Models

zenml.models special

Pydantic models for the various concepts in ZenML.

v2 special

base special

base

Base model definitions.

BaseDatedResponseBody (BaseResponseBody) pydantic-model

Base body model for entities that track a creation and update timestamp.

Used as a base class for all body models associated with responses. Features a creation and update timestamp.

Source code in zenml/models/v2/base/base.py

class BaseDatedResponseBody(BaseResponseBody):
    """Base body model for entities that track a creation and update timestamp.

    Used as a base class for all body models associated with responses.
    Features a creation and update timestamp.
    """

    created: datetime = Field(
        title="The timestamp when this resource was created."
    )
    updated: datetime = Field(
        title="The timestamp when this resource was last updated."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseIdentifiedResponse (BaseResponse[AnyDatedBody, AnyMetadata, AnyResources], Generic) pydantic-model

Base domain model for resources with DB representation.

Source code in zenml/models/v2/base/base.py

class BaseIdentifiedResponse(
    BaseResponse[AnyDatedBody, AnyMetadata, AnyResources],
    Generic[AnyDatedBody, AnyMetadata, AnyResources],
):
    """Base domain model for resources with DB representation."""

    id: UUID = Field(title="The unique resource id.")
    body: Optional["AnyDatedBody"] = Field(
        title="The body of the resource, "
        "containing at the minimum "
        "creation and updated fields."
    )
    metadata: Optional["AnyMetadata"] = Field(
        title="The metadata related to this resource."
    )
    resources: Optional["AnyResources"] = Field(
        title="The resources related to this resource."
    )
    permission_denied: bool = False

    # Helper functions
    def __hash__(self) -> int:
        """Implementation of hash magic method.

        Returns:
            Hash of the UUID.
        """
        return hash((type(self),) + tuple([self.id]))

    def __eq__(self, other: Any) -> bool:
        """Implementation of equality magic method.

        Args:
            other: The other object to compare to.

        Returns:
            True if the other object is of the same type and has the same UUID.
        """
        if isinstance(other, type(self)):
            return self.id == other.id
        else:
            return False

    def _validate_hydrated_version(
        self,
        hydrated_model: "BaseResponse[AnyDatedBody, AnyMetadata, AnyResources]",
    ) -> None:
        """Helper method to validate the values within the hydrated version.

        Args:
            hydrated_model: the hydrated version of the model.

        Raises:
            HydrationError: if the hydrated version has different values set
                for either the name of the body fields and the
                _method_body_mutation is set to ResponseBodyUpdate.DENY.
        """
        super()._validate_hydrated_version(hydrated_model)

        assert isinstance(hydrated_model, type(self))

        # Check if the ID is the same
        if self.id != hydrated_model.id:
            raise HydrationError(
                "The hydrated version of the model does not have the same id."
            )

    def get_hydrated_version(
        self,
    ) -> "BaseIdentifiedResponse[AnyDatedBody, AnyMetadata, AnyResources]":
        """Abstract method to fetch the hydrated version of the model.

        Raises:
            NotImplementedError: in case the method is not implemented.
        """
        raise NotImplementedError(
            "Please implement a `get_hydrated_version` method before "
            "using/hydrating the model."
        )

    def get_body(self) -> "AnyDatedBody":
        """Fetch the body of the entity.

        Returns:
            The body field of the response.

        Raises:
            IllegalOperationError: If the user lacks permission to access the
                entity represented by this response.
        """
        if self.permission_denied:
            raise IllegalOperationError(
                f"Missing permissions to access {type(self).__name__} with "
                f"ID {self.id}."
            )

        return super().get_body()

    def get_metadata(self) -> "AnyMetadata":
        """Fetch the metadata of the entity.

        Returns:
            The metadata field of the response.

        Raises:
            IllegalOperationError: If the user lacks permission to access this
                entity represented by this response.
        """
        if self.permission_denied:
            raise IllegalOperationError(
                f"Missing permissions to access {type(self).__name__} with "
                f"ID {self.id}."
            )

        return super().get_metadata()

    # Analytics
    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Fetches the analytics metadata for base response models.

        Returns:
            The analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        metadata["entity_id"] = self.id
        return metadata

    # Body and metadata properties
    @property
    def created(self) -> datetime:
        """The `created` property.

        Returns:
            the value of the property.
        """
        return self.get_body().created

    @property
    def updated(self) -> datetime:
        """The `updated` property.

        Returns:
            the value of the property.
        """
        return self.get_body().updated

created: datetime property readonly

The created property.

Returns:

Type	Description
datetime	the value of the property.

updated: datetime property readonly

The updated property.

Returns:

Type	Description
datetime	the value of the property.

__eq__(self, other) special

Implementation of equality magic method.

Parameters:

Name	Type	Description	Default
other	Any	The other object to compare to.	required

Returns:

Type	Description
bool	True if the other object is of the same type and has the same UUID.

Source code in zenml/models/v2/base/base.py

def __eq__(self, other: Any) -> bool:
    """Implementation of equality magic method.

    Args:
        other: The other object to compare to.

    Returns:
        True if the other object is of the same type and has the same UUID.
    """
    if isinstance(other, type(self)):
        return self.id == other.id
    else:
        return False

__hash__(self) special

Implementation of hash magic method.

Returns:

Type	Description
int	Hash of the UUID.

Source code in zenml/models/v2/base/base.py

def __hash__(self) -> int:
    """Implementation of hash magic method.

    Returns:
        Hash of the UUID.
    """
    return hash((type(self),) + tuple([self.id]))

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_analytics_metadata(self)

Fetches the analytics metadata for base response models.

Returns:

Type	Description
Dict[str, Any]	The analytics metadata.

Source code in zenml/models/v2/base/base.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for base response models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata["entity_id"] = self.id
    return metadata

get_body(self)

Fetch the body of the entity.

Returns:

Type	Description
AnyDatedBody	The body field of the response.

Exceptions:

Type	Description
IllegalOperationError	If the user lacks permission to access the entity represented by this response.

Source code in zenml/models/v2/base/base.py

def get_body(self) -> "AnyDatedBody":
    """Fetch the body of the entity.

    Returns:
        The body field of the response.

    Raises:
        IllegalOperationError: If the user lacks permission to access the
            entity represented by this response.
    """
    if self.permission_denied:
        raise IllegalOperationError(
            f"Missing permissions to access {type(self).__name__} with "
            f"ID {self.id}."
        )

    return super().get_body()

get_hydrated_version(self)

Abstract method to fetch the hydrated version of the model.

Exceptions:

Type	Description
NotImplementedError	in case the method is not implemented.

Source code in zenml/models/v2/base/base.py

def get_hydrated_version(
    self,
) -> "BaseIdentifiedResponse[AnyDatedBody, AnyMetadata, AnyResources]":
    """Abstract method to fetch the hydrated version of the model.

    Raises:
        NotImplementedError: in case the method is not implemented.
    """
    raise NotImplementedError(
        "Please implement a `get_hydrated_version` method before "
        "using/hydrating the model."
    )

get_metadata(self)

Fetch the metadata of the entity.

Returns:

Type	Description
AnyMetadata	The metadata field of the response.

Exceptions:

Type	Description
IllegalOperationError	If the user lacks permission to access this entity represented by this response.

Source code in zenml/models/v2/base/base.py

def get_metadata(self) -> "AnyMetadata":
    """Fetch the metadata of the entity.

    Returns:
        The metadata field of the response.

    Raises:
        IllegalOperationError: If the user lacks permission to access this
            entity represented by this response.
    """
    if self.permission_denied:
        raise IllegalOperationError(
            f"Missing permissions to access {type(self).__name__} with "
            f"ID {self.id}."
        )

    return super().get_metadata()

BaseRequest (BaseZenModel) pydantic-model

Base request model.

Used as a base class for all request models.

Source code in zenml/models/v2/base/base.py

class BaseRequest(BaseZenModel):
    """Base request model.

    Used as a base class for all request models.
    """

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseResponse (GenericModel, Generic, BaseZenModel) pydantic-model

Base domain model for all responses.

Source code in zenml/models/v2/base/base.py

class BaseResponse(
    GenericModel, Generic[AnyBody, AnyMetadata, AnyResources], BaseZenModel
):
    """Base domain model for all responses."""

    # Body and metadata pair
    body: Optional["AnyBody"] = Field(
        default=None, title="The body of the resource."
    )
    metadata: Optional["AnyMetadata"] = Field(
        default=None, title="The metadata related to this resource."
    )
    resources: Optional["AnyResources"] = Field(
        default=None, title="The resources related to this resource."
    )

    _response_update_strategy: ResponseUpdateStrategy = (
        ResponseUpdateStrategy.ALLOW
    )
    _warn_on_response_updates: bool = True

    def _validate_hydrated_version(
        self,
        hydrated_model: "BaseResponse[AnyBody, AnyMetadata, AnyResources]",
    ) -> None:
        """Helper method to validate the values within the hydrated version.

        Args:
            hydrated_model: the hydrated version of the model.

        Raises:
            HydrationError: if the hydrated version has different values set
                for either the name of the body fields and the
                _method_body_mutation is set to ResponseBodyUpdate.DENY.
        """
        # Check whether the metadata exists in the hydrated version
        if hydrated_model.metadata is None:
            raise HydrationError(
                "The hydrated model does not have a metadata field."
            )

        # Check if the name has changed
        if "name" in self.__fields__:
            original_name = getattr(self, "name")
            hydrated_name = getattr(hydrated_model, "name")

            if original_name != hydrated_name:
                if (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.ALLOW
                ):
                    setattr(self, "name", hydrated_name)

                    if self._warn_on_response_updates:
                        logger.warning(
                            f"The name of the entity has changed from "
                            f"`{original_name}` to `{hydrated_name}`."
                        )

                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.IGNORE
                ):
                    if self._warn_on_response_updates:
                        logger.warning(
                            f"Ignoring the name change in the hydrated version "
                            f"of the response: `{original_name}` to "
                            f"`{hydrated_name}`."
                        )
                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.DENY
                ):
                    raise HydrationError(
                        f"Failing the hydration, because there is a change in "
                        f"the name of the entity: `{original_name}` to "
                        f"`{hydrated_name}`."
                    )

        # Check all the fields in the body
        for field in self.get_body().__fields__:
            original_value = getattr(self.get_body(), field)
            hydrated_value = getattr(hydrated_model.get_body(), field)

            if original_value != hydrated_value:
                if (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.ALLOW
                ):
                    setattr(self.get_body(), field, hydrated_value)

                    if self._warn_on_response_updates:
                        logger.warning(
                            f"The field `{field}` in the body of the response "
                            f"has changed from `{original_value}` to "
                            f"`{hydrated_value}`."
                        )

                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.IGNORE
                ):
                    if self._warn_on_response_updates:
                        logger.warning(
                            f"Ignoring the change in the hydrated version of "
                            f"the field `{field}`: `{original_value}` -> "
                            f"`{hydrated_value}`."
                        )
                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.DENY
                ):
                    raise HydrationError(
                        f"Failing the hydration, because there is a change in "
                        f"the field `{field}`: `{original_value}` -> "
                        f"`{hydrated_value}`"
                    )

    def get_hydrated_version(
        self,
    ) -> "BaseResponse[AnyBody, AnyMetadata, AnyResources]":
        """Abstract method to fetch the hydrated version of the model.

        Raises:
            NotImplementedError: in case the method is not implemented.
        """
        raise NotImplementedError(
            "Please implement a `get_hydrated_version` method before "
            "using/hydrating the model."
        )

    def get_body(self) -> "AnyBody":
        """Fetch the body of the entity.

        Returns:
            The body field of the response.

        Raises:
            RuntimeError: If the body was not included in the response.
        """
        if not self.body:
            raise RuntimeError(
                f"Missing response body for {type(self).__name__}."
            )

        return self.body

    def get_metadata(self) -> "AnyMetadata":
        """Fetch the metadata of the entity.

        Returns:
            The metadata field of the response.
        """
        if self.metadata is None:
            # If the metadata is not there, check the class first.
            metadata_type = self.__fields__["metadata"].type_

            if len(metadata_type.__fields__):
                # If the metadata class defines any fields, fetch the metadata
                # through the hydrated version.
                hydrated_version = self.get_hydrated_version()
                self._validate_hydrated_version(hydrated_version)
                self.metadata = hydrated_version.metadata
            else:
                # Otherwise, use the metadata class to create an empty metadata
                # object.
                self.metadata = metadata_type()

        assert self.metadata is not None

        return self.metadata

    def get_resources(self) -> "AnyResources":
        """Fetch the resources related to this entity.

        Returns:
            The resources field of the response.

        Raises:
            RuntimeError: If the resources field was not included in the response.
        """
        if self.resources is None:
            # If the resources are not there, check the class first.
            resources_type = self.__fields__["resources"].type_

            if len(resources_type.__fields__):
                # If the resources class defines any fields, fetch the resources
                # through the hydrated version.
                hydrated_version = self.get_hydrated_version()
                self._validate_hydrated_version(hydrated_version)
                self.resources = hydrated_version.resources
            else:
                # Otherwise, use the resources class to create an empty
                # resources object.
                self.metadata = resources_type()

        if self.resources is None:
            raise RuntimeError(
                f"Missing response resources for {type(self).__name__}."
            )

        return self.resources

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_body(self)

Fetch the body of the entity.

Returns:

Type	Description
AnyBody	The body field of the response.

Exceptions:

Type	Description
RuntimeError	If the body was not included in the response.

Source code in zenml/models/v2/base/base.py

def get_body(self) -> "AnyBody":
    """Fetch the body of the entity.

    Returns:
        The body field of the response.

    Raises:
        RuntimeError: If the body was not included in the response.
    """
    if not self.body:
        raise RuntimeError(
            f"Missing response body for {type(self).__name__}."
        )

    return self.body

get_hydrated_version(self)

Abstract method to fetch the hydrated version of the model.

Exceptions:

Type	Description
NotImplementedError	in case the method is not implemented.

Source code in zenml/models/v2/base/base.py

def get_hydrated_version(
    self,
) -> "BaseResponse[AnyBody, AnyMetadata, AnyResources]":
    """Abstract method to fetch the hydrated version of the model.

    Raises:
        NotImplementedError: in case the method is not implemented.
    """
    raise NotImplementedError(
        "Please implement a `get_hydrated_version` method before "
        "using/hydrating the model."
    )

get_metadata(self)

Fetch the metadata of the entity.

Returns:

Type	Description
AnyMetadata	The metadata field of the response.

Source code in zenml/models/v2/base/base.py

def get_metadata(self) -> "AnyMetadata":
    """Fetch the metadata of the entity.

    Returns:
        The metadata field of the response.
    """
    if self.metadata is None:
        # If the metadata is not there, check the class first.
        metadata_type = self.__fields__["metadata"].type_

        if len(metadata_type.__fields__):
            # If the metadata class defines any fields, fetch the metadata
            # through the hydrated version.
            hydrated_version = self.get_hydrated_version()
            self._validate_hydrated_version(hydrated_version)
            self.metadata = hydrated_version.metadata
        else:
            # Otherwise, use the metadata class to create an empty metadata
            # object.
            self.metadata = metadata_type()

    assert self.metadata is not None

    return self.metadata

get_resources(self)

Fetch the resources related to this entity.

Returns:

Type	Description
AnyResources	The resources field of the response.

Exceptions:

Type	Description
RuntimeError	If the resources field was not included in the response.

Source code in zenml/models/v2/base/base.py

def get_resources(self) -> "AnyResources":
    """Fetch the resources related to this entity.

    Returns:
        The resources field of the response.

    Raises:
        RuntimeError: If the resources field was not included in the response.
    """
    if self.resources is None:
        # If the resources are not there, check the class first.
        resources_type = self.__fields__["resources"].type_

        if len(resources_type.__fields__):
            # If the resources class defines any fields, fetch the resources
            # through the hydrated version.
            hydrated_version = self.get_hydrated_version()
            self._validate_hydrated_version(hydrated_version)
            self.resources = hydrated_version.resources
        else:
            # Otherwise, use the resources class to create an empty
            # resources object.
            self.metadata = resources_type()

    if self.resources is None:
        raise RuntimeError(
            f"Missing response resources for {type(self).__name__}."
        )

    return self.resources

BaseResponseBody (BaseZenModel) pydantic-model

Base body model.

Source code in zenml/models/v2/base/base.py

class BaseResponseBody(BaseZenModel):
    """Base body model."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseResponseMetadata (BaseZenModel) pydantic-model

Base metadata model.

Used as a base class for all metadata models associated with responses.

Source code in zenml/models/v2/base/base.py

class BaseResponseMetadata(BaseZenModel):
    """Base metadata model.

    Used as a base class for all metadata models associated with responses.
    """

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseResponseResources (BaseZenModel) pydantic-model

Base resources model.

Used as a base class for all resource models associated with responses.

Source code in zenml/models/v2/base/base.py

class BaseResponseResources(BaseZenModel):
    """Base resources model.

    Used as a base class for all resource models associated with responses.
    """

    class Config:
        """Allows additional resources to be added."""

        extra = Extra.allow

Config

Allows additional resources to be added.

Source code in zenml/models/v2/base/base.py

class Config:
    """Allows additional resources to be added."""

    extra = Extra.allow

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseResponse[AnyDatedBody, AnyMetadata, AnyResources] (BaseResponse) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/base.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseZenModel (YAMLSerializationMixin, AnalyticsTrackedModelMixin) pydantic-model

Base model class for all ZenML models.

This class is used as a base class for all ZenML models. It provides functionality for tracking analytics events and proper encoding of SecretStr values.

Source code in zenml/models/v2/base/base.py

class BaseZenModel(YAMLSerializationMixin, AnalyticsTrackedModelMixin):
    """Base model class for all ZenML models.

    This class is used as a base class for all ZenML models. It provides
    functionality for tracking analytics events and proper encoding of
    SecretStr values.
    """

    class Config:
        """Pydantic configuration class."""

        # This is needed to allow the REST client and server to unpack SecretStr
        # values correctly.
        json_encoders = {
            SecretStr: lambda v: v.get_secret_value()
            if v is not None
            else None
        }

        # Allow extras on all models to support forwards and backwards
        # compatibility (e.g. new fields in newer versions of ZenML servers
        # are allowed to be present in older versions of ZenML clients and
        # vice versa).
        extra = "allow"

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/base.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

base_plugin_flavor

Plugin flavor model definitions.

BasePluginFlavorResponse (BaseResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources], Generic) pydantic-model

Base response for all Plugin Flavors.

Source code in zenml/models/v2/base/base_plugin_flavor.py

class BasePluginFlavorResponse(
    BaseResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources],
    Generic[AnyPluginBody, AnyPluginMetadata, AnyPluginResources],
):
    """Base response for all Plugin Flavors."""

    name: str = Field(title="Name of the flavor.")
    type: PluginType = Field(title="Type of the plugin.")
    subtype: PluginSubType = Field(title="Subtype of the plugin.")

    class Config:
        """Configuration for base plugin flavor response."""

        extra = Extra.ignore

    def get_hydrated_version(
        self,
    ) -> "BasePluginFlavorResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources]":
        """Abstract method to fetch the hydrated version of the model.

        Returns:
            Hydrated version of the PluginFlavorResponse
        """
        # TODO: shouldn't this call the Zen store ? The client should not have
        #  to know about the plugin flavor registry
        from zenml.zen_server.utils import plugin_flavor_registry

        plugin_flavor = plugin_flavor_registry().get_flavor_class(
            name=self.name, _type=self.type, subtype=self.subtype
        )
        return plugin_flavor.get_flavor_response_model(hydrate=True)

Config

Configuration for base plugin flavor response.

Source code in zenml/models/v2/base/base_plugin_flavor.py

class Config:
    """Configuration for base plugin flavor response."""

    extra = Extra.ignore

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Abstract method to fetch the hydrated version of the model.

Returns:

Type	Description
BasePluginFlavorResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources]	Hydrated version of the PluginFlavorResponse

Source code in zenml/models/v2/base/base_plugin_flavor.py

def get_hydrated_version(
    self,
) -> "BasePluginFlavorResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources]":
    """Abstract method to fetch the hydrated version of the model.

    Returns:
        Hydrated version of the PluginFlavorResponse
    """
    # TODO: shouldn't this call the Zen store ? The client should not have
    #  to know about the plugin flavor registry
    from zenml.zen_server.utils import plugin_flavor_registry

    plugin_flavor = plugin_flavor_registry().get_flavor_class(
        name=self.name, _type=self.type, subtype=self.subtype
    )
    return plugin_flavor.get_flavor_response_model(hydrate=True)

BasePluginResponseBody (BaseResponseBody) pydantic-model

Response body for plugins.

Source code in zenml/models/v2/base/base_plugin_flavor.py

class BasePluginResponseBody(BaseResponseBody):
    """Response body for plugins."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BasePluginResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for plugins.

Source code in zenml/models/v2/base/base_plugin_flavor.py

class BasePluginResponseMetadata(BaseResponseMetadata):
    """Response metadata for plugins."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BasePluginResponseResources (BaseResponseResources) pydantic-model

Response resources for plugins.

Source code in zenml/models/v2/base/base_plugin_flavor.py

class BasePluginResponseResources(BaseResponseResources):
    """Response resources for plugins."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources] (BaseResponse) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/base_plugin_flavor.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

filter

Base filter model definitions.

BaseFilter (BaseModel) pydantic-model

Class to unify all filter, paginate and sort request parameters.

This Model allows fine-grained filtering, sorting and pagination of resources.

Usage example for subclasses of this class:

ResourceListModel(
    name="contains:default",
    workspace="default"
    count_steps="gte:5"
    sort_by="created",
    page=2,
    size=20
)

Source code in zenml/models/v2/base/filter.py

class BaseFilter(BaseModel):
    """Class to unify all filter, paginate and sort request parameters.

    This Model allows fine-grained filtering, sorting and pagination of
    resources.

    Usage example for subclasses of this class:
    ```
    ResourceListModel(
        name="contains:default",
        workspace="default"
        count_steps="gte:5"
        sort_by="created",
        page=2,
        size=20
    )
    ```
    """

    # List of fields that cannot be used as filters.
    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        "sort_by",
        "page",
        "size",
        "logical_operator",
    ]

    # List of fields that are not even mentioned as options in the CLI.
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = []

    # List of fields that are wrapped with `fastapi.Query(default)` in API.
    API_MULTI_INPUT_PARAMS: ClassVar[List[str]] = []

    sort_by: str = Field(
        default="created", description="Which column to sort by."
    )
    logical_operator: LogicalOperators = Field(
        default=LogicalOperators.AND,
        description="Which logical operator to use between all filters "
        "['and', 'or']",
    )
    page: int = Field(
        default=PAGINATION_STARTING_PAGE, ge=1, description="Page number"
    )
    size: int = Field(
        default=PAGE_SIZE_DEFAULT,
        ge=1,
        le=PAGE_SIZE_MAXIMUM,
        description="Page size",
    )

    id: Optional[Union[UUID, str]] = Field(
        default=None, description="Id for this resource"
    )
    created: Optional[Union[datetime, str]] = Field(
        default=None, description="Created"
    )
    updated: Optional[Union[datetime, str]] = Field(
        default=None, description="Updated"
    )

    _rbac_configuration: Optional[
        Tuple[UUID, Dict[str, Optional[Set[UUID]]]]
    ] = None

    @validator("sort_by", pre=True)
    def validate_sort_by(cls, v: str) -> str:
        """Validate that the sort_column is a valid column with a valid operand.

        Args:
            v: The sort_by field value.

        Returns:
            The validated sort_by field value.

        Raises:
            ValidationError: If the sort_by field is not a string.
            ValueError: If the resource can't be sorted by this field.
        """
        # Somehow pydantic allows you to pass in int values, which will be
        #  interpreted as string, however within the validator they are still
        #  integers, which don't have a .split() method
        if not isinstance(v, str):
            raise ValidationError(
                f"str type expected for the sort_by field. "
                f"Received a {type(v)}"
            )
        column = v
        split_value = v.split(":", 1)
        if len(split_value) == 2:
            column = split_value[1]

            if split_value[0] not in SorterOps.values():
                logger.warning(
                    "Invalid operand used for column sorting. "
                    "Only the following operands are supported `%s`. "
                    "Defaulting to 'asc' on column `%s`.",
                    SorterOps.values(),
                    column,
                )
                v = column

        if column in cls.FILTER_EXCLUDE_FIELDS:
            raise ValueError(
                f"This resource can not be sorted by this field: '{v}'"
            )
        elif column in cls.__fields__:
            return v
        else:
            raise ValueError(
                "You can only sort by valid fields of this resource"
            )

    @root_validator(pre=True)
    def filter_ops(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Parse incoming filters to ensure all filters are legal.

        Args:
            values: The values of the class.

        Returns:
            The values of the class.
        """
        cls._generate_filter_list(values)
        return values

    @property
    def list_of_filters(self) -> List[Filter]:
        """Converts the class variables into a list of usable Filter Models.

        Returns:
            A list of Filter models.
        """
        return self._generate_filter_list(
            {key: getattr(self, key) for key in self.__fields__}
        )

    @property
    def sorting_params(self) -> Tuple[str, SorterOps]:
        """Converts the class variables into a list of usable Filter Models.

        Returns:
            A tuple of the column to sort by and the sorting operand.
        """
        column = self.sort_by
        # The default sorting operand is asc
        operator = SorterOps.ASCENDING

        # Check if user explicitly set an operand
        split_value = self.sort_by.split(":", 1)
        if len(split_value) == 2:
            column = split_value[1]
            operator = SorterOps(split_value[0])

        return column, operator

    def configure_rbac(
        self,
        authenticated_user_id: UUID,
        **column_allowed_ids: Optional[Set[UUID]],
    ) -> None:
        """Configure RBAC allowed column values.

        Args:
            authenticated_user_id: ID of the authenticated user. All entities
                owned by this user will be included.
            column_allowed_ids: Set of IDs per column to limit the query to.
                If given, the remaining filters will be applied to entities
                within this set only. If `None`, the remaining filters will
                applied to all entries in the table.
        """
        self._rbac_configuration = (authenticated_user_id, column_allowed_ids)

    def generate_rbac_filter(
        self,
        table: Type["AnySchema"],
    ) -> Optional["BooleanClauseList[Any]"]:
        """Generates an optional RBAC filter.

        Args:
            table: The query table.

        Returns:
            The RBAC filter.
        """
        from sqlmodel import or_

        if not self._rbac_configuration:
            return None

        expressions = []

        for column_name, allowed_ids in self._rbac_configuration[1].items():
            if allowed_ids is not None:
                expression = getattr(table, column_name).in_(allowed_ids)
                expressions.append(expression)

        if expressions and hasattr(table, "user_id"):
            # If `expressions` is not empty, we do not have full access to all
            # rows of the table. In this case, we also include rows which the
            # user owns.

            # Unowned entities are considered server-owned and can be seen
            # by anyone
            expressions.append(getattr(table, "user_id").is_(None))
            # The authenticated user owns this entity
            expressions.append(
                getattr(table, "user_id") == self._rbac_configuration[0]
            )

        if expressions:
            return or_(*expressions)
        else:
            return None

    @classmethod
    def _generate_filter_list(cls, values: Dict[str, Any]) -> List[Filter]:
        """Create a list of filters from a (column, value) dictionary.

        Args:
            values: A dictionary of column names and values to filter on.

        Returns:
            A list of filters.
        """
        list_of_filters: List[Filter] = []

        for key, value in values.items():
            # Ignore excluded filters
            if key in cls.FILTER_EXCLUDE_FIELDS:
                continue

            # Skip filtering for None values
            if value is None:
                continue

            # Determine the operator and filter value
            value, operator = cls._resolve_operator(value)

            # Define the filter
            filter = cls._define_filter(
                column=key, value=value, operator=operator
            )
            list_of_filters.append(filter)

        return list_of_filters

    @staticmethod
    def _resolve_operator(value: Any) -> Tuple[Any, GenericFilterOps]:
        """Determine the operator and filter value from a user-provided value.

        If the user-provided value is a string of the form "operator:value",
        then the operator is extracted and the value is returned. Otherwise,
        `GenericFilterOps.EQUALS` is used as default operator and the value
        is returned as-is.

        Args:
            value: The user-provided value.

        Returns:
            A tuple of the filter value and the operator.
        """
        operator = GenericFilterOps.EQUALS  # Default operator
        if isinstance(value, str):
            split_value = value.split(":", 1)
            if (
                len(split_value) == 2
                and split_value[0] in GenericFilterOps.values()
            ):
                value = split_value[1]
                operator = GenericFilterOps(split_value[0])
        return value, operator

    @classmethod
    def _define_filter(
        cls, column: str, value: Any, operator: GenericFilterOps
    ) -> Filter:
        """Define a filter for a given column.

        Args:
            column: The column to filter on.
            value: The value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.
        """
        # Create datetime filters
        if cls.is_datetime_field(column):
            return cls._define_datetime_filter(
                column=column,
                value=value,
                operator=operator,
            )

        # Create UUID filters
        if cls.is_uuid_field(column):
            return cls._define_uuid_filter(
                column=column,
                value=value,
                operator=operator,
            )

        # Create int filters
        if cls.is_int_field(column):
            return NumericFilter(
                operation=GenericFilterOps(operator),
                column=column,
                value=int(value),
            )

        # Create bool filters
        if cls.is_bool_field(column):
            return cls._define_bool_filter(
                column=column,
                value=value,
                operator=operator,
            )

        # Create str filters
        if cls.is_str_field(column):
            return StrFilter(
                operation=GenericFilterOps(operator),
                column=column,
                value=value,
            )

        # Handle unsupported datatypes
        logger.warning(
            f"The Datatype {cls.__fields__[column].type_} might not be "
            "supported for filtering. Defaulting to a string filter."
        )
        return StrFilter(
            operation=GenericFilterOps(operator),
            column=column,
            value=str(value),
        )

    @classmethod
    def is_datetime_field(cls, k: str) -> bool:
        """Checks if it's a datetime field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a datetime field, False otherwise.
        """
        return (
            issubclass(datetime, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is datetime
        )

    @classmethod
    def is_uuid_field(cls, k: str) -> bool:
        """Checks if it's a uuid field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a uuid field, False otherwise.
        """
        return (
            issubclass(UUID, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is UUID
        )

    @classmethod
    def is_int_field(cls, k: str) -> bool:
        """Checks if it's a int field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a int field, False otherwise.
        """
        return (
            issubclass(int, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is int
        )

    @classmethod
    def is_bool_field(cls, k: str) -> bool:
        """Checks if it's a bool field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a bool field, False otherwise.
        """
        return (
            issubclass(bool, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is bool
        )

    @classmethod
    def is_str_field(cls, k: str) -> bool:
        """Checks if it's a string field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a string field, False otherwise.
        """
        return (
            issubclass(str, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is str
        )

    @classmethod
    def is_sort_by_field(cls, k: str) -> bool:
        """Checks if it's a sort by field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a sort by field, False otherwise.
        """
        return (
            issubclass(str, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ == str
        ) and k == "sort_by"

    @staticmethod
    def _define_datetime_filter(
        column: str, value: Any, operator: GenericFilterOps
    ) -> NumericFilter:
        """Define a datetime filter for a given column.

