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
        model_name="my_model",  # link the artifact to a model
    )
]:
    return ...

Attributes:

Name	Type	Description
`name`	`Optional[str]`	The name of the artifact.
`version`	`Union[int, str]`	The version of the artifact.
`tags`	`Optional[List[str]]`	The tags of the artifact.
`model_name`	`Optional[str]`	The name of the model to link artifact to.
`model_version`	`Union[zenml.enums.ModelStages, str, int]`	The identifier of a version of the model to link the artifact to. It can be an exact version ("my_version"), exact version number (42), stage (ModelStages.PRODUCTION or "production"), or (ModelStages.LATEST or None) for the latest version (default).
`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
            model_name="my_model",  # link the artifact to a model
        )
    ]:
        return ...
    ```

    Attributes:
        name: The name of the artifact.
        version: The version of the artifact.
        tags: The tags of the artifact.
        model_name: The name of the model to link artifact to.
        model_version: The identifier of a version of the model to link the artifact
            to. It can be an exact version ("my_version"), exact version number
            (42), stage (ModelStages.PRODUCTION or "production"), or
            (ModelStages.LATEST or None) for the latest version (default).
        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

    model_name: Optional[str] = None
    model_version: Optional[Union[ModelStages, str, int]] = Field(
        default=None, union_mode="smart"
    )
    is_model_artifact: bool = False
    is_deployment_artifact: bool = False

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

    @model_validator(mode="after")
    def artifact_config_validator(self) -> "ArtifactConfig":
        """Model validator for the artifact config.

        Raises:
            ValueError: If both model_name and model_version is set incorrectly.

        Returns:
            the validated instance.
        """
        if self.model_name is not None and self.model_version is None:
            raise ValueError(
                f"Creation of new model version from {self.__class__.__name__} "
                "is not allowed. Please either keep `model_name` and "
                "`model_version` both `None` to get the model version from the "
                "step context or specify both at the same time. You can use "
                "`ModelStages.LATEST` as `model_version` when latest model "
                "version is desired."
            )
        return self

    @property
    def _model(self) -> Optional["Model"]:
        """The model linked to this artifact.

        Returns:
            The model or None if the model version cannot be determined.
        """
        try:
            model_ = get_step_context().model
        except (StepContextError, RuntimeError):
            model_ = None
        # Check if another model name was specified
        if (self.model_name is not None) and (
            model_ is None or model_.name != self.model_name
        ):
            # Create a new Model instance with the provided model name and version
            from zenml.model.model import Model

            on_the_fly_config = Model(
                name=self.model_name, version=self.model_version
            )
            return on_the_fly_config

        return model_

`artifact_config_validator(self)`

Model validator for the artifact config.

Exceptions:

Type	Description
`ValueError`	If both model_name and model_version is set incorrectly.

Returns:

Type	Description
`ArtifactConfig`	the validated instance.

Source code in zenml/artifacts/artifact_config.py

@model_validator(mode="after")
def artifact_config_validator(self) -> "ArtifactConfig":
    """Model validator for the artifact config.

    Raises:
        ValueError: If both model_name and model_version is set incorrectly.

    Returns:
        the validated instance.
    """
    if self.model_name is not None and self.model_version is None:
        raise ValueError(
            f"Creation of new model version from {self.__class__.__name__} "
            "is not allowed. Please either keep `model_name` and "
            "`model_version` both `None` to get the model version from the "
            "step context or specify both at the same time. You can use "
            "`ModelStages.LATEST` as `model_version` when latest model "
            "version is desired."
        )
    return self

`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 either a value associated with it that will be uploaded to the artifact store, or reference an artifact that is already registered in ZenML.

There are several ways to reference an existing artifact: - By providing an artifact ID. - By providing an artifact name and version. If no version is provided, the latest version of that artifact will be used.

Parameters:

Name	Description	Default
`value`	The artifact value.	required
`id`	The ID of an artifact that should be referenced by this external artifact.	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 either a value associated with it
    that will be uploaded to the artifact store, or reference an artifact
    that is already registered in ZenML.

    There are several ways to reference an existing artifact:
    - By providing an artifact ID.
    - By providing an artifact name and version. If no version is provided,
        the latest version of that artifact will be used.

    Args:
        value: The artifact value.
        id: The ID of an artifact that should be referenced by this external
            artifact.
        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 the value, id and name fields are set incorrectly.

