Artifacts

`zenml.artifacts` `special`

`artifact_config`

Artifact Config classes to support Model Control Plane feature.

`ArtifactConfig (BaseModel)`

Artifact configuration class.

Can be used in step definitions to define various artifact properties.

Examples:

@step
def my_step() -> Annotated[
    int, ArtifactConfig(
        name="my_artifact",  # override the default artifact name
        version=42,  # set a custom version
        tags=["tag1", "tag2"],  # set custom tags
    )
]:
    return ...

Attributes:

Name	Type	Description
`name`	`Optional[str]`	The name of the artifact.
`version`	`Union[str, int]`	The version of the artifact.
`tags`	`Optional[List[str]]`	The tags of the artifact.
`run_metadata`	`Optional[Dict[str, Union[str, int, float, bool, Dict[Any, Any], List[Any], Set[Any], Tuple[Any, ...], zenml.metadata.metadata_types.Uri, zenml.metadata.metadata_types.Path, zenml.metadata.metadata_types.DType, zenml.metadata.metadata_types.StorageSize]]]`	Metadata to add to the artifact.
`is_model_artifact`	`bool`	Whether the artifact is a model artifact.
`is_deployment_artifact`	`bool`	Whether the artifact is a deployment artifact.

Source code in zenml/artifacts/artifact_config.py

class ArtifactConfig(BaseModel):
    """Artifact configuration class.

    Can be used in step definitions to define various artifact properties.

    Example:
    ```python
    @step
    def my_step() -> Annotated[
        int, ArtifactConfig(
            name="my_artifact",  # override the default artifact name
            version=42,  # set a custom version
            tags=["tag1", "tag2"],  # set custom tags
        )
    ]:
        return ...
    ```

    Attributes:
        name: The name of the artifact.
        version: The version of the artifact.
        tags: The tags of the artifact.
        run_metadata: Metadata to add to the artifact.
        is_model_artifact: Whether the artifact is a model artifact.
        is_deployment_artifact: Whether the artifact is a deployment artifact.
    """

    name: Optional[str] = None
    version: Optional[Union[str, int]] = Field(
        default=None, union_mode="smart"
    )
    tags: Optional[List[str]] = None
    run_metadata: Optional[Dict[str, MetadataType]] = None

    is_model_artifact: bool = False
    is_deployment_artifact: bool = False

    @model_validator(mode="before")
    @classmethod
    @before_validator_handler
    def _remove_old_attributes(cls, data: Dict[str, Any]) -> Dict[str, Any]:
        """Remove old attributes that are not used anymore.

        Args:
            data: The model data.

        Returns:
            Model data without the removed attributes.
        """
        model_name = data.pop("model_name", None)
        model_version = data.pop("model_version", None)

        if model_name or model_version:
            logger.warning(
                "Specifying a model name or version for a step output "
                "artifact is not supported anymore."
            )

        return data

`external_artifact`

External artifact definition.

`ExternalArtifact (ExternalArtifactConfiguration)`

External artifacts can be used to provide values as input to ZenML steps.

ZenML steps accept either artifacts (=outputs of other steps), parameters (raw, JSON serializable values) or external artifacts. External artifacts can be used to provide any value as input to a step without needing to write an additional step that returns this value.

The external artifact needs to have a value associated with it that will be uploaded to the artifact store.

Parameters:

Name	Description	Default
`value`	The artifact value.	required
`materializer`	The materializer to use for saving the artifact value to the artifact store. Only used when `value` is provided.	required
`store_artifact_metadata`	Whether metadata for the artifact should be stored. Only used when `value` is provided.	required
`store_artifact_visualizations`	Whether visualizations for the artifact should be stored. Only used when `value` is provided.	required

Examples:

from zenml import step, pipeline
from zenml.artifacts.external_artifact import ExternalArtifact
import numpy as np

@step
def my_step(value: np.ndarray) -> None:
  print(value)

my_array = np.array([1, 2, 3])

@pipeline
def my_pipeline():
  my_step(value=ExternalArtifact(my_array))

Source code in zenml/artifacts/external_artifact.py

class ExternalArtifact(ExternalArtifactConfiguration):
    """External artifacts can be used to provide values as input to ZenML steps.

    ZenML steps accept either artifacts (=outputs of other steps), parameters
    (raw, JSON serializable values) or external artifacts. External artifacts
    can be used to provide any value as input to a step without needing to
    write an additional step that returns this value.

    The external artifact needs to have a value associated with it
    that will be uploaded to the artifact store.

    Args:
        value: The artifact value.
        materializer: The materializer to use for saving the artifact value
            to the artifact store. Only used when `value` is provided.
        store_artifact_metadata: Whether metadata for the artifact should
            be stored. Only used when `value` is provided.
        store_artifact_visualizations: Whether visualizations for the
            artifact should be stored. Only used when `value` is provided.

    Example:
    ```
    from zenml import step, pipeline
    from zenml.artifacts.external_artifact import ExternalArtifact
    import numpy as np

    @step
    def my_step(value: np.ndarray) -> None:
      print(value)

    my_array = np.array([1, 2, 3])

    @pipeline
    def my_pipeline():
      my_step(value=ExternalArtifact(my_array))
    ```
    """

    value: Optional[Any] = None
    materializer: Optional[MaterializerClassOrSource] = Field(
        default=None, union_mode="left_to_right"
    )
    store_artifact_metadata: bool = True
    store_artifact_visualizations: bool = True

    @model_validator(mode="after")
    def external_artifact_validator(self) -> "ExternalArtifact":
        """Model validator for the external artifact.

        Raises:
            ValueError: If an ID was set.

