Feast

zenml.integrations.feast

Initialization for Feast integration.

The Feast integration offers a way to connect to a Feast Feature Store. ZenML implements a dedicated stack component that you can access as part of your ZenML steps in the usual ways.

Attributes

FEAST = 'feast' module-attribute

FEAST_FEATURE_STORE_FLAVOR = 'feast' module-attribute

Classes

FeastIntegration

Bases: Integration

Definition of Feast integration for ZenML.

Functions

flavors() -> List[Type[Flavor]] classmethod

Declare the stack component flavors for the Feast integration.

Returns:

Type	Description
List[Type[Flavor]]	List of stack component flavors for this integration.

Source code in src/zenml/integrations/feast/__init__.py

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the Feast integration.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.feast.flavors import FeastFeatureStoreFlavor

    return [FeastFeatureStoreFlavor]

get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None
python_version	Optional[str]	The Python version to use for the requirements.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/feast/__init__.py

@classmethod
def get_requirements(cls, target_os: Optional[str] = None, python_version: Optional[str] = None
) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    from zenml.integrations.pandas import PandasIntegration

    return cls.REQUIREMENTS + \
        PandasIntegration.get_requirements(target_os=target_os, python_version=python_version)

Flavor

Class for ZenML Flavors.

Attributes

config_class: Type[StackComponentConfig] abstractmethod property

Returns StackComponentConfig config class.

Returns:

Type	Description
Type[StackComponentConfig]	The config class.

config_schema: Dict[str, Any] property

The config schema for a flavor.

Returns:

Type	Description
Dict[str, Any]	The config schema.

docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

implementation_class: Type[StackComponent] abstractmethod property

Implementation class for this flavor.

Returns:

Type	Description
Type[StackComponent]	The implementation class for this flavor.

logo_url: Optional[str] property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
Optional[str]	The flavor logo.

name: str abstractmethod property

The flavor name.

Returns:

Type	Description
str	The flavor name.

sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor SDK docs url.

service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for service connectors.

Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.

Returns:

Type	Description
Optional[ServiceConnectorRequirements]	Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]	connector is required for this flavor.

type: StackComponentType abstractmethod property

The stack component type.

Returns:

Type	Description
StackComponentType	The stack component type.

Functions

from_model(flavor_model: FlavorResponse) -> Flavor classmethod

Loads a flavor from a model.

Parameters:

Name	Type	Description	Default
flavor_model	FlavorResponse	The model to load from.	required

Raises:

Type	Description
CustomFlavorImportError	If the custom flavor can't be imported.
ImportError	If the flavor can't be imported.

Returns:

Type	Description
Flavor	The loaded flavor.

Source code in src/zenml/stack/flavor.py

@classmethod
def from_model(cls, flavor_model: FlavorResponse) -> "Flavor":
    """Loads a flavor from a model.

    Args:
        flavor_model: The model to load from.

    Raises:
        CustomFlavorImportError: If the custom flavor can't be imported.
        ImportError: If the flavor can't be imported.

    Returns:
        The loaded flavor.
    """
    try:
        flavor = source_utils.load(flavor_model.source)()
    except (ModuleNotFoundError, ImportError, NotImplementedError) as err:
        if flavor_model.is_custom:
            flavor_module, _ = flavor_model.source.rsplit(".", maxsplit=1)
            expected_file_path = os.path.join(
                source_utils.get_source_root(),
                flavor_module.replace(".", os.path.sep),
            )
            raise CustomFlavorImportError(
                f"Couldn't import custom flavor {flavor_model.name}: "
                f"{err}. Make sure the custom flavor class "
                f"`{flavor_model.source}` is importable. If it is part of "
                "a library, make sure it is installed. If "
                "it is a local code file, make sure it exists at "
                f"`{expected_file_path}.py`."
            )
        else:
            raise ImportError(
                f"Couldn't import flavor {flavor_model.name}: {err}"
            )
    return cast(Flavor, flavor)

generate_default_docs_url() -> str

Generate the doc urls for all inbuilt and integration flavors.

Note that this method is not going to be useful for custom flavors, which do not have any docs in the main zenml docs.