        Args:
            column: The column to filter on.
            value: The datetime value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.

        Raises:
            ValueError: If the value is not a valid datetime.
        """
        try:
            if isinstance(value, datetime):
                datetime_value = value
            else:
                datetime_value = datetime.strptime(
                    value, FILTERING_DATETIME_FORMAT
                )
        except ValueError as e:
            raise ValueError(
                "The datetime filter only works with values in the following "
                f"format: {FILTERING_DATETIME_FORMAT}"
            ) from e
        datetime_filter = NumericFilter(
            operation=GenericFilterOps(operator),
            column=column,
            value=datetime_value,
        )
        return datetime_filter

    @staticmethod
    def _define_uuid_filter(
        column: str, value: Any, operator: GenericFilterOps
    ) -> UUIDFilter:
        """Define a UUID filter for a given column.

        Args:
            column: The column to filter on.
            value: The UUID value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.

        Raises:
            ValueError: If the value is not a valid UUID.
        """
        # For equality checks, ensure that the value is a valid UUID.
        if operator == GenericFilterOps.EQUALS and not isinstance(value, UUID):
            try:
                UUID(value)
            except ValueError as e:
                raise ValueError(
                    "Invalid value passed as UUID query parameter."
                ) from e

        # Cast the value to string for further comparisons.
        value = str(value)

        # Generate the filter.
        uuid_filter = UUIDFilter(
            operation=GenericFilterOps(operator),
            column=column,
            value=value,
        )
        return uuid_filter

    @staticmethod
    def _define_bool_filter(
        column: str, value: Any, operator: GenericFilterOps
    ) -> BoolFilter:
        """Define a bool filter for a given column.

        Args:
            column: The column to filter on.
            value: The bool value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.
        """
        if GenericFilterOps(operator) != GenericFilterOps.EQUALS:
            logger.warning(
                "Boolean filters do not support any"
                "operation except for equals. Defaulting"
                "to an `equals` comparison."
            )
        return BoolFilter(
            operation=GenericFilterOps.EQUALS,
            column=column,
            value=bool(value),
        )

    @property
    def offset(self) -> int:
        """Returns the offset needed for the query on the data persistence layer.

        Returns:
            The offset for the query.
        """
        return self.size * (self.page - 1)

    def generate_filter(
        self, table: Type[SQLModel]
    ) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
        """Generate the filter for the query.

        Args:
            table: The Table that is being queried from.

        Returns:
            The filter expression for the query.

        Raises:
            RuntimeError: If a valid logical operator is not supplied.
        """
        from sqlalchemy import and_
        from sqlmodel import or_

        filters = []
        for column_filter in self.list_of_filters:
            filters.append(
                column_filter.generate_query_conditions(table=table)
            )
        for custom_filter in self.get_custom_filters():
            filters.append(custom_filter)
        if self.logical_operator == LogicalOperators.OR:
            return or_(False, *filters)
        elif self.logical_operator == LogicalOperators.AND:
            return and_(True, *filters)
        else:
            raise RuntimeError("No valid logical operator was supplied.")

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom filters.

        This can be overridden by subclasses to define custom filters that are
        not based on the columns of the underlying table.

        Returns:
            A list of custom filters.
        """
        return []

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        rbac_filter = self.generate_rbac_filter(table=table)

        if rbac_filter is not None:
            query = query.where(rbac_filter)

        filters = self.generate_filter(table=table)

        if filters is not None:
            query = query.where(filters)

        return query

    class Config:
        """Pydantic configuration class."""

        # all attributes with leading underscore are private and therefore
        # are mutable and not included in serialization
        underscore_attrs_are_private = True

created: Union[datetime.datetime, str] pydantic-field

Created

id: Union[uuid.UUID, str] pydantic-field

Id for this resource

list_of_filters: List[zenml.models.v2.base.filter.Filter] property readonly

Converts the class variables into a list of usable Filter Models.

Returns:

Type	Description
List[zenml.models.v2.base.filter.Filter]	A list of Filter models.

logical_operator: LogicalOperators pydantic-field

Which logical operator to use between all filters ['and', 'or']

offset: int property readonly

Returns the offset needed for the query on the data persistence layer.

Returns:

Type	Description
int	The offset for the query.

page: ConstrainedIntValue pydantic-field

Page number

size: ConstrainedIntValue pydantic-field

Page size

sort_by: str pydantic-field

Which column to sort by.

sorting_params: Tuple[str, zenml.enums.SorterOps] property readonly

Converts the class variables into a list of usable Filter Models.

Returns:

Type	Description
Tuple[str, zenml.enums.SorterOps]	A tuple of the column to sort by and the sorting operand.

updated: Union[datetime.datetime, str] pydantic-field

Updated

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/filter.py

class Config:
    """Pydantic configuration class."""

    # all attributes with leading underscore are private and therefore
    # are mutable and not included in serialization
    underscore_attrs_are_private = True

apply_filter(self, query, table)

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/base/filter.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    rbac_filter = self.generate_rbac_filter(table=table)

    if rbac_filter is not None:
        query = query.where(rbac_filter)

    filters = self.generate_filter(table=table)

    if filters is not None:
        query = query.where(filters)

    return query

configure_rbac(self, authenticated_user_id, **column_allowed_ids)

Configure RBAC allowed column values.

Parameters:

Name	Type	Description	Default
authenticated_user_id	UUID	ID of the authenticated user. All entities owned by this user will be included.	required
column_allowed_ids	Optional[Set[uuid.UUID]]	Set of IDs per column to limit the query to. If given, the remaining filters will be applied to entities within this set only. If None, the remaining filters will applied to all entries in the table.	{}

Source code in zenml/models/v2/base/filter.py

def configure_rbac(
    self,
    authenticated_user_id: UUID,
    **column_allowed_ids: Optional[Set[UUID]],
) -> None:
    """Configure RBAC allowed column values.

    Args:
        authenticated_user_id: ID of the authenticated user. All entities
            owned by this user will be included.
        column_allowed_ids: Set of IDs per column to limit the query to.
            If given, the remaining filters will be applied to entities
            within this set only. If `None`, the remaining filters will
            applied to all entries in the table.
    """
    self._rbac_configuration = (authenticated_user_id, column_allowed_ids)

filter_ops(values) classmethod

Parse incoming filters to ensure all filters are legal.

Parameters:

Name	Type	Description	Default
values	Dict[str, Any]	The values of the class.	required

Returns:

Type	Description
Dict[str, Any]	The values of the class.

Source code in zenml/models/v2/base/filter.py

@root_validator(pre=True)
def filter_ops(cls, values: Dict[str, Any]) -> Dict[str, Any]:
    """Parse incoming filters to ensure all filters are legal.

    Args:
        values: The values of the class.

    Returns:
        The values of the class.
    """
    cls._generate_filter_list(values)
    return values

generate_filter(self, table)

Generate the filter for the query.

Parameters:

Name	Type	Description	Default
table	Type[sqlmodel.main.SQLModel]	The Table that is being queried from.	required

Returns:

Type	Description
Union[BinaryExpression[Any], BooleanClauseList[Any]]	The filter expression for the query.

Exceptions:

Type	Description
RuntimeError	If a valid logical operator is not supplied.

Source code in zenml/models/v2/base/filter.py

def generate_filter(
    self, table: Type[SQLModel]
) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
    """Generate the filter for the query.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.

    Raises:
        RuntimeError: If a valid logical operator is not supplied.
    """
    from sqlalchemy import and_
    from sqlmodel import or_

    filters = []
    for column_filter in self.list_of_filters:
        filters.append(
            column_filter.generate_query_conditions(table=table)
        )
    for custom_filter in self.get_custom_filters():
        filters.append(custom_filter)
    if self.logical_operator == LogicalOperators.OR:
        return or_(False, *filters)
    elif self.logical_operator == LogicalOperators.AND:
        return and_(True, *filters)
    else:
        raise RuntimeError("No valid logical operator was supplied.")

generate_rbac_filter(self, table)

Generates an optional RBAC filter.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
Optional[BooleanClauseList[Any]]	The RBAC filter.

Source code in zenml/models/v2/base/filter.py

def generate_rbac_filter(
    self,
    table: Type["AnySchema"],
) -> Optional["BooleanClauseList[Any]"]:
    """Generates an optional RBAC filter.

    Args:
        table: The query table.

    Returns:
        The RBAC filter.
    """
    from sqlmodel import or_

    if not self._rbac_configuration:
        return None

    expressions = []

    for column_name, allowed_ids in self._rbac_configuration[1].items():
        if allowed_ids is not None:
            expression = getattr(table, column_name).in_(allowed_ids)
            expressions.append(expression)

    if expressions and hasattr(table, "user_id"):
        # If `expressions` is not empty, we do not have full access to all
        # rows of the table. In this case, we also include rows which the
        # user owns.

        # Unowned entities are considered server-owned and can be seen
        # by anyone
        expressions.append(getattr(table, "user_id").is_(None))
        # The authenticated user owns this entity
        expressions.append(
            getattr(table, "user_id") == self._rbac_configuration[0]
        )

    if expressions:
        return or_(*expressions)
    else:
        return None

get_custom_filters(self)

Get custom filters.

This can be overridden by subclasses to define custom filters that are not based on the columns of the underlying table.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/base/filter.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom filters.

    This can be overridden by subclasses to define custom filters that are
    not based on the columns of the underlying table.

    Returns:
        A list of custom filters.
    """
    return []

is_bool_field(k) classmethod

Checks if it's a bool field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a bool field, False otherwise.

Source code in zenml/models/v2/base/filter.py

@classmethod
def is_bool_field(cls, k: str) -> bool:
    """Checks if it's a bool field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a bool field, False otherwise.
    """
    return (
        issubclass(bool, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is bool
    )

is_datetime_field(k) classmethod

Checks if it's a datetime field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a datetime field, False otherwise.

Source code in zenml/models/v2/base/filter.py

@classmethod
def is_datetime_field(cls, k: str) -> bool:
    """Checks if it's a datetime field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a datetime field, False otherwise.
    """
    return (
        issubclass(datetime, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is datetime
    )

is_int_field(k) classmethod

Checks if it's a int field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a int field, False otherwise.

Source code in zenml/models/v2/base/filter.py

@classmethod
def is_int_field(cls, k: str) -> bool:
    """Checks if it's a int field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a int field, False otherwise.
    """
    return (
        issubclass(int, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is int
    )

is_sort_by_field(k) classmethod

Checks if it's a sort by field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a sort by field, False otherwise.

Source code in zenml/models/v2/base/filter.py

@classmethod
def is_sort_by_field(cls, k: str) -> bool:
    """Checks if it's a sort by field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a sort by field, False otherwise.
    """
    return (
        issubclass(str, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ == str
    ) and k == "sort_by"

is_str_field(k) classmethod

Checks if it's a string field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a string field, False otherwise.

Source code in zenml/models/v2/base/filter.py

@classmethod
def is_str_field(cls, k: str) -> bool:
    """Checks if it's a string field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a string field, False otherwise.
    """
    return (
        issubclass(str, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is str
    )

is_uuid_field(k) classmethod

Checks if it's a uuid field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a uuid field, False otherwise.

Source code in zenml/models/v2/base/filter.py

@classmethod
def is_uuid_field(cls, k: str) -> bool:
    """Checks if it's a uuid field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a uuid field, False otherwise.
    """
    return (
        issubclass(UUID, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is UUID
    )

validate_sort_by(v) classmethod

Validate that the sort_column is a valid column with a valid operand.

Parameters:

Name	Type	Description	Default
v	str	The sort_by field value.	required

Returns:

Type	Description
str	The validated sort_by field value.

Exceptions:

Type	Description
ValidationError	If the sort_by field is not a string.
ValueError	If the resource can't be sorted by this field.

Source code in zenml/models/v2/base/filter.py

@validator("sort_by", pre=True)
def validate_sort_by(cls, v: str) -> str:
    """Validate that the sort_column is a valid column with a valid operand.

    Args:
        v: The sort_by field value.

    Returns:
        The validated sort_by field value.

    Raises:
        ValidationError: If the sort_by field is not a string.
        ValueError: If the resource can't be sorted by this field.
    """
    # Somehow pydantic allows you to pass in int values, which will be
    #  interpreted as string, however within the validator they are still
    #  integers, which don't have a .split() method
    if not isinstance(v, str):
        raise ValidationError(
            f"str type expected for the sort_by field. "
            f"Received a {type(v)}"
        )
    column = v
    split_value = v.split(":", 1)
    if len(split_value) == 2:
        column = split_value[1]

        if split_value[0] not in SorterOps.values():
            logger.warning(
                "Invalid operand used for column sorting. "
                "Only the following operands are supported `%s`. "
                "Defaulting to 'asc' on column `%s`.",
                SorterOps.values(),
                column,
            )
            v = column

    if column in cls.FILTER_EXCLUDE_FIELDS:
        raise ValueError(
            f"This resource can not be sorted by this field: '{v}'"
        )
    elif column in cls.__fields__:
        return v
    else:
        raise ValueError(
            "You can only sort by valid fields of this resource"
        )

BoolFilter (Filter) pydantic-model

Filter for all Boolean fields.

Source code in zenml/models/v2/base/filter.py

class BoolFilter(Filter):
    """Filter for all Boolean fields."""

    ALLOWED_OPS: ClassVar[List[str]] = [GenericFilterOps.EQUALS]

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a boolean column.

        Args:
            column: The boolean column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """
        return column == self.value

generate_query_conditions_from_column(self, column)

Generate query conditions for a boolean column.

Parameters:

Name	Type	Description	Default
column	Any	The boolean column of an SQLModel table on which to filter.	required

Returns:

Type	Description
Any	A list of query conditions.

Source code in zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a boolean column.

    Args:
        column: The boolean column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """
    return column == self.value

Filter (BaseModel, ABC) pydantic-model

Filter for all fields.

A Filter is a combination of a column, a value that the user uses to filter on this column and an operation to use. The easiest example would be user equals aria with column=user, value=aria and the operation=equals.

All subclasses of this class will support different sets of operations. This operation set is defined in the ALLOWED_OPS class variable.

Source code in zenml/models/v2/base/filter.py

class Filter(BaseModel, ABC):
    """Filter for all fields.

    A Filter is a combination of a column, a value that the user uses to
    filter on this column and an operation to use. The easiest example
    would be `user equals aria` with column=`user`, value=`aria` and the
    operation=`equals`.

    All subclasses of this class will support different sets of operations.
    This operation set is defined in the ALLOWED_OPS class variable.
    """

    ALLOWED_OPS: ClassVar[List[str]] = []

    operation: GenericFilterOps
    column: str
    value: Any

    @validator("operation", pre=True)
    def validate_operation(cls, op: str) -> str:
        """Validate that the operation is a valid op for the field type.

        Args:
            op: The operation of this filter.

        Returns:
            The operation if it is valid.

        Raises:
            ValueError: If the operation is not valid for this field type.
        """
        if op not in cls.ALLOWED_OPS:
            raise ValueError(
                f"This datatype can not be filtered using this operation: "
                f"'{op}'. The allowed operations are: {cls.ALLOWED_OPS}"
            )
        else:
            return op

    def generate_query_conditions(
        self,
        table: Type[SQLModel],
    ) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
        """Generate the query conditions for the database.

        This method converts the Filter class into an appropriate SQLModel
        query condition, to be used when filtering on the Database.

        Args:
            table: The SQLModel table to use for the query creation

        Returns:
            A list of conditions that will be combined using the `and` operation
        """
        column = getattr(table, self.column)
        conditions = self.generate_query_conditions_from_column(column)
        return conditions  # type:ignore[no-any-return]

    @abstractmethod
    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions given the corresponding database column.

        This method should be overridden by subclasses to define how each
        supported operation in `self.ALLOWED_OPS` can be used to filter the
        given column by `self.value`.

        Args:
            column: The column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """

generate_query_conditions(self, table)

Generate the query conditions for the database.

This method converts the Filter class into an appropriate SQLModel query condition, to be used when filtering on the Database.

Parameters:

Name	Type	Description	Default
table	Type[sqlmodel.main.SQLModel]	The SQLModel table to use for the query creation	required

Returns:

Type	Description
Union[BinaryExpression[Any], BooleanClauseList[Any]]	A list of conditions that will be combined using the and operation

Source code in zenml/models/v2/base/filter.py

def generate_query_conditions(
    self,
    table: Type[SQLModel],
) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
    """Generate the query conditions for the database.

    This method converts the Filter class into an appropriate SQLModel
    query condition, to be used when filtering on the Database.

    Args:
        table: The SQLModel table to use for the query creation

    Returns:
        A list of conditions that will be combined using the `and` operation
    """
    column = getattr(table, self.column)
    conditions = self.generate_query_conditions_from_column(column)
    return conditions  # type:ignore[no-any-return]

generate_query_conditions_from_column(self, column)

Generate query conditions given the corresponding database column.

This method should be overridden by subclasses to define how each supported operation in self.ALLOWED_OPS can be used to filter the given column by self.value.

Parameters:

Name	Type	Description	Default
column	Any	The column of an SQLModel table on which to filter.	required

Returns:

Type	Description
Any	A list of query conditions.

Source code in zenml/models/v2/base/filter.py

@abstractmethod
def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions given the corresponding database column.

    This method should be overridden by subclasses to define how each
    supported operation in `self.ALLOWED_OPS` can be used to filter the
    given column by `self.value`.

    Args:
        column: The column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """

validate_operation(op) classmethod

Validate that the operation is a valid op for the field type.

Parameters:

Name	Type	Description	Default
op	str	The operation of this filter.	required

Returns:

Type	Description
str	The operation if it is valid.

Exceptions:

Type	Description
ValueError	If the operation is not valid for this field type.

Source code in zenml/models/v2/base/filter.py

@validator("operation", pre=True)
def validate_operation(cls, op: str) -> str:
    """Validate that the operation is a valid op for the field type.

    Args:
        op: The operation of this filter.

    Returns:
        The operation if it is valid.

    Raises:
        ValueError: If the operation is not valid for this field type.
    """
    if op not in cls.ALLOWED_OPS:
        raise ValueError(
            f"This datatype can not be filtered using this operation: "
            f"'{op}'. The allowed operations are: {cls.ALLOWED_OPS}"
        )
    else:
        return op

NumericFilter (Filter) pydantic-model

Filter for all numeric fields.

Source code in zenml/models/v2/base/filter.py

class NumericFilter(Filter):
    """Filter for all numeric fields."""

    value: Union[float, datetime]

    ALLOWED_OPS: ClassVar[List[str]] = [
        GenericFilterOps.EQUALS,
        GenericFilterOps.GT,
        GenericFilterOps.GTE,
        GenericFilterOps.LT,
        GenericFilterOps.LTE,
    ]

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a UUID column.

        Args:
            column: The UUID column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """
        if self.operation == GenericFilterOps.GTE:
            return column >= self.value
        if self.operation == GenericFilterOps.GT:
            return column > self.value
        if self.operation == GenericFilterOps.LTE:
            return column <= self.value
        if self.operation == GenericFilterOps.LT:
            return column < self.value
        return column == self.value

generate_query_conditions_from_column(self, column)

Generate query conditions for a UUID column.

Parameters:

Name	Type	Description	Default
column	Any	The UUID column of an SQLModel table on which to filter.	required

Returns:

Type	Description
Any	A list of query conditions.

Source code in zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a UUID column.

    Args:
        column: The UUID column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """
    if self.operation == GenericFilterOps.GTE:
        return column >= self.value
    if self.operation == GenericFilterOps.GT:
        return column > self.value
    if self.operation == GenericFilterOps.LTE:
        return column <= self.value
    if self.operation == GenericFilterOps.LT:
        return column < self.value
    return column == self.value

StrFilter (Filter) pydantic-model

Filter for all string fields.

Source code in zenml/models/v2/base/filter.py

class StrFilter(Filter):
    """Filter for all string fields."""

    ALLOWED_OPS: ClassVar[List[str]] = [
        GenericFilterOps.EQUALS,
        GenericFilterOps.STARTSWITH,
        GenericFilterOps.CONTAINS,
        GenericFilterOps.ENDSWITH,
    ]

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a string column.

        Args:
            column: The string column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """
        if self.operation == GenericFilterOps.CONTAINS:
            return column.like(f"%{self.value}%")
        if self.operation == GenericFilterOps.STARTSWITH:
            return column.startswith(f"{self.value}")
        if self.operation == GenericFilterOps.ENDSWITH:
            return column.endswith(f"{self.value}")
        return column == self.value

generate_query_conditions_from_column(self, column)

Generate query conditions for a string column.

Parameters:

Name	Type	Description	Default
column	Any	The string column of an SQLModel table on which to filter.	required

Returns:

Type	Description
Any	A list of query conditions.

Source code in zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a string column.

    Args:
        column: The string column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """
    if self.operation == GenericFilterOps.CONTAINS:
        return column.like(f"%{self.value}%")
    if self.operation == GenericFilterOps.STARTSWITH:
        return column.startswith(f"{self.value}")
    if self.operation == GenericFilterOps.ENDSWITH:
        return column.endswith(f"{self.value}")
    return column == self.value

UUIDFilter (StrFilter) pydantic-model

Filter for all uuid fields which are mostly treated like strings.

Source code in zenml/models/v2/base/filter.py

class UUIDFilter(StrFilter):
    """Filter for all uuid fields which are mostly treated like strings."""

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a UUID column.

        Args:
            column: The UUID column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """
        import sqlalchemy
        from sqlalchemy_utils.functions import cast_if

        # For equality checks, compare the UUID directly
        if self.operation == GenericFilterOps.EQUALS:
            return column == self.value

        # For all other operations, cast and handle the column as string
        return super().generate_query_conditions_from_column(
            column=cast_if(column, sqlalchemy.String)
        )

generate_query_conditions_from_column(self, column)

Generate query conditions for a UUID column.

Parameters:

Name	Type	Description	Default
column	Any	The UUID column of an SQLModel table on which to filter.	required

Returns:

Type	Description
Any	A list of query conditions.

Source code in zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a UUID column.

    Args:
        column: The UUID column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """
    import sqlalchemy
    from sqlalchemy_utils.functions import cast_if

    # For equality checks, compare the UUID directly
    if self.operation == GenericFilterOps.EQUALS:
        return column == self.value

    # For all other operations, cast and handle the column as string
    return super().generate_query_conditions_from_column(
        column=cast_if(column, sqlalchemy.String)
    )

internal

Utility methods for internal models.

server_owned_request_model(_cls)

Convert a request model to a model which does not require a user ID.

Parameters:

Name	Type	Description	Default
_cls	Type[~T]	The class to decorate	required

Returns:

Type	Description
Type[~T]	The decorated class.

Source code in zenml/models/v2/base/internal.py

def server_owned_request_model(_cls: Type[T]) -> Type[T]:
    """Convert a request model to a model which does not require a user ID.

    Args:
        _cls: The class to decorate

    Returns:
        The decorated class.
    """
    if user_field := _cls.__fields__.get("user", None):
        user_field.required = False
        user_field.allow_none = True
        user_field.default = None

    return _cls

page

Page model definitions.

Page (GenericModel, Generic) pydantic-model

Return Model for List Models to accommodate pagination.

Source code in zenml/models/v2/base/page.py

class Page(GenericModel, Generic[B]):
    """Return Model for List Models to accommodate pagination."""

    index: PositiveInt
    max_size: PositiveInt
    total_pages: NonNegativeInt
    total: NonNegativeInt
    items: List[B]

    __params_type__ = BaseFilter

    @property
    def size(self) -> int:
        """Return the item count of the page.

        Returns:
            The amount of items in the page.
        """
        return len(self.items)

    def __len__(self) -> int:
        """Return the item count of the page.

        This enables `len(page)`.

        Returns:
            The amount of items in the page.
        """
        return len(self.items)

    def __getitem__(self, index: int) -> B:
        """Return the item at the given index.

        This enables `page[index]`.

        Args:
            index: The index to get the item from.

        Returns:
            The item at the given index.
        """
        return self.items[index]

    def __iter__(self) -> Generator[B, None, None]:  # type: ignore[override]
        """Return an iterator over the items in the page.

        This enables `for item in page` loops, but breaks `dict(page)`.

        Yields:
            An iterator over the items in the page.
        """
        for item in self.items.__iter__():
            yield item

    def __contains__(self, item: B) -> bool:
        """Returns whether the page contains a specific item.

        This enables `item in page` checks.

        Args:
            item: The item to check for.

        Returns:
            Whether the item is in the page.
        """
        return item in self.items

    class Config:
        """Pydantic configuration class."""

        # This is needed to allow the REST API server to unpack SecretStr
        # values correctly before sending them to the client.
        json_encoders = {
            SecretStr: lambda v: v.get_secret_value() if v else None
        }

size: int property readonly

Return the item count of the page.

Returns:

Type	Description
int	The amount of items in the page.

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/page.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST API server to unpack SecretStr
    # values correctly before sending them to the client.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value() if v else None
    }

__params_type__ (BaseModel) pydantic-model

Class to unify all filter, paginate and sort request parameters.

This Model allows fine-grained filtering, sorting and pagination of resources.

Usage example for subclasses of this class:

ResourceListModel(
    name="contains:default",
    workspace="default"
    count_steps="gte:5"
    sort_by="created",
    page=2,
    size=20
)

Source code in zenml/models/v2/base/page.py

class BaseFilter(BaseModel):
    """Class to unify all filter, paginate and sort request parameters.

    This Model allows fine-grained filtering, sorting and pagination of
    resources.

    Usage example for subclasses of this class:
    ```
    ResourceListModel(
        name="contains:default",
        workspace="default"
        count_steps="gte:5"
        sort_by="created",
        page=2,
        size=20
    )
    ```
    """

    # List of fields that cannot be used as filters.
    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        "sort_by",
        "page",
        "size",
        "logical_operator",
    ]

    # List of fields that are not even mentioned as options in the CLI.
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = []

    # List of fields that are wrapped with `fastapi.Query(default)` in API.
    API_MULTI_INPUT_PARAMS: ClassVar[List[str]] = []

    sort_by: str = Field(
        default="created", description="Which column to sort by."
    )
    logical_operator: LogicalOperators = Field(
        default=LogicalOperators.AND,
        description="Which logical operator to use between all filters "
        "['and', 'or']",
    )
    page: int = Field(
        default=PAGINATION_STARTING_PAGE, ge=1, description="Page number"
    )
    size: int = Field(
        default=PAGE_SIZE_DEFAULT,
        ge=1,
        le=PAGE_SIZE_MAXIMUM,
        description="Page size",
    )

    id: Optional[Union[UUID, str]] = Field(
        default=None, description="Id for this resource"
    )
    created: Optional[Union[datetime, str]] = Field(
        default=None, description="Created"
    )
    updated: Optional[Union[datetime, str]] = Field(
        default=None, description="Updated"
    )

    _rbac_configuration: Optional[
        Tuple[UUID, Dict[str, Optional[Set[UUID]]]]
    ] = None

    @validator("sort_by", pre=True)
    def validate_sort_by(cls, v: str) -> str:
        """Validate that the sort_column is a valid column with a valid operand.

        Args:
            v: The sort_by field value.

        Returns:
            The validated sort_by field value.

        Raises:
            ValidationError: If the sort_by field is not a string.
            ValueError: If the resource can't be sorted by this field.
        """
        # Somehow pydantic allows you to pass in int values, which will be
        #  interpreted as string, however within the validator they are still
        #  integers, which don't have a .split() method
        if not isinstance(v, str):
            raise ValidationError(
                f"str type expected for the sort_by field. "
                f"Received a {type(v)}"
            )
        column = v
        split_value = v.split(":", 1)
        if len(split_value) == 2:
            column = split_value[1]

            if split_value[0] not in SorterOps.values():
                logger.warning(
                    "Invalid operand used for column sorting. "
                    "Only the following operands are supported `%s`. "
                    "Defaulting to 'asc' on column `%s`.",
                    SorterOps.values(),
                    column,
                )
                v = column

        if column in cls.FILTER_EXCLUDE_FIELDS:
            raise ValueError(
                f"This resource can not be sorted by this field: '{v}'"
            )
        elif column in cls.__fields__:
            return v
        else:
            raise ValueError(
                "You can only sort by valid fields of this resource"
            )

    @root_validator(pre=True)
    def filter_ops(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Parse incoming filters to ensure all filters are legal.

        Args:
            values: The values of the class.

        Returns:
            The values of the class.
        """
        cls._generate_filter_list(values)
        return values

    @property
    def list_of_filters(self) -> List[Filter]:
        """Converts the class variables into a list of usable Filter Models.

        Returns:
            A list of Filter models.
        """
        return self._generate_filter_list(
            {key: getattr(self, key) for key in self.__fields__}
        )

    @property
    def sorting_params(self) -> Tuple[str, SorterOps]:
        """Converts the class variables into a list of usable Filter Models.

        Returns:
            A tuple of the column to sort by and the sorting operand.
        """
        column = self.sort_by
        # The default sorting operand is asc
        operator = SorterOps.ASCENDING

        # Check if user explicitly set an operand
        split_value = self.sort_by.split(":", 1)
        if len(split_value) == 2:
            column = split_value[1]
            operator = SorterOps(split_value[0])

        return column, operator

    def configure_rbac(
        self,
        authenticated_user_id: UUID,
        **column_allowed_ids: Optional[Set[UUID]],
    ) -> None:
        """Configure RBAC allowed column values.

