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Deepchecks

Deepchecks integration for ZenML.

The Deepchecks integration provides a way to validate your data in your pipelines. It includes a way to detect data anomalies and define checks to ensure quality of data.

The integration includes custom materializers to store and visualize Deepchecks SuiteResults.

DeepchecksIntegration

Bases: Integration

Definition of Deepchecks integration for ZenML.

Source code in src/zenml/integrations/deepchecks/__init__.py 
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class DeepchecksIntegration(Integration):
    """Definition of [Deepchecks](https://github.com/deepchecks/deepchecks) integration for ZenML."""

    NAME = DEEPCHECKS
    REQUIREMENTS = [
        "deepchecks[vision]~=0.18.0",
        "torchvision>=0.14.0",
        "opencv-python==4.5.5.64",  # pin to same version
        "opencv-python-headless==4.5.5.64",  # pin to same version
        "tenacity!=8.4.0",  # https://github.com/jd/tenacity/issues/471
        # The deepchecks integrations requires pandas to work.
        # However, their version 0.18.0 is still not compatible with
        # pandas>=2.2.0, so we limit the version here.
        "pandas<2.2.0",
    ]

    APT_PACKAGES = ["ffmpeg", "libsm6", "libxext6"]
    REQUIREMENTS_IGNORED_ON_UNINSTALL = ["pandas", "torchvision", "tenacity"]

    @classmethod
    def activate(cls) -> None:
        """Activate the Deepchecks integration."""
        from zenml.integrations.deepchecks import materializers  # noqa

    @classmethod
    def get_requirements(cls, target_os: 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.

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

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

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

        Returns:
            List of stack component flavors for this integration.
        """
        from zenml.integrations.deepchecks.flavors import (
            DeepchecksDataValidatorFlavor,
        )

        return [DeepchecksDataValidatorFlavor]

activate() classmethod

Activate the Deepchecks integration.

Source code in src/zenml/integrations/deepchecks/__init__.py 
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@classmethod
def activate(cls) -> None:
    """Activate the Deepchecks integration."""
    from zenml.integrations.deepchecks import materializers  # noqa

flavors() classmethod

Declare the stack component flavors for the Deepchecks integration.

Returns:

Type Description
List[Type[Flavor]]

List of stack component flavors for this integration.
Source code in src/zenml/integrations/deepchecks/__init__.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the Deepchecks integration.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.deepchecks.flavors import (
        DeepchecksDataValidatorFlavor,
    )

    return [DeepchecksDataValidatorFlavor]

get_requirements(target_os=None) 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

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/deepchecks/__init__.py 
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@classmethod
def get_requirements(cls, target_os: 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.

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

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