Returns:

Type	Description
str	The complete url to the zenml documentation

Source code in src/zenml/stack/flavor.py

def generate_default_docs_url(self) -> str:
    """Generate the doc urls for all inbuilt and integration flavors.

    Note that this method is not going to be useful for custom flavors,
    which do not have any docs in the main zenml docs.

    Returns:
        The complete url to the zenml documentation
    """
    from zenml import __version__

    component_type = self.type.plural.replace("_", "-")
    name = self.name.replace("_", "-")

    try:
        is_latest = is_latest_zenml_version()
    except RuntimeError:
        # We assume in error cases that we are on the latest version
        is_latest = True

    if is_latest:
        base = "https://docs.zenml.io"
    else:
        base = f"https://zenml-io.gitbook.io/zenml-legacy-documentation/v/{__version__}"
    return f"{base}/stack-components/{component_type}/{name}"

generate_default_sdk_docs_url() -> str

Generate SDK docs url for a flavor.

Returns:

Type	Description
str	The complete url to the zenml SDK docs

Source code in src/zenml/stack/flavor.py

def generate_default_sdk_docs_url(self) -> str:
    """Generate SDK docs url for a flavor.

    Returns:
        The complete url to the zenml SDK docs
    """
    from zenml import __version__

    base = f"https://sdkdocs.zenml.io/{__version__}"

    component_type = self.type.plural

    if "zenml.integrations" in self.__module__:
        # Get integration name out of module path which will look something
        #  like this "zenml.integrations.<integration>....
        integration = self.__module__.split(
            "zenml.integrations.", maxsplit=1
        )[1].split(".")[0]

        # Get the config class name to point to the specific class
        config_class_name = self.config_class.__name__

        return (
            f"{base}/integration_code_docs"
            f"/integrations-{integration}"
            f"#zenml.integrations.{integration}.flavors.{config_class_name}"
        )

    else:
        return (
            f"{base}/core_code_docs/core-{component_type}/"
            f"#{self.__module__}"
        )

to_model(integration: Optional[str] = None, is_custom: bool = True) -> FlavorRequest

Converts a flavor to a model.

Parameters:

Name	Type	Description	Default
integration	Optional[str]	The integration to use for the model.	None
is_custom	bool	Whether the flavor is a custom flavor.	True

Returns:

Type	Description
FlavorRequest	The model.

Source code in src/zenml/stack/flavor.py

def to_model(
    self,
    integration: Optional[str] = None,
    is_custom: bool = True,
) -> FlavorRequest:
    """Converts a flavor to a model.

    Args:
        integration: The integration to use for the model.
        is_custom: Whether the flavor is a custom flavor.

    Returns:
        The model.
    """
    connector_requirements = self.service_connector_requirements
    connector_type = (
        connector_requirements.connector_type
        if connector_requirements
        else None
    )
    resource_type = (
        connector_requirements.resource_type
        if connector_requirements
        else None
    )
    resource_id_attr = (
        connector_requirements.resource_id_attr
        if connector_requirements
        else None
    )

    model = FlavorRequest(
        name=self.name,
        type=self.type,
        source=source_utils.resolve(self.__class__).import_path,
        config_schema=self.config_schema,
        connector_type=connector_type,
        connector_resource_type=resource_type,
        connector_resource_id_attr=resource_id_attr,
        integration=integration,
        logo_url=self.logo_url,
        docs_url=self.docs_url,
        sdk_docs_url=self.sdk_docs_url,
        is_custom=is_custom,
    )
    return model

Integration

Base class for integration in ZenML.

Functions

activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py

@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""

check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type	Description
bool	True if all required packages are installed, False otherwise.