        Args:
            authenticated_user_id: ID of the authenticated user. All entities
                owned by this user will be included.
            column_allowed_ids: Set of IDs per column to limit the query to.
                If given, the remaining filters will be applied to entities
                within this set only. If `None`, the remaining filters will
                applied to all entries in the table.
        """
        self._rbac_configuration = (authenticated_user_id, column_allowed_ids)

    def generate_rbac_filter(
        self,
        table: Type["AnySchema"],
    ) -> Optional["BooleanClauseList[Any]"]:
        """Generates an optional RBAC filter.

        Args:
            table: The query table.

        Returns:
            The RBAC filter.
        """
        from sqlmodel import or_

        if not self._rbac_configuration:
            return None

        expressions = []

        for column_name, allowed_ids in self._rbac_configuration[1].items():
            if allowed_ids is not None:
                expression = getattr(table, column_name).in_(allowed_ids)
                expressions.append(expression)

        if expressions and hasattr(table, "user_id"):
            # If `expressions` is not empty, we do not have full access to all
            # rows of the table. In this case, we also include rows which the
            # user owns.

            # Unowned entities are considered server-owned and can be seen
            # by anyone
            expressions.append(getattr(table, "user_id").is_(None))
            # The authenticated user owns this entity
            expressions.append(
                getattr(table, "user_id") == self._rbac_configuration[0]
            )

        if expressions:
            return or_(*expressions)
        else:
            return None

    @classmethod
    def _generate_filter_list(cls, values: Dict[str, Any]) -> List[Filter]:
        """Create a list of filters from a (column, value) dictionary.

        Args:
            values: A dictionary of column names and values to filter on.

        Returns:
            A list of filters.
        """
        list_of_filters: List[Filter] = []

        for key, value in values.items():
            # Ignore excluded filters
            if key in cls.FILTER_EXCLUDE_FIELDS:
                continue

            # Skip filtering for None values
            if value is None:
                continue

            # Determine the operator and filter value
            value, operator = cls._resolve_operator(value)

            # Define the filter
            filter = cls._define_filter(
                column=key, value=value, operator=operator
            )
            list_of_filters.append(filter)

        return list_of_filters

    @staticmethod
    def _resolve_operator(value: Any) -> Tuple[Any, GenericFilterOps]:
        """Determine the operator and filter value from a user-provided value.

        If the user-provided value is a string of the form "operator:value",
        then the operator is extracted and the value is returned. Otherwise,
        `GenericFilterOps.EQUALS` is used as default operator and the value
        is returned as-is.

        Args:
            value: The user-provided value.

        Returns:
            A tuple of the filter value and the operator.
        """
        operator = GenericFilterOps.EQUALS  # Default operator
        if isinstance(value, str):
            split_value = value.split(":", 1)
            if (
                len(split_value) == 2
                and split_value[0] in GenericFilterOps.values()
            ):
                value = split_value[1]
                operator = GenericFilterOps(split_value[0])
        return value, operator

    @classmethod
    def _define_filter(
        cls, column: str, value: Any, operator: GenericFilterOps
    ) -> Filter:
        """Define a filter for a given column.

        Args:
            column: The column to filter on.
            value: The value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.
        """
        # Create datetime filters
        if cls.is_datetime_field(column):
            return cls._define_datetime_filter(
                column=column,
                value=value,
                operator=operator,
            )

        # Create UUID filters
        if cls.is_uuid_field(column):
            return cls._define_uuid_filter(
                column=column,
                value=value,
                operator=operator,
            )

        # Create int filters
        if cls.is_int_field(column):
            return NumericFilter(
                operation=GenericFilterOps(operator),
                column=column,
                value=int(value),
            )

        # Create bool filters
        if cls.is_bool_field(column):
            return cls._define_bool_filter(
                column=column,
                value=value,
                operator=operator,
            )

        # Create str filters
        if cls.is_str_field(column):
            return StrFilter(
                operation=GenericFilterOps(operator),
                column=column,
                value=value,
            )

        # Handle unsupported datatypes
        logger.warning(
            f"The Datatype {cls.__fields__[column].type_} might not be "
            "supported for filtering. Defaulting to a string filter."
        )
        return StrFilter(
            operation=GenericFilterOps(operator),
            column=column,
            value=str(value),
        )

    @classmethod
    def is_datetime_field(cls, k: str) -> bool:
        """Checks if it's a datetime field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a datetime field, False otherwise.
        """
        return (
            issubclass(datetime, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is datetime
        )

    @classmethod
    def is_uuid_field(cls, k: str) -> bool:
        """Checks if it's a uuid field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a uuid field, False otherwise.
        """
        return (
            issubclass(UUID, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is UUID
        )

    @classmethod
    def is_int_field(cls, k: str) -> bool:
        """Checks if it's a int field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a int field, False otherwise.
        """
        return (
            issubclass(int, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is int
        )

    @classmethod
    def is_bool_field(cls, k: str) -> bool:
        """Checks if it's a bool field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a bool field, False otherwise.
        """
        return (
            issubclass(bool, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is bool
        )

    @classmethod
    def is_str_field(cls, k: str) -> bool:
        """Checks if it's a string field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a string field, False otherwise.
        """
        return (
            issubclass(str, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ is str
        )

    @classmethod
    def is_sort_by_field(cls, k: str) -> bool:
        """Checks if it's a sort by field.

        Args:
            k: The key to check.

        Returns:
            True if the field is a sort by field, False otherwise.
        """
        return (
            issubclass(str, get_args(cls.__fields__[k].type_))
            or cls.__fields__[k].type_ == str
        ) and k == "sort_by"

    @staticmethod
    def _define_datetime_filter(
        column: str, value: Any, operator: GenericFilterOps
    ) -> NumericFilter:
        """Define a datetime filter for a given column.

        Args:
            column: The column to filter on.
            value: The datetime value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.

        Raises:
            ValueError: If the value is not a valid datetime.
        """
        try:
            if isinstance(value, datetime):
                datetime_value = value
            else:
                datetime_value = datetime.strptime(
                    value, FILTERING_DATETIME_FORMAT
                )
        except ValueError as e:
            raise ValueError(
                "The datetime filter only works with values in the following "
                f"format: {FILTERING_DATETIME_FORMAT}"
            ) from e
        datetime_filter = NumericFilter(
            operation=GenericFilterOps(operator),
            column=column,
            value=datetime_value,
        )
        return datetime_filter

    @staticmethod
    def _define_uuid_filter(
        column: str, value: Any, operator: GenericFilterOps
    ) -> UUIDFilter:
        """Define a UUID filter for a given column.

        Args:
            column: The column to filter on.
            value: The UUID value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.

        Raises:
            ValueError: If the value is not a valid UUID.
        """
        # For equality checks, ensure that the value is a valid UUID.
        if operator == GenericFilterOps.EQUALS and not isinstance(value, UUID):
            try:
                UUID(value)
            except ValueError as e:
                raise ValueError(
                    "Invalid value passed as UUID query parameter."
                ) from e

        # Cast the value to string for further comparisons.
        value = str(value)

        # Generate the filter.
        uuid_filter = UUIDFilter(
            operation=GenericFilterOps(operator),
            column=column,
            value=value,
        )
        return uuid_filter

    @staticmethod
    def _define_bool_filter(
        column: str, value: Any, operator: GenericFilterOps
    ) -> BoolFilter:
        """Define a bool filter for a given column.

        Args:
            column: The column to filter on.
            value: The bool value by which to filter.
            operator: The operator to use for filtering.

        Returns:
            A Filter object.
        """
        if GenericFilterOps(operator) != GenericFilterOps.EQUALS:
            logger.warning(
                "Boolean filters do not support any"
                "operation except for equals. Defaulting"
                "to an `equals` comparison."
            )
        return BoolFilter(
            operation=GenericFilterOps.EQUALS,
            column=column,
            value=bool(value),
        )

    @property
    def offset(self) -> int:
        """Returns the offset needed for the query on the data persistence layer.

        Returns:
            The offset for the query.
        """
        return self.size * (self.page - 1)

    def generate_filter(
        self, table: Type[SQLModel]
    ) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
        """Generate the filter for the query.

        Args:
            table: The Table that is being queried from.

        Returns:
            The filter expression for the query.

        Raises:
            RuntimeError: If a valid logical operator is not supplied.
        """
        from sqlalchemy import and_
        from sqlmodel import or_

        filters = []
        for column_filter in self.list_of_filters:
            filters.append(
                column_filter.generate_query_conditions(table=table)
            )
        for custom_filter in self.get_custom_filters():
            filters.append(custom_filter)
        if self.logical_operator == LogicalOperators.OR:
            return or_(False, *filters)
        elif self.logical_operator == LogicalOperators.AND:
            return and_(True, *filters)
        else:
            raise RuntimeError("No valid logical operator was supplied.")

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom filters.

        This can be overridden by subclasses to define custom filters that are
        not based on the columns of the underlying table.

        Returns:
            A list of custom filters.
        """
        return []

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        rbac_filter = self.generate_rbac_filter(table=table)

        if rbac_filter is not None:
            query = query.where(rbac_filter)

        filters = self.generate_filter(table=table)

        if filters is not None:
            query = query.where(filters)

        return query

    class Config:
        """Pydantic configuration class."""

        # all attributes with leading underscore are private and therefore
        # are mutable and not included in serialization
        underscore_attrs_are_private = True

created: Union[datetime.datetime, str] pydantic-field

Created

id: Union[uuid.UUID, str] pydantic-field

Id for this resource

list_of_filters: List[zenml.models.v2.base.filter.Filter] property readonly

Converts the class variables into a list of usable Filter Models.

Returns:

Type	Description
List[zenml.models.v2.base.filter.Filter]	A list of Filter models.

logical_operator: LogicalOperators pydantic-field

Which logical operator to use between all filters ['and', 'or']

offset: int property readonly

Returns the offset needed for the query on the data persistence layer.

Returns:

Type	Description
int	The offset for the query.

page: ConstrainedIntValue pydantic-field

Page number

size: ConstrainedIntValue pydantic-field

Page size

sort_by: str pydantic-field

Which column to sort by.

sorting_params: Tuple[str, zenml.enums.SorterOps] property readonly

Converts the class variables into a list of usable Filter Models.

Returns:

Type	Description
Tuple[str, zenml.enums.SorterOps]	A tuple of the column to sort by and the sorting operand.

updated: Union[datetime.datetime, str] pydantic-field

Updated

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/page.py

class Config:
    """Pydantic configuration class."""

    # all attributes with leading underscore are private and therefore
    # are mutable and not included in serialization
    underscore_attrs_are_private = True

apply_filter(self, query, table)

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/base/page.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    rbac_filter = self.generate_rbac_filter(table=table)

    if rbac_filter is not None:
        query = query.where(rbac_filter)

    filters = self.generate_filter(table=table)

    if filters is not None:
        query = query.where(filters)

    return query

configure_rbac(self, authenticated_user_id, **column_allowed_ids)

Configure RBAC allowed column values.

Parameters:

Name	Type	Description	Default
authenticated_user_id	UUID	ID of the authenticated user. All entities owned by this user will be included.	required
column_allowed_ids	Optional[Set[uuid.UUID]]	Set of IDs per column to limit the query to. If given, the remaining filters will be applied to entities within this set only. If None, the remaining filters will applied to all entries in the table.	{}

Source code in zenml/models/v2/base/page.py

def configure_rbac(
    self,
    authenticated_user_id: UUID,
    **column_allowed_ids: Optional[Set[UUID]],
) -> None:
    """Configure RBAC allowed column values.

    Args:
        authenticated_user_id: ID of the authenticated user. All entities
            owned by this user will be included.
        column_allowed_ids: Set of IDs per column to limit the query to.
            If given, the remaining filters will be applied to entities
            within this set only. If `None`, the remaining filters will
            applied to all entries in the table.
    """
    self._rbac_configuration = (authenticated_user_id, column_allowed_ids)

filter_ops(values) classmethod

Parse incoming filters to ensure all filters are legal.

Parameters:

Name	Type	Description	Default
values	Dict[str, Any]	The values of the class.	required

Returns:

Type	Description
Dict[str, Any]	The values of the class.

Source code in zenml/models/v2/base/page.py

@root_validator(pre=True)
def filter_ops(cls, values: Dict[str, Any]) -> Dict[str, Any]:
    """Parse incoming filters to ensure all filters are legal.

    Args:
        values: The values of the class.

    Returns:
        The values of the class.
    """
    cls._generate_filter_list(values)
    return values

generate_filter(self, table)

Generate the filter for the query.

Parameters:

Name	Type	Description	Default
table	Type[sqlmodel.main.SQLModel]	The Table that is being queried from.	required

Returns:

Type	Description
Union[BinaryExpression[Any], BooleanClauseList[Any]]	The filter expression for the query.

Exceptions:

Type	Description
RuntimeError	If a valid logical operator is not supplied.

Source code in zenml/models/v2/base/page.py

def generate_filter(
    self, table: Type[SQLModel]
) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
    """Generate the filter for the query.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.

    Raises:
        RuntimeError: If a valid logical operator is not supplied.
    """
    from sqlalchemy import and_
    from sqlmodel import or_

    filters = []
    for column_filter in self.list_of_filters:
        filters.append(
            column_filter.generate_query_conditions(table=table)
        )
    for custom_filter in self.get_custom_filters():
        filters.append(custom_filter)
    if self.logical_operator == LogicalOperators.OR:
        return or_(False, *filters)
    elif self.logical_operator == LogicalOperators.AND:
        return and_(True, *filters)
    else:
        raise RuntimeError("No valid logical operator was supplied.")

generate_rbac_filter(self, table)

Generates an optional RBAC filter.

Parameters:

Name	Type	Description	Default
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
Optional[BooleanClauseList[Any]]	The RBAC filter.

Source code in zenml/models/v2/base/page.py

def generate_rbac_filter(
    self,
    table: Type["AnySchema"],
) -> Optional["BooleanClauseList[Any]"]:
    """Generates an optional RBAC filter.

    Args:
        table: The query table.

    Returns:
        The RBAC filter.
    """
    from sqlmodel import or_

    if not self._rbac_configuration:
        return None

    expressions = []

    for column_name, allowed_ids in self._rbac_configuration[1].items():
        if allowed_ids is not None:
            expression = getattr(table, column_name).in_(allowed_ids)
            expressions.append(expression)

    if expressions and hasattr(table, "user_id"):
        # If `expressions` is not empty, we do not have full access to all
        # rows of the table. In this case, we also include rows which the
        # user owns.

        # Unowned entities are considered server-owned and can be seen
        # by anyone
        expressions.append(getattr(table, "user_id").is_(None))
        # The authenticated user owns this entity
        expressions.append(
            getattr(table, "user_id") == self._rbac_configuration[0]
        )

    if expressions:
        return or_(*expressions)
    else:
        return None

get_custom_filters(self)

Get custom filters.

This can be overridden by subclasses to define custom filters that are not based on the columns of the underlying table.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/base/page.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom filters.

    This can be overridden by subclasses to define custom filters that are
    not based on the columns of the underlying table.

    Returns:
        A list of custom filters.
    """
    return []

is_bool_field(k) classmethod

Checks if it's a bool field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a bool field, False otherwise.

Source code in zenml/models/v2/base/page.py

@classmethod
def is_bool_field(cls, k: str) -> bool:
    """Checks if it's a bool field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a bool field, False otherwise.
    """
    return (
        issubclass(bool, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is bool
    )

is_datetime_field(k) classmethod

Checks if it's a datetime field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a datetime field, False otherwise.

Source code in zenml/models/v2/base/page.py

@classmethod
def is_datetime_field(cls, k: str) -> bool:
    """Checks if it's a datetime field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a datetime field, False otherwise.
    """
    return (
        issubclass(datetime, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is datetime
    )

is_int_field(k) classmethod

Checks if it's a int field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a int field, False otherwise.

Source code in zenml/models/v2/base/page.py

@classmethod
def is_int_field(cls, k: str) -> bool:
    """Checks if it's a int field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a int field, False otherwise.
    """
    return (
        issubclass(int, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is int
    )

is_sort_by_field(k) classmethod

Checks if it's a sort by field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a sort by field, False otherwise.

Source code in zenml/models/v2/base/page.py

@classmethod
def is_sort_by_field(cls, k: str) -> bool:
    """Checks if it's a sort by field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a sort by field, False otherwise.
    """
    return (
        issubclass(str, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ == str
    ) and k == "sort_by"

is_str_field(k) classmethod

Checks if it's a string field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a string field, False otherwise.

Source code in zenml/models/v2/base/page.py

@classmethod
def is_str_field(cls, k: str) -> bool:
    """Checks if it's a string field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a string field, False otherwise.
    """
    return (
        issubclass(str, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is str
    )

is_uuid_field(k) classmethod

Checks if it's a uuid field.

Parameters:

Name	Type	Description	Default
k	str	The key to check.	required

Returns:

Type	Description
bool	True if the field is a uuid field, False otherwise.

Source code in zenml/models/v2/base/page.py

@classmethod
def is_uuid_field(cls, k: str) -> bool:
    """Checks if it's a uuid field.

    Args:
        k: The key to check.

    Returns:
        True if the field is a uuid field, False otherwise.
    """
    return (
        issubclass(UUID, get_args(cls.__fields__[k].type_))
        or cls.__fields__[k].type_ is UUID
    )

validate_sort_by(v) classmethod

Validate that the sort_column is a valid column with a valid operand.

Parameters:

Name	Type	Description	Default
v	str	The sort_by field value.	required

Returns:

Type	Description
str	The validated sort_by field value.

Exceptions:

Type	Description
ValidationError	If the sort_by field is not a string.
ValueError	If the resource can't be sorted by this field.

Source code in zenml/models/v2/base/page.py

@validator("sort_by", pre=True)
def validate_sort_by(cls, v: str) -> str:
    """Validate that the sort_column is a valid column with a valid operand.

    Args:
        v: The sort_by field value.

    Returns:
        The validated sort_by field value.

    Raises:
        ValidationError: If the sort_by field is not a string.
        ValueError: If the resource can't be sorted by this field.
    """
    # Somehow pydantic allows you to pass in int values, which will be
    #  interpreted as string, however within the validator they are still
    #  integers, which don't have a .split() method
    if not isinstance(v, str):
        raise ValidationError(
            f"str type expected for the sort_by field. "
            f"Received a {type(v)}"
        )
    column = v
    split_value = v.split(":", 1)
    if len(split_value) == 2:
        column = split_value[1]

        if split_value[0] not in SorterOps.values():
            logger.warning(
                "Invalid operand used for column sorting. "
                "Only the following operands are supported `%s`. "
                "Defaulting to 'asc' on column `%s`.",
                SorterOps.values(),
                column,
            )
            v = column

    if column in cls.FILTER_EXCLUDE_FIELDS:
        raise ValueError(
            f"This resource can not be sorted by this field: '{v}'"
        )
    elif column in cls.__fields__:
        return v
    else:
        raise ValueError(
            "You can only sort by valid fields of this resource"
        )

__contains__(self, item) special

Returns whether the page contains a specific item.

This enables item in page checks.

Parameters:

Name	Type	Description	Default
item	~B	The item to check for.	required

Returns:

Type	Description
bool	Whether the item is in the page.

Source code in zenml/models/v2/base/page.py

def __contains__(self, item: B) -> bool:
    """Returns whether the page contains a specific item.

    This enables `item in page` checks.

    Args:
        item: The item to check for.

    Returns:
        Whether the item is in the page.
    """
    return item in self.items

__getitem__(self, index) special

Return the item at the given index.

This enables page[index].

Parameters:

Name	Type	Description	Default
index	int	The index to get the item from.	required

Returns:

Type	Description
~B	The item at the given index.

Source code in zenml/models/v2/base/page.py

def __getitem__(self, index: int) -> B:
    """Return the item at the given index.

    This enables `page[index]`.

    Args:
        index: The index to get the item from.

    Returns:
        The item at the given index.
    """
    return self.items[index]

__iter__(self) special

Return an iterator over the items in the page.

This enables for item in page loops, but breaks dict(page).

Yields:

Type	Description
Generator[~B, NoneType, NoneType]	An iterator over the items in the page.

Source code in zenml/models/v2/base/page.py

def __iter__(self) -> Generator[B, None, None]:  # type: ignore[override]
    """Return an iterator over the items in the page.

    This enables `for item in page` loops, but breaks `dict(page)`.

    Yields:
        An iterator over the items in the page.
    """
    for item in self.items.__iter__():
        yield item

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

__len__(self) special

Return the item count of the page.

This enables len(page).

Returns:

Type	Description
int	The amount of items in the page.

Source code in zenml/models/v2/base/page.py

def __len__(self) -> int:
    """Return the item count of the page.

    This enables `len(page)`.

    Returns:
        The amount of items in the page.
    """
    return len(self.items)

scoped

Scoped model definitions.

BaseIdentifiedResponse[UserBody, UserMetadata, UserResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][UserBody, UserMetadata, UserResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/scoped.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserScopedFilter (BaseFilter) pydantic-model

Model to enable advanced user-based scoping.

Source code in zenml/models/v2/base/scoped.py

class UserScopedFilter(BaseFilter):
    """Model to enable advanced user-based scoping."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "scope_user",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.CLI_EXCLUDE_FIELDS,
        "scope_user",
    ]
    scope_user: Optional[UUID] = Field(
        default=None,
        description="The user to scope this query to.",
    )

    def set_scope_user(self, user_id: UUID) -> None:
        """Set the user that is performing the filtering to scope the response.

        Args:
            user_id: The user ID to scope the response to.
        """
        self.scope_user = user_id

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)

        if self.scope_user:
            query = query.where(getattr(table, "user_id") == self.scope_user)

        return query

scope_user: UUID pydantic-field

The user to scope this query to.

apply_filter(self, query, table)

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/base/scoped.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)

    if self.scope_user:
        query = query.where(getattr(table, "user_id") == self.scope_user)

    return query

set_scope_user(self, user_id)

Set the user that is performing the filtering to scope the response.

Parameters:

Name	Type	Description	Default
user_id	UUID	The user ID to scope the response to.	required

Source code in zenml/models/v2/base/scoped.py

def set_scope_user(self, user_id: UUID) -> None:
    """Set the user that is performing the filtering to scope the response.

    Args:
        user_id: The user ID to scope the response to.
    """
    self.scope_user = user_id

UserScopedRequest (BaseRequest) pydantic-model

Base user-owned request model.

Used as a base class for all domain models that are "owned" by a user.

Source code in zenml/models/v2/base/scoped.py

class UserScopedRequest(BaseRequest):
    """Base user-owned request model.

    Used as a base class for all domain models that are "owned" by a user.
    """

    user: UUID = Field(title="The id of the user that created this resource.")

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Fetches the analytics metadata for user scoped models.

        Returns:
            The analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        metadata["user_id"] = self.user
        return metadata

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_analytics_metadata(self)

Fetches the analytics metadata for user scoped models.

Returns:

Type	Description
Dict[str, Any]	The analytics metadata.

Source code in zenml/models/v2/base/scoped.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for user scoped models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata["user_id"] = self.user
    return metadata

UserScopedResponse (BaseIdentifiedResponse[UserBody, UserMetadata, UserResources], Generic) pydantic-model

Base user-owned model.

Used as a base class for all domain models that are "owned" by a user.

Source code in zenml/models/v2/base/scoped.py

class UserScopedResponse(
    BaseIdentifiedResponse[UserBody, UserMetadata, UserResources],
    Generic[UserBody, UserMetadata, UserResources],
):
    """Base user-owned model.

    Used as a base class for all domain models that are "owned" by a user.
    """

    # Analytics
    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Fetches the analytics metadata for user scoped models.

        Returns:
            The analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        if self.user is not None:
            metadata["user_id"] = self.user.id
        return metadata

    # Body and metadata properties
    @property
    def user(self) -> Optional["UserResponse"]:
        """The `user` property.

        Returns:
            the value of the property.
        """
        return self.get_body().user

user: Optional[UserResponse] property readonly

The user property.

Returns:

Type	Description
Optional[UserResponse]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_analytics_metadata(self)

Fetches the analytics metadata for user scoped models.

Returns:

Type	Description
Dict[str, Any]	The analytics metadata.

Source code in zenml/models/v2/base/scoped.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for user scoped models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    if self.user is not None:
        metadata["user_id"] = self.user.id
    return metadata

UserScopedResponseBody (BaseDatedResponseBody) pydantic-model

Base user-owned body.

Source code in zenml/models/v2/base/scoped.py

class UserScopedResponseBody(BaseDatedResponseBody):
    """Base user-owned body."""

    user: Optional["UserResponse"] = Field(
        title="The user who created this resource."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserScopedResponseMetadata (BaseResponseMetadata) pydantic-model

Base user-owned metadata.

Source code in zenml/models/v2/base/scoped.py

class UserScopedResponseMetadata(BaseResponseMetadata):
    """Base user-owned metadata."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserScopedResponseResources (BaseResponseResources) pydantic-model

Base class for all resource models associated with the user.

Source code in zenml/models/v2/base/scoped.py

class UserScopedResponseResources(BaseResponseResources):
    """Base class for all resource models associated with the user."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources] (UserScopedResponse, BaseIdentifiedResponse[UserBody, UserMetadata, UserResources][WorkspaceBody, WorkspaceMetadata, WorkspaceResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/base/scoped.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedFilter (BaseFilter) pydantic-model

Model to enable advanced scoping with workspace.

Source code in zenml/models/v2/base/scoped.py

class WorkspaceScopedFilter(BaseFilter):
    """Model to enable advanced scoping with workspace."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "scope_workspace",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.CLI_EXCLUDE_FIELDS,
        "scope_workspace",
    ]
    scope_workspace: Optional[UUID] = Field(
        default=None,
        description="The workspace to scope this query to.",
    )

    def set_scope_workspace(self, workspace_id: UUID) -> None:
        """Set the workspace to scope this response.

        Args:
            workspace_id: The workspace to scope this response to.
        """
        self.scope_workspace = workspace_id

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        from sqlmodel import or_

        query = super().apply_filter(query=query, table=table)

        if self.scope_workspace:
            scope_filter = or_(
                getattr(table, "workspace_id") == self.scope_workspace,
                getattr(table, "workspace_id").is_(None),
            )
            query = query.where(scope_filter)

        return query

scope_workspace: UUID pydantic-field

The workspace to scope this query to.

apply_filter(self, query, table)

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/base/scoped.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    from sqlmodel import or_

    query = super().apply_filter(query=query, table=table)

    if self.scope_workspace:
        scope_filter = or_(
            getattr(table, "workspace_id") == self.scope_workspace,
            getattr(table, "workspace_id").is_(None),
        )
        query = query.where(scope_filter)

    return query

set_scope_workspace(self, workspace_id)

Set the workspace to scope this response.

Parameters:

Name	Type	Description	Default
workspace_id	UUID	The workspace to scope this response to.	required

Source code in zenml/models/v2/base/scoped.py

def set_scope_workspace(self, workspace_id: UUID) -> None:
    """Set the workspace to scope this response.

    Args:
        workspace_id: The workspace to scope this response to.
    """
    self.scope_workspace = workspace_id

WorkspaceScopedRequest (UserScopedRequest) pydantic-model

Base workspace-scoped request domain model.

Used as a base class for all domain models that are workspace-scoped.

Source code in zenml/models/v2/base/scoped.py

class WorkspaceScopedRequest(UserScopedRequest):
    """Base workspace-scoped request domain model.

    Used as a base class for all domain models that are workspace-scoped.
    """

    workspace: UUID = Field(
        title="The workspace to which this resource belongs."
    )

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Fetches the analytics metadata for workspace scoped models.

        Returns:
            The analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        metadata["workspace_id"] = self.workspace
        return metadata

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_analytics_metadata(self)

Fetches the analytics metadata for workspace scoped models.

Returns:

Type	Description
Dict[str, Any]	The analytics metadata.

Source code in zenml/models/v2/base/scoped.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for workspace scoped models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata["workspace_id"] = self.workspace
    return metadata

WorkspaceScopedResponse (UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources], Generic) pydantic-model

Base workspace-scoped domain model.

Used as a base class for all domain models that are workspace-scoped.

Source code in zenml/models/v2/base/scoped.py

class WorkspaceScopedResponse(
    UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources],
    Generic[WorkspaceBody, WorkspaceMetadata, WorkspaceResources],
):
    """Base workspace-scoped domain model.

    Used as a base class for all domain models that are workspace-scoped.
    """

    # Body and metadata properties
    @property
    def workspace(self) -> "WorkspaceResponse":
        """The workspace property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().workspace

workspace: WorkspaceResponse property readonly

The workspace property.

Returns:

Type	Description
WorkspaceResponse	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponseBody (UserScopedResponseBody) pydantic-model

Base workspace-scoped body.

Source code in zenml/models/v2/base/scoped.py

class WorkspaceScopedResponseBody(UserScopedResponseBody):
    """Base workspace-scoped body."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponseMetadata (UserScopedResponseMetadata) pydantic-model

Base workspace-scoped metadata.

Source code in zenml/models/v2/base/scoped.py

class WorkspaceScopedResponseMetadata(UserScopedResponseMetadata):
    """Base workspace-scoped metadata."""

    workspace: "WorkspaceResponse" = Field(
        title="The workspace of this resource."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponseResources (UserScopedResponseResources) pydantic-model

Base workspace-scoped resources.

Source code in zenml/models/v2/base/scoped.py

class WorkspaceScopedResponseResources(UserScopedResponseResources):
    """Base workspace-scoped resources."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedTaggableFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced scoping with workspace and tagging.

Source code in zenml/models/v2/base/scoped.py

class WorkspaceScopedTaggableFilter(WorkspaceScopedFilter):
    """Model to enable advanced scoping with workspace and tagging."""

    tag: Optional[str] = Field(
        description="Tag to apply to the filter query.", default=None
    )

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "tag",
    ]

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        from zenml.zen_stores.schemas import TagResourceSchema

        query = super().apply_filter(query=query, table=table)
        if self.tag:
            query = (
                query.join(getattr(table, "tags"))
                .join(TagResourceSchema.tag)
                .distinct()
            )

        return query

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom tag filters.