        Returns:
            The validated instance.
        """
        if self.id:
            raise ValueError(
                "External artifacts can only be initialized with a value."
            )

        return self

    def upload_by_value(self) -> UUID:
        """Uploads the artifact by value.

        Returns:
            The uploaded artifact ID.
        """
        from zenml.artifacts.utils import save_artifact

        artifact_name = f"external_{uuid4()}"
        uri = os.path.join("external_artifacts", artifact_name)
        logger.info("Uploading external artifact to '%s'.", uri)

        artifact = save_artifact(
            name=artifact_name,
            data=self.value,
            extract_metadata=self.store_artifact_metadata,
            include_visualizations=self.store_artifact_visualizations,
            materializer=self.materializer,
            uri=uri,
            has_custom_name=False,
            save_type=ArtifactSaveType.EXTERNAL,
        )

        # To avoid duplicate uploads, switch to referencing the uploaded
        # artifact by ID
        self.id = artifact.id
        self.value = None

        logger.info("Finished uploading external artifact %s.", self.id)
        return self.id

    @property
    def config(self) -> ExternalArtifactConfiguration:
        """Returns the lightweight config without hard for JSON properties.

        Returns:
            The config object to be evaluated in runtime by step interface.
        """
        return ExternalArtifactConfiguration(
            id=self.id,
        )

`config: ExternalArtifactConfiguration` `property` `readonly`

Returns the lightweight config without hard for JSON properties.

Returns:

Type	Description
`ExternalArtifactConfiguration`	The config object to be evaluated in runtime by step interface.

`external_artifact_validator(self)`

Model validator for the external artifact.

Exceptions:

Type	Description
`ValueError`	If an ID was set.

Returns:

Type	Description
`ExternalArtifact`	The validated instance.

Source code in zenml/artifacts/external_artifact.py

@model_validator(mode="after")
def external_artifact_validator(self) -> "ExternalArtifact":
    """Model validator for the external artifact.

    Raises:
        ValueError: If an ID was set.

    Returns:
        The validated instance.
    """
    if self.id:
        raise ValueError(
            "External artifacts can only be initialized with a value."
        )

    return self

`upload_by_value(self)`

Uploads the artifact by value.

Returns:

Type	Description
`UUID`	The uploaded artifact ID.

Source code in zenml/artifacts/external_artifact.py

def upload_by_value(self) -> UUID:
    """Uploads the artifact by value.

    Returns:
        The uploaded artifact ID.
    """
    from zenml.artifacts.utils import save_artifact

    artifact_name = f"external_{uuid4()}"
    uri = os.path.join("external_artifacts", artifact_name)
    logger.info("Uploading external artifact to '%s'.", uri)

    artifact = save_artifact(
        name=artifact_name,
        data=self.value,
        extract_metadata=self.store_artifact_metadata,
        include_visualizations=self.store_artifact_visualizations,
        materializer=self.materializer,
        uri=uri,
        has_custom_name=False,
        save_type=ArtifactSaveType.EXTERNAL,
    )

    # To avoid duplicate uploads, switch to referencing the uploaded
    # artifact by ID
    self.id = artifact.id
    self.value = None

    logger.info("Finished uploading external artifact %s.", self.id)
    return self.id

`external_artifact_config`

External artifact definition.

`ExternalArtifactConfiguration (BaseModel)`

External artifact configuration.

Lightweight class to pass in the steps for runtime inference.

Source code in zenml/artifacts/external_artifact_config.py

class ExternalArtifactConfiguration(BaseModel):
    """External artifact configuration.

    Lightweight class to pass in the steps for runtime inference.
    """

    id: Optional[UUID] = None

    @model_validator(mode="before")
    @classmethod
    @before_validator_handler
    def _remove_old_attributes(cls, data: Dict[str, Any]) -> Dict[str, Any]:
        """Remove old attributes that are not used anymore.

        Args:
            data: The model data.

        Returns:
            Model data without the removed attributes.
        """
        data.pop("name", None)
        data.pop("version", None)
        data.pop("model", None)
        return data

    def get_artifact_version_id(self) -> UUID:
        """Get the artifact.

        Returns:
            The artifact ID.

        Raises:
            RuntimeError: If the artifact store of the referenced artifact
                is not the same as the one in the active stack.
            RuntimeError: If neither the ID nor the name of the artifact was
                provided.
        """
        from zenml.client import Client

        client = Client()

        if self.id:
            response = client.get_artifact_version(self.id)
        else:
            raise RuntimeError(
                "The ID of the artifact must be provided. "
                "- If you created this ExternalArtifact from a value, please "
                "ensure that `upload_by_value` was called before trying to "
                "fetch the artifact ID.\n- If you specified an artifact name "
                "or model name for this external artifact, this functionality "
                "was removed from the ExternalArtifact class. Use Client "
                "methods instead to dynamically fetch an artifact via name or "
                "from a model instead."
            )

        artifact_store_id = client.active_stack.artifact_store.id
        if response.artifact_store_id != artifact_store_id:
            raise RuntimeError(
                f"The artifact {response.name} (ID: {response.id}) "
                "referenced by an external artifact is not stored in the "
                "artifact store of the active stack. This will lead to "
                "issues loading the artifact. Please make sure to only "
                "reference artifact versions stored in your active artifact "
                "store."
            )

        return self.id

`get_artifact_version_id(self)`

Get the artifact.

Returns:

Type	Description
`UUID`	The artifact ID.

Exceptions:

Type	Description
`RuntimeError`	If the artifact store of the referenced artifact is not the same as the one in the active stack.
`RuntimeError`	If neither the ID nor the name of the artifact was provided.

Source code in zenml/artifacts/external_artifact_config.py

def get_artifact_version_id(self) -> UUID:
    """Get the artifact.