Source code in src/zenml/integrations/integration.py

@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for requirement in cls.get_requirements():
        parsed_requirement = Requirement(requirement)

        if not requirement_installed(parsed_requirement):
            logger.debug(
                "Requirement '%s' for integration '%s' is not installed "
                "or installed with the wrong version.",
                requirement,
                cls.NAME,
            )
            return False

        dependencies = get_dependencies(parsed_requirement)

        for dependency in dependencies:
            if not requirement_installed(dependency):
                logger.debug(
                    "Requirement '%s' for integration '%s' is not "
                    "installed or installed with the wrong version.",
                    dependency,
                    cls.NAME,
                )
                return False

    logger.debug(
        f"Integration '{cls.NAME}' is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True

flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type	Description
List[Type[Flavor]]	A list of new stack component flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []

get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None
python_version	Optional[str]	The Python version to use for the requirements.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/integration.py

@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: Optional[str] = None,
) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS

get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/integration.py

@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret

plugin_flavors() -> List[Type[BasePluginFlavor]] classmethod

Abstract method to declare new plugin flavors.

Returns:

Type	Description
List[Type[BasePluginFlavor]]	A list of new plugin flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

    Returns:
        A list of new plugin flavors.
    """
    return []

Modules

feature_stores

Feast Feature Store integration for ZenML.

Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept in sync between the two. It also offers a centralized registry where features (and feature schemas) are stored for use within a team or wider organization. Feature stores are a relatively recent addition to commonly-used machine learning stacks. Feast is a leading open-source feature store, first developed by Gojek in collaboration with Google.

Classes

FeastFeatureStore(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, environment: Optional[Dict[str, str]] = None, secrets: Optional[List[UUID]] = None, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseFeatureStore

Class to interact with the Feast feature store.

Source code in src/zenml/stack/stack_component.py

def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    environment: Optional[Dict[str, str]] = None,
    secrets: Optional[List[UUID]] = None,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        environment: Environment variables to set when running on this
            component.
        secrets: Secrets to set as environment variables when running on
            this component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.environment = environment or {}
    self.secrets = secrets or []
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None

Attributes

config: FeastFeatureStoreConfig property

Returns the FeastFeatureStoreConfig config.

Returns:

Type	Description
FeastFeatureStoreConfig	The configuration.

Functions

get_data_sources() -> List[str]

Returns the data sources' names.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The data sources' names.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_data_sources(self) -> List[str]:
    """Returns the data sources' names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The data sources' names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_data_sources()]

get_entities() -> List[str]

Returns the entity names.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The entity names.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_entities(self) -> List[str]:
    """Returns the entity names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The entity names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_entities()]

get_feast_version() -> str

Returns the version of Feast used.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
str	The version of Feast currently being used.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feast_version(self) -> str:
    """Returns the version of Feast used.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The version of Feast currently being used.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return str(fs.version())

get_feature_services() -> List[FeatureService]

Returns the feature services.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[FeatureService]	The feature services.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feature_services(self) -> List[FeatureService]:
    """Returns the feature services.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The feature services.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    feature_services: List[FeatureService] = list(
        fs.list_feature_services()
    )

    return feature_services

get_feature_views() -> List[str]

Returns the feature view names.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The feature view names.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feature_views(self) -> List[str]:
    """Returns the feature view names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The feature view names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_feature_views()]

get_historical_features(entity_df: Union[pd.DataFrame, str], features: Union[List[str], FeatureService], full_feature_names: bool = False) -> pd.DataFrame

Returns the historical features for training or batch scoring.

Parameters:

Name	Type	Description	Default
entity_df	Union[DataFrame, str]	The entity DataFrame or entity name.	required
features	Union[List[str], FeatureService]	The features to retrieve or a FeatureService.	required
full_feature_names	bool	Whether to return the full feature names.	False

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
DataFrame	The historical features as a Pandas DataFrame.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_historical_features(
    self,
    entity_df: Union[pd.DataFrame, str],
    features: Union[List[str], FeatureService],
    full_feature_names: bool = False,
) -> pd.DataFrame:
    """Returns the historical features for training or batch scoring.

    Args:
        entity_df: The entity DataFrame or entity name.
        features: The features to retrieve or a FeatureService.
        full_feature_names: Whether to return the full feature names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The historical features as a Pandas DataFrame.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)

    return fs.get_historical_features(
        entity_df=entity_df,
        features=features,
        full_feature_names=full_feature_names,
    ).to_df()

get_online_features(entity_rows: List[Dict[str, Any]], features: Union[List[str], FeatureService], full_feature_names: bool = False) -> Dict[str, Any]

Returns the latest online feature data.

