise RuntimeError(
            "Unable to determine source root because the main module does not "
            "have an associated file. This could be because you're running in "
            "an interactive Python environment. If you are trying to run from "
            "within a Jupyter notebook, please run `zenml init` from the root "
            "where your notebook is located and restart your notebook server."
        )

    path = Path(main_module.__file__).resolve().parent
    return str(path)

get_resolved_notebook_sources() -> Dict[str, str]

Get all notebook sources that were resolved in this process.

Returns:

Type	Description
Dict[str, str]	Dictionary mapping the import path of notebook sources to the code
Dict[str, str]	of their notebook cell.

Source code in src/zenml/utils/source_utils.py

def get_resolved_notebook_sources() -> Dict[str, str]:
    """Get all notebook sources that were resolved in this process.

    Returns:
        Dictionary mapping the import path of notebook sources to the code
        of their notebook cell.
    """
    return _resolved_notebook_sources.copy()

get_source_root() -> str

Get the source root.

The source root will be determined in the following order: - The manually specified custom source root if it was set. - The ZenML repository directory if one exists in the current working directory or any parent directories. - The parent directory of the main module file.

Returns:

Type	Description
str	The source root.

Source code in src/zenml/utils/source_utils.py

def get_source_root() -> str:
    """Get the source root.

    The source root will be determined in the following order:
    - The manually specified custom source root if it was set.
    - The ZenML repository directory if one exists in the current working
      directory or any parent directories.
    - The parent directory of the main module file.

    Returns:
        The source root.
    """
    if _CUSTOM_SOURCE_ROOT:
        logger.debug("Using custom source root: %s", _CUSTOM_SOURCE_ROOT)
        return _CUSTOM_SOURCE_ROOT

    from zenml.client import Client

    repo_root = Client.find_repository()
    if repo_root:
        logger.debug("Using repository root as source root: %s", repo_root)
        return str(repo_root.resolve())

    implicit_source_root = get_implicit_source_root()
    logger.debug(
        "Using main module parent directory as source root: %s",
        implicit_source_root,
    )
    return implicit_source_root

get_source_type(module: ModuleType) -> SourceType

Get the type of a source.

Parameters:

Name	Type	Description	Default
module	ModuleType	The module for which to get the source type.	required

Returns:

Type	Description
SourceType	The source type.

Source code in src/zenml/utils/source_utils.py

def get_source_type(module: ModuleType) -> SourceType:
    """Get the type of a source.

    Args:
        module: The module for which to get the source type.

    Returns:
        The source type.
    """
    if module.__name__ in _notebook_modules:
        return SourceType.NOTEBOOK

    try:
        file_path = inspect.getfile(module)
    except (TypeError, OSError):
        if module.__name__ == "__main__" and Environment.in_notebook():
            return SourceType.NOTEBOOK

        return SourceType.BUILTIN

    if is_internal_module(module_name=module.__name__):
        return SourceType.INTERNAL

    if is_distribution_package_file(
        file_path=file_path, module_name=module.__name__
    ):
        return SourceType.DISTRIBUTION_PACKAGE

    if is_standard_lib_file(file_path=file_path):
        return SourceType.BUILTIN

    # Make sure to check for distribution packages before this to catch the
    # case when a virtual environment is inside our source root
    if is_user_file(file_path=file_path):
        return SourceType.USER

    return SourceType.UNKNOWN

is_distribution_package_file(file_path: str, module_name: str) -> bool

Checks if a file/module belongs to a distribution package.

Parameters:

Name	Type	Description	Default
file_path	str	The file path to check.	required
module_name	str	The module name.	required

Returns:

Type	Description
bool	True if the file/module belongs to a distribution package, False
bool	otherwise.

Source code in src/zenml/utils/source_utils.py

def is_distribution_package_file(file_path: str, module_name: str) -> bool:
    """Checks if a file/module belongs to a distribution package.

    Args:
        file_path: The file path to check.
        module_name: The module name.