        Returns:
            A list of custom filters.
        """
        from zenml.zen_stores.schemas import TagSchema

        custom_filters = super().get_custom_filters()
        if self.tag:
            custom_filters.append(col(TagSchema.name) == self.tag)  # type: ignore[arg-type]

        return custom_filters

tag: str pydantic-field

Tag to apply to the filter query.

apply_filter(self, query, table)

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/base/scoped.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    from zenml.zen_stores.schemas import TagResourceSchema

    query = super().apply_filter(query=query, table=table)
    if self.tag:
        query = (
            query.join(getattr(table, "tags"))
            .join(TagResourceSchema.tag)
            .distinct()
        )

    return query

get_custom_filters(self)

Get custom tag filters.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/base/scoped.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom tag filters.

    Returns:
        A list of custom filters.
    """
    from zenml.zen_stores.schemas import TagSchema

    custom_filters = super().get_custom_filters()
    if self.tag:
        custom_filters.append(col(TagSchema.name) == self.tag)  # type: ignore[arg-type]

    return custom_filters

update

Utility methods for base models.

update_model(_cls)

Base update model.

This is used as a decorator on top of request models to convert them into update models where the fields are optional and can be set to None.

Parameters:

Name	Type	Description	Default
_cls	Type[T]	The class to decorate	required

Returns:

Type	Description
Type[T]	The decorated class.

Source code in zenml/models/v2/base/update.py

def update_model(_cls: Type["T"]) -> Type["T"]:
    """Base update model.

    This is used as a decorator on top of request models to convert them
    into update models where the fields are optional and can be set to None.

    Args:
        _cls: The class to decorate

    Returns:
        The decorated class.
    """
    for _, value in _cls.__fields__.items():
        value.required = False
        value.allow_none = True

    _cls.__config__.extra = Extra.ignore

    return _cls

core special

action_flavor

Action flavor model definitions.

ActionFlavorResponse (BasePluginFlavorResponse[ActionFlavorResponseBody, ActionFlavorResponseMetadata, ActionFlavorResponseResources]) pydantic-model

Response model for Action Flavors.

Source code in zenml/models/v2/core/action_flavor.py

class ActionFlavorResponse(
    BasePluginFlavorResponse[
        ActionFlavorResponseBody,
        ActionFlavorResponseMetadata,
        ActionFlavorResponseResources,
    ]
):
    """Response model for Action Flavors."""

    # Body and metadata properties
    @property
    def config_schema(self) -> Dict[str, Any]:
        """The `source_config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config_schema

config_schema: Dict[str, Any] property readonly

The source_config_schema property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ActionFlavorResponseBody (BasePluginResponseBody) pydantic-model

Response body for action flavors.

Source code in zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseBody(BasePluginResponseBody):
    """Response body for action flavors."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ActionFlavorResponseMetadata (BasePluginResponseMetadata) pydantic-model

Response metadata for action flavors.

Source code in zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseMetadata(BasePluginResponseMetadata):
    """Response metadata for action flavors."""

    config_schema: Dict[str, Any]

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ActionFlavorResponseResources (BasePluginResponseResources) pydantic-model

Response resources for action flavors.

Source code in zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseResources(BasePluginResponseResources):
    """Response resources for action flavors."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BasePluginFlavorResponse[ActionFlavorResponseBody, ActionFlavorResponseMetadata, ActionFlavorResponseResources] (BasePluginFlavorResponse, BaseResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources][ActionFlavorResponseBody, ActionFlavorResponseMetadata, ActionFlavorResponseResources]) pydantic-model

Config

Configuration for base plugin flavor response.

Source code in zenml/models/v2/core/action_flavor.py

class Config:
    """Configuration for base plugin flavor response."""

    extra = Extra.ignore

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

api_key

Models representing API keys.

APIKey (BaseModel) pydantic-model

Encoded model for API keys.

Source code in zenml/models/v2/core/api_key.py

class APIKey(BaseModel):
    """Encoded model for API keys."""

    id: UUID
    key: str

    @classmethod
    def decode_api_key(cls, encoded_key: str) -> "APIKey":
        """Decodes an API key from a base64 string.

        Args:
            encoded_key: The encoded API key.

        Returns:
            The decoded API key.

        Raises:
            ValueError: If the key is not valid.
        """
        if encoded_key.startswith(ZENML_API_KEY_PREFIX):
            encoded_key = encoded_key[len(ZENML_API_KEY_PREFIX) :]
        try:
            json_key = b64_decode(encoded_key)
            return cls.parse_raw(json_key)
        except Exception:
            raise ValueError("Invalid API key.")

    def encode(self) -> str:
        """Encodes the API key in a base64 string that includes the key ID and prefix.

        Returns:
            The encoded API key.
        """
        encoded_key = b64_encode(self.json())
        return f"{ZENML_API_KEY_PREFIX}{encoded_key}"

decode_api_key(encoded_key) classmethod

Decodes an API key from a base64 string.

Parameters:

Name	Type	Description	Default
encoded_key	str	The encoded API key.	required

Returns:

Type	Description
APIKey	The decoded API key.

Exceptions:

Type	Description
ValueError	If the key is not valid.

Source code in zenml/models/v2/core/api_key.py

@classmethod
def decode_api_key(cls, encoded_key: str) -> "APIKey":
    """Decodes an API key from a base64 string.

    Args:
        encoded_key: The encoded API key.

    Returns:
        The decoded API key.

    Raises:
        ValueError: If the key is not valid.
    """
    if encoded_key.startswith(ZENML_API_KEY_PREFIX):
        encoded_key = encoded_key[len(ZENML_API_KEY_PREFIX) :]
    try:
        json_key = b64_decode(encoded_key)
        return cls.parse_raw(json_key)
    except Exception:
        raise ValueError("Invalid API key.")

encode(self)

Encodes the API key in a base64 string that includes the key ID and prefix.

Returns:

Type	Description
str	The encoded API key.

Source code in zenml/models/v2/core/api_key.py

def encode(self) -> str:
    """Encodes the API key in a base64 string that includes the key ID and prefix.

    Returns:
        The encoded API key.
    """
    encoded_key = b64_encode(self.json())
    return f"{ZENML_API_KEY_PREFIX}{encoded_key}"

APIKeyFilter (BaseFilter) pydantic-model

Filter model for API keys.

Source code in zenml/models/v2/core/api_key.py

class APIKeyFilter(BaseFilter):
    """Filter model for API keys."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "service_account",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.CLI_EXCLUDE_FIELDS,
        "service_account",
    ]

    service_account: Optional[UUID] = Field(
        default=None,
        description="The service account to scope this query to.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Name of the API key",
    )
    description: Optional[str] = Field(
        default=None,
        title="Filter by the API key description.",
    )
    active: Optional[Union[bool, str]] = Field(
        default=None,
        title="Whether the API key is active.",
    )
    last_login: Optional[Union[datetime, str]] = Field(
        default=None, title="Time when the API key was last used to log in."
    )
    last_rotated: Optional[Union[datetime, str]] = Field(
        default=None, title="Time when the API key was last rotated."
    )

    def set_service_account(self, service_account_id: UUID) -> None:
        """Set the service account by which to scope this query.

        Args:
            service_account_id: The service account ID.
        """
        self.service_account = service_account_id

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Override to apply the service account scope as an additional filter.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)

        if self.service_account:
            scope_filter = (
                getattr(table, "service_account_id") == self.service_account
            )
            query = query.where(scope_filter)

        return query

name: str pydantic-field

Name of the API key

service_account: UUID pydantic-field

The service account to scope this query to.

apply_filter(self, query, table)

Override to apply the service account scope as an additional filter.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/core/api_key.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to apply the service account scope as an additional filter.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)

    if self.service_account:
        scope_filter = (
            getattr(table, "service_account_id") == self.service_account
        )
        query = query.where(scope_filter)

    return query

set_service_account(self, service_account_id)

Set the service account by which to scope this query.

Parameters:

Name	Type	Description	Default
service_account_id	UUID	The service account ID.	required

Source code in zenml/models/v2/core/api_key.py

def set_service_account(self, service_account_id: UUID) -> None:
    """Set the service account by which to scope this query.

    Args:
        service_account_id: The service account ID.
    """
    self.service_account = service_account_id

APIKeyInternalResponse (APIKeyResponse) pydantic-model

Response model for API keys used internally.

Source code in zenml/models/v2/core/api_key.py

class APIKeyInternalResponse(APIKeyResponse):
    """Response model for API keys used internally."""

    previous_key: Optional[str] = Field(
        default=None,
        title="The previous API key. Only set if the key was rotated.",
    )

    def verify_key(
        self,
        key: str,
    ) -> bool:
        """Verifies a given key against the stored (hashed) key(s).

        Args:
            key: Input key to be verified.

        Returns:
            True if the keys match.
        """
        # even when the hashed key is not set, we still want to execute
        # the hash verification to protect against response discrepancy
        # attacks (https://cwe.mitre.org/data/definitions/204.html)
        key_hash: Optional[str] = None
        context = CryptContext(schemes=["bcrypt"], deprecated="auto")
        if self.key is not None and self.active:
            key_hash = self.key
        result = context.verify(key, key_hash)

        # same for the previous key, if set and if it's still valid
        key_hash = None
        if (
            self.previous_key is not None
            and self.last_rotated is not None
            and self.active
            and self.retain_period_minutes > 0
        ):
            # check if the previous key is still valid
            if datetime.utcnow() - self.last_rotated < timedelta(
                minutes=self.retain_period_minutes
            ):
                key_hash = self.previous_key
        previous_result = context.verify(key, key_hash)

        return result or previous_result

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

verify_key(self, key)

Verifies a given key against the stored (hashed) key(s).

Parameters:

Name	Type	Description	Default
key	str	Input key to be verified.	required

Returns:

Type	Description
bool	True if the keys match.

Source code in zenml/models/v2/core/api_key.py

def verify_key(
    self,
    key: str,
) -> bool:
    """Verifies a given key against the stored (hashed) key(s).

    Args:
        key: Input key to be verified.

    Returns:
        True if the keys match.
    """
    # even when the hashed key is not set, we still want to execute
    # the hash verification to protect against response discrepancy
    # attacks (https://cwe.mitre.org/data/definitions/204.html)
    key_hash: Optional[str] = None
    context = CryptContext(schemes=["bcrypt"], deprecated="auto")
    if self.key is not None and self.active:
        key_hash = self.key
    result = context.verify(key, key_hash)

    # same for the previous key, if set and if it's still valid
    key_hash = None
    if (
        self.previous_key is not None
        and self.last_rotated is not None
        and self.active
        and self.retain_period_minutes > 0
    ):
        # check if the previous key is still valid
        if datetime.utcnow() - self.last_rotated < timedelta(
            minutes=self.retain_period_minutes
        ):
            key_hash = self.previous_key
    previous_result = context.verify(key, key_hash)

    return result or previous_result

APIKeyInternalUpdate (APIKeyUpdate) pydantic-model

Update model for API keys used internally.

Source code in zenml/models/v2/core/api_key.py

class APIKeyInternalUpdate(APIKeyUpdate):
    """Update model for API keys used internally."""

    update_last_login: bool = Field(
        default=False,
        title="Whether to update the last login timestamp.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

APIKeyRequest (BaseRequest) pydantic-model

Request model for API keys.

Source code in zenml/models/v2/core/api_key.py

class APIKeyRequest(BaseRequest):
    """Request model for API keys."""

    name: str = Field(
        title="The name of the API Key.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    description: Optional[str] = Field(
        default=None,
        title="The description of the API Key.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

APIKeyResponse (BaseIdentifiedResponse[APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources]) pydantic-model

Response model for API keys.

Source code in zenml/models/v2/core/api_key.py

class APIKeyResponse(
    BaseIdentifiedResponse[
        APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources
    ]
):
    """Response model for API keys."""

    name: str = Field(
        title="The name of the API Key.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    _warn_on_response_updates = False

    def get_hydrated_version(self) -> "APIKeyResponse":
        """Get the hydrated version of this API key.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_api_key(
            service_account_id=self.service_account.id,
            api_key_name_or_id=self.id,
        )

    # Helper functions
    def set_key(self, key: str) -> None:
        """Sets the API key and encodes it.

        Args:
            key: The API key value to be set.
        """
        self.get_body().key = APIKey(id=self.id, key=key).encode()

    # Body and metadata properties
    @property
    def key(self) -> Optional[str]:
        """The `key` property.

        Returns:
            the value of the property.
        """
        return self.get_body().key

    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def service_account(self) -> "ServiceAccountResponse":
        """The `service_account` property.

        Returns:
            the value of the property.
        """
        return self.get_body().service_account

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def retain_period_minutes(self) -> int:
        """The `retain_period_minutes` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().retain_period_minutes

    @property
    def last_login(self) -> Optional[datetime]:
        """The `last_login` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().last_login

    @property
    def last_rotated(self) -> Optional[datetime]:
        """The `last_rotated` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().last_rotated

active: bool property readonly

The active property.

Returns:

Type	Description
bool	the value of the property.

description: str property readonly

The description property.

Returns:

Type	Description
str	the value of the property.

key: Optional[str] property readonly

The key property.

Returns:

Type	Description
Optional[str]	the value of the property.

last_login: Optional[datetime.datetime] property readonly

The last_login property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

last_rotated: Optional[datetime.datetime] property readonly

The last_rotated property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

retain_period_minutes: int property readonly

The retain_period_minutes property.

Returns:

Type	Description
int	the value of the property.

service_account: ServiceAccountResponse property readonly

The service_account property.

Returns:

Type	Description
ServiceAccountResponse	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this API key.

Returns:

Type	Description
APIKeyResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/api_key.py

def get_hydrated_version(self) -> "APIKeyResponse":
    """Get the hydrated version of this API key.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_api_key(
        service_account_id=self.service_account.id,
        api_key_name_or_id=self.id,
    )

set_key(self, key)

Sets the API key and encodes it.

Parameters:

Name	Type	Description	Default
key	str	The API key value to be set.	required

Source code in zenml/models/v2/core/api_key.py

def set_key(self, key: str) -> None:
    """Sets the API key and encodes it.

    Args:
        key: The API key value to be set.
    """
    self.get_body().key = APIKey(id=self.id, key=key).encode()

APIKeyResponseBody (BaseDatedResponseBody) pydantic-model

Response body for API keys.

Source code in zenml/models/v2/core/api_key.py

class APIKeyResponseBody(BaseDatedResponseBody):
    """Response body for API keys."""

    key: Optional[str] = Field(
        default=None,
        title="The API key. Only set immediately after creation or rotation.",
    )
    active: bool = Field(
        default=True,
        title="Whether the API key is active.",
    )
    service_account: "ServiceAccountResponse" = Field(
        title="The service account associated with this API key."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

APIKeyResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for API keys.

Source code in zenml/models/v2/core/api_key.py

class APIKeyResponseMetadata(BaseResponseMetadata):
    """Response metadata for API keys."""

    description: str = Field(
        default="",
        title="The description of the API Key.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    retain_period_minutes: int = Field(
        title="Number of minutes for which the previous key is still valid "
        "after it has been rotated.",
    )
    last_login: Optional[datetime] = Field(
        default=None, title="Time when the API key was last used to log in."
    )
    last_rotated: Optional[datetime] = Field(
        default=None, title="Time when the API key was last rotated."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

APIKeyResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the APIKey entity.

Source code in zenml/models/v2/core/api_key.py

class APIKeyResponseResources(BaseResponseResources):
    """Class for all resource models associated with the APIKey entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

APIKeyRotateRequest (BaseModel) pydantic-model

Request model for API key rotation.

Source code in zenml/models/v2/core/api_key.py

class APIKeyRotateRequest(BaseModel):
    """Request model for API key rotation."""

    retain_period_minutes: int = Field(
        default=0,
        title="Number of minutes for which the previous key is still valid "
        "after it has been rotated.",
    )

APIKeyUpdate (APIKeyRequest) pydantic-model

Update model for API keys.

Source code in zenml/models/v2/core/api_key.py

@update_model
class APIKeyUpdate(APIKeyRequest):
    """Update model for API keys."""

    active: Optional[bool] = Field(
        default=True,
        title="Whether the API key is active.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseIdentifiedResponse[APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][APIKeyResponseBody, APIKeyResponseMetadata, APIKeyResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/api_key.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

artifact

Models representing artifacts.

ArtifactFilter (WorkspaceScopedTaggableFilter) pydantic-model

Model to enable advanced filtering of artifacts.

Source code in zenml/models/v2/core/artifact.py

class ArtifactFilter(WorkspaceScopedTaggableFilter):
    """Model to enable advanced filtering of artifacts."""

    name: Optional[str] = None
    has_custom_name: Optional[bool] = None

ArtifactRequest (BaseRequest) pydantic-model

Artifact request model.

Source code in zenml/models/v2/core/artifact.py

class ArtifactRequest(BaseRequest):
    """Artifact request model."""

    name: str = Field(
        title="Name of the artifact.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )
    tags: Optional[List[str]] = Field(
        title="Artifact tags.",
        description="Should be a list of plain strings, e.g., ['tag1', 'tag2']",
        default=None,
    )

tags: List[str] pydantic-field

Should be a list of plain strings, e.g., ['tag1', 'tag2']

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactResponse (BaseIdentifiedResponse[ArtifactResponseBody, ArtifactResponseMetadata, ArtifactResponseResources]) pydantic-model

Artifact response model.

Source code in zenml/models/v2/core/artifact.py

class ArtifactResponse(
    BaseIdentifiedResponse[
        ArtifactResponseBody,
        ArtifactResponseMetadata,
        ArtifactResponseResources,
    ]
):
    """Artifact response model."""

    def get_hydrated_version(self) -> "ArtifactResponse":
        """Get the hydrated version of this artifact.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact(self.id)

    name: str = Field(
        title="Name of the output in the parent step.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    # Body and metadata properties
    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def latest_version_name(self) -> Optional[str]:
        """The `latest_version_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_name

    @property
    def latest_version_id(self) -> Optional[UUID]:
        """The `latest_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_id

    @property
    def has_custom_name(self) -> bool:
        """The `has_custom_name` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().has_custom_name

    # Helper methods
    @property
    def versions(self) -> Dict[str, "ArtifactVersionResponse"]:
        """Get a list of all versions of this artifact.

        Returns:
            A list of all versions of this artifact.
        """
        from zenml.client import Client

        responses = Client().list_artifact_versions(name=self.name)
        return {str(response.version): response for response in responses}

has_custom_name: bool property readonly

The has_custom_name property.

Returns:

Type	Description
bool	the value of the property.

latest_version_id: Optional[uuid.UUID] property readonly

The latest_version_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

latest_version_name: Optional[str] property readonly

The latest_version_name property.

Returns:

Type	Description
Optional[str]	the value of the property.

tags: List[zenml.models.v2.core.tag.TagResponse] property readonly

The tags property.

Returns:

Type	Description
List[zenml.models.v2.core.tag.TagResponse]	the value of the property.

versions: Dict[str, ArtifactVersionResponse] property readonly

Get a list of all versions of this artifact.

Returns:

Type	Description
Dict[str, ArtifactVersionResponse]	A list of all versions of this artifact.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this artifact.

Returns:

Type	Description
ArtifactResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/artifact.py

def get_hydrated_version(self) -> "ArtifactResponse":
    """Get the hydrated version of this artifact.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_artifact(self.id)

ArtifactResponseBody (BaseDatedResponseBody) pydantic-model

Response body for artifacts.

Source code in zenml/models/v2/core/artifact.py

class ArtifactResponseBody(BaseDatedResponseBody):
    """Response body for artifacts."""

    tags: List[TagResponse] = Field(
        title="Tags associated with the model",
    )
    latest_version_name: Optional[str]
    latest_version_id: Optional[UUID]

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for artifacts.

Source code in zenml/models/v2/core/artifact.py

class ArtifactResponseMetadata(BaseResponseMetadata):
    """Response metadata for artifacts."""

    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the Artifact Entity.

Source code in zenml/models/v2/core/artifact.py

class ArtifactResponseResources(BaseResponseResources):
    """Class for all resource models associated with the Artifact Entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactUpdate (BaseModel) pydantic-model

Artifact update model.

Source code in zenml/models/v2/core/artifact.py

class ArtifactUpdate(BaseModel):
    """Artifact update model."""

    name: Optional[str] = None
    add_tags: Optional[List[str]] = None
    remove_tags: Optional[List[str]] = None
    has_custom_name: Optional[bool] = None

BaseIdentifiedResponse[ArtifactResponseBody, ArtifactResponseMetadata, ArtifactResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][ArtifactResponseBody, ArtifactResponseMetadata, ArtifactResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/artifact.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

artifact_version

Models representing artifact versions.

ArtifactVersionFilter (WorkspaceScopedTaggableFilter) pydantic-model

Model to enable advanced filtering of artifact versions.

Source code in zenml/models/v2/core/artifact_version.py

class ArtifactVersionFilter(WorkspaceScopedTaggableFilter):
    """Model to enable advanced filtering of artifact versions."""

    # `name` and `only_unused` refer to properties related to other entities
    #  rather than a field in the db, hence they needs to be handled
    #  explicitly
    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedTaggableFilter.FILTER_EXCLUDE_FIELDS,
        "name",
        "only_unused",
        "has_custom_name",
    ]
    artifact_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="ID of the artifact to which this version belongs.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Name of the artifact to which this version belongs.",
    )
    version: Optional[str] = Field(
        default=None,
        description="Version of the artifact",
    )
    version_number: Optional[Union[int, str]] = Field(
        default=None,
        description="Version of the artifact if it is an integer",
    )
    uri: Optional[str] = Field(
        default=None,
        description="Uri of the artifact",
    )
    materializer: Optional[str] = Field(
        default=None,
        description="Materializer used to produce the artifact",
    )
    type: Optional[str] = Field(
        default=None,
        description="Type of the artifact",
    )
    data_type: Optional[str] = Field(
        default=None,
        description="Datatype of the artifact",
    )
    artifact_store_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Artifact store for this artifact"
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace for this artifact"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User that produced this artifact"
    )
    only_unused: Optional[bool] = Field(
        default=False, description="Filter only for unused artifacts"
    )
    has_custom_name: Optional[bool] = Field(
        default=None,
        description="Filter only artifacts with/without custom names.",
    )

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom filters.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters()

        from sqlalchemy import and_
        from sqlmodel import select

        from zenml.zen_stores.schemas.artifact_schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
        )
        from zenml.zen_stores.schemas.step_run_schemas import (
            StepRunInputArtifactSchema,
            StepRunOutputArtifactSchema,
        )

        if self.name:
            value, filter_operator = self._resolve_operator(self.name)
            filter_ = StrFilter(
                operation=GenericFilterOps(filter_operator),
                column="name",
                value=value,
            )
            artifact_name_filter = and_(  # type: ignore[type-var]
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                filter_.generate_query_conditions(ArtifactSchema),
            )
            custom_filters.append(artifact_name_filter)

        if self.only_unused:
            unused_filter = and_(
                ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                    select(StepRunOutputArtifactSchema.artifact_id)
                ),
                ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                    select(StepRunInputArtifactSchema.artifact_id)
                ),
            )
            custom_filters.append(unused_filter)

        if self.has_custom_name is not None:
            custom_name_filter = and_(  # type: ignore[type-var]
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                ArtifactSchema.has_custom_name == self.has_custom_name,
            )
            custom_filters.append(custom_name_filter)

        return custom_filters

artifact_id: Union[uuid.UUID, str] pydantic-field

ID of the artifact to which this version belongs.

artifact_store_id: Union[uuid.UUID, str] pydantic-field

Artifact store for this artifact

data_type: str pydantic-field

Datatype of the artifact

has_custom_name: bool pydantic-field

Filter only artifacts with/without custom names.

materializer: str pydantic-field

Materializer used to produce the artifact

name: str pydantic-field

Name of the artifact to which this version belongs.

only_unused: bool pydantic-field

Filter only for unused artifacts

type: str pydantic-field

Type of the artifact

uri: str pydantic-field

Uri of the artifact

user_id: Union[uuid.UUID, str] pydantic-field

User that produced this artifact

version: str pydantic-field

Version of the artifact

version_number: Union[int, str] pydantic-field

Version of the artifact if it is an integer

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace for this artifact

get_custom_filters(self)

Get custom filters.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/core/artifact_version.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom filters.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters()

    from sqlalchemy import and_
    from sqlmodel import select

    from zenml.zen_stores.schemas.artifact_schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
    )
    from zenml.zen_stores.schemas.step_run_schemas import (
        StepRunInputArtifactSchema,
        StepRunOutputArtifactSchema,
    )

    if self.name:
        value, filter_operator = self._resolve_operator(self.name)
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        artifact_name_filter = and_(  # type: ignore[type-var]
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            filter_.generate_query_conditions(ArtifactSchema),
        )
        custom_filters.append(artifact_name_filter)

    if self.only_unused:
        unused_filter = and_(
            ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                select(StepRunOutputArtifactSchema.artifact_id)
            ),
            ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                select(StepRunInputArtifactSchema.artifact_id)
            ),
        )
        custom_filters.append(unused_filter)

    if self.has_custom_name is not None:
        custom_name_filter = and_(  # type: ignore[type-var]
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            ArtifactSchema.has_custom_name == self.has_custom_name,
        )
        custom_filters.append(custom_name_filter)

    return custom_filters

ArtifactVersionRequest (WorkspaceScopedRequest) pydantic-model

Request model for artifact versions.

Source code in zenml/models/v2/core/artifact_version.py

class ArtifactVersionRequest(WorkspaceScopedRequest):
    """Request model for artifact versions."""

    artifact_id: UUID = Field(
        title="ID of the artifact to which this version belongs.",
    )
    version: Union[str, int] = Field(
        title="Version of the artifact.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )
    type: ArtifactType = Field(title="Type of the artifact.")
    artifact_store_id: Optional[UUID] = Field(
        title="ID of the artifact store in which this artifact is stored.",
        default=None,
    )
    uri: str = Field(
        title="URI of the artifact.", max_length=TEXT_FIELD_MAX_LENGTH
    )
    materializer: Source = Field(
        title="Materializer class to use for this artifact.",
    )
    data_type: Source = Field(
        title="Data type of the artifact.",
    )
    tags: Optional[List[str]] = Field(
        title="Tags of the artifact.",
        description="Should be a list of plain strings, e.g., ['tag1', 'tag2']",
        default=None,
    )
    visualizations: Optional[List["ArtifactVisualizationRequest"]] = Field(
        default=None, title="Visualizations of the artifact."
    )

    _convert_source = convert_source_validator("materializer", "data_type")

tags: List[str] pydantic-field

Should be a list of plain strings, e.g., ['tag1', 'tag2']

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactVersionResponse (WorkspaceScopedResponse[ArtifactVersionResponseBody, ArtifactVersionResponseMetadata, ArtifactVersionResponseResources]) pydantic-model

Response model for artifact versions.

Source code in zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponse(
    WorkspaceScopedResponse[
        ArtifactVersionResponseBody,
        ArtifactVersionResponseMetadata,
        ArtifactVersionResponseResources,
    ]
):
    """Response model for artifact versions."""

    def get_hydrated_version(self) -> "ArtifactVersionResponse":
        """Get the hydrated version of this artifact version.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact_version(self.id)

    # Body and metadata properties
    @property
    def artifact(self) -> "ArtifactResponse":
        """The `artifact` property.

        Returns:
            the value of the property.
        """
        return self.get_body().artifact

    @property
    def version(self) -> Union[str, int]:
        """The `version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().version

    @property
    def uri(self) -> str:
        """The `uri` property.

        Returns:
            the value of the property.
        """
        return self.get_body().uri

    @property
    def type(self) -> ArtifactType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def producer_pipeline_run_id(self) -> Optional[UUID]:
        """The `producer_pipeline_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().producer_pipeline_run_id

    @property
    def artifact_store_id(self) -> Optional[UUID]:
        """The `artifact_store_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().artifact_store_id

    @property
    def producer_step_run_id(self) -> Optional[UUID]:
        """The `producer_step_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().producer_step_run_id

    @property
    def visualizations(
        self,
    ) -> Optional[List["ArtifactVisualizationResponse"]]:
        """The `visualizations` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().visualizations

    @property
    def run_metadata(self) -> Dict[str, "RunMetadataResponse"]:
        """The `metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    @property
    def materializer(self) -> Source:
        """The `materializer` property.

        Returns:
            the value of the property.
        """
        return self.get_body().materializer

    @property
    def data_type(self) -> Source:
        """The `data_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().data_type

    # Helper methods
    @property
    def name(self) -> str:
        """The `name` property.

        Returns:
            the value of the property.
        """
        return self.artifact.name

    @property
    def step(self) -> "StepRunResponse":
        """Get the step that produced this artifact.

        Returns:
            The step that produced this artifact.
        """
        from zenml.artifacts.utils import get_producer_step_of_artifact

        return get_producer_step_of_artifact(self)

    @property
    def run(self) -> "PipelineRunResponse":
        """Get the pipeline run that produced this artifact.

        Returns:
            The pipeline run that produced this artifact.
        """
        from zenml.client import Client

        return Client().get_pipeline_run(self.step.pipeline_run_id)

    def load(self) -> Any:
        """Materializes (loads) the data stored in this artifact.

        Returns:
            The materialized data.
        """
        from zenml.artifacts.utils import load_artifact_from_response

        return load_artifact_from_response(self)

    def download_files(self, path: str, overwrite: bool = False) -> None:
        """Downloads data for an artifact with no materializing.

        Any artifacts will be saved as a zip file to the given path.

        Args:
            path: The path to save the binary data to.
            overwrite: Whether to overwrite the file if it already exists.

        Raises:
            ValueError: If the path does not end with '.zip'.
        """
        if not path.endswith(".zip"):
            raise ValueError(
                "The path should end with '.zip' to save the binary data."
            )
        from zenml.artifacts.utils import (
            download_artifact_files_from_response,
        )

        download_artifact_files_from_response(
            self,
            path=path,
            overwrite=overwrite,
        )

    def read(self) -> Any:
        """(Deprecated) Materializes (loads) the data stored in this artifact.