        Returns:
            the validated instance.
        """
        deprecation_msg = (
            "Parameter `{param}` or `ExternalArtifact` will be deprecated "
            "in upcoming releases. Please use `{substitute}` instead."
        )
        for param, substitute in [
            ["id", "Client().get_artifact_version(name_id_or_prefix=<id>)"],
            [
                "name",
                "Client().get_artifact_version(name_id_or_prefix=<name>)",
            ],
            [
                "version",
                "Client().get_artifact_version(name_id_or_prefix=<name>,version=<version>)",
            ],
            [
                "model",
                "Client().get_model_version(<model_name>,<model_version>).get_artifact(name)",
            ],
        ]:
            if getattr(self, param, None):
                logger.warning(
                    deprecation_msg.format(
                        param=param,
                        substitute=substitute,
                    )
                )
        options = [
            getattr(self, field, None) is not None
            for field in ["value", "id", "name"]
        ]
        if sum(options) > 1:
            raise ValueError(
                "Only one of `value`, `id`, or `name` can be provided when "
                "creating an external artifact."
            )
        elif sum(options) == 0:
            raise ValueError(
                "Either `value`, `id`, or `name` must be provided when "
                "creating an external artifact."
            )
        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,
            manual_save=False,
        )

        # 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,
            name=self.name,
            version=self.version,
            model=self.model,
        )

`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 the value, id and name fields are set incorrectly.

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 the value, id and name fields are set incorrectly.

    Returns:
        the validated instance.
    """
    deprecation_msg = (
        "Parameter `{param}` or `ExternalArtifact` will be deprecated "
        "in upcoming releases. Please use `{substitute}` instead."
    )
    for param, substitute in [
        ["id", "Client().get_artifact_version(name_id_or_prefix=<id>)"],
        [
            "name",
            "Client().get_artifact_version(name_id_or_prefix=<name>)",
        ],
        [
            "version",
            "Client().get_artifact_version(name_id_or_prefix=<name>,version=<version>)",
        ],
        [
            "model",
            "Client().get_model_version(<model_name>,<model_version>).get_artifact(name)",
        ],
    ]:
        if getattr(self, param, None):
            logger.warning(
                deprecation_msg.format(
                    param=param,
                    substitute=substitute,
                )
            )
    options = [
        getattr(self, field, None) is not None
        for field in ["value", "id", "name"]
    ]
    if sum(options) > 1:
        raise ValueError(
            "Only one of `value`, `id`, or `name` can be provided when "
            "creating an external artifact."
        )
    elif sum(options) == 0:
        raise ValueError(
            "Either `value`, `id`, or `name` must be provided when "
            "creating an external artifact."
        )
    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,
        manual_save=False,
    )

    # 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
    name: Optional[str] = None
    version: Optional[str] = None
    model: Optional[Model] = None

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

        Raises:
            ValueError: if both version and model fields are set.

        Returns:
            the validated instance.
        """
        if self.version and self.model:
            raise ValueError(
                "Cannot provide both `version` and `model` when "
                "creating an external artifact."
            )
        return self

    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)
        elif self.name:
            if self.version:
                response = client.get_artifact_version(
                    self.name, version=self.version
                )
            elif self.model:
                response_ = self.model.get_artifact(self.name)
                if not isinstance(response_, ArtifactVersionResponse):
                    raise RuntimeError(
                        f"Failed to pull artifact `{self.name}` from the Model "
                        f"(name=`{self.model.name}`, version="
                        f"`{self.model.version}`). Please validate the "
                        "input and try again."
                    )
                response = response_
            else:
                response = client.get_artifact_version(self.name)
        else:
            raise RuntimeError(
                "Either the ID or name 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."
            )

        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."
            )

        self.id = response.id

        return self.id

`external_artifact_validator(self)`

Model validator for the external artifact configuration.

Exceptions:

Type	Description
`ValueError`	if both version and model fields are set.

Returns:

Type	Description
`ExternalArtifactConfiguration`	the validated instance.

Source code in zenml/artifacts/external_artifact_config.py

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

    Raises:
        ValueError: if both version and model fields are set.