    Returns:
        True if the file/module belongs to a distribution package, False
        otherwise.
    """
    absolute_file_path = Path(file_path).resolve()

    for path in site.getsitepackages() + [site.getusersitepackages()]:
        if Path(path).resolve() in absolute_file_path.parents:
            return True

    # TODO: The previous check does not detect editable installs because
    # the site packages dir only contains a reference to the source files,
    # not the actual files. That means currently editable installs get a
    # source type UNKNOWN which might or might not lead to issues.

    return False

is_internal_module(module_name: str) -> bool

Checks if a module is internal (=part of the zenml package).

Parameters:

Name	Type	Description	Default
module_name	str	Name of the module to check.	required

Returns:

Type	Description
bool	True if the module is internal, False otherwise.

Source code in src/zenml/utils/source_utils.py

def is_internal_module(module_name: str) -> bool:
    """Checks if a module is internal (=part of the zenml package).

    Args:
        module_name: Name of the module to check.

    Returns:
        True if the module is internal, False otherwise.
    """
    return module_name.split(".", maxsplit=1)[0] == "zenml"

is_standard_lib_file(file_path: str) -> bool

Checks if a file belongs to the Python standard library.

Parameters:

Name	Type	Description	Default
file_path	str	The file path to check.	required

Returns:

Type	Description
bool	True if the file belongs to the Python standard library, False
bool	otherwise.

Source code in src/zenml/utils/source_utils.py

def is_standard_lib_file(file_path: str) -> bool:
    """Checks if a file belongs to the Python standard library.

    Args:
        file_path: The file path to check.

    Returns:
        True if the file belongs to the Python standard library, False
        otherwise.
    """
    stdlib_root = get_python_lib(standard_lib=True)
    logger.debug("Standard library root: %s", stdlib_root)
    return Path(stdlib_root).resolve() in Path(file_path).resolve().parents

is_user_file(file_path: str) -> bool

Checks if a file is a user file.

Parameters:

Name	Type	Description	Default
file_path	str	The file path to check.	required

Returns:

Type	Description
bool	True if the file is a user file, False otherwise.

Source code in src/zenml/utils/source_utils.py

def is_user_file(file_path: str) -> bool:
    """Checks if a file is a user file.

    Args:
        file_path: The file path to check.

    Returns:
        True if the file is a user file, False otherwise.
    """
    source_root = get_source_root()
    return Path(source_root) in Path(file_path).resolve().parents

load(source: Union[Source, str]) -> Any

Load a source or import path.

Parameters:

Name	Type	Description	Default
source	Union[Source, str]	The source to load.	required

Returns:

Type	Description
Any	The loaded object.

Source code in src/zenml/utils/source_utils.py

def load(source: Union[Source, str]) -> Any:
    """Load a source or import path.

    Args:
        source: The source to load.

    Returns:
        The loaded object.
    """
    if isinstance(source, str):
        source = Source.from_import_path(source)

    # The types of some objects don't exist in the `builtin` module
    # so we need to manually handle it here
    if source.import_path == NoneTypeSource.import_path:
        return NoneType
    elif source.import_path == FunctionTypeSource.import_path:
        return FunctionType
    elif source.import_path == BuiltinFunctionTypeSource.import_path:
        return BuiltinFunctionType

    import_root = None
    if source.type == SourceType.CODE_REPOSITORY:
        source = CodeRepositorySource.model_validate(dict(source))
        _warn_about_potential_source_loading_issues(source=source)
        import_root = get_source_root()
    elif source.type == SourceType.DISTRIBUTION_PACKAGE:
        source = DistributionPackageSource.model_validate(dict(source))
        if source.version:
            current_package_version = _get_package_version(
                package_name=source.package_name
            )
            if current_package_version != source.version:
                logger.warning(
                    "The currently installed version `%s` of package `%s` "
                    "does not match the source version `%s`. This might lead "
                    "to unexpected behavior when using the source object `%s`.",
                    current_package_version,
                    source.package_name,
                    source.version,
                    source.import_path,
                )
    elif source.type == SourceType.NOTEBOOK:
        if Environment.in_notebook():
            # If we're in a notebook, we don't need to do anything as the
            # loading from the __main__ module should work just fine.
            pass
        else:
            notebook_source = NotebookSource.model_validate(dict(source))
            return _try_to_load_notebook_source(notebook_source)
    elif source.type in {SourceType.USER, SourceType.UNKNOWN}:
        # Unknown source might also refer to a user file, include source
        # root in python path just to be sure
        import_root = get_source_root()