    Returns:
        The artifact ID.

    Raises:
        RuntimeError: If the artifact store of the referenced artifact
            is not the same as the one in the active stack.
        RuntimeError: If neither the ID nor the name of the artifact was
            provided.
    """
    from zenml.client import Client

    client = Client()

    if self.id:
        response = client.get_artifact_version(self.id)
    else:
        raise RuntimeError(
            "The ID of the artifact must be provided. "
            "- If you created this ExternalArtifact from a value, please "
            "ensure that `upload_by_value` was called before trying to "
            "fetch the artifact ID.\n- If you specified an artifact name "
            "or model name for this external artifact, this functionality "
            "was removed from the ExternalArtifact class. Use Client "
            "methods instead to dynamically fetch an artifact via name or "
            "from a model instead."
        )

    artifact_store_id = client.active_stack.artifact_store.id
    if response.artifact_store_id != artifact_store_id:
        raise RuntimeError(
            f"The artifact {response.name} (ID: {response.id}) "
            "referenced by an external artifact is not stored in the "
            "artifact store of the active stack. This will lead to "
            "issues loading the artifact. Please make sure to only "
            "reference artifact versions stored in your active artifact "
            "store."
        )

    return self.id

`preexisting_data_materializer`

Only-load materializer for directories.

`PreexistingDataMaterializer (BaseMaterializer)`

Materializer to load directories from the artifact store.

This materializer is very special, since it do not implement save logic at all. The save of the data to some URI inside the artifact store shall happen outside and is in user's responsibility.

This materializer solely supports the register_artifact function.

Source code in zenml/artifacts/preexisting_data_materializer.py

class PreexistingDataMaterializer(BaseMaterializer):
    """Materializer to load directories from the artifact store.

    This materializer is very special, since it do not implement save
    logic at all. The save of the data to some URI inside the artifact store
    shall happen outside and is in user's responsibility.

    This materializer solely supports the `register_artifact` function.
    """

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (Path,)
    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA
    SKIP_REGISTRATION: ClassVar[bool] = True

    def load(self, data_type: Type[Any]) -> Any:
        """Copy the artifact file(s) to a local temp directory.

        Args:
            data_type: Unused.

        Returns:
            Path to the local directory that contains the artifact files.
        """
        with self.get_temporary_directory(delete_at_exit=False) as temp_dir:
            if fileio.isdir(self.uri):
                self._copy_directory(src=self.uri, dst=temp_dir)
                return Path(temp_dir)
            else:
                dst = os.path.join(temp_dir, os.path.split(self.uri)[-1])
                fileio.copy(src=self.uri, dst=dst)
                return Path(dst)

    def save(self, data: Any) -> None:
        """Store the directory in the artifact store.

        Args:
            data: Path to a local directory to store.

        Raises:
            NotImplementedError: Always
        """
        raise NotImplementedError(
            "`PreexistingDataMaterializer` can only be used in the "
            "context of `register_artifact` function, "
            "which expects the data to be already properly saved in "
            "the Artifact Store, thus `save` logic makes no sense here."
        )

    @staticmethod
    def _copy_directory(src: str, dst: str) -> None:
        """Recursively copy a directory.

        Args:
            src: The directory to copy.
            dst: Where to copy the directory to.
        """
        for src_dir, _, files in fileio.walk(src):
            src_dir_ = str(src_dir)
            dst_dir = str(os.path.join(dst, os.path.relpath(src_dir_, src)))
            fileio.makedirs(dst_dir)

            for file in files:
                file_ = str(file)
                src_file = os.path.join(src_dir_, file_)
                dst_file = os.path.join(dst_dir, file_)
                fileio.copy(src_file, dst_file)

`load(self, data_type)`

Copy the artifact file(s) to a local temp directory.

Parameters:

Name	Type	Description	Default
`data_type`	`Type[Any]`	Unused.	required

Returns:

Type	Description
`Any`	Path to the local directory that contains the artifact files.

Source code in zenml/artifacts/preexisting_data_materializer.py

def load(self, data_type: Type[Any]) -> Any:
    """Copy the artifact file(s) to a local temp directory.

    Args:
        data_type: Unused.

    Returns:
        Path to the local directory that contains the artifact files.
    """
    with self.get_temporary_directory(delete_at_exit=False) as temp_dir:
        if fileio.isdir(self.uri):
            self._copy_directory(src=self.uri, dst=temp_dir)
            return Path(temp_dir)
        else:
            dst = os.path.join(temp_dir, os.path.split(self.uri)[-1])
            fileio.copy(src=self.uri, dst=dst)
            return Path(dst)

`save(self, data)`

Store the directory in the artifact store.

Parameters:

Name	Type	Description	Default
`data`	`Any`	Path to a local directory to store.	required

Exceptions:

Type	Description
`NotImplementedError`	Always

Source code in zenml/artifacts/preexisting_data_materializer.py

def save(self, data: Any) -> None:
    """Store the directory in the artifact store.

    Args:
        data: Path to a local directory to store.

    Raises:
        NotImplementedError: Always
    """
    raise NotImplementedError(
        "`PreexistingDataMaterializer` can only be used in the "
        "context of `register_artifact` function, "
        "which expects the data to be already properly saved in "
        "the Artifact Store, thus `save` logic makes no sense here."
    )

`unmaterialized_artifact`

Unmaterialized artifact class.

`UnmaterializedArtifact (ArtifactVersionResponse)`

Unmaterialized artifact class.

Typing a step input to have this type will cause ZenML to not materialize the artifact. This is useful for steps that need to access the artifact metadata instead of the actual artifact data.