        Returns:
            The materialized data.
        """
        logger.warning(
            "`artifact.read()` is deprecated and will be removed in a future "
            "release. Please use `artifact.load()` instead."
        )
        return self.load()

    def visualize(self, title: Optional[str] = None) -> None:
        """Visualize the artifact in notebook environments.

        Args:
            title: Optional title to show before the visualizations.
        """
        from zenml.utils.visualization_utils import visualize_artifact

        visualize_artifact(self, title=title)

artifact: ArtifactResponse property readonly

The artifact property.

Returns:

Type	Description
ArtifactResponse	the value of the property.

artifact_store_id: Optional[uuid.UUID] property readonly

The artifact_store_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

data_type: Source property readonly

The data_type property.

Returns:

Type	Description
Source	the value of the property.

materializer: Source property readonly

The materializer property.

Returns:

Type	Description
Source	the value of the property.

name: str property readonly

The name property.

Returns:

Type	Description
str	the value of the property.

producer_pipeline_run_id: Optional[uuid.UUID] property readonly

The producer_pipeline_run_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

producer_step_run_id: Optional[uuid.UUID] property readonly

The producer_step_run_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

run: PipelineRunResponse property readonly

Get the pipeline run that produced this artifact.

Returns:

Type	Description
PipelineRunResponse	The pipeline run that produced this artifact.

run_metadata: Dict[str, RunMetadataResponse] property readonly

The metadata property.

Returns:

Type	Description
Dict[str, RunMetadataResponse]	the value of the property.

step: StepRunResponse property readonly

Get the step that produced this artifact.

Returns:

Type	Description
StepRunResponse	The step that produced this artifact.

tags: List[zenml.models.v2.core.tag.TagResponse] property readonly

The tags property.

Returns:

Type	Description
List[zenml.models.v2.core.tag.TagResponse]	the value of the property.

type: ArtifactType property readonly

The type property.

Returns:

Type	Description
ArtifactType	the value of the property.

uri: str property readonly

The uri property.

Returns:

Type	Description
str	the value of the property.

version: Union[str, int] property readonly

The version property.

Returns:

Type	Description
Union[str, int]	the value of the property.

visualizations: Optional[List[ArtifactVisualizationResponse]] property readonly

The visualizations property.

Returns:

Type	Description
Optional[List[ArtifactVisualizationResponse]]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

download_files(self, path, overwrite=False)

Downloads data for an artifact with no materializing.

Any artifacts will be saved as a zip file to the given path.

Parameters:

Name	Type	Description	Default
path	str	The path to save the binary data to.	required
overwrite	bool	Whether to overwrite the file if it already exists.	False

Exceptions:

Type	Description
ValueError	If the path does not end with '.zip'.

Source code in zenml/models/v2/core/artifact_version.py

def download_files(self, path: str, overwrite: bool = False) -> None:
    """Downloads data for an artifact with no materializing.

    Any artifacts will be saved as a zip file to the given path.

    Args:
        path: The path to save the binary data to.
        overwrite: Whether to overwrite the file if it already exists.

    Raises:
        ValueError: If the path does not end with '.zip'.
    """
    if not path.endswith(".zip"):
        raise ValueError(
            "The path should end with '.zip' to save the binary data."
        )
    from zenml.artifacts.utils import (
        download_artifact_files_from_response,
    )

    download_artifact_files_from_response(
        self,
        path=path,
        overwrite=overwrite,
    )

get_hydrated_version(self)

Get the hydrated version of this artifact version.

Returns:

Type	Description
ArtifactVersionResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/artifact_version.py

def get_hydrated_version(self) -> "ArtifactVersionResponse":
    """Get the hydrated version of this artifact version.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_artifact_version(self.id)

load(self)

Materializes (loads) the data stored in this artifact.

Returns:

Type	Description
Any	The materialized data.

Source code in zenml/models/v2/core/artifact_version.py

def load(self) -> Any:
    """Materializes (loads) the data stored in this artifact.

    Returns:
        The materialized data.
    """
    from zenml.artifacts.utils import load_artifact_from_response

    return load_artifact_from_response(self)

read(self)

(Deprecated) Materializes (loads) the data stored in this artifact.

Returns:

Type	Description
Any	The materialized data.

Source code in zenml/models/v2/core/artifact_version.py

def read(self) -> Any:
    """(Deprecated) Materializes (loads) the data stored in this artifact.

    Returns:
        The materialized data.
    """
    logger.warning(
        "`artifact.read()` is deprecated and will be removed in a future "
        "release. Please use `artifact.load()` instead."
    )
    return self.load()

visualize(self, title=None)

Visualize the artifact in notebook environments.

Parameters:

Name	Type	Description	Default
title	Optional[str]	Optional title to show before the visualizations.	None

Source code in zenml/models/v2/core/artifact_version.py

def visualize(self, title: Optional[str] = None) -> None:
    """Visualize the artifact in notebook environments.

    Args:
        title: Optional title to show before the visualizations.
    """
    from zenml.utils.visualization_utils import visualize_artifact

    visualize_artifact(self, title=title)

ArtifactVersionResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for artifact versions.

Source code in zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponseBody(WorkspaceScopedResponseBody):
    """Response body for artifact versions."""

    artifact: ArtifactResponse = Field(
        title="Artifact to which this version belongs."
    )
    version: Union[str, int] = Field(
        title="Version of the artifact.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    uri: str = Field(
        title="URI of the artifact.", max_length=TEXT_FIELD_MAX_LENGTH
    )
    type: ArtifactType = Field(title="Type of the artifact.")
    materializer: Source = Field(
        title="Materializer class to use for this artifact.",
    )
    data_type: Source = Field(
        title="Data type of the artifact.",
    )
    tags: List[TagResponse] = Field(
        title="Tags associated with the model",
    )
    producer_pipeline_run_id: Optional[UUID] = Field(
        title="The ID of the pipeline run that generated this artifact version."
    )

    _convert_source = convert_source_validator("materializer", "data_type")

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactVersionResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for artifact versions.

Source code in zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for artifact versions."""

    artifact_store_id: Optional[UUID] = Field(
        title="ID of the artifact store in which this artifact is stored.",
        default=None,
    )
    producer_step_run_id: Optional[UUID] = Field(
        title="ID of the step run that produced this artifact.",
        default=None,
    )
    visualizations: Optional[List["ArtifactVisualizationResponse"]] = Field(
        default=None, title="Visualizations of the artifact."
    )
    run_metadata: Dict[str, "RunMetadataResponse"] = Field(
        default={}, title="Metadata of the artifact."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactVersionResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the artifact version entity.

Source code in zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the artifact version entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactVersionUpdate (BaseModel) pydantic-model

Artifact version update model.

Source code in zenml/models/v2/core/artifact_version.py

class ArtifactVersionUpdate(BaseModel):
    """Artifact version update model."""

    name: Optional[str] = None
    add_tags: Optional[List[str]] = None
    remove_tags: Optional[List[str]] = None

LazyArtifactVersionResponse (ArtifactVersionResponse) pydantic-model

Lazy artifact version response.

Used if the artifact version is accessed from the model in a pipeline context available only during pipeline compilation.

Source code in zenml/models/v2/core/artifact_version.py

class LazyArtifactVersionResponse(ArtifactVersionResponse):
    """Lazy artifact version response.

    Used if the artifact version is accessed from the model in
    a pipeline context available only during pipeline compilation.
    """

    id: Optional[UUID] = None  # type: ignore[assignment]
    _lazy_load_name: Optional[str] = None
    _lazy_load_version: Optional[str] = None
    _lazy_load_model: "Model"

    def get_body(self) -> None:  # type: ignore[override]
        """Protects from misuse of the lazy loader.

        Raises:
            RuntimeError: always
        """
        raise RuntimeError("Cannot access artifact body before pipeline runs.")

    def get_metadata(self) -> None:  # type: ignore[override]
        """Protects from misuse of the lazy loader.

        Raises:
            RuntimeError: always
        """
        raise RuntimeError(
            "Cannot access artifact metadata before pipeline runs."
        )

    @property
    def run_metadata(self) -> Dict[str, "RunMetadataResponse"]:
        """The `metadata` property in lazy loading mode.

        Returns:
            getter of lazy responses for internal use.
        """
        from zenml.metadata.lazy_load import RunMetadataLazyGetter

        return RunMetadataLazyGetter(  # type: ignore[return-value]
            self._lazy_load_model,
            self._lazy_load_name,
            self._lazy_load_version,
        )

run_metadata: Dict[str, RunMetadataResponse] property readonly

The metadata property in lazy loading mode.

Returns:

Type	Description
Dict[str, RunMetadataResponse]	getter of lazy responses for internal use.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_body(self)

Protects from misuse of the lazy loader.

Exceptions:

Type	Description
RuntimeError	always

Source code in zenml/models/v2/core/artifact_version.py

def get_body(self) -> None:  # type: ignore[override]
    """Protects from misuse of the lazy loader.

    Raises:
        RuntimeError: always
    """
    raise RuntimeError("Cannot access artifact body before pipeline runs.")

get_metadata(self)

Protects from misuse of the lazy loader.

Exceptions:

Type	Description
RuntimeError	always

Source code in zenml/models/v2/core/artifact_version.py

def get_metadata(self) -> None:  # type: ignore[override]
    """Protects from misuse of the lazy loader.

    Raises:
        RuntimeError: always
    """
    raise RuntimeError(
        "Cannot access artifact metadata before pipeline runs."
    )

WorkspaceScopedResponse[ArtifactVersionResponseBody, ArtifactVersionResponseMetadata, ArtifactVersionResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][ArtifactVersionResponseBody, ArtifactVersionResponseMetadata, ArtifactVersionResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/artifact_version.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

artifact_visualization

Models representing artifact visualizations.

ArtifactVisualizationRequest (BaseRequest) pydantic-model

Request model for artifact visualization.

Source code in zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationRequest(BaseRequest):
    """Request model for artifact visualization."""

    type: VisualizationType
    uri: str

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactVisualizationResponse (BaseIdentifiedResponse[ArtifactVisualizationResponseBody, ArtifactVisualizationResponseMetadata, ArtifactVisualizationResponseResources]) pydantic-model

Response model for artifact visualizations.

Source code in zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationResponse(
    BaseIdentifiedResponse[
        ArtifactVisualizationResponseBody,
        ArtifactVisualizationResponseMetadata,
        ArtifactVisualizationResponseResources,
    ]
):
    """Response model for artifact visualizations."""

    def get_hydrated_version(self) -> "ArtifactVisualizationResponse":
        """Get the hydrated version of this artifact visualization.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact_visualization(self.id)

    # Body and metadata properties
    @property
    def type(self) -> VisualizationType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def uri(self) -> str:
        """The `uri` property.

        Returns:
            the value of the property.
        """
        return self.get_body().uri

    @property
    def artifact_version_id(self) -> UUID:
        """The `artifact_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().artifact_version_id

artifact_version_id: UUID property readonly

The artifact_version_id property.

Returns:

Type	Description
UUID	the value of the property.

type: VisualizationType property readonly

The type property.

Returns:

Type	Description
VisualizationType	the value of the property.

uri: str property readonly

The uri property.

Returns:

Type	Description
str	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this artifact visualization.

Returns:

Type	Description
ArtifactVisualizationResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/artifact_visualization.py

def get_hydrated_version(self) -> "ArtifactVisualizationResponse":
    """Get the hydrated version of this artifact visualization.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_artifact_visualization(self.id)

ArtifactVisualizationResponseBody (BaseDatedResponseBody) pydantic-model

Response body for artifact visualizations.

Source code in zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationResponseBody(BaseDatedResponseBody):
    """Response body for artifact visualizations."""

    type: VisualizationType
    uri: str

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactVisualizationResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata model for artifact visualizations.

Source code in zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationResponseMetadata(BaseResponseMetadata):
    """Response metadata model for artifact visualizations."""

    artifact_version_id: UUID

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ArtifactVisualizationResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the artifact visualization.

Source code in zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationResponseResources(BaseResponseResources):
    """Class for all resource models associated with the artifact visualization."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

BaseIdentifiedResponse[ArtifactVisualizationResponseBody, ArtifactVisualizationResponseMetadata, ArtifactVisualizationResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][ArtifactVisualizationResponseBody, ArtifactVisualizationResponseMetadata, ArtifactVisualizationResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/artifact_visualization.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

code_reference

Models representing code references.

BaseIdentifiedResponse[CodeReferenceResponseBody, CodeReferenceResponseMetadata, CodeReferenceResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][CodeReferenceResponseBody, CodeReferenceResponseMetadata, CodeReferenceResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/code_reference.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeReferenceRequest (BaseRequest) pydantic-model

Request model for code references.

Source code in zenml/models/v2/core/code_reference.py

class CodeReferenceRequest(BaseRequest):
    """Request model for code references."""

    commit: str = Field(description="The commit of the code reference.")
    subdirectory: str = Field(
        description="The subdirectory of the code reference."
    )
    code_repository: UUID = Field(
        description="The repository of the code reference."
    )

code_repository: UUID pydantic-field required

The repository of the code reference.

commit: str pydantic-field required

The commit of the code reference.

subdirectory: str pydantic-field required

The subdirectory of the code reference.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeReferenceResponse (BaseIdentifiedResponse[CodeReferenceResponseBody, CodeReferenceResponseMetadata, CodeReferenceResponseResources]) pydantic-model

Response model for code references.

Source code in zenml/models/v2/core/code_reference.py

class CodeReferenceResponse(
    BaseIdentifiedResponse[
        CodeReferenceResponseBody,
        CodeReferenceResponseMetadata,
        CodeReferenceResponseResources,
    ]
):
    """Response model for code references."""

    def get_hydrated_version(self) -> "CodeReferenceResponse":
        """Get the hydrated version of this code reference.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_code_reference(self.id)

    # Body and metadata properties
    @property
    def commit(self) -> str:
        """The `commit` property.

        Returns:
            the value of the property.
        """
        return self.get_body().commit

    @property
    def subdirectory(self) -> str:
        """The `subdirectory` property.

        Returns:
            the value of the property.
        """
        return self.get_body().subdirectory

    @property
    def code_repository(self) -> "CodeRepositoryResponse":
        """The `code_repository` property.

        Returns:
            the value of the property.
        """
        return self.get_body().code_repository

code_repository: CodeRepositoryResponse property readonly

The code_repository property.

Returns:

Type	Description
CodeRepositoryResponse	the value of the property.

commit: str property readonly

The commit property.

Returns:

Type	Description
str	the value of the property.

subdirectory: str property readonly

The subdirectory property.

Returns:

Type	Description
str	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this code reference.

Returns:

Type	Description
CodeReferenceResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/code_reference.py

def get_hydrated_version(self) -> "CodeReferenceResponse":
    """Get the hydrated version of this code reference.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_code_reference(self.id)

CodeReferenceResponseBody (BaseDatedResponseBody) pydantic-model

Response body for code references.

Source code in zenml/models/v2/core/code_reference.py

class CodeReferenceResponseBody(BaseDatedResponseBody):
    """Response body for code references."""

    commit: str = Field(description="The commit of the code reference.")
    subdirectory: str = Field(
        description="The subdirectory of the code reference."
    )
    code_repository: "CodeRepositoryResponse" = Field(
        description="The repository of the code reference."
    )

code_repository: CodeRepositoryResponse pydantic-field required

The repository of the code reference.

commit: str pydantic-field required

The commit of the code reference.

subdirectory: str pydantic-field required

The subdirectory of the code reference.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeReferenceResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for code references.

Source code in zenml/models/v2/core/code_reference.py

class CodeReferenceResponseMetadata(BaseResponseMetadata):
    """Response metadata for code references."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeReferenceResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the code reference entity.

Source code in zenml/models/v2/core/code_reference.py

class CodeReferenceResponseResources(BaseResponseResources):
    """Class for all resource models associated with the code reference entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

code_repository

Models representing code repositories.

CodeRepositoryFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all code repositories.

Source code in zenml/models/v2/core/code_repository.py

class CodeRepositoryFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all code repositories."""

    name: Optional[str] = Field(
        description="Name of the code repository.",
    )
    workspace_id: Union[UUID, str, None] = Field(
        description="Workspace of the code repository."
    )
    user_id: Union[UUID, str, None] = Field(
        description="User that created the code repository."
    )

name: str pydantic-field

Name of the code repository.

user_id: Union[uuid.UUID, str] pydantic-field

User that created the code repository.

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the code repository.

CodeRepositoryRequest (WorkspaceScopedRequest) pydantic-model

Request model for code repositories.

Source code in zenml/models/v2/core/code_repository.py

class CodeRepositoryRequest(WorkspaceScopedRequest):
    """Request model for code repositories."""

    name: str = Field(
        title="The name of the code repository.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    config: Dict[str, Any] = Field(
        description="Configuration for the code repository."
    )
    source: Source = Field(description="The code repository source.")
    logo_url: Optional[str] = Field(
        description="Optional URL of a logo (png, jpg or svg) for the "
        "code repository."
    )
    description: Optional[str] = Field(
        description="Code repository description.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

config: Dict[str, Any] pydantic-field required

Configuration for the code repository.

description: ConstrainedStrValue pydantic-field

Code repository description.

logo_url: str pydantic-field

Optional URL of a logo (png, jpg or svg) for the code repository.

source: Source pydantic-field required

The code repository source.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeRepositoryResponse (WorkspaceScopedResponse[CodeRepositoryResponseBody, CodeRepositoryResponseMetadata, CodeRepositoryResponseResources]) pydantic-model

Response model for code repositories.

Source code in zenml/models/v2/core/code_repository.py

class CodeRepositoryResponse(
    WorkspaceScopedResponse[
        CodeRepositoryResponseBody,
        CodeRepositoryResponseMetadata,
        CodeRepositoryResponseResources,
    ]
):
    """Response model for code repositories."""

    name: str = Field(
        title="The name of the code repository.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "CodeRepositoryResponse":
        """Get the hydrated version of this code repository.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_code_repository(self.id)

    # Body and metadata properties
    @property
    def source(self) -> Source:
        """The `source` property.

        Returns:
            the value of the property.
        """
        return self.get_body().source

    @property
    def logo_url(self) -> Optional[str]:
        """The `logo_url` property.

        Returns:
            the value of the property.
        """
        return self.get_body().logo_url

    @property
    def config(self) -> Dict[str, Any]:
        """The `config` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

config: Dict[str, Any] property readonly

The config property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

description: Optional[str] property readonly

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

logo_url: Optional[str] property readonly

The logo_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

source: Source property readonly

The source property.

Returns:

Type	Description
Source	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this code repository.

Returns:

Type	Description
CodeRepositoryResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/code_repository.py

def get_hydrated_version(self) -> "CodeRepositoryResponse":
    """Get the hydrated version of this code repository.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_code_repository(self.id)

CodeRepositoryResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for code repositories.

Source code in zenml/models/v2/core/code_repository.py

class CodeRepositoryResponseBody(WorkspaceScopedResponseBody):
    """Response body for code repositories."""

    source: Source = Field(description="The code repository source.")
    logo_url: Optional[str] = Field(
        default=None,
        description="Optional URL of a logo (png, jpg or svg) for the "
        "code repository.",
    )

logo_url: str pydantic-field

Optional URL of a logo (png, jpg or svg) for the code repository.

source: Source pydantic-field required

The code repository source.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeRepositoryResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for code repositories.

Source code in zenml/models/v2/core/code_repository.py

class CodeRepositoryResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for code repositories."""

    config: Dict[str, Any] = Field(
        description="Configuration for the code repository."
    )
    description: Optional[str] = Field(
        default=None,
        description="Code repository description.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

config: Dict[str, Any] pydantic-field required

Configuration for the code repository.

description: ConstrainedStrValue pydantic-field

Code repository description.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeRepositoryResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the code repository entity.

Source code in zenml/models/v2/core/code_repository.py

class CodeRepositoryResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the code repository entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

CodeRepositoryUpdate (CodeRepositoryRequest) pydantic-model

Update model for code repositories.

Source code in zenml/models/v2/core/code_repository.py

@update_model
class CodeRepositoryUpdate(CodeRepositoryRequest):
    """Update model for code repositories."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[CodeRepositoryResponseBody, CodeRepositoryResponseMetadata, CodeRepositoryResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][CodeRepositoryResponseBody, CodeRepositoryResponseMetadata, CodeRepositoryResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/code_repository.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

component

Models representing components.

ComponentBase (BaseModel) pydantic-model

Base model for components.

Source code in zenml/models/v2/core/component.py

class ComponentBase(BaseModel):
    """Base model for components."""

    name: str = Field(
        title="The name of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    type: StackComponentType = Field(
        title="The type of the stack component.",
    )

    flavor: str = Field(
        title="The flavor of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    configuration: Dict[str, Any] = Field(
        title="The stack component configuration.",
    )

    connector_resource_id: Optional[str] = Field(
        default=None,
        description="The ID of a specific resource instance to "
        "gain access to through the connector",
    )

    labels: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The stack component labels.",
    )

    component_spec_path: Optional[str] = Field(
        default=None,
        title="The path to the component spec used for mlstacks deployments.",
    )

connector_resource_id: str pydantic-field

The ID of a specific resource instance to gain access to through the connector

ComponentFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all ComponentModels.

The Component Model needs additional scoping. As such the _scope_user field can be set to the user that is doing the filtering. The generate_filter() method of the baseclass is overwritten to include the scoping.

Source code in zenml/models/v2/core/component.py

class ComponentFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all ComponentModels.

    The Component Model needs additional scoping. As such the `_scope_user`
    field can be set to the user that is doing the filtering. The
    `generate_filter()` method of the baseclass is overwritten to include the
    scoping.
    """

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "scope_type",
        "stack_id",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        "scope_type",
    ]
    scope_type: Optional[str] = Field(
        default=None,
        description="The type to scope this query to.",
    )

    name: Optional[str] = Field(
        default=None,
        description="Name of the stack component",
    )
    flavor: Optional[str] = Field(
        default=None,
        description="Flavor of the stack component",
    )
    type: Optional[str] = Field(
        default=None,
        description="Type of the stack component",
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of the stack component"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User of the stack component"
    )
    connector_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Connector linked to the stack component"
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Stack of the stack component"
    )

    def set_scope_type(self, component_type: str) -> None:
        """Set the type of component on which to perform the filtering to scope the response.

        Args:
            component_type: The type of component to scope the query to.
        """
        self.scope_type = component_type

    def generate_filter(
        self, table: Type["SQLModel"]
    ) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
        """Generate the filter for the query.

        Stack components can be scoped by type to narrow the search.

        Args:
            table: The Table that is being queried from.

        Returns:
            The filter expression for the query.
        """
        from sqlalchemy import and_, or_

        from zenml.zen_stores.schemas import (
            StackComponentSchema,
            StackCompositionSchema,
        )

        base_filter = super().generate_filter(table)
        if self.scope_type:
            type_filter = getattr(table, "type") == self.scope_type
            return and_(base_filter, type_filter)

        if self.stack_id:
            operator = (
                or_ if self.logical_operator == LogicalOperators.OR else and_
            )

            stack_filter = and_(  # type: ignore[type-var]
                StackCompositionSchema.stack_id == self.stack_id,
                StackCompositionSchema.component_id == StackComponentSchema.id,
            )
            base_filter = operator(base_filter, stack_filter)

        return base_filter

connector_id: Union[uuid.UUID, str] pydantic-field

Connector linked to the stack component

flavor: str pydantic-field

Flavor of the stack component

name: str pydantic-field

Name of the stack component

scope_type: str pydantic-field

The type to scope this query to.

stack_id: Union[uuid.UUID, str] pydantic-field

Stack of the stack component

type: str pydantic-field

Type of the stack component

user_id: Union[uuid.UUID, str] pydantic-field

User of the stack component

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the stack component

generate_filter(self, table)

Generate the filter for the query.

Stack components can be scoped by type to narrow the search.

Parameters:

Name	Type	Description	Default
table	Type[SQLModel]	The Table that is being queried from.	required

Returns:

Type	Description
Union[BinaryExpression[Any], BooleanClauseList[Any]]	The filter expression for the query.

Source code in zenml/models/v2/core/component.py

def generate_filter(
    self, table: Type["SQLModel"]
) -> Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]:
    """Generate the filter for the query.

    Stack components can be scoped by type to narrow the search.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.
    """
    from sqlalchemy import and_, or_

    from zenml.zen_stores.schemas import (
        StackComponentSchema,
        StackCompositionSchema,
    )

    base_filter = super().generate_filter(table)
    if self.scope_type:
        type_filter = getattr(table, "type") == self.scope_type
        return and_(base_filter, type_filter)

    if self.stack_id:
        operator = (
            or_ if self.logical_operator == LogicalOperators.OR else and_
        )

        stack_filter = and_(  # type: ignore[type-var]
            StackCompositionSchema.stack_id == self.stack_id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
        )
        base_filter = operator(base_filter, stack_filter)

    return base_filter

set_scope_type(self, component_type)

Set the type of component on which to perform the filtering to scope the response.

Parameters:

Name	Type	Description	Default
component_type	str	The type of component to scope the query to.	required

Source code in zenml/models/v2/core/component.py

def set_scope_type(self, component_type: str) -> None:
    """Set the type of component on which to perform the filtering to scope the response.

    Args:
        component_type: The type of component to scope the query to.
    """
    self.scope_type = component_type

ComponentRequest (ComponentBase, WorkspaceScopedRequest) pydantic-model

Request model for components.

Source code in zenml/models/v2/core/component.py

class ComponentRequest(ComponentBase, WorkspaceScopedRequest):
    """Request model for components."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["type", "flavor"]

    connector: Optional[UUID] = Field(
        default=None,
        title="The service connector linked to this stack component.",
    )

    @validator("name")
    def name_cant_be_a_secret_reference(cls, name: str) -> str:
        """Validator to ensure that the given name is not a secret reference.

        Args:
            name: The name to validate.

        Returns:
            The name if it is not a secret reference.

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

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

name_cant_be_a_secret_reference(name) classmethod

Validator to ensure that the given name is not a secret reference.

Parameters:

Name	Type	Description	Default
name	str	The name to validate.	required

Returns:

Type	Description
str	The name if it is not a secret reference.

Exceptions:

Type	Description
ValueError	If the name is a secret reference.

Source code in zenml/models/v2/core/component.py

@validator("name")
def name_cant_be_a_secret_reference(cls, name: str) -> str:
    """Validator to ensure that the given name is not a secret reference.

    Args:
        name: The name to validate.

    Returns:
        The name if it is not a secret reference.

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

ComponentResponse (WorkspaceScopedResponse[ComponentResponseBody, ComponentResponseMetadata, ComponentResponseResources]) pydantic-model

Response model for components.

Source code in zenml/models/v2/core/component.py

class ComponentResponse(
    WorkspaceScopedResponse[
        ComponentResponseBody,
        ComponentResponseMetadata,
        ComponentResponseResources,
    ]
):
    """Response model for components."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["type", "flavor"]

    name: str = Field(
        title="The name of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ComponentResponse":
        """Get the hydrated version of this component.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_stack_component(self.id)

    # Body and metadata properties
    @property
    def type(self) -> StackComponentType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def flavor(self) -> str:
        """The `flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().flavor

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    @property
    def labels(self) -> Optional[Dict[str, Any]]:
        """The `labels` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().labels

    @property
    def component_spec_path(self) -> Optional[str]:
        """The `component_spec_path` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().component_spec_path

    @property
    def connector_resource_id(self) -> Optional[str]:
        """The `connector_resource_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_resource_id

    @property
    def connector(self) -> Optional["ServiceConnectorResponse"]:
        """The `connector` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector

component_spec_path: Optional[str] property readonly

The component_spec_path property.

Returns:

Type	Description
Optional[str]	the value of the property.

configuration: Dict[str, Any] property readonly

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

connector: Optional[ServiceConnectorResponse] property readonly

The connector property.

Returns:

Type	Description
Optional[ServiceConnectorResponse]	the value of the property.

connector_resource_id: Optional[str] property readonly

The connector_resource_id property.

Returns:

Type	Description
Optional[str]	the value of the property.

flavor: str property readonly

The flavor property.

Returns:

Type	Description
str	the value of the property.

labels: Optional[Dict[str, Any]] property readonly

The labels property.

Returns:

Type	Description
Optional[Dict[str, Any]]	the value of the property.

type: StackComponentType property readonly

The type property.

Returns:

Type	Description
StackComponentType	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this component.

Returns:

Type	Description
ComponentResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/component.py

def get_hydrated_version(self) -> "ComponentResponse":
    """Get the hydrated version of this component.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_stack_component(self.id)

ComponentResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for components.

Source code in zenml/models/v2/core/component.py

class ComponentResponseBody(WorkspaceScopedResponseBody):
    """Response body for components."""

    type: StackComponentType = Field(
        title="The type of the stack component.",
    )
    flavor: str = Field(
        title="The flavor of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ComponentResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for components.

Source code in zenml/models/v2/core/component.py

class ComponentResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for components."""

    configuration: Dict[str, Any] = Field(
        title="The stack component configuration.",
    )
    labels: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The stack component labels.",
    )
    component_spec_path: Optional[str] = Field(
        default=None,
        title="The path to the component spec used for mlstacks deployments.",
    )
    connector_resource_id: Optional[str] = Field(
        default=None,
        description="The ID of a specific resource instance to "
        "gain access to through the connector",
    )
    connector: Optional["ServiceConnectorResponse"] = Field(
        default=None,
        title="The service connector linked to this stack component.",
    )

connector_resource_id: str pydantic-field

The ID of a specific resource instance to gain access to through the connector

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ComponentResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the component entity.

Source code in zenml/models/v2/core/component.py

class ComponentResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the component entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ComponentUpdate (ComponentRequest) pydantic-model

Update model for stack components.

Source code in zenml/models/v2/core/component.py

@update_model
class ComponentUpdate(ComponentRequest):
    """Update model for stack components."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

InternalComponentRequest (ComponentRequest) pydantic-model

Internal component request model.