    Returns:
        the validated instance.
    """
    if self.version and self.model:
        raise ValueError(
            "Cannot provide both `version` and `model` when "
            "creating an external artifact."
        )
    return self

`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)
    elif self.name:
        if self.version:
            response = client.get_artifact_version(
                self.name, version=self.version
            )
        elif self.model:
            response_ = self.model.get_artifact(self.name)
            if not isinstance(response_, ArtifactVersionResponse):
                raise RuntimeError(
                    f"Failed to pull artifact `{self.name}` from the Model "
                    f"(name=`{self.model.name}`, version="
                    f"`{self.model.version}`). Please validate the "
                    "input and try again."
                )
            response = response_
        else:
            response = client.get_artifact_version(self.name)
    else:
        raise RuntimeError(
            "Either the ID or name 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."
        )

    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."
        )

    self.id = response.id

    return self.id

`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:
            artifact_versions.extend(step.outputs.values())
    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)
    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 of a step
    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 of 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 of 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.
    """
    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)

`save_artifact(data, name, version=None, tags=None, extract_metadata=True, include_visualizations=True, has_custom_name=True, user_metadata=None, materializer=None, uri=None, is_model_artifact=False, is_deployment_artifact=False, manual_save=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`
`has_custom_name`	`bool`	If the artifact name is custom and should be listed in the dashboard "Artifacts" tab.	`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`
`manual_save`	`bool`	If this function is called manually and should therefore link the artifact to the current step run.	`True`

Returns:

Type	Description
`ArtifactVersionResponse`	The saved artifact response.

Exceptions:

Type	Description
`RuntimeError`	If artifact URI already exists.
`EntityExistsError`	If artifact version already exists.

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,
    has_custom_name: 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,
    manual_save: 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.
        has_custom_name: If the artifact name is custom and should be listed in
            the dashboard "Artifacts" tab.
        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.
        manual_save: If this function is called manually and should therefore
            link the artifact to the current step run.

    Returns:
        The saved artifact response.

    Raises:
        RuntimeError: If artifact URI already exists.
        EntityExistsError: If artifact version already exists.
    """
    from zenml.materializers.materializer_registry import (
        materializer_registry,
    )
    from zenml.utils import source_utils

    client = Client()

    # Get or create the artifact
    try:
        artifact = client.list_artifacts(name=name)[0]
        if artifact.has_custom_name != has_custom_name:
            client.update_artifact(
                name_id_or_prefix=artifact.id, has_custom_name=has_custom_name
            )
    except IndexError:
        try:
            artifact = client.zen_store.create_artifact(
                ArtifactRequest(
                    name=name,
                    has_custom_name=has_custom_name,
                    tags=tags,
                )
            )
        except EntityExistsError:
            artifact = client.list_artifacts(name=name)[0]

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

    # Build and check the artifact URI
    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 manual_save and artifact_store.exists(uri):
        # This check is only necessary for manual saves as we already check
        # it when creating the directory for step output artifacts
        other_artifacts = client.list_artifact_versions(uri=uri, size=1)
        if other_artifacts and (other_artifact := other_artifacts[0]):
            raise RuntimeError(
                f"Cannot save new artifact {name} version to URI "
                f"{uri} because the URI is already used by artifact "
                f"{other_artifact.name} (version {other_artifact.version})."
            )
    artifact_store.makedirs(uri)

    # Find and initialize the right materializer class
    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)]
    materializer_object = materializer_class(uri)

    # Force URIs to have forward slashes
    materializer_object.uri = materializer_object.uri.replace("\\", "/")

    # Save the artifact to the artifact store
    data_type = type(data)
    materializer_object.validate_type_compatibility(data_type)
    materializer_object.save(data)

    # Save visualizations of the artifact
    visualizations: List[ArtifactVisualizationRequest] = []
    if include_visualizations:
        try:
            vis_data = materializer_object.save_visualizations(data)
            for vis_uri, vis_type in vis_data.items():
                vis_model = ArtifactVisualizationRequest(
                    type=vis_type,
                    uri=vis_uri,
                )
                visualizations.append(vis_model)
        except Exception as e:
            logger.warning(
                f"Failed to save visualization for output artifact '{name}': "
                f"{e}"
            )

    # Save metadata of the artifact
    artifact_metadata: Dict[str, "MetadataType"] = {}
    if extract_metadata:
        try:
            artifact_metadata = materializer_object.extract_full_metadata(data)
            artifact_metadata.update(user_metadata or {})
        except Exception as e:
            logger.warning(
                f"Failed to extract metadata for output artifact '{name}': {e}"
            )