Usage example:

from zenml import step
from zenml.artifacts.unmaterialized_artifact import UnmaterializedArtifact

@step
def my_step(input_artifact: UnmaterializedArtifact):
    print(input_artifact.uri)

Source code in zenml/artifacts/unmaterialized_artifact.py

class UnmaterializedArtifact(ArtifactVersionResponse):
    """Unmaterialized artifact class.

    Typing a step input to have this type will cause ZenML to not materialize
    the artifact. This is useful for steps that need to access the artifact
    metadata instead of the actual artifact data.

    Usage example:

    ```python
    from zenml import step
    from zenml.artifacts.unmaterialized_artifact import UnmaterializedArtifact

    @step
    def my_step(input_artifact: UnmaterializedArtifact):
        print(input_artifact.uri)
    ```
    """

`model_post_init(/, self, context)`

We need to both initialize private attributes and call the user-defined model_post_init method.

Source code in zenml/artifacts/unmaterialized_artifact.py

def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None:
    """We need to both initialize private attributes and call the user-defined model_post_init
    method.
    """
    init_private_attributes(self, context)
    original_model_post_init(self, context)

`utils`

Utility functions for handling artifacts.

`download_artifact_files_from_response(artifact, path, overwrite=False)`

Download the given artifact into a file.

Parameters:

Name	Type	Description	Default
`artifact`	`ArtifactVersionResponse`	The artifact to download.	required
`path`	`str`	The path to which to download the artifact.	required
`overwrite`	`bool`	Whether to overwrite the file if it already exists.	`False`

Exceptions:

Type	Description
`FileExistsError`	If the file already exists and `overwrite` is `False`.
`Exception`	If the artifact could not be downloaded to the zip file.

Source code in zenml/artifacts/utils.py

def download_artifact_files_from_response(
    artifact: "ArtifactVersionResponse",
    path: str,
    overwrite: bool = False,
) -> None:
    """Download the given artifact into a file.

    Args:
        artifact: The artifact to download.
        path: The path to which to download the artifact.
        overwrite: Whether to overwrite the file if it already exists.

    Raises:
        FileExistsError: If the file already exists and `overwrite` is `False`.
        Exception: If the artifact could not be downloaded to the zip file.
    """
    if not overwrite and fileio.exists(path):
        raise FileExistsError(
            f"File '{path}' already exists and `overwrite` is set to `False`."
        )

    artifact_store = _get_artifact_store_from_response_or_from_active_stack(
        artifact=artifact
    )

    if filepaths := artifact_store.listdir(artifact.uri):
        # save a zipfile to 'path' containing all the files
        # in 'filepaths' with compression
        try:
            with zipfile.ZipFile(path, "w", zipfile.ZIP_DEFLATED) as zipf:
                for file in filepaths:
                    # Ensure 'file' is a string for path operations
                    # and ZIP entry naming
                    file_str = (
                        file.decode() if isinstance(file, bytes) else file
                    )
                    file_path = str(Path(artifact.uri) / file_str)
                    with artifact_store.open(
                        name=file_path, mode="rb"
                    ) as store_file:
                        # Use a loop to read and write chunks of the file
                        # instead of reading the entire file into memory
                        CHUNK_SIZE = 8192
                        while True:
                            if file_content := store_file.read(CHUNK_SIZE):
                                zipf.writestr(file_str, file_content)
                            else:
                                break
        except Exception as e:
            logger.error(
                f"Failed to save artifact '{artifact.id}' to zip file "
                f" '{path}': {e}"
            )
            raise

`get_artifacts_versions_of_pipeline_run(pipeline_run, only_produced=False)`

Get all artifact versions produced during a pipeline run.

Parameters:

Name	Type	Description	Default
`pipeline_run`	`PipelineRunResponse`	The pipeline run.	required
`only_produced`	`bool`	If only artifact versions produced by the pipeline run should be returned or also cached artifact versions.	`False`

Returns:

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

Source code in zenml/artifacts/utils.py

def get_artifacts_versions_of_pipeline_run(
    pipeline_run: "PipelineRunResponse", only_produced: bool = False
) -> List["ArtifactVersionResponse"]:
    """Get all artifact versions produced during a pipeline run.

    Args:
        pipeline_run: The pipeline run.
        only_produced: If only artifact versions produced by the pipeline run
            should be returned or also cached artifact versions.

    Returns:
        A list of all artifact versions produced during the pipeline run.
    """
    artifact_versions: List["ArtifactVersionResponse"] = []
    for step in pipeline_run.steps.values():
        if not only_produced or step.status == ExecutionStatus.COMPLETED:
            for output in step.outputs.values():
                artifact_versions.extend(output)
    return artifact_versions

`get_producer_step_of_artifact(artifact)`

Get the step run that produced a given artifact.

Parameters:

Name	Type	Description	Default
`artifact`	`ArtifactVersionResponse`	The artifact.	required

Returns:

Type	Description
`StepRunResponse`	The step run that produced the artifact.

Exceptions:

Type	Description
`RuntimeError`	If the run that created the artifact no longer exists.

Source code in zenml/artifacts/utils.py

def get_producer_step_of_artifact(
    artifact: "ArtifactVersionResponse",
) -> "StepRunResponse":
    """Get the step run that produced a given artifact.

    Args:
        artifact: The artifact.

    Returns:
        The step run that produced the artifact.

    Raises:
        RuntimeError: If the run that created the artifact no longer exists.
    """
    if not artifact.producer_step_run_id:
        raise RuntimeError(
            f"The run that produced the artifact with id '{artifact.id}' no "
            "longer exists. This can happen if the run was deleted."
        )
    return Client().get_run_step(artifact.producer_step_run_id)

`load_artifact(name_or_id, version=None)`

Load an artifact.