Parameters:

Name	Type	Description	Default
entity_rows	List[Dict[str, Any]]	The entity rows to retrieve.	required
features	Union[List[str], FeatureService]	The features to retrieve or a FeatureService.	required
full_feature_names	bool	Whether to return the full feature names.	False

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
Dict[str, Any]	The latest online feature data as a dictionary.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_online_features(
    self,
    entity_rows: List[Dict[str, Any]],
    features: Union[List[str], FeatureService],
    full_feature_names: bool = False,
) -> Dict[str, Any]:
    """Returns the latest online feature data.

    Args:
        entity_rows: The entity rows to retrieve.
        features: The features to retrieve or a FeatureService.
        full_feature_names: Whether to return the full feature names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The latest online feature data as a dictionary.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)

    return fs.get_online_features(  # type: ignore[no-any-return]
        entity_rows=entity_rows,
        features=features,
        full_feature_names=full_feature_names,
    ).to_dict()

get_project() -> str

Returns the project name.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
str	The project name.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_project(self) -> str:
    """Returns the project name.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The project name.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return str(fs.project)

get_registry() -> BaseRegistry

Returns the feature store registry.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
BaseRegistry	The registry.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_registry(self) -> BaseRegistry:
    """Returns the feature store registry.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The registry.
    """
    fs: FeatureStore = FeatureStore(repo_path=self.config.feast_repo)
    return fs.registry

Modules

feast_feature_store

Implementation of the Feast Feature Store for ZenML.

Classes

FeastFeatureStore(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, environment: Optional[Dict[str, str]] = None, secrets: Optional[List[UUID]] = None, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseFeatureStore

Class to interact with the Feast feature store.

Source code in src/zenml/stack/stack_component.py

def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    environment: Optional[Dict[str, str]] = None,
    secrets: Optional[List[UUID]] = None,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        environment: Environment variables to set when running on this
            component.
        secrets: Secrets to set as environment variables when running on
            this component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.environment = environment or {}
    self.secrets = secrets or []
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None

Attributes

config: FeastFeatureStoreConfig property

Returns the FeastFeatureStoreConfig config.

Returns:

Type	Description
FeastFeatureStoreConfig	The configuration.

Functions

get_data_sources() -> List[str]

Returns the data sources' names.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The data sources' names.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_data_sources(self) -> List[str]:
    """Returns the data sources' names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The data sources' names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_data_sources()]

get_entities() -> List[str]

Returns the entity names.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The entity names.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_entities(self) -> List[str]:
    """Returns the entity names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The entity names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_entities()]

get_feast_version() -> str

Returns the version of Feast used.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
str	The version of Feast currently being used.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feast_version(self) -> str:
    """Returns the version of Feast used.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The version of Feast currently being used.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return str(fs.version())

get_feature_services() -> List[FeatureService]

Returns the feature services.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[FeatureService]	The feature services.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feature_services(self) -> List[FeatureService]:
    """Returns the feature services.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The feature services.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    feature_services: List[FeatureService] = list(
        fs.list_feature_services()
    )

    return feature_services

get_feature_views() -> List[str]

Returns the feature view names.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The feature view names.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feature_views(self) -> List[str]:
    """Returns the feature view names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The feature view names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_feature_views()]

get_historical_features(entity_df: Union[pd.DataFrame, str], features: Union[List[str], FeatureService], full_feature_names: bool = False) -> pd.DataFrame

Returns the historical features for training or batch scoring.

Parameters:

Name	Type	Description	Default
entity_df	Union[DataFrame, str]	The entity DataFrame or entity name.	required
features	Union[List[str], FeatureService]	The features to retrieve or a FeatureService.	required
full_feature_names	bool	Whether to return the full feature names.	False

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
DataFrame	The historical features as a Pandas DataFrame.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_historical_features(
    self,
    entity_df: Union[pd.DataFrame, str],
    features: Union[List[str], FeatureService],
    full_feature_names: bool = False,
) -> pd.DataFrame:
    """Returns the historical features for training or batch scoring.

