Develop a custom artifact store

Learning how to develop a custom artifact store.

Before diving into the specifics of this component type, it is beneficial to familiarize yourself with our general guide to writing custom component flavors in ZenML. This guide provides an essential understanding of ZenML's component flavor concepts.

ZenML comes equipped with Artifact Store implementations that you can use to store artifacts on a local filesystem or in the managed AWS, GCP, or Azure cloud object storage services. However, if you need to use a different type of object storage service as a backend for your ZenML Artifact Store, you can extend ZenML to provide your own custom Artifact Store implementation.

Base Abstraction

The Artifact Store establishes one of the main components in every ZenML stack. Now, let us take a deeper dive into the fundamentals behind its abstraction, namely the BaseArtifactStore class:

  1. As ZenML only supports filesystem-based artifact stores, it features a configuration parameter called path, which will indicate the root path of the artifact store. When registering an artifact store, users will have to define this parameter.

  2. Moreover, there is another variable in the config class called SUPPORTED_SCHEMES. This is a class variable that needs to be defined in every subclass of the base artifact store configuration. It indicates the supported file path schemes for the corresponding implementation. For instance, for the Azure artifact store, this set will be defined as {"abfs://", "az://"}.

  3. Lastly, the base class features a set of abstractmethods: open, copyfile,exists,glob,isdir,listdir ,makedirs,mkdir,remove, rename,rmtree,stat,walk. In the implementation of every ArtifactStore flavor, it is required to define these methods with respect to the flavor at hand.

Putting all these considerations together, we end up with the following implementation:

from zenml.enums import StackComponentType
from zenml.stack import StackComponent, StackComponentConfig

PathType = Union[bytes, str]

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

    path: str

    SUPPORTED_SCHEMES: ClassVar[Set[str]]

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

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

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

    def exists(self, path: PathType) -> bool:
        """Returns `True` if the given path exists."""

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

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

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

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

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

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

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

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

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

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

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

    def name(self) -> Type["BaseArtifactStore"]:
        """Returns the name of the flavor."""

    def type(self) -> StackComponentType:
        """Returns the flavor type."""
        return StackComponentType.ARTIFACT_STORE

    def config_class(self) -> Type[StackComponentConfig]:
        """Config class."""
        return BaseArtifactStoreConfig

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

This is a slimmed-down version of the base implementation which aims to highlight the abstraction layer. In order to see the full implementation and get the complete docstrings, please check the SDK docs .

The effect on the

If you created an instance of an artifact store, added it to your stack, and activated the stack, it will create a filesystem each time you run a ZenML pipeline and make it available to the module.

This means that when you utilize a method such as with a file path that starts with one of the SUPPORTED_SCHEMES within your steps or materializers, it will be able to use the open(...) method that you defined within your artifact store.

Build your own custom artifact store

If you want to implement your own custom Artifact Store, you can follow the following steps:

  1. Create a class that inherits from the BaseArtifactStore class and implements the abstract methods.

  2. Create a class that inherits from the BaseArtifactStoreConfig class and fill in the SUPPORTED_SCHEMES based on your file system.

  3. Bring both of these classes together by inheriting from the BaseArtifactStoreFlavor class.

Once you are done with the implementation, you can register it through the CLI. Please ensure you point to the flavor class via dot notation:

zenml artifact-store flavor register <>

For example, if your flavor class MyArtifactStoreFlavor is defined in flavors/, you'd register it by doing:

zenml artifact-store flavor register flavors.my_flavor.MyArtifactStoreFlavor

ZenML resolves the flavor class by taking the path where you initialized zenml (via zenml init) as the starting point of resolution. Therefore, please ensure you follow the best practice of initializing zenml at the root of your repository.

If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually, it's better to not have to rely on this mechanism and initialize zenml at the root.

Afterward, you should see the new custom artifact store flavor in the list of available artifact store flavors:

zenml artifact-store flavor list

It is important to draw attention to when and how these base abstractions are coming into play in a ZenML workflow.

  • The CustomArtifactStoreFlavor class is imported and utilized upon the creation of the custom flavor through the CLI.

  • The CustomArtifactStoreConfig class is imported when someone tries to register/update a stack component with this custom flavor. Especially, during the registration process of the stack component, the config will be used to validate the values given by the user. As Config objects are inherently pydantic objects, you can also add your own custom validators here.

  • The CustomArtifactStore only comes into play when the component is ultimately in use.

The design behind this interaction lets us separate the configuration of the flavor from its implementation. This way we can register flavors and components even when the major dependencies behind their implementation are not installed in our local setting (assuming the CustomArtifactStoreFlavor and the CustomArtifactStoreConfig are implemented in a different module/path than the actual CustomArtifactStore).

Enabling Artifact Visualizations with Custom Artifact Stores

ZenML automatically saves visualizations for many common data types and allows you to view these visualizations in the ZenML dashboard. Under the hood, this works by saving the visualizations together with the artifacts in the artifact store.

In order to load and display these visualizations, ZenML needs to be able to load and access the corresponding artifact store. This means that your custom artifact store needs to be configured in a way that allows authenticating to the back-end without relying on the local environment, e.g., by embedding the authentication credentials in the stack component configuration or by referencing a secret.

Furthermore, for deployed ZenML instances, you need to install the package dependencies of your artifact store implementation in the environment where you have deployed ZenML. See the Documentation on deploying ZenML with custom Docker images for more information on how to do that.

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