Source code in zenml/models/v2/core/component.py

@server_owned_request_model
class InternalComponentRequest(ComponentRequest):
    """Internal component request model."""

    pass

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[ComponentResponseBody, ComponentResponseMetadata, ComponentResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][ComponentResponseBody, ComponentResponseMetadata, ComponentResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/component.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

device

Models representing devices.

OAuthDeviceFilter (UserScopedFilter) pydantic-model

Model to enable advanced filtering of OAuth2 devices.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceFilter(UserScopedFilter):
    """Model to enable advanced filtering of OAuth2 devices."""

    expires: Optional[Union[datetime, str, None]] = Field(
        default=None, description="The expiration date of the OAuth2 device."
    )
    client_id: Union[UUID, str, None] = Field(
        default=None, description="The client ID of the OAuth2 device."
    )
    status: Union[OAuthDeviceStatus, str, None] = Field(
        default=None, description="The status of the OAuth2 device."
    )
    trusted_device: Union[bool, str, None] = Field(
        default=None,
        description="Whether the OAuth2 device was marked as trusted.",
    )
    failed_auth_attempts: Union[int, str, None] = Field(
        default=None,
        description="The number of failed authentication attempts.",
    )
    last_login: Optional[Union[datetime, str, None]] = Field(
        default=None, description="The date of the last successful login."
    )

client_id: Union[uuid.UUID, str] pydantic-field

The client ID of the OAuth2 device.

expires: Union[datetime.datetime, str] pydantic-field

The expiration date of the OAuth2 device.

failed_auth_attempts: Union[int, str] pydantic-field

The number of failed authentication attempts.

last_login: Union[datetime.datetime, str] pydantic-field

The date of the last successful login.

status: Union[zenml.enums.OAuthDeviceStatus, str] pydantic-field

The status of the OAuth2 device.

trusted_device: Union[bool, str] pydantic-field

Whether the OAuth2 device was marked as trusted.

OAuthDeviceInternalRequest (BaseRequest) pydantic-model

Internal request model for OAuth2 devices.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceInternalRequest(BaseRequest):
    """Internal request model for OAuth2 devices."""

    client_id: UUID = Field(description="The client ID of the OAuth2 device.")
    expires_in: int = Field(
        description="The number of seconds after which the OAuth2 device "
        "expires and can no longer be used for authentication."
    )
    os: Optional[str] = Field(
        default=None,
        description="The operating system of the device used for "
        "authentication.",
    )
    ip_address: Optional[str] = Field(
        default=None,
        description="The IP address of the device used for authentication.",
    )
    hostname: Optional[str] = Field(
        default=None,
        description="The hostname of the device used for authentication.",
    )
    python_version: Optional[str] = Field(
        default=None,
        description="The Python version of the device used for authentication.",
    )
    zenml_version: Optional[str] = Field(
        default=None,
        description="The ZenML version of the device used for authentication.",
    )
    city: Optional[str] = Field(
        default=None,
        description="The city where the device is located.",
    )
    region: Optional[str] = Field(
        default=None,
        description="The region where the device is located.",
    )
    country: Optional[str] = Field(
        default=None,
        description="The country where the device is located.",
    )

city: str pydantic-field

The city where the device is located.

client_id: UUID pydantic-field required

The client ID of the OAuth2 device.

country: str pydantic-field

The country where the device is located.

expires_in: int pydantic-field required

The number of seconds after which the OAuth2 device expires and can no longer be used for authentication.

hostname: str pydantic-field

The hostname of the device used for authentication.

ip_address: str pydantic-field

The IP address of the device used for authentication.

os: str pydantic-field

The operating system of the device used for authentication.

python_version: str pydantic-field

The Python version of the device used for authentication.

region: str pydantic-field

The region where the device is located.

zenml_version: str pydantic-field

The ZenML version of the device used for authentication.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

OAuthDeviceInternalResponse (OAuthDeviceResponse) pydantic-model

OAuth2 device response model used internally for authentication.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceInternalResponse(OAuthDeviceResponse):
    """OAuth2 device response model used internally for authentication."""

    user_code: str = Field(
        title="The user code.",
    )
    device_code: str = Field(
        title="The device code.",
    )

    def _verify_code(
        self,
        code: str,
        code_hash: Optional[str],
    ) -> bool:
        """Verifies a given code against the stored (hashed) code.

        Args:
            code: The code to verify.
            code_hash: The hashed code to verify against.

        Returns:
            True if the code is valid, False otherwise.
        """
        context = CryptContext(schemes=["bcrypt"], deprecated="auto")
        result = context.verify(code, code_hash)

        return result

    def verify_user_code(
        self,
        user_code: str,
    ) -> bool:
        """Verifies a given user code against the stored (hashed) user code.

        Args:
            user_code: The user code to verify.

        Returns:
            True if the user code is valid, False otherwise.
        """
        return self._verify_code(user_code, self.user_code)

    def verify_device_code(
        self,
        device_code: str,
    ) -> bool:
        """Verifies a given device code against the stored (hashed) device code.

        Args:
            device_code: The device code to verify.

        Returns:
            True if the device code is valid, False otherwise.
        """
        return self._verify_code(device_code, self.device_code)

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

verify_device_code(self, device_code)

Verifies a given device code against the stored (hashed) device code.

Parameters:

Name	Type	Description	Default
device_code	str	The device code to verify.	required

Returns:

Type	Description
bool	True if the device code is valid, False otherwise.

Source code in zenml/models/v2/core/device.py

def verify_device_code(
    self,
    device_code: str,
) -> bool:
    """Verifies a given device code against the stored (hashed) device code.

    Args:
        device_code: The device code to verify.

    Returns:
        True if the device code is valid, False otherwise.
    """
    return self._verify_code(device_code, self.device_code)

verify_user_code(self, user_code)

Verifies a given user code against the stored (hashed) user code.

Parameters:

Name	Type	Description	Default
user_code	str	The user code to verify.	required

Returns:

Type	Description
bool	True if the user code is valid, False otherwise.

Source code in zenml/models/v2/core/device.py

def verify_user_code(
    self,
    user_code: str,
) -> bool:
    """Verifies a given user code against the stored (hashed) user code.

    Args:
        user_code: The user code to verify.

    Returns:
        True if the user code is valid, False otherwise.
    """
    return self._verify_code(user_code, self.user_code)

OAuthDeviceInternalUpdate (OAuthDeviceUpdate) pydantic-model

OAuth2 device update model used internally for authentication.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceInternalUpdate(OAuthDeviceUpdate):
    """OAuth2 device update model used internally for authentication."""

    user_id: Optional[UUID] = Field(
        default=None, description="User that owns the OAuth2 device."
    )
    status: Optional[OAuthDeviceStatus] = Field(
        default=None, description="The new status of the OAuth2 device."
    )
    expires_in: Optional[int] = Field(
        default=None,
        description="Set the device to expire in the given number of seconds. "
        "If the value is 0 or negative, the device is set to never expire.",
    )
    failed_auth_attempts: Optional[int] = Field(
        default=None,
        description="Set the number of failed authentication attempts.",
    )
    trusted_device: Optional[bool] = Field(
        default=None,
        description="Whether to mark the OAuth2 device as trusted. A trusted "
        "device has a much longer validity time.",
    )
    update_last_login: bool = Field(
        default=False, description="Whether to update the last login date."
    )
    generate_new_codes: bool = Field(
        default=False,
        description="Whether to generate new user and device codes.",
    )
    os: Optional[str] = Field(
        default=None,
        description="The operating system of the device used for "
        "authentication.",
    )
    ip_address: Optional[str] = Field(
        default=None,
        description="The IP address of the device used for authentication.",
    )
    hostname: Optional[str] = Field(
        default=None,
        description="The hostname of the device used for authentication.",
    )
    python_version: Optional[str] = Field(
        default=None,
        description="The Python version of the device used for authentication.",
    )
    zenml_version: Optional[str] = Field(
        default=None,
        description="The ZenML version of the device used for authentication.",
    )
    city: Optional[str] = Field(
        default=None,
        description="The city where the device is located.",
    )
    region: Optional[str] = Field(
        default=None,
        description="The region where the device is located.",
    )
    country: Optional[str] = Field(
        default=None,
        description="The country where the device is located.",
    )

city: str pydantic-field

The city where the device is located.

country: str pydantic-field

The country where the device is located.

expires_in: int pydantic-field

Set the device to expire in the given number of seconds. If the value is 0 or negative, the device is set to never expire.

failed_auth_attempts: int pydantic-field

Set the number of failed authentication attempts.

generate_new_codes: bool pydantic-field

Whether to generate new user and device codes.

hostname: str pydantic-field

The hostname of the device used for authentication.

ip_address: str pydantic-field

The IP address of the device used for authentication.

os: str pydantic-field

The operating system of the device used for authentication.

python_version: str pydantic-field

The Python version of the device used for authentication.

region: str pydantic-field

The region where the device is located.

status: OAuthDeviceStatus pydantic-field

The new status of the OAuth2 device.

trusted_device: bool pydantic-field

Whether to mark the OAuth2 device as trusted. A trusted device has a much longer validity time.

update_last_login: bool pydantic-field

Whether to update the last login date.

user_id: UUID pydantic-field

User that owns the OAuth2 device.

zenml_version: str pydantic-field

The ZenML version of the device used for authentication.

OAuthDeviceResponse (UserScopedResponse[OAuthDeviceResponseBody, OAuthDeviceResponseMetadata, OAuthDeviceResponseResources]) pydantic-model

Response model for OAuth2 devices.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceResponse(
    UserScopedResponse[
        OAuthDeviceResponseBody,
        OAuthDeviceResponseMetadata,
        OAuthDeviceResponseResources,
    ]
):
    """Response model for OAuth2 devices."""

    _warn_on_response_updates = False

    def get_hydrated_version(self) -> "OAuthDeviceResponse":
        """Get the hydrated version of this OAuth2 device.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_authorized_device(self.id)

    # Body and metadata properties
    @property
    def client_id(self) -> UUID:
        """The `client_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().client_id

    @property
    def expires(self) -> Optional[datetime]:
        """The `expires` property.

        Returns:
            the value of the property.
        """
        return self.get_body().expires

    @property
    def trusted_device(self) -> bool:
        """The `trusted_device` property.

        Returns:
            the value of the property.
        """
        return self.get_body().trusted_device

    @property
    def status(self) -> OAuthDeviceStatus:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().status

    @property
    def os(self) -> Optional[str]:
        """The `os` property.

        Returns:
            the value of the property.
        """
        return self.get_body().os

    @property
    def ip_address(self) -> Optional[str]:
        """The `ip_address` property.

        Returns:
            the value of the property.
        """
        return self.get_body().ip_address

    @property
    def hostname(self) -> Optional[str]:
        """The `hostname` property.

        Returns:
            the value of the property.
        """
        return self.get_body().hostname

    @property
    def python_version(self) -> Optional[str]:
        """The `python_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().python_version

    @property
    def zenml_version(self) -> Optional[str]:
        """The `zenml_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().zenml_version

    @property
    def city(self) -> Optional[str]:
        """The `city` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().city

    @property
    def region(self) -> Optional[str]:
        """The `region` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().region

    @property
    def country(self) -> Optional[str]:
        """The `country` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().country

    @property
    def failed_auth_attempts(self) -> int:
        """The `failed_auth_attempts` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().failed_auth_attempts

    @property
    def last_login(self) -> Optional[datetime]:
        """The `last_login` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().last_login

city: Optional[str] property readonly

The city property.

Returns:

Type	Description
Optional[str]	the value of the property.

client_id: UUID property readonly

The client_id property.

Returns:

Type	Description
UUID	the value of the property.

country: Optional[str] property readonly

The country property.

Returns:

Type	Description
Optional[str]	the value of the property.

expires: Optional[datetime.datetime] property readonly

The expires property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

failed_auth_attempts: int property readonly

The failed_auth_attempts property.

Returns:

Type	Description
int	the value of the property.

hostname: Optional[str] property readonly

The hostname property.

Returns:

Type	Description
Optional[str]	the value of the property.

ip_address: Optional[str] property readonly

The ip_address property.

Returns:

Type	Description
Optional[str]	the value of the property.

last_login: Optional[datetime.datetime] property readonly

The last_login property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

os: Optional[str] property readonly

The os property.

Returns:

Type	Description
Optional[str]	the value of the property.

python_version: Optional[str] property readonly

The python_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

region: Optional[str] property readonly

The region property.

Returns:

Type	Description
Optional[str]	the value of the property.

status: OAuthDeviceStatus property readonly

The status property.

Returns:

Type	Description
OAuthDeviceStatus	the value of the property.

trusted_device: bool property readonly

The trusted_device property.

Returns:

Type	Description
bool	the value of the property.

zenml_version: Optional[str] property readonly

The zenml_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this OAuth2 device.

Returns:

Type	Description
OAuthDeviceResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/device.py

def get_hydrated_version(self) -> "OAuthDeviceResponse":
    """Get the hydrated version of this OAuth2 device.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_authorized_device(self.id)

OAuthDeviceResponseBody (UserScopedResponseBody) pydantic-model

Response body for OAuth2 devices.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceResponseBody(UserScopedResponseBody):
    """Response body for OAuth2 devices."""

    client_id: UUID = Field(description="The client ID of the OAuth2 device.")
    expires: Optional[datetime] = Field(
        default=None,
        description="The expiration date of the OAuth2 device after which "
        "the device is no longer valid and cannot be used for "
        "authentication.",
    )
    trusted_device: bool = Field(
        description="Whether the OAuth2 device was marked as trusted. A "
        "trusted device has a much longer validity time.",
    )
    status: OAuthDeviceStatus = Field(
        description="The status of the OAuth2 device."
    )
    os: Optional[str] = Field(
        default=None,
        description="The operating system of the device used for "
        "authentication.",
    )
    ip_address: Optional[str] = Field(
        default=None,
        description="The IP address of the device used for authentication.",
    )
    hostname: Optional[str] = Field(
        default=None,
        description="The hostname of the device used for authentication.",
    )

client_id: UUID pydantic-field required

The client ID of the OAuth2 device.

expires: datetime pydantic-field

The expiration date of the OAuth2 device after which the device is no longer valid and cannot be used for authentication.

hostname: str pydantic-field

The hostname of the device used for authentication.

ip_address: str pydantic-field

The IP address of the device used for authentication.

os: str pydantic-field

The operating system of the device used for authentication.

status: OAuthDeviceStatus pydantic-field required

The status of the OAuth2 device.

trusted_device: bool pydantic-field required

Whether the OAuth2 device was marked as trusted. A trusted device has a much longer validity time.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

OAuthDeviceResponseMetadata (UserScopedResponseMetadata) pydantic-model

Response metadata for OAuth2 devices.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceResponseMetadata(UserScopedResponseMetadata):
    """Response metadata for OAuth2 devices."""

    python_version: Optional[str] = Field(
        default=None,
        description="The Python version of the device used for authentication.",
    )
    zenml_version: Optional[str] = Field(
        default=None,
        description="The ZenML version of the device used for authentication.",
    )
    city: Optional[str] = Field(
        default=None,
        description="The city where the device is located.",
    )
    region: Optional[str] = Field(
        default=None,
        description="The region where the device is located.",
    )
    country: Optional[str] = Field(
        default=None,
        description="The country where the device is located.",
    )
    failed_auth_attempts: int = Field(
        description="The number of failed authentication attempts.",
    )
    last_login: Optional[datetime] = Field(
        description="The date of the last successful login."
    )

city: str pydantic-field

The city where the device is located.

country: str pydantic-field

The country where the device is located.

failed_auth_attempts: int pydantic-field required

The number of failed authentication attempts.

last_login: datetime pydantic-field

The date of the last successful login.

python_version: str pydantic-field

The Python version of the device used for authentication.

region: str pydantic-field

The region where the device is located.

zenml_version: str pydantic-field

The ZenML version of the device used for authentication.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

OAuthDeviceResponseResources (UserScopedResponseResources) pydantic-model

Class for all resource models associated with the OAuthDevice entity.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceResponseResources(UserScopedResponseResources):
    """Class for all resource models associated with the OAuthDevice entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

OAuthDeviceUpdate (BaseModel) pydantic-model

OAuth2 device update model.

Source code in zenml/models/v2/core/device.py

class OAuthDeviceUpdate(BaseModel):
    """OAuth2 device update model."""

    locked: Optional[bool] = Field(
        default=None,
        description="Whether to lock or unlock the OAuth2 device. A locked "
        "device cannot be used for authentication.",
    )

locked: bool pydantic-field

Whether to lock or unlock the OAuth2 device. A locked device cannot be used for authentication.

UserScopedResponse[OAuthDeviceResponseBody, OAuthDeviceResponseMetadata, OAuthDeviceResponseResources] (UserScopedResponse, BaseIdentifiedResponse[UserBody, UserMetadata, UserResources][OAuthDeviceResponseBody, OAuthDeviceResponseMetadata, OAuthDeviceResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/device.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

event_source

Collection of all models concerning event configurations.

EventSourceFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all EventSourceModels.

Source code in zenml/models/v2/core/event_source.py

class EventSourceFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all EventSourceModels."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the event source",
    )
    flavor: Optional[str] = Field(
        default=None,
        description="Flavor of the event source",
    )
    plugin_subtype: Optional[str] = Field(
        title="The plugin sub type of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

flavor: str pydantic-field

Flavor of the event source

name: str pydantic-field

Name of the event source

EventSourceRequest (WorkspaceScopedRequest) pydantic-model

BaseModel for all event sources.

Source code in zenml/models/v2/core/event_source.py

class EventSourceRequest(WorkspaceScopedRequest):
    """BaseModel for all event sources."""

    name: str = Field(
        title="The name of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    flavor: str = Field(
        title="The flavor of event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The plugin subtype of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: str = Field(
        default="",
        title="The description of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    configuration: Dict[str, Any] = Field(
        title="The event source configuration.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceResponse (WorkspaceScopedResponse[EventSourceResponseBody, EventSourceResponseMetadata, EventSourceResponseResources]) pydantic-model

Response model for event sources.

Source code in zenml/models/v2/core/event_source.py

class EventSourceResponse(
    WorkspaceScopedResponse[
        EventSourceResponseBody,
        EventSourceResponseMetadata,
        EventSourceResponseResources,
    ]
):
    """Response model for event sources."""

    name: str = Field(
        title="The name of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "EventSourceResponse":
        """Get the hydrated version of this event source.

        Returns:
            An instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_event_source(self.id)

    # Body and metadata properties
    @property
    def flavor(self) -> str:
        """The `flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().flavor

    @property
    def is_active(self) -> bool:
        """The `is_active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_active

    @property
    def plugin_subtype(self) -> PluginSubType:
        """The `plugin_subtype` property.

        Returns:
            the value of the property.
        """
        return self.get_body().plugin_subtype

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    def set_configuration(self, configuration: Dict[str, Any]) -> None:
        """Set the `configuration` property.

        Args:
            configuration: The value to set.
        """
        self.get_metadata().configuration = configuration

configuration: Dict[str, Any] property readonly

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

description: str property readonly

The description property.

Returns:

Type	Description
str	the value of the property.

flavor: str property readonly

The flavor property.

Returns:

Type	Description
str	the value of the property.

is_active: bool property readonly

The is_active property.

Returns:

Type	Description
bool	the value of the property.

plugin_subtype: PluginSubType property readonly

The plugin_subtype property.

Returns:

Type	Description
PluginSubType	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this event source.

Returns:

Type	Description
EventSourceResponse	An instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/event_source.py

def get_hydrated_version(self) -> "EventSourceResponse":
    """Get the hydrated version of this event source.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_event_source(self.id)

set_configuration(self, configuration)

Set the configuration property.

Parameters:

Name	Type	Description	Default
configuration	Dict[str, Any]	The value to set.	required

Source code in zenml/models/v2/core/event_source.py

def set_configuration(self, configuration: Dict[str, Any]) -> None:
    """Set the `configuration` property.

    Args:
        configuration: The value to set.
    """
    self.get_metadata().configuration = configuration

EventSourceResponseBody (WorkspaceScopedResponseBody) pydantic-model

ResponseBody for event sources.

Source code in zenml/models/v2/core/event_source.py

class EventSourceResponseBody(WorkspaceScopedResponseBody):
    """ResponseBody for event sources."""

    flavor: str = Field(
        title="The flavor of event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The plugin subtype of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    is_active: bool = Field(
        title="Whether the event source is active.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for event sources.

Source code in zenml/models/v2/core/event_source.py

class EventSourceResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for event sources."""

    description: str = Field(
        default="",
        title="The description of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Dict[str, Any] = Field(
        title="The event source configuration.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the code repository entity.

Source code in zenml/models/v2/core/event_source.py

class EventSourceResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the code repository entity."""

    triggers: Page[TriggerResponse] = Field(
        title="The triggers configured with this event source.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceUpdate (BaseZenModel) pydantic-model

Update model for event sources.

Source code in zenml/models/v2/core/event_source.py

class EventSourceUpdate(BaseZenModel):
    """Update model for event sources."""

    name: Optional[str] = Field(
        default=None,
        title="The updated name of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The updated description of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The updated event source configuration.",
    )
    is_active: Optional[bool] = Field(
        default=None,
        title="The status of the event source.",
    )

    @classmethod
    def from_response(
        cls, response: "EventSourceResponse"
    ) -> "EventSourceUpdate":
        """Create an update model from a response model.

        Args:
            response: The response model to create the update model from.

        Returns:
            The update model.
        """
        return EventSourceUpdate(
            name=response.name,
            description=response.description,
            configuration=copy.deepcopy(response.configuration),
            is_active=response.is_active,
        )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

from_response(response) classmethod

Create an update model from a response model.

Parameters:

Name	Type	Description	Default
response	EventSourceResponse	The response model to create the update model from.	required

Returns:

Type	Description
EventSourceUpdate	The update model.

Source code in zenml/models/v2/core/event_source.py

@classmethod
def from_response(
    cls, response: "EventSourceResponse"
) -> "EventSourceUpdate":
    """Create an update model from a response model.

    Args:
        response: The response model to create the update model from.

    Returns:
        The update model.
    """
    return EventSourceUpdate(
        name=response.name,
        description=response.description,
        configuration=copy.deepcopy(response.configuration),
        is_active=response.is_active,
    )

WorkspaceScopedResponse[EventSourceResponseBody, EventSourceResponseMetadata, EventSourceResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][EventSourceResponseBody, EventSourceResponseMetadata, EventSourceResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/event_source.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

event_source_flavor

Models representing event source flavors..

BasePluginFlavorResponse[EventSourceFlavorResponseBody, EventSourceFlavorResponseMetadata, EventSourceFlavorResponseResources] (BasePluginFlavorResponse, BaseResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources][EventSourceFlavorResponseBody, EventSourceFlavorResponseMetadata, EventSourceFlavorResponseResources]) pydantic-model

Config

Configuration for base plugin flavor response.

Source code in zenml/models/v2/core/event_source_flavor.py

class Config:
    """Configuration for base plugin flavor response."""

    extra = Extra.ignore

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceFlavorResponse (BasePluginFlavorResponse[EventSourceFlavorResponseBody, EventSourceFlavorResponseMetadata, EventSourceFlavorResponseResources]) pydantic-model

Response model for Event Source Flavors.

Source code in zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponse(
    BasePluginFlavorResponse[
        EventSourceFlavorResponseBody,
        EventSourceFlavorResponseMetadata,
        EventSourceFlavorResponseResources,
    ]
):
    """Response model for Event Source Flavors."""

    # Body and metadata properties
    @property
    def source_config_schema(self) -> Dict[str, Any]:
        """The `source_config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().source_config_schema

    @property
    def filter_config_schema(self) -> Dict[str, Any]:
        """The `filter_config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().filter_config_schema

filter_config_schema: Dict[str, Any] property readonly

The filter_config_schema property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

source_config_schema: Dict[str, Any] property readonly

The source_config_schema property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceFlavorResponseBody (BasePluginResponseBody) pydantic-model

Response body for event flavors.

Source code in zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponseBody(BasePluginResponseBody):
    """Response body for event flavors."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceFlavorResponseMetadata (BasePluginResponseMetadata) pydantic-model

Response metadata for event flavors.

Source code in zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponseMetadata(BasePluginResponseMetadata):
    """Response metadata for event flavors."""

    source_config_schema: Dict[str, Any]
    filter_config_schema: Dict[str, Any]

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

EventSourceFlavorResponseResources (BasePluginResponseResources) pydantic-model

Response resources for event source flavors.

Source code in zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponseResources(BasePluginResponseResources):
    """Response resources for event source flavors."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

flavor

Models representing flavors.

FlavorFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all Flavors.

Source code in zenml/models/v2/core/flavor.py

class FlavorFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all Flavors."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the flavor",
    )
    type: Optional[str] = Field(
        default=None,
        description="Stack Component Type of the stack flavor",
    )
    integration: Optional[str] = Field(
        default=None,
        description="Integration associated with the flavor",
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of the stack"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User of the stack"
    )

integration: str pydantic-field

Integration associated with the flavor

name: str pydantic-field

Name of the flavor

type: str pydantic-field

Stack Component Type of the stack flavor

user_id: Union[uuid.UUID, str] pydantic-field

User of the stack

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the stack

FlavorRequest (UserScopedRequest) pydantic-model

Request model for flavors.

Source code in zenml/models/v2/core/flavor.py

class FlavorRequest(UserScopedRequest):
    """Request model for flavors."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "type",
        "integration",
    ]

    name: str = Field(
        title="The name of the Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    type: StackComponentType = Field(title="The type of the Flavor.")
    config_schema: Dict[str, Any] = Field(
        title="The JSON schema of this flavor's corresponding configuration.",
    )
    connector_type: Optional[str] = Field(
        default=None,
        title="The type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_type: Optional[str] = Field(
        default=None,
        title="The resource type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_id_attr: Optional[str] = Field(
        default=None,
        title="The name of an attribute in the stack component configuration "
        "that plays the role of resource ID when linked to a service "
        "connector.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    source: str = Field(
        title="The path to the module which contains this Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    integration: Optional[str] = Field(
        title="The name of the integration that the Flavor belongs to.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to a png,"
        "svg or jpg can be attached.",
    )
    docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to docs, within docs.zenml.io.",
    )
    sdk_docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to SDK docs,"
        "within sdkdocs.zenml.io.",
    )
    is_custom: bool = Field(
        title="Whether or not this flavor is a custom, user created flavor.",
        default=True,
    )
    workspace: Optional[UUID] = Field(
        default=None, title="The workspace to which this resource belongs."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

FlavorResponse (UserScopedResponse[FlavorResponseBody, FlavorResponseMetadata, FlavorResponseResources]) pydantic-model

Response model for flavors.

Source code in zenml/models/v2/core/flavor.py

class FlavorResponse(
    UserScopedResponse[
        FlavorResponseBody, FlavorResponseMetadata, FlavorResponseResources
    ]
):
    """Response model for flavors."""

    # Analytics
    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "id",
        "type",
        "integration",
    ]

    name: str = Field(
        title="The name of the Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "FlavorResponse":
        """Get the hydrated version of the flavor.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_flavor(self.id)

    # Helper methods
    @property
    def connector_requirements(
        self,
    ) -> Optional["ServiceConnectorRequirements"]:
        """Returns the connector requirements for the flavor.

        Returns:
            The connector requirements for the flavor.
        """
        from zenml.models import (
            ServiceConnectorRequirements,
        )

        if not self.connector_resource_type:
            return None

        return ServiceConnectorRequirements(
            connector_type=self.connector_type,
            resource_type=self.connector_resource_type,
            resource_id_attr=self.connector_resource_id_attr,
        )

    # Body and metadata properties
    @property
    def type(self) -> StackComponentType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def integration(self) -> Optional[str]:
        """The `integration` property.

        Returns:
            the value of the property.
        """
        return self.get_body().integration

    @property
    def logo_url(self) -> Optional[str]:
        """The `logo_url` property.

        Returns:
            the value of the property.
        """
        return self.get_body().logo_url

    @property
    def workspace(self) -> Optional["WorkspaceResponse"]:
        """The `workspace` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().workspace

    @property
    def config_schema(self) -> Dict[str, Any]:
        """The `config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config_schema

    @property
    def connector_type(self) -> Optional[str]:
        """The `connector_type` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_type

    @property
    def connector_resource_type(self) -> Optional[str]:
        """The `connector_resource_type` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_resource_type

    @property
    def connector_resource_id_attr(self) -> Optional[str]:
        """The `connector_resource_id_attr` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_resource_id_attr

    @property
    def source(self) -> str:
        """The `source` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().source

    @property
    def docs_url(self) -> Optional[str]:
        """The `docs_url` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().docs_url

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """The `sdk_docs_url` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().sdk_docs_url

    @property
    def is_custom(self) -> bool:
        """The `is_custom` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().is_custom

config_schema: Dict[str, Any] property readonly

The config_schema property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

connector_requirements: Optional[ServiceConnectorRequirements] property readonly

Returns the connector requirements for the flavor.

Returns:

Type	Description
Optional[ServiceConnectorRequirements]	The connector requirements for the flavor.

connector_resource_id_attr: Optional[str] property readonly

The connector_resource_id_attr property.

Returns:

Type	Description
Optional[str]	the value of the property.

connector_resource_type: Optional[str] property readonly

The connector_resource_type property.

Returns:

Type	Description
Optional[str]	the value of the property.

connector_type: Optional[str] property readonly

The connector_type property.

Returns:

Type	Description
Optional[str]	the value of the property.

docs_url: Optional[str] property readonly

The docs_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

integration: Optional[str] property readonly

The integration property.

Returns:

Type	Description
Optional[str]	the value of the property.

is_custom: bool property readonly

The is_custom property.

Returns:

Type	Description
bool	the value of the property.

logo_url: Optional[str] property readonly

The logo_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

sdk_docs_url: Optional[str] property readonly

The sdk_docs_url property.

Returns:

Type	Description
Optional[str]	the value of the property.

source: str property readonly

The source property.

Returns:

Type	Description
str	the value of the property.

type: StackComponentType property readonly

The type property.