Parameters:

Name	Type	Description	Default
`name_or_id`	`Union[str, uuid.UUID]`	The name or ID of the artifact to load.	required
`version`	`Optional[str]`	The version of the artifact to load, if `name_or_id` is a name. If not provided, the latest version will be loaded.	`None`

Returns:

Type	Description
`Any`	The loaded artifact.

Source code in zenml/artifacts/utils.py

def load_artifact(
    name_or_id: Union[str, UUID],
    version: Optional[str] = None,
) -> Any:
    """Load an artifact.

    Args:
        name_or_id: The name or ID of the artifact to load.
        version: The version of the artifact to load, if `name_or_id` is a
            name. If not provided, the latest version will be loaded.

    Returns:
        The loaded artifact.
    """
    artifact = Client().get_artifact_version(name_or_id, version)
    return load_artifact_from_response(artifact)

`load_artifact_from_response(artifact)`

Load the given artifact into memory.

Parameters:

Name	Type	Description	Default
`artifact`	`ArtifactVersionResponse`	The artifact to load.	required

Returns:

Type	Description
`Any`	The artifact loaded into memory.

Source code in zenml/artifacts/utils.py

def load_artifact_from_response(artifact: "ArtifactVersionResponse") -> Any:
    """Load the given artifact into memory.

    Args:
        artifact: The artifact to load.

    Returns:
        The artifact loaded into memory.
    """
    artifact_store = _get_artifact_store_from_response_or_from_active_stack(
        artifact=artifact
    )

    return _load_artifact_from_uri(
        materializer=artifact.materializer,
        data_type=artifact.data_type,
        uri=artifact.uri,
        artifact_store=artifact_store,
    )

`load_artifact_visualization(artifact, index=0, zen_store=None, encode_image=False)`

Load a visualization of the given artifact.

Parameters:

Name	Type	Description	Default
`artifact`	`ArtifactVersionResponse`	The artifact to visualize.	required
`index`	`int`	The index of the visualization to load.	`0`
`zen_store`	`Optional[BaseZenStore]`	The ZenStore to use for finding the artifact store. If not provided, the client's ZenStore will be used.	`None`
`encode_image`	`bool`	Whether to base64 encode image visualizations.	`False`

Returns:

Type	Description
`LoadedVisualization`	The loaded visualization.

Exceptions:

Type	Description
`DoesNotExistException`	If the artifact does not have the requested visualization or if the visualization was not found in the artifact store.

Source code in zenml/artifacts/utils.py

def load_artifact_visualization(
    artifact: "ArtifactVersionResponse",
    index: int = 0,
    zen_store: Optional["BaseZenStore"] = None,
    encode_image: bool = False,
) -> LoadedVisualization:
    """Load a visualization of the given artifact.

    Args:
        artifact: The artifact to visualize.
        index: The index of the visualization to load.
        zen_store: The ZenStore to use for finding the artifact store. If not
            provided, the client's ZenStore will be used.
        encode_image: Whether to base64 encode image visualizations.

    Returns:
        The loaded visualization.

    Raises:
        DoesNotExistException: If the artifact does not have the requested
            visualization or if the visualization was not found in the artifact
            store.
    """
    # Get the visualization to load
    if not artifact.visualizations:
        raise DoesNotExistException(
            f"Artifact '{artifact.id}' has no visualizations."
        )
    if index < 0 or index >= len(artifact.visualizations):
        raise DoesNotExistException(
            f"Artifact '{artifact.id}' only has {len(artifact.visualizations)} "
            f"visualizations, but index {index} was requested."
        )
    visualization = artifact.visualizations[index]

    # Load the visualization from the artifact's artifact store
    if not artifact.artifact_store_id:
        raise DoesNotExistException(
            f"Artifact '{artifact.id}' cannot be visualized because the "
            "underlying artifact store was deleted."
        )
    artifact_store = _load_artifact_store(
        artifact_store_id=artifact.artifact_store_id, zen_store=zen_store
    )
    try:
        mode = "rb" if visualization.type == VisualizationType.IMAGE else "r"
        value = _load_file_from_artifact_store(
            uri=visualization.uri,
            artifact_store=artifact_store,
            mode=mode,
        )

        # Encode image visualizations if requested
        if visualization.type == VisualizationType.IMAGE and encode_image:
            value = base64.b64encode(bytes(value))

        return LoadedVisualization(type=visualization.type, value=value)
    finally:
        artifact_store.cleanup()

`load_model_from_metadata(model_uri)`

Load a zenml model artifact from a json file.

This function is used to load information from a Yaml file that was created by the save_model_metadata function. The information in the Yaml file is used to load the model into memory in the inference environment.

Parameters:

Name	Type	Description	Default
`model_uri`	`str`	the artifact to extract the metadata from.	required

Returns:

Type	Description
`Any`	The ML model object loaded into memory.

Source code in zenml/artifacts/utils.py

def load_model_from_metadata(model_uri: str) -> Any:
    """Load a zenml model artifact from a json file.

    This function is used to load information from a Yaml file that was created
    by the save_model_metadata function. The information in the Yaml file is
    used to load the model into memory in the inference environment.

    Args:
        model_uri: the artifact to extract the metadata from.