    Args:
        entity_df: The entity DataFrame or entity name.
        features: The features to retrieve or a FeatureService.
        full_feature_names: Whether to return the full feature names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The historical features as a Pandas DataFrame.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)

    return fs.get_historical_features(
        entity_df=entity_df,
        features=features,
        full_feature_names=full_feature_names,
    ).to_df()

get_online_features(entity_rows: List[Dict[str, Any]], features: Union[List[str], FeatureService], full_feature_names: bool = False) -> Dict[str, Any]

Returns the latest online feature data.

Parameters:

Name	Type	Description	Default
entity_rows	List[Dict[str, Any]]	The entity rows to retrieve.	required
features	Union[List[str], FeatureService]	The features to retrieve or a FeatureService.	required
full_feature_names	bool	Whether to return the full feature names.	False

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
Dict[str, Any]	The latest online feature data as a dictionary.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_online_features(
    self,
    entity_rows: List[Dict[str, Any]],
    features: Union[List[str], FeatureService],
    full_feature_names: bool = False,
) -> Dict[str, Any]:
    """Returns the latest online feature data.

    Args:
        entity_rows: The entity rows to retrieve.
        features: The features to retrieve or a FeatureService.
        full_feature_names: Whether to return the full feature names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The latest online feature data as a dictionary.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)

    return fs.get_online_features(  # type: ignore[no-any-return]
        entity_rows=entity_rows,
        features=features,
        full_feature_names=full_feature_names,
    ).to_dict()

get_project() -> str

Returns the project name.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
str	The project name.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_project(self) -> str:
    """Returns the project name.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The project name.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return str(fs.project)

get_registry() -> BaseRegistry

Returns the feature store registry.

Raise

ConnectionError: If the online component (Redis) is not available.

Returns:

Type	Description
BaseRegistry	The registry.

Source code in src/zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_registry(self) -> BaseRegistry:
    """Returns the feature store registry.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The registry.
    """
    fs: FeatureStore = FeatureStore(repo_path=self.config.feast_repo)
    return fs.registry

Functions

flavors

Feast integration flavors.

Classes

FeastFeatureStoreConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseFeatureStoreConfig

Config for Feast feature store.

Configuration for connecting to Feast feature stores. Field descriptions are defined inline using Field() descriptors.

Source code in src/zenml/stack/stack_component.py

def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)

Attributes

is_local: bool property

Checks if this stack component is running locally.

Returns:

Type	Description
bool	True if this config is for a local component, False otherwise.

FeastFeatureStoreFlavor

Bases: BaseFeatureStoreFlavor

Feast Feature store flavor.

Attributes

config_class: Type[FeastFeatureStoreConfig] property

Returns FeastFeatureStoreConfig config class.

Returns:

Type	Description
Type[FeastFeatureStoreConfig]	The config class.

docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

implementation_class: Type[FeastFeatureStore] property

Implementation class for this flavor.

Returns:

Type	Description
Type[FeastFeatureStore]	The implementation class.

logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name: str property

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor SDK docs url.

Modules

feast_feature_store_flavor

Feast feature store flavor.

Classes

FeastFeatureStoreConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseFeatureStoreConfig

Config for Feast feature store.

Configuration for connecting to Feast feature stores. Field descriptions are defined inline using Field() descriptors.

Source code in src/zenml/stack/stack_component.py

def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)

Attributes

is_local: bool property

Checks if this stack component is running locally.

Returns:

Type	Description
bool	True if this config is for a local component, False otherwise.

FeastFeatureStoreFlavor

Bases: BaseFeatureStoreFlavor

Feast Feature store flavor.

Attributes

config_class: Type[FeastFeatureStoreConfig] property

Returns FeastFeatureStoreConfig config class.

Returns:

Type	Description
Type[FeastFeatureStoreConfig]	The config class.

docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

implementation_class: Type[FeastFeatureStore] property

Implementation class for this flavor.

Returns:

Type	Description
Type[FeastFeatureStore]	The implementation class.

logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name: str property

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor SDK docs url.