Implement a custom stack component
How to write a custom stack component flavor
When building a sophisticated MLOps Platform, 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.
Before we get into the topic of creating custom stack component flavors, let us briefly discuss some 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.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 follow 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."""
...
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
BaseArtifactStoreConfig, 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.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.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:
PathType = Union[bytes, str]
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."""
@property
def name(self) -> str:
"""The name of the flavor."""
return 'my_artifact_store'
@property
def implementation_class(self) -> Type["BaseArtifactStore"]:
"""Implementation class for this flavor."""
from ... import MyArtifactStore
return MyArtifactStore
@property
def config_class(self) -> Type[StackComponentConfig]:
"""Configuration class for this flavor."""
from ... import MyArtifactStoreConfig
return MyArtifactStoreConfig
Once your implementation is complete, you can register it through the CLI. Please ensure you point to the flavor class via dot notation:
zenml artifact-store flavor register <path.to.MyArtifactStoreFlavor>
For example, if your flavor class
MyArtifactStoreFlavor is defined in flavors/my_flavor.py, 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
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 <ARTIFACT_STORE_NAME> \
--flavor=my_artifact_store \
...
zenml stack register <STACK_NAME> \
--artifact-store <ARTIFACT_STORE_NAME> \
--path='some-path' \
--my_param=3
If you would like to automatically track some metadata about your custom stack component with each pipeline run, check out the Tracking Custom Stack Component Metadata section.
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
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 Component | Description |
Orchestrating the runs of your pipeline | |
Storage for the artifacts created by your pipelines | |
Store for your containers | |
Execution of individual steps in specialized runtime environments | |
Services/platforms responsible for online model serving | |
Management of your data/features | |
Tracking your ML experiments | |
Sending alerts through specified channels | |
Annotating and labeling data | |
Validating and monitoring your data |
Last modified 7d ago