    Returns:
        The ML model object loaded into memory.
    """
    # Load the model from its metadata
    artifact_versions_by_uri = Client().list_artifact_versions(uri=model_uri)
    if artifact_versions_by_uri.total == 1:
        artifact_store = (
            _get_artifact_store_from_response_or_from_active_stack(
                artifact_versions_by_uri.items[0]
            )
        )
    else:
        artifact_store = Client().active_stack.artifact_store

    with artifact_store.open(
        os.path.join(model_uri, MODEL_METADATA_YAML_FILE_NAME), "r"
    ) as f:
        metadata = read_yaml(f.name)
    data_type = metadata["datatype"]
    materializer = metadata["materializer"]
    model = _load_artifact_from_uri(
        materializer=materializer,
        data_type=data_type,
        uri=model_uri,
        artifact_store=artifact_store,
    )

    # Switch to eval mode if the model is a torch model
    try:
        import torch.nn as nn

        if isinstance(model, nn.Module):
            model.eval()
    except ImportError:
        pass

    return model

`log_artifact_metadata(metadata, artifact_name=None, artifact_version=None)`

Log artifact metadata.

This function can be used to log metadata for either existing artifact versions or artifact versions that are newly created in the same step.

Parameters:

Name	Type	Description	Default
`metadata`	`Dict[str, MetadataType]`	The metadata to log.	required
`artifact_name`	`Optional[str]`	The name of the artifact to log metadata for. Can be omitted when being called inside a step with only one output.	`None`
`artifact_version`	`Optional[str]`	The version of the artifact to log metadata for. If not provided, when being called inside a step that produces an artifact named `artifact_name`, the metadata will be associated to the corresponding newly created artifact. Or, if not provided when being called outside a step, or in a step that does not produce any artifact named `artifact_name`, the metadata will be associated to the latest version of that artifact.	`None`

Exceptions:

Type	Description
`ValueError`	If no artifact name is provided and the function is not called inside a step with a single output, or, if neither an artifact nor an output with the given name exists.

Source code in zenml/artifacts/utils.py

def log_artifact_metadata(
    metadata: Dict[str, "MetadataType"],
    artifact_name: Optional[str] = None,
    artifact_version: Optional[str] = None,
) -> None:
    """Log artifact metadata.

    This function can be used to log metadata for either existing artifact
    versions or artifact versions that are newly created in the same step.

    Args:
        metadata: The metadata to log.
        artifact_name: The name of the artifact to log metadata for. Can
            be omitted when being called inside a step with only one output.
        artifact_version: The version of the artifact to log metadata for. If
            not provided, when being called inside a step that produces an
            artifact named `artifact_name`, the metadata will be associated to
            the corresponding newly created artifact. Or, if not provided when
            being called outside a step, or in a step that does not produce
            any artifact named `artifact_name`, the metadata will be associated
            to the latest version of that artifact.

    Raises:
        ValueError: If no artifact name is provided and the function is not
            called inside a step with a single output, or, if neither an
            artifact nor an output with the given name exists.
    """
    logger.warning(
        "The `log_artifact_metadata` function is deprecated and will soon be "
        "removed. Please use `log_metadata` instead."
    )
    try:
        step_context = get_step_context()
        in_step_outputs = (artifact_name in step_context._outputs) or (
            not artifact_name and len(step_context._outputs) == 1
        )
    except RuntimeError:
        step_context = None
        in_step_outputs = False

    if not step_context or not in_step_outputs or artifact_version:
        if not artifact_name:
            raise ValueError(
                "Artifact name must be provided unless the function is called "
                "inside a step with a single output."
            )
        client = Client()
        response = client.get_artifact_version(artifact_name, artifact_version)
        client.create_run_metadata(
            metadata=metadata,
            resource_id=response.id,
            resource_type=MetadataResourceTypes.ARTIFACT_VERSION,
        )

    else:
        try:
            step_context.add_output_metadata(
                metadata=metadata, output_name=artifact_name
            )
        except StepContextError as e:
            raise ValueError(e)

`register_artifact(folder_or_file_uri, name, version=None, tags=None, has_custom_name=True, is_model_artifact=False, is_deployment_artifact=False, artifact_metadata={})`

Register existing data stored in the artifact store as a ZenML Artifact.

Parameters:

Name	Type	Description	Default
`folder_or_file_uri`	`str`	The full URI within the artifact store to the folder or to the file.	required
`name`	`str`	The name of the artifact.	required
`version`	`Union[int, str]`	The version of the artifact. If not provided, a new auto-incremented version will be used.	`None`
`tags`	`Optional[List[str]]`	Tags to associate with the artifact.	`None`
`has_custom_name`	`bool`	If the artifact name is custom and should be listed in the dashboard "Artifacts" tab.	`True`
`is_model_artifact`	`bool`	If the artifact is a model artifact.	`False`
`is_deployment_artifact`	`bool`	If the artifact is a deployment artifact.	`False`
`artifact_metadata`	`Dict[str, MetadataType]`	Metadata dictionary to attach to the artifact version.	`{}`

Returns:

Type	Description
`ArtifactVersionResponse`	The saved artifact response.

Exceptions:

Type	Description
`FileNotFoundError`	If the folder URI is outside of the artifact store bounds.

Source code in zenml/artifacts/utils.py

def register_artifact(
    folder_or_file_uri: str,
    name: str,
    version: Optional[Union[int, str]] = None,
    tags: Optional[List[str]] = None,
    has_custom_name: bool = True,
    is_model_artifact: bool = False,
    is_deployment_artifact: bool = False,
    artifact_metadata: Dict[str, "MetadataType"] = {},
) -> "ArtifactVersionResponse":
    """Register existing data stored in the artifact store as a ZenML Artifact.

    Args:
        folder_or_file_uri: The full URI within the artifact store to the folder
            or to the file.
        name: The name of the artifact.
        version: The version of the artifact. If not provided, a new
            auto-incremented version will be used.
        tags: Tags to associate with the artifact.
        has_custom_name: If the artifact name is custom and should be listed in
            the dashboard "Artifacts" tab.
        is_model_artifact: If the artifact is a model artifact.
        is_deployment_artifact: If the artifact is a deployment artifact.
        artifact_metadata: Metadata dictionary to attach to the artifact version.