Returns:

Type	Description
StackComponentType	the value of the property.

workspace: Optional[WorkspaceResponse] property readonly

The workspace property.

Returns:

Type	Description
Optional[WorkspaceResponse]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of the flavor.

Returns:

Type	Description
FlavorResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/flavor.py

def get_hydrated_version(self) -> "FlavorResponse":
    """Get the hydrated version of the flavor.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_flavor(self.id)

FlavorResponseBody (UserScopedResponseBody) pydantic-model

Response body for flavor.

Source code in zenml/models/v2/core/flavor.py

class FlavorResponseBody(UserScopedResponseBody):
    """Response body for flavor."""

    type: StackComponentType = Field(title="The type of the Flavor.")
    integration: Optional[str] = Field(
        title="The name of the integration that the Flavor belongs to.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to a png,"
        "svg or jpg can be attached.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

FlavorResponseMetadata (UserScopedResponseMetadata) pydantic-model

Response metadata for flavors.

Source code in zenml/models/v2/core/flavor.py

class FlavorResponseMetadata(UserScopedResponseMetadata):
    """Response metadata for flavors."""

    workspace: Optional["WorkspaceResponse"] = Field(
        title="The project of this resource."
    )
    config_schema: Dict[str, Any] = Field(
        title="The JSON schema of this flavor's corresponding configuration.",
    )
    connector_type: Optional[str] = Field(
        default=None,
        title="The type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_type: Optional[str] = Field(
        default=None,
        title="The resource type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_id_attr: Optional[str] = Field(
        default=None,
        title="The name of an attribute in the stack component configuration "
        "that plays the role of resource ID when linked to a service "
        "connector.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    source: str = Field(
        title="The path to the module which contains this Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to docs, within docs.zenml.io.",
    )
    sdk_docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to SDK docs,"
        "within sdkdocs.zenml.io.",
    )
    is_custom: bool = Field(
        title="Whether or not this flavor is a custom, user created flavor.",
        default=True,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

FlavorResponseResources (UserScopedResponseResources) pydantic-model

Class for all resource models associated with the flavor entity.

Source code in zenml/models/v2/core/flavor.py

class FlavorResponseResources(UserScopedResponseResources):
    """Class for all resource models associated with the flavor entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

FlavorUpdate (FlavorRequest) pydantic-model

Update model for flavors.

Source code in zenml/models/v2/core/flavor.py

@update_model
class FlavorUpdate(FlavorRequest):
    """Update model for flavors."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

InternalFlavorRequest (FlavorRequest) pydantic-model

Internal flavor request model.

Source code in zenml/models/v2/core/flavor.py

@server_owned_request_model
class InternalFlavorRequest(FlavorRequest):
    """Internal flavor request model."""

    pass

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserScopedResponse[FlavorResponseBody, FlavorResponseMetadata, FlavorResponseResources] (UserScopedResponse, BaseIdentifiedResponse[UserBody, UserMetadata, UserResources][FlavorResponseBody, FlavorResponseMetadata, FlavorResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/flavor.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

logs

Models representing logs.

BaseIdentifiedResponse[LogsResponseBody, LogsResponseMetadata, LogsResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][LogsResponseBody, LogsResponseMetadata, LogsResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/logs.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

LogsRequest (BaseRequest) pydantic-model

Request model for logs.

Source code in zenml/models/v2/core/logs.py

class LogsRequest(BaseRequest):
    """Request model for logs."""

    uri: str = Field(
        title="The uri of the logs file",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    artifact_store_id: Union[str, UUID] = Field(
        title="The artifact store ID to associate the logs with.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

LogsResponse (BaseIdentifiedResponse[LogsResponseBody, LogsResponseMetadata, LogsResponseResources]) pydantic-model

Response model for logs.

Source code in zenml/models/v2/core/logs.py

class LogsResponse(
    BaseIdentifiedResponse[
        LogsResponseBody, LogsResponseMetadata, LogsResponseResources
    ]
):
    """Response model for logs."""

    def get_hydrated_version(self) -> "LogsResponse":
        """Get the hydrated version of these logs.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_logs(self.id)

    # Body and metadata properties
    @property
    def uri(self) -> str:
        """The `uri` property.

        Returns:
            the value of the property.
        """
        return self.get_body().uri

    @property
    def step_run_id(self) -> Optional[Union[str, UUID]]:
        """The `step_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().step_run_id

    @property
    def pipeline_run_id(self) -> Optional[Union[str, UUID]]:
        """The `pipeline_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_run_id

    @property
    def artifact_store_id(self) -> Union[str, UUID]:
        """The `artifact_store_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().artifact_store_id

artifact_store_id: Union[str, uuid.UUID] property readonly

The artifact_store_id property.

Returns:

Type	Description
Union[str, uuid.UUID]	the value of the property.

pipeline_run_id: Union[uuid.UUID, str] property readonly

The pipeline_run_id property.

Returns:

Type	Description
Union[uuid.UUID, str]	the value of the property.

step_run_id: Union[uuid.UUID, str] property readonly

The step_run_id property.

Returns:

Type	Description
Union[uuid.UUID, str]	the value of the property.

uri: str property readonly

The uri property.

Returns:

Type	Description
str	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of these logs.

Returns:

Type	Description
LogsResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/logs.py

def get_hydrated_version(self) -> "LogsResponse":
    """Get the hydrated version of these logs.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_logs(self.id)

LogsResponseBody (BaseDatedResponseBody) pydantic-model

Response body for logs.

Source code in zenml/models/v2/core/logs.py

class LogsResponseBody(BaseDatedResponseBody):
    """Response body for logs."""

    uri: str = Field(
        title="The uri of the logs file",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

LogsResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for logs.

Source code in zenml/models/v2/core/logs.py

class LogsResponseMetadata(BaseResponseMetadata):
    """Response metadata for logs."""

    step_run_id: Optional[Union[str, UUID]] = Field(
        title="Step ID to associate the logs with.",
        default=None,
        description="When this is set, pipeline_run_id should be set to None.",
    )
    pipeline_run_id: Optional[Union[str, UUID]] = Field(
        title="Pipeline run ID to associate the logs with.",
        default=None,
        description="When this is set, step_run_id should be set to None.",
    )
    artifact_store_id: Union[str, UUID] = Field(
        title="The artifact store ID to associate the logs with.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

pipeline_run_id: Union[uuid.UUID, str] pydantic-field

When this is set, step_run_id should be set to None.

step_run_id: Union[uuid.UUID, str] pydantic-field

When this is set, pipeline_run_id should be set to None.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

LogsResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the Logs entity.

Source code in zenml/models/v2/core/logs.py

class LogsResponseResources(BaseResponseResources):
    """Class for all resource models associated with the Logs entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

model

Models representing models.

ModelFilter (WorkspaceScopedTaggableFilter) pydantic-model

Model to enable advanced filtering of all Workspaces.

Source code in zenml/models/v2/core/model.py

class ModelFilter(WorkspaceScopedTaggableFilter):
    """Model to enable advanced filtering of all Workspaces."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the Model",
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of the Model"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User of the Model"
    )

    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedTaggableFilter.CLI_EXCLUDE_FIELDS,
        "workspace_id",
        "user_id",
    ]

name: str pydantic-field

Name of the Model

user_id: Union[uuid.UUID, str] pydantic-field

User of the Model

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the Model

ModelRequest (WorkspaceScopedRequest) pydantic-model

Request model for models.

Source code in zenml/models/v2/core/model.py

class ModelRequest(WorkspaceScopedRequest):
    """Request model for models."""

    name: str = Field(
        title="The name of the model",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    license: Optional[str] = Field(
        title="The license model created under",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="The description of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    audience: Optional[str] = Field(
        title="The target audience of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    use_cases: Optional[str] = Field(
        title="The use cases of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    limitations: Optional[str] = Field(
        title="The know limitations of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    trade_offs: Optional[str] = Field(
        title="The trade offs of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    ethics: Optional[str] = Field(
        title="The ethical implications of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    tags: Optional[List[str]] = Field(
        title="Tags associated with the model",
    )
    save_models_to_registry: bool = Field(
        title="Whether to save all ModelArtifacts to Model Registry",
        default=True,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelResponse (WorkspaceScopedResponse[ModelResponseBody, ModelResponseMetadata, ModelResponseResources]) pydantic-model

Response model for models.

Source code in zenml/models/v2/core/model.py

class ModelResponse(
    WorkspaceScopedResponse[
        ModelResponseBody, ModelResponseMetadata, ModelResponseResources
    ]
):
    """Response model for models."""

    name: str = Field(
        title="The name of the model",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ModelResponse":
        """Get the hydrated version of this model.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_model(self.id)

    # Body and metadata properties
    @property
    def tags(self) -> List["TagResponse"]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def latest_version_name(self) -> Optional[str]:
        """The `latest_version_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_name

    @property
    def latest_version_id(self) -> Optional[UUID]:
        """The `latest_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_id

    @property
    def license(self) -> Optional[str]:
        """The `license` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().license

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def audience(self) -> Optional[str]:
        """The `audience` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().audience

    @property
    def use_cases(self) -> Optional[str]:
        """The `use_cases` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().use_cases

    @property
    def limitations(self) -> Optional[str]:
        """The `limitations` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().limitations

    @property
    def trade_offs(self) -> Optional[str]:
        """The `trade_offs` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().trade_offs

    @property
    def ethics(self) -> Optional[str]:
        """The `ethics` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().ethics

    @property
    def save_models_to_registry(self) -> bool:
        """The `save_models_to_registry` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().save_models_to_registry

    # Helper functions
    @property
    def versions(self) -> List["Model"]:
        """List all versions of the model.

        Returns:
            The list of all model version.
        """
        from zenml.client import Client

        client = Client()
        model_versions = depaginate(
            partial(client.list_model_versions, model_name_or_id=self.id)
        )
        return [
            mv.to_model_class(suppress_class_validation_warnings=True)
            for mv in model_versions
        ]

audience: Optional[str] property readonly

The audience property.

Returns:

Type	Description
Optional[str]	the value of the property.

description: Optional[str] property readonly

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

ethics: Optional[str] property readonly

The ethics property.

Returns:

Type	Description
Optional[str]	the value of the property.

latest_version_id: Optional[uuid.UUID] property readonly

The latest_version_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

latest_version_name: Optional[str] property readonly

The latest_version_name property.

Returns:

Type	Description
Optional[str]	the value of the property.

license: Optional[str] property readonly

The license property.

Returns:

Type	Description
Optional[str]	the value of the property.

limitations: Optional[str] property readonly

The limitations property.

Returns:

Type	Description
Optional[str]	the value of the property.

save_models_to_registry: bool property readonly

The save_models_to_registry property.

Returns:

Type	Description
bool	the value of the property.

tags: List[TagResponse] property readonly

The tags property.

Returns:

Type	Description
List[TagResponse]	the value of the property.

trade_offs: Optional[str] property readonly

The trade_offs property.

Returns:

Type	Description
Optional[str]	the value of the property.

use_cases: Optional[str] property readonly

The use_cases property.

Returns:

Type	Description
Optional[str]	the value of the property.

versions: List[Model] property readonly

List all versions of the model.

Returns:

Type	Description
List[Model]	The list of all model version.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this model.

Returns:

Type	Description
ModelResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/model.py

def get_hydrated_version(self) -> "ModelResponse":
    """Get the hydrated version of this model.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_model(self.id)

ModelResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for models.

Source code in zenml/models/v2/core/model.py

class ModelResponseBody(WorkspaceScopedResponseBody):
    """Response body for models."""

    tags: List["TagResponse"] = Field(
        title="Tags associated with the model",
    )
    latest_version_name: Optional[str]
    latest_version_id: Optional[UUID]

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for models.

Source code in zenml/models/v2/core/model.py

class ModelResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for models."""

    license: Optional[str] = Field(
        title="The license model created under",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="The description of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    audience: Optional[str] = Field(
        title="The target audience of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    use_cases: Optional[str] = Field(
        title="The use cases of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    limitations: Optional[str] = Field(
        title="The know limitations of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    trade_offs: Optional[str] = Field(
        title="The trade offs of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    ethics: Optional[str] = Field(
        title="The ethical implications of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    save_models_to_registry: bool = Field(
        title="Whether to save all ModelArtifacts to Model Registry",
        default=True,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the model entity.

Source code in zenml/models/v2/core/model.py

class ModelResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the model entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelUpdate (BaseModel) pydantic-model

Update model for models.

Source code in zenml/models/v2/core/model.py

class ModelUpdate(BaseModel):
    """Update model for models."""

    name: Optional[str] = None
    license: Optional[str] = None
    description: Optional[str] = None
    audience: Optional[str] = None
    use_cases: Optional[str] = None
    limitations: Optional[str] = None
    trade_offs: Optional[str] = None
    ethics: Optional[str] = None
    add_tags: Optional[List[str]] = None
    remove_tags: Optional[List[str]] = None
    save_models_to_registry: Optional[bool] = None

WorkspaceScopedResponse[ModelResponseBody, ModelResponseMetadata, ModelResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][ModelResponseBody, ModelResponseMetadata, ModelResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/model.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

model_version

Models representing model versions.

ModelVersionFilter (WorkspaceScopedTaggableFilter) pydantic-model

Filter model for model versions.

Source code in zenml/models/v2/core/model_version.py

class ModelVersionFilter(WorkspaceScopedTaggableFilter):
    """Filter model for model versions."""

    name: Optional[str] = Field(
        default=None,
        description="The name of the Model Version",
    )
    number: Optional[int] = Field(
        default=None,
        description="The number of the Model Version",
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="The workspace of the Model Version"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="The user of the Model Version"
    )
    stage: Optional[Union[str, ModelStages]] = Field(
        description="The model version stage", default=None
    )

    _model_id: UUID = PrivateAttr(None)

    def set_scope_model(self, model_name_or_id: Union[str, UUID]) -> None:
        """Set the model to scope this response.

        Args:
            model_name_or_id: The model to scope this response to.
        """
        try:
            model_id = UUID(str(model_name_or_id))
        except ValueError:
            from zenml.client import Client

            model_id = Client().get_model(model_name_or_id).id

        self._model_id = model_id

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)

        if self._model_id:
            query = query.where(getattr(table, "model_id") == self._model_id)

        return query

name: str pydantic-field

The name of the Model Version

number: int pydantic-field

The number of the Model Version

stage: Union[str, zenml.enums.ModelStages] pydantic-field

The model version stage

user_id: Union[uuid.UUID, str] pydantic-field

The user of the Model Version

workspace_id: Union[uuid.UUID, str] pydantic-field

The workspace of the Model Version

apply_filter(self, query, table)

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/core/model_version.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)

    if self._model_id:
        query = query.where(getattr(table, "model_id") == self._model_id)

    return query

set_scope_model(self, model_name_or_id)

Set the model to scope this response.

Parameters:

Name	Type	Description	Default
model_name_or_id	Union[str, uuid.UUID]	The model to scope this response to.	required

Source code in zenml/models/v2/core/model_version.py

def set_scope_model(self, model_name_or_id: Union[str, UUID]) -> None:
    """Set the model to scope this response.

    Args:
        model_name_or_id: The model to scope this response to.
    """
    try:
        model_id = UUID(str(model_name_or_id))
    except ValueError:
        from zenml.client import Client

        model_id = Client().get_model(model_name_or_id).id

    self._model_id = model_id

ModelVersionRequest (WorkspaceScopedRequest) pydantic-model

Request model for model versions.

Source code in zenml/models/v2/core/model_version.py

class ModelVersionRequest(WorkspaceScopedRequest):
    """Request model for model versions."""

    name: Optional[str] = Field(
        description="The name of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        description="The description of the model version",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    stage: Optional[str] = Field(
        description="The stage of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )

    number: Optional[int] = Field(
        description="The number of the model version",
    )
    model: UUID = Field(
        description="The ID of the model containing version",
    )
    tags: Optional[List[str]] = Field(
        title="Tags associated with the model version",
    )

description: ConstrainedStrValue pydantic-field

The description of the model version

model: UUID pydantic-field required

The ID of the model containing version

name: ConstrainedStrValue pydantic-field

The name of the model version

number: int pydantic-field

The number of the model version

stage: ConstrainedStrValue pydantic-field

The stage of the model version

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionResponse (WorkspaceScopedResponse[ModelVersionResponseBody, ModelVersionResponseMetadata, ModelVersionResponseResources]) pydantic-model

Response model for model versions.

Source code in zenml/models/v2/core/model_version.py

class ModelVersionResponse(
    WorkspaceScopedResponse[
        ModelVersionResponseBody,
        ModelVersionResponseMetadata,
        ModelVersionResponseResources,
    ]
):
    """Response model for model versions."""

    name: Optional[str] = Field(
        description="The name of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )

    @property
    def stage(self) -> Optional[str]:
        """The `stage` property.

        Returns:
            the value of the property.
        """
        return self.get_body().stage

    @property
    def number(self) -> int:
        """The `number` property.

        Returns:
            the value of the property.
        """
        return self.get_body().number

    @property
    def model(self) -> "ModelResponse":
        """The `model` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model

    @property
    def model_artifact_ids(self) -> Dict[str, Dict[str, UUID]]:
        """The `model_artifact_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_artifact_ids

    @property
    def data_artifact_ids(self) -> Dict[str, Dict[str, UUID]]:
        """The `data_artifact_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().data_artifact_ids

    @property
    def deployment_artifact_ids(self) -> Dict[str, Dict[str, UUID]]:
        """The `deployment_artifact_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().deployment_artifact_ids

    @property
    def pipeline_run_ids(self) -> Dict[str, UUID]:
        """The `pipeline_run_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().pipeline_run_ids

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def run_metadata(self) -> Optional[Dict[str, "RunMetadataResponse"]]:
        """The `run_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    def get_hydrated_version(self) -> "ModelVersionResponse":
        """Get the hydrated version of this model version.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_model_version(self.id)

    # Helper functions
    def to_model_class(
        self,
        was_created_in_this_run: bool = False,
        suppress_class_validation_warnings: bool = False,
    ) -> "Model":
        """Convert response model to Model object.

        Args:
            was_created_in_this_run: Whether model version was created during
                the current run.
            suppress_class_validation_warnings: internally used to suppress
                repeated warnings.

        Returns:
            Model object
        """
        from zenml.model.model import Model

        mv = Model(
            name=self.model.name,
            license=self.model.license,
            description=self.description,
            audience=self.model.audience,
            use_cases=self.model.use_cases,
            limitations=self.model.limitations,
            trade_offs=self.model.trade_offs,
            ethics=self.model.ethics,
            tags=[t.name for t in self.tags],
            version=self.name,
            was_created_in_this_run=was_created_in_this_run,
            suppress_class_validation_warnings=suppress_class_validation_warnings,
        )
        mv._id = self.id

        return mv

    @property
    def model_artifacts(
        self,
    ) -> Dict[str, Dict[str, "ArtifactVersionResponse"]]:
        """Get all model artifacts linked to this model version.

        Returns:
            Dictionary of model artifacts with versions as
            Dict[str, Dict[str, ArtifactResponse]]
        """
        from zenml.client import Client

        return {
            name: {
                version: Client().get_artifact_version(a)
                for version, a in self.model_artifact_ids[name].items()
            }
            for name in self.model_artifact_ids
        }

    @property
    def data_artifacts(
        self,
    ) -> Dict[str, Dict[str, "ArtifactVersionResponse"]]:
        """Get all data artifacts linked to this model version.

        Returns:
            Dictionary of data artifacts with versions as
            Dict[str, Dict[str, ArtifactResponse]]
        """
        from zenml.client import Client

        return {
            name: {
                version: Client().get_artifact_version(a)
                for version, a in self.data_artifact_ids[name].items()
            }
            for name in self.data_artifact_ids
        }

    @property
    def deployment_artifacts(
        self,
    ) -> Dict[str, Dict[str, "ArtifactVersionResponse"]]:
        """Get all deployment artifacts linked to this model version.

        Returns:
            Dictionary of deployment artifacts with versions as
            Dict[str, Dict[str, ArtifactResponse]]
        """
        from zenml.client import Client

        return {
            name: {
                version: Client().get_artifact_version(a)
                for version, a in self.deployment_artifact_ids[name].items()
            }
            for name in self.deployment_artifact_ids
        }

    @property
    def pipeline_runs(self) -> Dict[str, "PipelineRunResponse"]:
        """Get all pipeline runs linked to this version.

        Returns:
            Dictionary of Pipeline Runs as PipelineRunResponseModel
        """
        from zenml.client import Client

        return {
            name: Client().get_pipeline_run(pr)
            for name, pr in self.pipeline_run_ids.items()
        }

    def _get_linked_object(
        self,
        collection: Dict[str, Dict[str, UUID]],
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the artifact linked to this model version given type.

        Args:
            collection: The collection to search in (one of
                self.model_artifact_ids, self.data_artifact_ids,
                self.deployment_artifact_ids)
            name: The name of the artifact to retrieve.
            version: The version of the artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of an artifact from collection or None
        """
        from zenml.client import Client

        client = Client()

        if name not in collection:
            return None
        if version is None:
            version = max(collection[name].keys())
        return client.get_artifact_version(collection[name][version])

    def get_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the artifact linked to this model version.

        Args:
            name: The name of the artifact to retrieve.
            version: The version of the artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of an artifact or None
        """
        all_artifact_ids = {
            **self.model_artifact_ids,
            **self.data_artifact_ids,
            **self.deployment_artifact_ids,
        }
        return self._get_linked_object(all_artifact_ids, name, version)

    def get_model_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the model artifact linked to this model version.

        Args:
            name: The name of the model artifact to retrieve.
            version: The version of the model artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of the model artifact or None
        """
        return self._get_linked_object(self.model_artifact_ids, name, version)

    def get_data_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the data artifact linked to this model version.

        Args:
            name: The name of the data artifact to retrieve.
            version: The version of the data artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of the data artifact or None
        """
        return self._get_linked_object(
            self.data_artifact_ids,
            name,
            version,
        )

    def get_deployment_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the deployment artifact linked to this model version.

        Args:
            name: The name of the deployment artifact to retrieve.
            version: The version of the deployment artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of the deployment artifact or None
        """
        return self._get_linked_object(
            self.deployment_artifact_ids,
            name,
            version,
        )

    def get_pipeline_run(self, name: str) -> "PipelineRunResponse":
        """Get pipeline run linked to this version.

        Args:
            name: The name of the pipeline run to retrieve.

        Returns:
            PipelineRun as PipelineRunResponseModel
        """
        from zenml.client import Client

        return Client().get_pipeline_run(self.pipeline_run_ids[name])

    def set_stage(
        self, stage: Union[str, ModelStages], force: bool = False
    ) -> None:
        """Sets this Model Version to a desired stage.

        Args:
            stage: the target stage for model version.
            force: whether to force archiving of current model version in
                target stage or raise.

        Raises:
            ValueError: if model_stage is not valid.
        """
        from zenml.client import Client

        stage = getattr(stage, "value", stage)
        if stage not in [stage.value for stage in ModelStages]:
            raise ValueError(f"`{stage}` is not a valid model stage.")

        Client().update_model_version(
            model_name_or_id=self.model.id,
            version_name_or_id=self.id,
            stage=stage,
            force=force,
        )

    # TODO in https://zenml.atlassian.net/browse/OSS-2433
    # def generate_model_card(self, template_name: str) -> str:
    #     """Return HTML/PDF based on input template"""

data_artifact_ids: Dict[str, Dict[str, uuid.UUID]] property readonly

The data_artifact_ids property.

Returns:

Type	Description
Dict[str, Dict[str, uuid.UUID]]	the value of the property.

data_artifacts: Dict[str, Dict[str, ArtifactVersionResponse]] property readonly

Get all data artifacts linked to this model version.

Returns:

Type	Description
Dict[str, Dict[str, ArtifactVersionResponse]]	Dictionary of data artifacts with versions as Dict[str, Dict[str, ArtifactResponse]]

deployment_artifact_ids: Dict[str, Dict[str, uuid.UUID]] property readonly

The deployment_artifact_ids property.

Returns:

Type	Description
Dict[str, Dict[str, uuid.UUID]]	the value of the property.

deployment_artifacts: Dict[str, Dict[str, ArtifactVersionResponse]] property readonly

Get all deployment artifacts linked to this model version.

Returns:

Type	Description
Dict[str, Dict[str, ArtifactVersionResponse]]	Dictionary of deployment artifacts with versions as Dict[str, Dict[str, ArtifactResponse]]

description: Optional[str] property readonly

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

model: ModelResponse property readonly

The model property.

Returns:

Type	Description
ModelResponse	the value of the property.

model_artifact_ids: Dict[str, Dict[str, uuid.UUID]] property readonly

The model_artifact_ids property.

Returns:

Type	Description
Dict[str, Dict[str, uuid.UUID]]	the value of the property.

model_artifacts: Dict[str, Dict[str, ArtifactVersionResponse]] property readonly

Get all model artifacts linked to this model version.

Returns:

Type	Description
Dict[str, Dict[str, ArtifactVersionResponse]]	Dictionary of model artifacts with versions as Dict[str, Dict[str, ArtifactResponse]]

name: ConstrainedStrValue pydantic-field

The name of the model version

number: int property readonly

The number property.

Returns:

Type	Description
int	the value of the property.

pipeline_run_ids: Dict[str, uuid.UUID] property readonly

The pipeline_run_ids property.

Returns:

Type	Description
Dict[str, uuid.UUID]	the value of the property.

pipeline_runs: Dict[str, PipelineRunResponse] property readonly

Get all pipeline runs linked to this version.

Returns:

Type	Description
Dict[str, PipelineRunResponse]	Dictionary of Pipeline Runs as PipelineRunResponseModel

run_metadata: Optional[Dict[str, RunMetadataResponse]] property readonly

The run_metadata property.

Returns:

Type	Description
Optional[Dict[str, RunMetadataResponse]]	the value of the property.

stage: Optional[str] property readonly

The stage property.

Returns:

Type	Description
Optional[str]	the value of the property.

tags: List[zenml.models.v2.core.tag.TagResponse] property readonly

The tags property.

Returns:

Type	Description
List[zenml.models.v2.core.tag.TagResponse]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_artifact(self, name, version=None)

Get the artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the artifact to retrieve.	required
version	Optional[str]	The version of the artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of an artifact or None

Source code in zenml/models/v2/core/model_version.py

def get_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the artifact linked to this model version.

    Args:
        name: The name of the artifact to retrieve.
        version: The version of the artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of an artifact or None
    """
    all_artifact_ids = {
        **self.model_artifact_ids,
        **self.data_artifact_ids,
        **self.deployment_artifact_ids,
    }
    return self._get_linked_object(all_artifact_ids, name, version)

get_data_artifact(self, name, version=None)

Get the data artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the data artifact to retrieve.	required
version	Optional[str]	The version of the data artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of the data artifact or None

Source code in zenml/models/v2/core/model_version.py

def get_data_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the data artifact linked to this model version.

    Args:
        name: The name of the data artifact to retrieve.
        version: The version of the data artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the data artifact or None
    """
    return self._get_linked_object(
        self.data_artifact_ids,
        name,
        version,
    )

get_deployment_artifact(self, name, version=None)

Get the deployment artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the deployment artifact to retrieve.	required
version	Optional[str]	The version of the deployment artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of the deployment artifact or None

Source code in zenml/models/v2/core/model_version.py

def get_deployment_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the deployment artifact linked to this model version.

    Args:
        name: The name of the deployment artifact to retrieve.
        version: The version of the deployment artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the deployment artifact or None
    """
    return self._get_linked_object(
        self.deployment_artifact_ids,
        name,
        version,
    )

get_hydrated_version(self)

Get the hydrated version of this model version.

Returns:

Type	Description
ModelVersionResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/model_version.py

def get_hydrated_version(self) -> "ModelVersionResponse":
    """Get the hydrated version of this model version.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_model_version(self.id)

get_model_artifact(self, name, version=None)

Get the model artifact linked to this model version.

Parameters:

Name	Type	Description	Default
name	str	The name of the model artifact to retrieve.	required
version	Optional[str]	The version of the model artifact to retrieve (None for latest/non-versioned)	None

Returns:

Type	Description
Optional[ArtifactVersionResponse]	Specific version of the model artifact or None

Source code in zenml/models/v2/core/model_version.py

def get_model_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the model artifact linked to this model version.

    Args:
        name: The name of the model artifact to retrieve.
        version: The version of the model artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the model artifact or None
    """
    return self._get_linked_object(self.model_artifact_ids, name, version)

get_pipeline_run(self, name)

Get pipeline run linked to this version.

Parameters:

Name	Type	Description	Default
name	str	The name of the pipeline run to retrieve.	required

Returns:

Type	Description
PipelineRunResponse	PipelineRun as PipelineRunResponseModel

Source code in zenml/models/v2/core/model_version.py

def get_pipeline_run(self, name: str) -> "PipelineRunResponse":
    """Get pipeline run linked to this version.

    Args:
        name: The name of the pipeline run to retrieve.

    Returns:
        PipelineRun as PipelineRunResponseModel
    """
    from zenml.client import Client

    return Client().get_pipeline_run(self.pipeline_run_ids[name])

set_stage(self, stage, force=False)

Sets this Model Version to a desired stage.

Parameters:

Name	Type	Description	Default
stage	Union[str, zenml.enums.ModelStages]	the target stage for model version.	required
force	bool	whether to force archiving of current model version in target stage or raise.	False

Exceptions:

Type	Description
ValueError	if model_stage is not valid.

Source code in zenml/models/v2/core/model_version.py

def set_stage(
    self, stage: Union[str, ModelStages], force: bool = False
) -> None:
    """Sets this Model Version to a desired stage.

    Args:
        stage: the target stage for model version.
        force: whether to force archiving of current model version in
            target stage or raise.

    Raises:
        ValueError: if model_stage is not valid.
    """
    from zenml.client import Client

    stage = getattr(stage, "value", stage)
    if stage not in [stage.value for stage in ModelStages]:
        raise ValueError(f"`{stage}` is not a valid model stage.")

    Client().update_model_version(
        model_name_or_id=self.model.id,
        version_name_or_id=self.id,
        stage=stage,
        force=force,
    )

to_model_class(self, was_created_in_this_run=False, suppress_class_validation_warnings=False)

Convert response model to Model object.