    if _should_load_from_main_module(source):
        # This source points to the __main__ module of the current process.
        # If we were to load the module here, we would load the same python
        # file with a different module name, which would rerun all top-level
        # code. To avoid this, we instead load the source from the __main__
        # module which is already loaded.
        module = sys.modules["__main__"]
    else:
        module = _load_module(
            module_name=source.module, import_root=import_root
        )

    if source.attribute:
        obj = getattr(module, source.attribute)
    else:
        obj = module

    return obj

load_and_validate_class(source: Union[str, Source], expected_class: Type[Any]) -> Type[Any]

Loads a source class and validates its class.

Parameters:

Name	Type	Description	Default
source	Union[str, Source]	The source.	required
expected_class	Type[Any]	The class that the source should resolve to.	required

Raises:

Type	Description
TypeError	If the source does not resolve to the expected class.

Returns:

Type	Description
Type[Any]	The resolved source class.

Source code in src/zenml/utils/source_utils.py

def load_and_validate_class(
    source: Union[str, Source], expected_class: Type[Any]
) -> Type[Any]:
    """Loads a source class and validates its class.

    Args:
        source: The source.
        expected_class: The class that the source should resolve to.

    Raises:
        TypeError: If the source does not resolve to the expected class.

    Returns:
        The resolved source class.
    """
    obj = load(source)

    if isinstance(obj, type) and issubclass(obj, expected_class):
        return obj
    else:
        raise TypeError(
            f"Error while loading `{source}`. Expected class "
            f"{expected_class.__name__}, got {obj} instead."
        )

prepend_python_path(path: str) -> Iterator[None]

Context manager to temporarily prepend a path to the python path.

Parameters:

Name	Type	Description	Default
path	str	Path that will be prepended to sys.path for the duration of the context manager.	required

Yields:

Type	Description
None	None

Source code in src/zenml/utils/source_utils.py

@contextlib.contextmanager
def prepend_python_path(path: str) -> Iterator[None]:
    """Context manager to temporarily prepend a path to the python path.

    Args:
        path: Path that will be prepended to sys.path for the duration of
            the context manager.

    Yields:
        None
    """
    try:
        sys.path.insert(0, path)
        yield
    finally:
        sys.path.remove(path)

resolve(obj: Union[Type[Any], Callable[..., Any], ModuleType, FunctionType, BuiltinFunctionType, NoneType], skip_validation: bool = False) -> Source

Resolve an object.

Parameters:

Name	Type	Description	Default
obj	Union[Type[Any], Callable[..., Any], ModuleType, FunctionType, BuiltinFunctionType, NoneType]	The object to resolve.	required
skip_validation	bool	If True, the validation that the object exist in the module is skipped.	False

Raises:

Type	Description
RuntimeError	If the object can't be resolved.

Returns:

Type	Description
Source	The source of the resolved object.

Source code in src/zenml/utils/source_utils.py

def resolve(
    obj: Union[
        Type[Any],
        Callable[..., Any],
        ModuleType,
        FunctionType,
        BuiltinFunctionType,
        NoneType,
    ],
    skip_validation: bool = False,
) -> Source:
    """Resolve an object.

    Args:
        obj: The object to resolve.
        skip_validation: If True, the validation that the object exist in the
            module is skipped.

    Raises:
        RuntimeError: If the object can't be resolved.

    Returns:
        The source of the resolved object.
    """
    # The types of some objects don't exist in the `builtin` module
    # so we need to manually handle it here
    if obj is NoneType:
        return NoneTypeSource
    elif obj is FunctionType:
        return FunctionTypeSource
    elif obj is BuiltinFunctionType:
        return BuiltinFunctionTypeSource
    elif source := getattr(obj, ZENML_SOURCE_ATTRIBUTE_NAME, None):
        assert isinstance(source, Source)
        return source
    elif isinstance(obj, ModuleType):
        module = obj
        attribute_name = None
    else:
        module = sys.modules[obj.__module__]
        attribute_name = obj.__name__  # type: ignore[union-attr]