    Returns:
        The saved artifact response.

    Raises:
        FileNotFoundError: If the folder URI is outside of the artifact store
            bounds.
    """
    client = Client()

    # Get the current artifact store
    artifact_store = client.active_stack.artifact_store

    if not folder_or_file_uri.startswith(artifact_store.path):
        raise FileNotFoundError(
            f"Folder `{folder_or_file_uri}` is outside of "
            f"artifact store bounds `{artifact_store.path}`"
        )

    _check_if_artifact_with_given_uri_already_registered(
        artifact_store=artifact_store,
        uri=folder_or_file_uri,
        name=name,
    )

    artifact_version_request = ArtifactVersionRequest(
        artifact_name=name,
        version=version,
        tags=tags,
        type=ArtifactType.DATA,
        save_type=ArtifactSaveType.PREEXISTING,
        uri=folder_or_file_uri,
        materializer=source_utils.resolve(PreexistingDataMaterializer),
        data_type=source_utils.resolve(Path),
        user=Client().active_user.id,
        workspace=Client().active_workspace.id,
        artifact_store_id=artifact_store.id,
        has_custom_name=has_custom_name,
        metadata=validate_metadata(artifact_metadata)
        if artifact_metadata
        else None,
    )
    artifact_version = client.zen_store.create_artifact_version(
        artifact_version=artifact_version_request
    )

    _link_artifact_version_to_the_step_and_model(
        artifact_version=artifact_version,
        is_model_artifact=is_model_artifact,
        is_deployment_artifact=is_deployment_artifact,
    )

    return artifact_version

`save_artifact(data, name, version=None, tags=None, extract_metadata=True, include_visualizations=True, user_metadata=None, materializer=None, uri=None, is_model_artifact=False, is_deployment_artifact=False, save_type=<ArtifactSaveType.MANUAL: 'manual'>, has_custom_name=True)`

Upload and publish an artifact.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the artifact.	required
`data`	`Any`	The artifact data.	required
`version`	`Union[int, str]`	The version of the artifact. If not provided, a new auto-incremented version will be used.	`None`
`tags`	`Optional[List[str]]`	Tags to associate with the artifact.	`None`
`extract_metadata`	`bool`	If artifact metadata should be extracted and returned.	`True`
`include_visualizations`	`bool`	If artifact visualizations should be generated.	`True`
`user_metadata`	`Optional[Dict[str, MetadataType]]`	User-provided metadata to store with the artifact.	`None`
`materializer`	`Optional[MaterializerClassOrSource]`	The materializer to use for saving the artifact to the artifact store.	`None`
`uri`	`Optional[str]`	The URI within the artifact store to upload the artifact to. If not provided, the artifact will be uploaded to `custom_artifacts/{name}/{version}`.	`None`
`is_model_artifact`	`bool`	If the artifact is a model artifact.	`False`
`is_deployment_artifact`	`bool`	If the artifact is a deployment artifact.	`False`
`save_type`	`ArtifactSaveType`	The type of save operation that created the artifact version.	`<ArtifactSaveType.MANUAL: 'manual'>`
`has_custom_name`	`bool`	If the artifact name is custom and should be listed in the dashboard "Artifacts" tab.	`True`

Returns:

Type	Description
`ArtifactVersionResponse`	The saved artifact response.

Source code in zenml/artifacts/utils.py

def save_artifact(
    data: Any,
    name: str,
    version: Optional[Union[int, str]] = None,
    tags: Optional[List[str]] = None,
    extract_metadata: bool = True,
    include_visualizations: bool = True,
    user_metadata: Optional[Dict[str, "MetadataType"]] = None,
    materializer: Optional["MaterializerClassOrSource"] = None,
    uri: Optional[str] = None,
    is_model_artifact: bool = False,
    is_deployment_artifact: bool = False,
    # TODO: remove these once external artifact does not use this function anymore
    save_type: ArtifactSaveType = ArtifactSaveType.MANUAL,
    has_custom_name: bool = True,
) -> "ArtifactVersionResponse":
    """Upload and publish an artifact.

    Args:
        name: The name of the artifact.
        data: The artifact data.
        version: The version of the artifact. If not provided, a new
            auto-incremented version will be used.
        tags: Tags to associate with the artifact.
        extract_metadata: If artifact metadata should be extracted and returned.
        include_visualizations: If artifact visualizations should be generated.
        user_metadata: User-provided metadata to store with the artifact.
        materializer: The materializer to use for saving the artifact to the
            artifact store.
        uri: The URI within the artifact store to upload the artifact
            to. If not provided, the artifact will be uploaded to
            `custom_artifacts/{name}/{version}`.
        is_model_artifact: If the artifact is a model artifact.
        is_deployment_artifact: If the artifact is a deployment artifact.
        save_type: The type of save operation that created the artifact version.
        has_custom_name: If the artifact name is custom and should be listed in
            the dashboard "Artifacts" tab.