    # Create the artifact version
    def _create_version() -> Optional[ArtifactVersionResponse]:
        artifact_version = ArtifactVersionRequest(
            artifact_id=artifact.id,
            version=version,
            tags=tags,
            type=materializer_object.ASSOCIATED_ARTIFACT_TYPE,
            uri=materializer_object.uri,
            materializer=source_utils.resolve(materializer_object.__class__),
            data_type=source_utils.resolve(data_type),
            user=Client().active_user.id,
            workspace=Client().active_workspace.id,
            artifact_store_id=artifact_store.id,
            visualizations=visualizations,
            has_custom_name=has_custom_name,
        )
        try:
            return client.zen_store.create_artifact_version(
                artifact_version=artifact_version
            )
        except EntityExistsError:
            return None

    response = None
    if not version:
        retries_made = 0
        for i in range(MAX_RETRIES_FOR_VERSIONED_ENTITY_CREATION):
            # Get new artifact version
            version = _get_new_artifact_version(name)
            if response := _create_version():
                break
            # smoothed exponential back-off, it will go as 0.2, 0.3,
            # 0.45, 0.68, 1.01, 1.52, 2.28, 3.42, 5.13, 7.69, ...
            sleep = 0.2 * 1.5**i
            logger.debug(
                f"Failed to create artifact version `{version}` for "
                f"artifact `{name}`. Retrying in {sleep}..."
            )
            time.sleep(sleep)
            retries_made += 1
        if not response:
            raise EntityExistsError(
                f"Failed to create new artifact version for artifact "
                f"`{name}`. Retried {retries_made} times. "
                "This could be driven by exceptionally high concurrency of "
                "pipeline runs. Please, reach out to us on ZenML Slack for support."
            )
    else:
        response = _create_version()
        if not response:
            raise EntityExistsError(
                f"Failed to create artifact version `{version}` for artifact "
                f"`{name}`. Given version already exists."
            )
    if artifact_metadata:
        client.create_run_metadata(
            metadata=artifact_metadata,
            resource_id=response.id,
            resource_type=MetadataResourceTypes.ARTIFACT_VERSION,
        )

    if manual_save:
        try:
            error_message = "step run"
            step_context = get_step_context()
            step_run = step_context.step_run
            client.zen_store.update_run_step(
                step_run_id=step_run.id,
                step_run_update=StepRunUpdate(
                    saved_artifact_versions={name: response.id}
                ),
            )
            error_message = "model"
            model = step_context.model
            if model:
                from zenml.model.utils import link_artifact_to_model

                link_artifact_to_model(
                    artifact_version_id=response.id,
                    model=model,
                    is_model_artifact=is_model_artifact,
                    is_deployment_artifact=is_deployment_artifact,
                )
        except (RuntimeError, StepContextError):
            logger.debug(f"Unable to link saved artifact to {error_message}.")

    return response

`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)

artifact_config_validator(self)

external_artifact

ExternalArtifact (ExternalArtifactConfiguration)

config: ExternalArtifactConfiguration property readonly

external_artifact_validator(self)

upload_by_value(self)

external_artifact_config

ExternalArtifactConfiguration (BaseModel)

external_artifact_validator(self)

get_artifact_version_id(self)

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)

save_artifact(data, name, version=None, tags=None, extract_metadata=True, include_visualizations=True, has_custom_name=True, user_metadata=None, materializer=None, uri=None, is_model_artifact=False, is_deployment_artifact=False, manual_save=True)

save_model_metadata(model_artifact)

`zenml.artifacts` `special`

`artifact_config`

`ArtifactConfig (BaseModel)`

`artifact_config_validator(self)`

`external_artifact`

`ExternalArtifact (ExternalArtifactConfiguration)`

`config: ExternalArtifactConfiguration` `property` `readonly`

`external_artifact_validator(self)`

`upload_by_value(self)`

`external_artifact_config`

`ExternalArtifactConfiguration (BaseModel)`

`external_artifact_validator(self)`

`get_artifact_version_id(self)`

`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)`

`save_artifact(data, name, version=None, tags=None, extract_metadata=True, include_visualizations=True, has_custom_name=True, user_metadata=None, materializer=None, uri=None, is_model_artifact=False, is_deployment_artifact=False, manual_save=True)`

`save_model_metadata(model_artifact)`