    if (
        not (skip_validation or getattr(obj, "_DOCS_BUILDING_MODE", False))
        and attribute_name
        and getattr(module, attribute_name, None) is not obj
    ):
        raise RuntimeError(
            f"Unable to resolve object `{obj}`. For the resolving to work, the "
            "class or function must be defined as top-level code (= it must "
            "get defined when importing the module) and not inside a function/"
            f"if-condition. Please make sure that your `{module.__name__}` "
            f"module has a top-level attribute `{attribute_name}` that "
            "holds the object you want to resolve."
        )

    module_name = module.__name__
    if module_name == "__main__":
        module_name = _resolve_module(module)

    source_type = get_source_type(module=module)

    if source_type == SourceType.USER:
        from zenml.utils import code_repository_utils

        local_repo_context = (
            code_repository_utils.find_active_code_repository()
        )

        if local_repo_context and not local_repo_context.has_local_changes:
            module_name = _resolve_module(module)

            source_root = get_source_root()
            subdir = PurePath(source_root).relative_to(local_repo_context.root)

            return CodeRepositorySource(
                repository_id=local_repo_context.code_repository.id,
                commit=local_repo_context.current_commit,
                subdirectory=subdir.as_posix(),
                module=module_name,
                attribute=attribute_name,
                type=SourceType.CODE_REPOSITORY,
            )

        module_name = _resolve_module(module)
    elif source_type == SourceType.DISTRIBUTION_PACKAGE:
        package_name = _get_package_for_module(module_name=module_name)
        if package_name:
            package_version = _get_package_version(package_name=package_name)
            return DistributionPackageSource(
                module=module_name,
                attribute=attribute_name,
                package_name=package_name,
                version=package_version,
                type=source_type,
            )
        else:
            # Fallback to an unknown source if we can't find the package
            source_type = SourceType.UNKNOWN
    elif source_type == SourceType.NOTEBOOK:
        source = NotebookSource(
            module="__main__",
            attribute=attribute_name,
            type=source_type,
        )

        if module_name in _notebook_modules:
            source.replacement_module = module_name
            source.artifact_store_id = _notebook_modules[module_name]
        elif cell_code := notebook_utils.load_notebook_cell_code(obj):
            replacement_module = (
                notebook_utils.compute_cell_replacement_module_name(
                    cell_code=cell_code
                )
            )
            source.replacement_module = replacement_module
            _resolved_notebook_sources[source.import_path] = cell_code

        return source

    return Source(
        module=module_name, attribute=attribute_name, type=source_type
    )

set_custom_source_root(source_root: Optional[str]) -> None

Sets a custom source root.

If set this has the highest priority and will always be used as the source root.

Parameters:

Name	Type	Description	Default
source_root	Optional[str]	The source root to use.	required

Source code in src/zenml/utils/source_utils.py

def set_custom_source_root(source_root: Optional[str]) -> None:
    """Sets a custom source root.

    If set this has the highest priority and will always be used as the source
    root.

    Args:
        source_root: The source root to use.
    """
    logger.debug("Setting custom source root: %s", source_root)
    global _CUSTOM_SOURCE_ROOT
    _CUSTOM_SOURCE_ROOT = source_root

validate_source_class(source: Union[Source, str], expected_class: Type[Any]) -> bool

Validates that a source resolves to a certain class.

Parameters:

Name	Type	Description	Default
source	Union[Source, str]	The source to validate.	required
expected_class	Type[Any]	The class that the source should resolve to.	required

Returns:

Type	Description
bool	True if the source resolves to the expected class, False otherwise.

Source code in src/zenml/utils/source_utils.py

def validate_source_class(
    source: Union[Source, str], expected_class: Type[Any]
) -> bool:
    """Validates that a source resolves to a certain class.

    Args:
        source: The source to validate.
        expected_class: The class that the source should resolve to.

    Returns:
        True if the source resolves to the expected class, False otherwise.
    """
    try:
        obj = load(source)
    except Exception:
        return False

    if isinstance(obj, type) and issubclass(obj, expected_class):
        return True
    else:
        return False

Modules