    Returns:
        The saved artifact response.
    """
    from zenml.materializers.materializer_registry import (
        materializer_registry,
    )
    from zenml.utils import source_utils

    client = Client()
    artifact_store = client.active_stack.artifact_store

    if not uri:
        uri = os.path.join("custom_artifacts", name, str(uuid4()))
    if not uri.startswith(artifact_store.path):
        uri = os.path.join(artifact_store.path, uri)

    if save_type == ArtifactSaveType.MANUAL:
        # This check is only necessary for manual saves as we already check
        # it when creating the directory for step output artifacts
        _check_if_artifact_with_given_uri_already_registered(
            artifact_store=artifact_store,
            uri=uri,
            name=name,
        )

    if isinstance(materializer, type):
        materializer_class = materializer
    elif materializer:
        materializer_class = source_utils.load_and_validate_class(
            materializer, expected_class=BaseMaterializer
        )
    else:
        materializer_class = materializer_registry[type(data)]

    artifact_version_request = _store_artifact_data_and_prepare_request(
        data=data,
        name=name,
        uri=uri,
        materializer_class=materializer_class,
        save_type=save_type,
        version=version,
        tags=tags,
        store_metadata=extract_metadata,
        store_visualizations=include_visualizations,
        has_custom_name=has_custom_name,
        metadata=user_metadata,
    )
    artifact_version = client.zen_store.create_artifact_version(
        artifact_version=artifact_version_request
    )

    if save_type == ArtifactSaveType.MANUAL:
        _link_artifact_version_to_the_step_and_model(
            artifact_version=artifact_version,
            is_model_artifact=is_model_artifact,
            is_deployment_artifact=is_deployment_artifact,
        )

    return artifact_version

`save_model_metadata(model_artifact)`

Save a zenml model artifact metadata to a YAML file.

This function is used to extract and save information from a zenml model artifact such as the model type and materializer. The extracted information will be the key to loading the model into memory in the inference environment.

datatype: the model type. This is the path to the model class. materializer: The path to the materializer class.

Parameters:

Name	Type	Description	Default
`model_artifact`	`ArtifactVersionResponse`	the artifact to extract the metadata from.	required

Returns:

Type	Description
`str`	The path to the temporary file where the model metadata is saved

Source code in zenml/artifacts/utils.py

def save_model_metadata(model_artifact: "ArtifactVersionResponse") -> str:
    """Save a zenml model artifact metadata to a YAML file.

    This function is used to extract and save information from a zenml model
    artifact such as the model type and materializer. The extracted information
    will be the key to loading the model into memory in the inference
    environment.

    datatype: the model type. This is the path to the model class.
    materializer: The path to the materializer class.

    Args:
        model_artifact: the artifact to extract the metadata from.

    Returns:
        The path to the temporary file where the model metadata is saved
    """
    metadata = dict()
    metadata["datatype"] = model_artifact.data_type
    metadata["materializer"] = model_artifact.materializer

    with tempfile.NamedTemporaryFile(
        mode="w", suffix=".yaml", delete=False
    ) as f:
        write_yaml(f.name, metadata)
    return f.name

Artifacts

zenml.artifacts special

artifact_config

ArtifactConfig (BaseModel)

external_artifact

ExternalArtifact (ExternalArtifactConfiguration)

config: ExternalArtifactConfiguration property readonly

external_artifact_validator(self)

upload_by_value(self)

external_artifact_config

ExternalArtifactConfiguration (BaseModel)

get_artifact_version_id(self)

preexisting_data_materializer

PreexistingDataMaterializer (BaseMaterializer)

load(self, data_type)

save(self, data)

unmaterialized_artifact

UnmaterializedArtifact (ArtifactVersionResponse)

model_post_init(/, self, context)

utils

download_artifact_files_from_response(artifact, path, overwrite=False)

get_artifacts_versions_of_pipeline_run(pipeline_run, only_produced=False)

get_producer_step_of_artifact(artifact)

load_artifact(name_or_id, version=None)

load_artifact_from_response(artifact)

load_artifact_visualization(artifact, index=0, zen_store=None, encode_image=False)

load_model_from_metadata(model_uri)

log_artifact_metadata(metadata, artifact_name=None, artifact_version=None)

register_artifact(folder_or_file_uri, name, version=None, tags=None, has_custom_name=True, is_model_artifact=False, is_deployment_artifact=False, artifact_metadata={})

save_artifact(data, name, version=None, tags=None, extract_metadata=True, include_visualizations=True, user_metadata=None, materializer=None, uri=None, is_model_artifact=False, is_deployment_artifact=False, save_type=<ArtifactSaveType.MANUAL: 'manual'>, has_custom_name=True)

save_model_metadata(model_artifact)

`zenml.artifacts` `special`

`artifact_config`

`ArtifactConfig (BaseModel)`

`external_artifact`

`ExternalArtifact (ExternalArtifactConfiguration)`

`config: ExternalArtifactConfiguration` `property` `readonly`

`external_artifact_validator(self)`

`upload_by_value(self)`

`external_artifact_config`

`ExternalArtifactConfiguration (BaseModel)`

`get_artifact_version_id(self)`

`preexisting_data_materializer`

`PreexistingDataMaterializer (BaseMaterializer)`

`load(self, data_type)`

`save(self, data)`

`unmaterialized_artifact`

`UnmaterializedArtifact (ArtifactVersionResponse)`

`model_post_init(/, self, context)`

`utils`

`download_artifact_files_from_response(artifact, path, overwrite=False)`

`get_artifacts_versions_of_pipeline_run(pipeline_run, only_produced=False)`

`get_producer_step_of_artifact(artifact)`

`load_artifact(name_or_id, version=None)`

`load_artifact_from_response(artifact)`

`load_artifact_visualization(artifact, index=0, zen_store=None, encode_image=False)`

`load_model_from_metadata(model_uri)`

`log_artifact_metadata(metadata, artifact_name=None, artifact_version=None)`

`register_artifact(folder_or_file_uri, name, version=None, tags=None, has_custom_name=True, is_model_artifact=False, is_deployment_artifact=False, artifact_metadata={})`

`save_artifact(data, name, version=None, tags=None, extract_metadata=True, include_visualizations=True, user_metadata=None, materializer=None, uri=None, is_model_artifact=False, is_deployment_artifact=False, save_type=<ArtifactSaveType.MANUAL: 'manual'>, has_custom_name=True)`

`save_model_metadata(model_artifact)`