When building sophisticated ML workflows, you will often need to come up with custom-tailored solutions. Sometimes, this might even require you to use custom components for your infrastructure or tooling. That is exactly why the stack component flavors in ZenML are designed to be modular and straightforward to extend. Using ZenML’s base abstractions, you can create your own stack component flavor and use it in your stack.

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Base Abstractions

Before we get into the topic of creating custom stack component flavors, let us briefly discuss some of the important design choices behind the abstraction of a ZenML flavor. The overall implementation revolves around three base interfaces, namely the StackComponent, the StackComponentConfig, and the Flavor.

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Base Abstraction 1: StackComponent

The StackComponent is utilized as an interface to define the logic behind the functionality of a flavor. For instance, you can take a look at the BaseArtifactStore example down below. By inheriting from the StackComponent, the BaseArtifactStore establishes the interface for all artifact stores. Any flavor of an artifact store needs to follows the standards set by this base class. 
class StackComponent:
    """Abstract StackComponent class for all components of a ZenML stack."""

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

    # --- public interface ---

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

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

    ...

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Base Abstraction 2: StackComponentConfig

As its name suggests, the StackComponentConfig is used to configure a stack component instance. It is separated from the actual implementation on purpose. This way, ZenML can use this class to validate the configuration of a stack component during its registration/update, without having to import heavy (or even non-installed) dependencies. Let us continue with the same example up above and take a look at the BaseArtifactStoreConfig. 
from pydantic import BaseModel

class StackComponentConfig(BaseModel):
    """Base class for all ZenML stack component configs."""

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

    path: str

    SUPPORTED_SCHEMES: ClassVar[Set[str]]

    @root_validator(skip_on_failure=True)
    def _ensure_artifact_store(cls, values: Dict[str, Any]) -> Any:
        """Validator function for the Artifact Stores.

        Checks whether supported schemes are defined and the given path is
        supported.
        """
        ...
There are a few things to unpack here. Let’s talk about Pydantic first. Pydantic is a library for data validation and settings management. By using their BaseModel as a base class, ZenML is able to configure and serialize these configuration properties while being able to add a validation layer to each implementation. If you take a closer look at the example above, you will see that, through the BaseArtifacStoreConfig, each artifact store will require users to define a path variable along with a list of SUPPORTED_SCHEMES. Using this configuration class, ZenML can check if the given path is actually supported.
Similar to the example above, you can use class variables by denoting them with the ClassVar[..], which are also excluded from the serialization.

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Base Abstraction 3: Flavor

Ultimately, the Flavor abstraction is responsible for bringing the implementation of a StackComponent together with the corresponding StackComponentConfig definition to create a Flavor. 
class Flavor:
    """Base class for ZenML Flavors."""

    @property
    @abstractmethod
    def name(self) -> str:
        """The name of the flavor."""

    @property
    @abstractmethod
    def type(self) -> StackComponentType:
        """The type of the flavor."""

    @property
    @abstractmethod
    def implementation_class(self) -> Type[StackComponent]:
        """Implementation class for this flavor."""

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

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

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

    @property
    def config_class(self) -> Type[StackComponentConfig]:
        """Config class for this flavor."""
        return BaseArtifactStoreConfig
Following the same example, the BaseArtifactStoreFlavor sets the correct type property and introduces the BaseArtifactStoreConfig as the default configuration for all ZenML artifact stores.

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Implementing a Custom Stack Component Flavor

Using all the abstraction layers above, let us create a custom artifact store flavor, starting with the configuration. 
from zenml.artifact_stores import BaseArtifactStoreConfig

class MyArtifactStoreConfig(BaseArtifactStoreConfig):
    """Custom artifact store implementation."""

    my_param: int  # Adding a custom parameter on top of the `path` variable
With the configuration defined, we can move on to the logic behind the implementation: 
from zenml.artifact_stores import BaseArtifactStore

class MyArtifactStore(BaseArtifactStore):
    """Custom artifact store implementation."""

    def open(self, name: PathType, mode: str = "r") -> Any:
        """Custom logic goes here."""
        ...

    def exists(self, path: PathType) -> bool:
        """Custom logic goes here."""
        ...

    def my_custom_method(self):
        """Custom method here."""
        print(self.config.path)     # The configuration properties are available
        print(self.config.my_param) # under self.config
Now, let us bring these two classes together through a Flavor. Make sure that you give your flavor a unique name here. 
from zenml.artifact_stores import BaseArtifactStoreFlavor

class MyArtifactStoreFlavor(BaseArtifactStoreFlavor):
    """Custom artifact store implementation."""

    def name(self) -> str:
        """The name of the flavor."""
        return 'my_artifact_store'

    def implementation_class(self) -> Type["BaseArtifactStore"]:
        """Implementation class for this flavor."""
        from ... import MyArtifactStore
        return MyArtifactStore

    def config_class(self) -> Type[StackComponentConfig]:
        """Configuration class for this flavor."""
        from ... import MyArtifactStoreConfig
        return MyArtifactStoreConfig

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Managing a Custom Stack Component Flavor

Once your implementation is complete, you can register it through the CLI: 
zenml artifact-store flavor register
Afterwards, you should see the new custom artifact store flavor in the list of available artifact store flavors: 
zenml artifact-store flavor list
And that’s it, you now have defined a custom stack component flavor that you can use in any of your stacks just like any other flavor you used before, e.g.: 
zenml artifact-store register  \
    --flavor=my_artifact_store \
    ...

zenml stack register  \
    --artifact-store  \
    --path='some-path' \
    --my_param=3
If your custom stack component flavor requires special setup before it can be used, check out the Managing Stack Component States section for more details.
Check out this short (< 3 minutes) video on how to quickly get some more information about the registered flavors available to you:

Describe MLOps Stack Component Flavors

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Extending Specific Stack Components

If you would like to learn more about how to build a custom stack component flavor for a specific stack component, please check the links below:
Type of Stack ComponentDescription
OrchestratorOrchestrating the runs of your pipeline
Artifact StoreStorage for the artifacts created by your pipelines
Container RegistryStore for your containers
Secrets ManagerCentralized location for the storage of your secrets
Step OperatorExecution of individual steps in specialized runtime environments
Model DeployerServices/platforms responsible for online model serving
Feature StoreManagement of your data/features
Experiment TrackerTracking your ML experiments
AlerterSending alerts through specified channels
AnnotatorAnnotating and labeling data
Data ValidatorValidating and monitoring your data