Parameters:

Name	Type	Description	Default
was_created_in_this_run	bool	Whether model version was created during the current run.	False
suppress_class_validation_warnings	bool	internally used to suppress repeated warnings.	False

Returns:

Type	Description
Model	Model object

Source code in zenml/models/v2/core/model_version.py

def to_model_class(
    self,
    was_created_in_this_run: bool = False,
    suppress_class_validation_warnings: bool = False,
) -> "Model":
    """Convert response model to Model object.

    Args:
        was_created_in_this_run: Whether model version was created during
            the current run.
        suppress_class_validation_warnings: internally used to suppress
            repeated warnings.

    Returns:
        Model object
    """
    from zenml.model.model import Model

    mv = Model(
        name=self.model.name,
        license=self.model.license,
        description=self.description,
        audience=self.model.audience,
        use_cases=self.model.use_cases,
        limitations=self.model.limitations,
        trade_offs=self.model.trade_offs,
        ethics=self.model.ethics,
        tags=[t.name for t in self.tags],
        version=self.name,
        was_created_in_this_run=was_created_in_this_run,
        suppress_class_validation_warnings=suppress_class_validation_warnings,
    )
    mv._id = self.id

    return mv

ModelVersionResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for model versions.

Source code in zenml/models/v2/core/model_version.py

class ModelVersionResponseBody(WorkspaceScopedResponseBody):
    """Response body for model versions."""

    stage: Optional[str] = Field(
        description="The stage of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    number: int = Field(
        description="The number of the model version",
    )
    model: "ModelResponse" = Field(
        description="The model containing version",
    )
    model_artifact_ids: Dict[str, Dict[str, UUID]] = Field(
        description="Model artifacts linked to the model version",
        default={},
    )
    data_artifact_ids: Dict[str, Dict[str, UUID]] = Field(
        description="Data artifacts linked to the model version",
        default={},
    )
    deployment_artifact_ids: Dict[str, Dict[str, UUID]] = Field(
        description="Deployment artifacts linked to the model version",
        default={},
    )
    pipeline_run_ids: Dict[str, UUID] = Field(
        description="Pipeline runs linked to the model version",
        default={},
    )
    tags: List[TagResponse] = Field(
        title="Tags associated with the model version", default=[]
    )

data_artifact_ids: Dict[str, Dict[str, uuid.UUID]] pydantic-field

Data artifacts linked to the model version

deployment_artifact_ids: Dict[str, Dict[str, uuid.UUID]] pydantic-field

Deployment artifacts linked to the model version

model: ModelResponse pydantic-field required

The model containing version

model_artifact_ids: Dict[str, Dict[str, uuid.UUID]] pydantic-field

Model artifacts linked to the model version

number: int pydantic-field required

The number of the model version

pipeline_run_ids: Dict[str, uuid.UUID] pydantic-field

Pipeline runs linked to the model version

stage: ConstrainedStrValue pydantic-field

The stage of the model version

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for model versions.

Source code in zenml/models/v2/core/model_version.py

class ModelVersionResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for model versions."""

    description: Optional[str] = Field(
        description="The description of the model version",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    run_metadata: Dict[str, "RunMetadataResponse"] = Field(
        description="Metadata linked to the model version",
        default={},
    )

description: ConstrainedStrValue pydantic-field

The description of the model version

run_metadata: Dict[str, RunMetadataResponse] pydantic-field

Metadata linked to the model version

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the model version entity.

Source code in zenml/models/v2/core/model_version.py

class ModelVersionResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the model version entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionUpdate (BaseModel) pydantic-model

Update model for model versions.

Source code in zenml/models/v2/core/model_version.py

class ModelVersionUpdate(BaseModel):
    """Update model for model versions."""

    model: UUID = Field(
        description="The ID of the model containing version",
    )
    stage: Optional[Union[str, ModelStages]] = Field(
        description="Target model version stage to be set", default=None
    )
    force: bool = Field(
        description="Whether existing model version in target stage should be "
        "silently archived or an error should be raised.",
        default=False,
    )
    name: Optional[str] = Field(
        description="Target model version name to be set",
        default=None,
    )
    description: Optional[str] = Field(
        description="Target model version description to be set",
        default=None,
    )
    add_tags: Optional[List[str]] = Field(
        description="Tags to be added to the model version",
        default=None,
    )
    remove_tags: Optional[List[str]] = Field(
        description="Tags to be removed from the model version",
        default=None,
    )

    @validator("stage")
    def _validate_stage(cls, stage: str) -> str:
        stage = getattr(stage, "value", stage)
        if stage is not None and stage not in [
            stage.value for stage in ModelStages
        ]:
            raise ValueError(f"`{stage}` is not a valid model stage.")
        return stage

add_tags: List[str] pydantic-field

Tags to be added to the model version

description: str pydantic-field

Target model version description to be set

force: bool pydantic-field

Whether existing model version in target stage should be silently archived or an error should be raised.

model: UUID pydantic-field required

The ID of the model containing version

name: str pydantic-field

Target model version name to be set

remove_tags: List[str] pydantic-field

Tags to be removed from the model version

stage: Union[str, zenml.enums.ModelStages] pydantic-field

Target model version stage to be set

WorkspaceScopedResponse[ModelVersionResponseBody, ModelVersionResponseMetadata, ModelVersionResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][ModelVersionResponseBody, ModelVersionResponseMetadata, ModelVersionResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/model_version.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

model_version_artifact

Models representing the link between model versions and artifacts.

BaseIdentifiedResponse[ModelVersionArtifactResponseBody, BaseResponseMetadata, ModelVersionArtifactResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][ModelVersionArtifactResponseBody, BaseResponseMetadata, ModelVersionArtifactResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/model_version_artifact.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionArtifactFilter (WorkspaceScopedFilter) pydantic-model

Model version pipeline run links filter model.

Source code in zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactFilter(WorkspaceScopedFilter):
    """Model version pipeline run links filter model."""

    # Artifact name and type are not DB fields and need to be handled separately
    FILTER_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "artifact_name",
        "only_data_artifacts",
        "only_model_artifacts",
        "only_deployment_artifacts",
        "has_custom_name",
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        "only_data_artifacts",
        "only_model_artifacts",
        "only_deployment_artifacts",
        "has_custom_name",
        "model_id",
        "model_version_id",
        "user_id",
        "workspace_id",
        "updated",
        "id",
    ]

    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="The workspace of the Model Version"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="The user of the Model Version"
    )
    model_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Filter by model ID"
    )
    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Filter by model version ID"
    )
    artifact_version_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Filter by artifact ID"
    )
    artifact_name: Optional[str] = Field(
        default=None,
        description="Name of the artifact",
    )
    only_data_artifacts: Optional[bool] = False
    only_model_artifacts: Optional[bool] = False
    only_deployment_artifacts: Optional[bool] = False
    has_custom_name: Optional[bool] = None

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom filters.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters()

        from sqlalchemy import and_

        from zenml.zen_stores.schemas.artifact_schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
        )
        from zenml.zen_stores.schemas.model_schemas import (
            ModelVersionArtifactSchema,
        )

        if self.artifact_name:
            value, filter_operator = self._resolve_operator(self.artifact_name)
            filter_ = StrFilter(
                operation=GenericFilterOps(filter_operator),
                column="name",
                value=value,
            )
            artifact_name_filter = and_(  # type: ignore[type-var]
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                filter_.generate_query_conditions(ArtifactSchema),
            )
            custom_filters.append(artifact_name_filter)

        if self.only_data_artifacts:
            data_artifact_filter = and_(
                ModelVersionArtifactSchema.is_model_artifact.is_(False),  # type: ignore[attr-defined]
                ModelVersionArtifactSchema.is_deployment_artifact.is_(False),  # type: ignore[attr-defined]
            )
            custom_filters.append(data_artifact_filter)

        if self.only_model_artifacts:
            model_artifact_filter = and_(
                ModelVersionArtifactSchema.is_model_artifact.is_(True),  # type: ignore[attr-defined]
            )
            custom_filters.append(model_artifact_filter)

        if self.only_deployment_artifacts:
            deployment_artifact_filter = and_(
                ModelVersionArtifactSchema.is_deployment_artifact.is_(True),  # type: ignore[attr-defined]
            )
            custom_filters.append(deployment_artifact_filter)

        if self.has_custom_name is not None:
            custom_name_filter = and_(  # type: ignore[type-var]
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                ArtifactSchema.has_custom_name == self.has_custom_name,
            )
            custom_filters.append(custom_name_filter)

        return custom_filters

artifact_name: str pydantic-field

Name of the artifact

artifact_version_id: Union[uuid.UUID, str] pydantic-field

Filter by artifact ID

model_id: Union[uuid.UUID, str] pydantic-field

Filter by model ID

model_version_id: Union[uuid.UUID, str] pydantic-field

Filter by model version ID

user_id: Union[uuid.UUID, str] pydantic-field

The user of the Model Version

workspace_id: Union[uuid.UUID, str] pydantic-field

The workspace of the Model Version

get_custom_filters(self)

Get custom filters.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/core/model_version_artifact.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom filters.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters()

    from sqlalchemy import and_

    from zenml.zen_stores.schemas.artifact_schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
    )
    from zenml.zen_stores.schemas.model_schemas import (
        ModelVersionArtifactSchema,
    )

    if self.artifact_name:
        value, filter_operator = self._resolve_operator(self.artifact_name)
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        artifact_name_filter = and_(  # type: ignore[type-var]
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            filter_.generate_query_conditions(ArtifactSchema),
        )
        custom_filters.append(artifact_name_filter)

    if self.only_data_artifacts:
        data_artifact_filter = and_(
            ModelVersionArtifactSchema.is_model_artifact.is_(False),  # type: ignore[attr-defined]
            ModelVersionArtifactSchema.is_deployment_artifact.is_(False),  # type: ignore[attr-defined]
        )
        custom_filters.append(data_artifact_filter)

    if self.only_model_artifacts:
        model_artifact_filter = and_(
            ModelVersionArtifactSchema.is_model_artifact.is_(True),  # type: ignore[attr-defined]
        )
        custom_filters.append(model_artifact_filter)

    if self.only_deployment_artifacts:
        deployment_artifact_filter = and_(
            ModelVersionArtifactSchema.is_deployment_artifact.is_(True),  # type: ignore[attr-defined]
        )
        custom_filters.append(deployment_artifact_filter)

    if self.has_custom_name is not None:
        custom_name_filter = and_(  # type: ignore[type-var]
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            ArtifactSchema.has_custom_name == self.has_custom_name,
        )
        custom_filters.append(custom_name_filter)

    return custom_filters

ModelVersionArtifactRequest (WorkspaceScopedRequest) pydantic-model

Request model for links between model versions and artifacts.

Source code in zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactRequest(WorkspaceScopedRequest):
    """Request model for links between model versions and artifacts."""

    model: UUID
    model_version: UUID
    artifact_version: UUID
    is_model_artifact: bool = False
    is_deployment_artifact: bool = False

    @validator("is_deployment_artifact")
    def _validate_is_endpoint_artifact(
        cls, is_deployment_artifact: bool, values: Dict[str, Any]
    ) -> bool:
        is_model_artifact = values.get("is_model_artifact", False)
        if is_model_artifact and is_deployment_artifact:
            raise ValueError(
                "Artifact cannot be a model artifact and deployment artifact "
                "at the same time."
            )
        return is_deployment_artifact

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionArtifactResponse (BaseIdentifiedResponse[ModelVersionArtifactResponseBody, BaseResponseMetadata, ModelVersionArtifactResponseResources]) pydantic-model

Response model for links between model versions and artifacts.

Source code in zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactResponse(
    BaseIdentifiedResponse[
        ModelVersionArtifactResponseBody,
        BaseResponseMetadata,
        ModelVersionArtifactResponseResources,
    ]
):
    """Response model for links between model versions and artifacts."""

    # Body and metadata properties
    @property
    def model(self) -> UUID:
        """The `model` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model

    @property
    def model_version(self) -> UUID:
        """The `model_version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_version

    @property
    def artifact_version(self) -> "ArtifactVersionResponse":
        """The `artifact_version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().artifact_version

    @property
    def is_model_artifact(self) -> bool:
        """The `is_model_artifact` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_model_artifact

    @property
    def is_deployment_artifact(self) -> bool:
        """The `is_deployment_artifact` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_deployment_artifact

artifact_version: ArtifactVersionResponse property readonly

The artifact_version property.

Returns:

Type	Description
ArtifactVersionResponse	the value of the property.

is_deployment_artifact: bool property readonly

The is_deployment_artifact property.

Returns:

Type	Description
bool	the value of the property.

is_model_artifact: bool property readonly

The is_model_artifact property.

Returns:

Type	Description
bool	the value of the property.

model: UUID property readonly

The model property.

Returns:

Type	Description
UUID	the value of the property.

model_version: UUID property readonly

The model_version property.

Returns:

Type	Description
UUID	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionArtifactResponseBody (BaseDatedResponseBody) pydantic-model

Response body for links between model versions and artifacts.

Source code in zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactResponseBody(BaseDatedResponseBody):
    """Response body for links between model versions and artifacts."""

    model: UUID
    model_version: UUID
    artifact_version: "ArtifactVersionResponse"
    is_model_artifact: bool = False
    is_deployment_artifact: bool = False

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionArtifactResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the model version artifact entity.

Source code in zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactResponseResources(BaseResponseResources):
    """Class for all resource models associated with the model version artifact entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

model_version_pipeline_run

Models representing the link between model versions and pipeline runs.

BaseIdentifiedResponse[ModelVersionPipelineRunResponseBody, BaseResponseMetadata, ModelVersionPipelineRunResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][ModelVersionPipelineRunResponseBody, BaseResponseMetadata, ModelVersionPipelineRunResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/model_version_pipeline_run.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionPipelineRunFilter (WorkspaceScopedFilter) pydantic-model

Model version pipeline run links filter model.

Source code in zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunFilter(WorkspaceScopedFilter):
    """Model version pipeline run links filter model."""

    # Pipeline run name is not a DB field and needs to be handled separately
    FILTER_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "pipeline_run_name",
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        "model_id",
        "model_version_id",
        "user_id",
        "workspace_id",
        "updated",
        "id",
    ]

    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="The workspace of the Model Version"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="The user of the Model Version"
    )
    model_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Filter by model ID"
    )
    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Filter by model version ID"
    )
    pipeline_run_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Filter by pipeline run ID"
    )
    pipeline_run_name: Optional[str] = Field(
        default=None,
        description="Name of the pipeline run",
    )

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom filters.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters()

        from sqlalchemy import and_

        from zenml.zen_stores.schemas.model_schemas import (
            ModelVersionPipelineRunSchema,
        )
        from zenml.zen_stores.schemas.pipeline_run_schemas import (
            PipelineRunSchema,
        )

        if self.pipeline_run_name:
            value, filter_operator = self._resolve_operator(
                self.pipeline_run_name
            )
            filter_ = StrFilter(
                operation=GenericFilterOps(filter_operator),
                column="name",
                value=value,
            )
            pipeline_run_name_filter = and_(  # type: ignore[type-var]
                ModelVersionPipelineRunSchema.pipeline_run_id
                == PipelineRunSchema.id,
                filter_.generate_query_conditions(PipelineRunSchema),
            )
            custom_filters.append(pipeline_run_name_filter)

        return custom_filters

model_id: Union[uuid.UUID, str] pydantic-field

Filter by model ID

model_version_id: Union[uuid.UUID, str] pydantic-field

Filter by model version ID

pipeline_run_id: Union[uuid.UUID, str] pydantic-field

Filter by pipeline run ID

pipeline_run_name: str pydantic-field

Name of the pipeline run

user_id: Union[uuid.UUID, str] pydantic-field

The user of the Model Version

workspace_id: Union[uuid.UUID, str] pydantic-field

The workspace of the Model Version

get_custom_filters(self)

Get custom filters.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/core/model_version_pipeline_run.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom filters.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters()

    from sqlalchemy import and_

    from zenml.zen_stores.schemas.model_schemas import (
        ModelVersionPipelineRunSchema,
    )
    from zenml.zen_stores.schemas.pipeline_run_schemas import (
        PipelineRunSchema,
    )

    if self.pipeline_run_name:
        value, filter_operator = self._resolve_operator(
            self.pipeline_run_name
        )
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        pipeline_run_name_filter = and_(  # type: ignore[type-var]
            ModelVersionPipelineRunSchema.pipeline_run_id
            == PipelineRunSchema.id,
            filter_.generate_query_conditions(PipelineRunSchema),
        )
        custom_filters.append(pipeline_run_name_filter)

    return custom_filters

ModelVersionPipelineRunRequest (WorkspaceScopedRequest) pydantic-model

Request model for links between model versions and pipeline runs.

Source code in zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunRequest(WorkspaceScopedRequest):
    """Request model for links between model versions and pipeline runs."""

    model: UUID
    model_version: UUID
    pipeline_run: UUID

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionPipelineRunResponse (BaseIdentifiedResponse[ModelVersionPipelineRunResponseBody, BaseResponseMetadata, ModelVersionPipelineRunResponseResources]) pydantic-model

Response model for links between model versions and pipeline runs.

Source code in zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunResponse(
    BaseIdentifiedResponse[
        ModelVersionPipelineRunResponseBody,
        BaseResponseMetadata,
        ModelVersionPipelineRunResponseResources,
    ]
):
    """Response model for links between model versions and pipeline runs."""

    # Body and metadata properties
    @property
    def model(self) -> UUID:
        """The `model` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model

    @property
    def model_version(self) -> UUID:
        """The `model_version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_version

    @property
    def pipeline_run(self) -> "PipelineRunResponse":
        """The `pipeline_run` property.

        Returns:
            the value of the property.
        """
        return self.get_body().pipeline_run

model: UUID property readonly

The model property.

Returns:

Type	Description
UUID	the value of the property.

model_version: UUID property readonly

The model_version property.

Returns:

Type	Description
UUID	the value of the property.

pipeline_run: PipelineRunResponse property readonly

The pipeline_run property.

Returns:

Type	Description
PipelineRunResponse	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionPipelineRunResponseBody (BaseDatedResponseBody) pydantic-model

Response body for links between model versions and pipeline runs.

Source code in zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunResponseBody(BaseDatedResponseBody):
    """Response body for links between model versions and pipeline runs."""

    model: UUID
    model_version: UUID
    pipeline_run: PipelineRunResponse

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ModelVersionPipelineRunResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the model version pipeline run entity.

Source code in zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunResponseResources(BaseResponseResources):
    """Class for all resource models associated with the model version pipeline run entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

pipeline

Models representing pipelines.

BaseResponse[PipelineNamespaceResponseBody, PipelineNamespaceResponseMetadata, PipelineNamespaceResponseResources] (BaseResponse) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/pipeline.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineFilter (WorkspaceScopedFilter) pydantic-model

Pipeline filter model.

Source code in zenml/models/v2/core/pipeline.py

class PipelineFilter(WorkspaceScopedFilter):
    """Pipeline filter model."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline",
    )
    version: Optional[str] = Field(
        default=None,
        description="Version of the Pipeline",
    )
    version_hash: Optional[str] = Field(
        default=None,
        description="Version hash of the Pipeline",
    )
    docstring: Optional[str] = Field(
        default=None,
        description="Docstring of the Pipeline",
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of the Pipeline"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User of the Pipeline"
    )

docstring: str pydantic-field

Docstring of the Pipeline

name: str pydantic-field

Name of the Pipeline

user_id: Union[uuid.UUID, str] pydantic-field

User of the Pipeline

version: str pydantic-field

Version of the Pipeline

version_hash: str pydantic-field

Version hash of the Pipeline

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the Pipeline

PipelineNamespaceFilter (BaseFilter) pydantic-model

Pipeline namespace filter model.

Source code in zenml/models/v2/core/pipeline.py

class PipelineNamespaceFilter(BaseFilter):
    """Pipeline namespace filter model."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the pipeline namespace.",
    )

name: str pydantic-field

Name of the pipeline namespace.

PipelineNamespaceResponse (BaseResponse[PipelineNamespaceResponseBody, PipelineNamespaceResponseMetadata, PipelineNamespaceResponseResources]) pydantic-model

Response model for pipeline namespaces.

Source code in zenml/models/v2/core/pipeline.py

class PipelineNamespaceResponse(
    BaseResponse[
        PipelineNamespaceResponseBody,
        PipelineNamespaceResponseMetadata,
        PipelineNamespaceResponseResources,
    ]
):
    """Response model for pipeline namespaces."""

    name: str = Field(
        title="The name of the pipeline namespace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "PipelineNamespaceResponse":
        """Get the hydrated version of this pipeline namespace.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        return self

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this pipeline namespace.

Returns:

Type	Description
PipelineNamespaceResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/pipeline.py

def get_hydrated_version(self) -> "PipelineNamespaceResponse":
    """Get the hydrated version of this pipeline namespace.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    return self

PipelineNamespaceResponseBody (BaseResponseBody) pydantic-model

Response body for pipeline namespaces.

Source code in zenml/models/v2/core/pipeline.py

class PipelineNamespaceResponseBody(BaseResponseBody):
    """Response body for pipeline namespaces."""

    latest_run_id: Optional[UUID] = Field(
        default=None,
        title="The ID of the latest run of the pipeline namespace.",
    )
    latest_run_status: Optional[ExecutionStatus] = Field(
        default=None,
        title="The status of the latest run of the pipeline namespace.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineNamespaceResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for pipeline namespaces.

Source code in zenml/models/v2/core/pipeline.py

class PipelineNamespaceResponseMetadata(BaseResponseMetadata):
    """Response metadata for pipeline namespaces."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineNamespaceResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the pipeline namespace entity.

Source code in zenml/models/v2/core/pipeline.py

class PipelineNamespaceResponseResources(BaseResponseResources):
    """Class for all resource models associated with the pipeline namespace entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineRequest (WorkspaceScopedRequest) pydantic-model

Request model for pipelines.

Source code in zenml/models/v2/core/pipeline.py

class PipelineRequest(WorkspaceScopedRequest):
    """Request model for pipelines."""

    name: str = Field(
        title="The name of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    version: str = Field(
        title="The version of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    version_hash: str = Field(
        title="The version hash of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    docstring: Optional[str] = Field(
        title="The docstring of the pipeline.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    spec: PipelineSpec = Field(title="The spec of the pipeline.")

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineResponse (WorkspaceScopedResponse[PipelineResponseBody, PipelineResponseMetadata, PipelineResponseResources]) pydantic-model

Response model for pipelines.

Source code in zenml/models/v2/core/pipeline.py

class PipelineResponse(
    WorkspaceScopedResponse[
        PipelineResponseBody,
        PipelineResponseMetadata,
        PipelineResponseResources,
    ]
):
    """Response model for pipelines."""

    name: str = Field(
        title="The name of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "PipelineResponse":
        """Get the hydrated version of this pipeline.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_pipeline(self.id)

    # Helper methods
    def get_runs(self, **kwargs: Any) -> List["PipelineRunResponse"]:
        """Get runs of this pipeline.

        Can be used to fetch runs other than `self.runs` and supports
        fine-grained filtering and pagination.

        Args:
            **kwargs: Further arguments for filtering or pagination that are
                passed to `client.list_pipeline_runs()`.

        Returns:
            List of runs of this pipeline.
        """
        from zenml.client import Client

        return Client().list_pipeline_runs(pipeline_id=self.id, **kwargs).items

    @property
    def runs(self) -> List["PipelineRunResponse"]:
        """Returns the 20 most recent runs of this pipeline in descending order.

        Returns:
            The 20 most recent runs of this pipeline in descending order.
        """
        return self.get_runs()

    @property
    def num_runs(self) -> int:
        """Returns the number of runs of this pipeline.

        Returns:
            The number of runs of this pipeline.
        """
        from zenml.client import Client

        return Client().list_pipeline_runs(pipeline_id=self.id, size=1).total

    @property
    def last_run(self) -> "PipelineRunResponse":
        """Returns the last run of this pipeline.

        Returns:
            The last run of this pipeline.

        Raises:
            RuntimeError: If no runs were found for this pipeline.
        """
        runs = self.get_runs(size=1)
        if not runs:
            raise RuntimeError(
                f"No runs found for pipeline '{self.name}' with id {self.id}."
            )
        return runs[0]

    @property
    def last_successful_run(self) -> "PipelineRunResponse":
        """Returns the last successful run of this pipeline.

        Returns:
            The last successful run of this pipeline.

        Raises:
            RuntimeError: If no successful runs were found for this pipeline.
        """
        runs = self.get_runs(status=ExecutionStatus.COMPLETED, size=1)
        if not runs:
            raise RuntimeError(
                f"No successful runs found for pipeline '{self.name}' with id "
                f"{self.id}."
            )
        return runs[0]

    # Body and metadata properties
    @property
    def status(self) -> Optional[List[ExecutionStatus]]:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().status

    @property
    def version(self) -> str:
        """The `version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().version

    @property
    def spec(self) -> PipelineSpec:
        """The `spec` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().spec

    @property
    def version_hash(self) -> str:
        """The `version_hash` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().version_hash

    @property
    def docstring(self) -> Optional[str]:
        """The `docstring` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().docstring

docstring: Optional[str] property readonly

The docstring property.

Returns:

Type	Description
Optional[str]	the value of the property.

last_run: PipelineRunResponse property readonly

Returns the last run of this pipeline.

Returns:

Type	Description
PipelineRunResponse	The last run of this pipeline.

Exceptions:

Type	Description
RuntimeError	If no runs were found for this pipeline.

last_successful_run: PipelineRunResponse property readonly

Returns the last successful run of this pipeline.

Returns:

Type	Description
PipelineRunResponse	The last successful run of this pipeline.

Exceptions:

Type	Description
RuntimeError	If no successful runs were found for this pipeline.

num_runs: int property readonly

Returns the number of runs of this pipeline.

Returns:

Type	Description
int	The number of runs of this pipeline.

runs: List[PipelineRunResponse] property readonly

Returns the 20 most recent runs of this pipeline in descending order.

Returns:

Type	Description
List[PipelineRunResponse]	The 20 most recent runs of this pipeline in descending order.

spec: PipelineSpec property readonly

The spec property.

Returns:

Type	Description
PipelineSpec	the value of the property.

status: Optional[List[zenml.enums.ExecutionStatus]] property readonly

The status property.

Returns:

Type	Description
Optional[List[zenml.enums.ExecutionStatus]]	the value of the property.

version: str property readonly

The version property.

Returns:

Type	Description
str	the value of the property.

version_hash: str property readonly

The version_hash property.

Returns:

Type	Description
str	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this pipeline.

Returns:

Type	Description
PipelineResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/pipeline.py

def get_hydrated_version(self) -> "PipelineResponse":
    """Get the hydrated version of this pipeline.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_pipeline(self.id)

get_runs(self, **kwargs)

Get runs of this pipeline.

Can be used to fetch runs other than self.runs and supports fine-grained filtering and pagination.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Further arguments for filtering or pagination that are passed to client.list_pipeline_runs().	{}

Returns:

Type	Description
List[PipelineRunResponse]	List of runs of this pipeline.

Source code in zenml/models/v2/core/pipeline.py

def get_runs(self, **kwargs: Any) -> List["PipelineRunResponse"]:
    """Get runs of this pipeline.

    Can be used to fetch runs other than `self.runs` and supports
    fine-grained filtering and pagination.

    Args:
        **kwargs: Further arguments for filtering or pagination that are
            passed to `client.list_pipeline_runs()`.

    Returns:
        List of runs of this pipeline.
    """
    from zenml.client import Client

    return Client().list_pipeline_runs(pipeline_id=self.id, **kwargs).items

PipelineResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for pipelines.

Source code in zenml/models/v2/core/pipeline.py

class PipelineResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipelines."""

    status: Optional[List[ExecutionStatus]] = Field(
        default=None, title="The status of the last 3 Pipeline Runs."
    )
    version: str = Field(
        title="The version of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for pipelines.

Source code in zenml/models/v2/core/pipeline.py

class PipelineResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipelines."""

    version_hash: str = Field(
        title="The version hash of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    spec: PipelineSpec = Field(title="The spec of the pipeline.")
    docstring: Optional[str] = Field(
        title="The docstring of the pipeline.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the pipeline entity.

Source code in zenml/models/v2/core/pipeline.py

class PipelineResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the pipeline entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineUpdate (PipelineRequest) pydantic-model

Update model for pipelines.

Source code in zenml/models/v2/core/pipeline.py

@update_model
class PipelineUpdate(PipelineRequest):
    """Update model for pipelines."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[PipelineResponseBody, PipelineResponseMetadata, PipelineResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][PipelineResponseBody, PipelineResponseMetadata, PipelineResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/pipeline.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

pipeline_build

Models representing pipeline builds.

PipelineBuildBase (BaseZenModel) pydantic-model

Base model for pipeline builds.

Source code in zenml/models/v2/core/pipeline_build.py

class PipelineBuildBase(BaseZenModel):
    """Base model for pipeline builds."""

    images: Dict[str, BuildItem] = Field(
        default={}, title="The images of this build."
    )
    is_local: bool = Field(
        title="Whether the build images are stored in a container registry "
        "or locally.",
    )
    contains_code: bool = Field(
        title="Whether any image of the build contains user code.",
    )
    zenml_version: Optional[str] = Field(
        title="The version of ZenML used for this build."
    )
    python_version: Optional[str] = Field(
        title="The Python version used for this build."
    )

    # Helper methods
    @property
    def requires_code_download(self) -> bool:
        """Whether the build requires code download.

        Returns:
            Whether the build requires code download.
        """
        return any(
            item.requires_code_download for item in self.images.values()
        )

    @staticmethod
    def get_image_key(component_key: str, step: Optional[str] = None) -> str:
        """Get the image key.

        Args:
            component_key: The component key.
            step: The pipeline step for which the image was built.

        Returns:
            The image key.
        """
        if step:
            return f"{step}.{component_key}"
        else:
            return component_key

    def get_image(self, component_key: str, step: Optional[str] = None) -> str:
        """Get the image built for a specific key.

        Args:
            component_key: The key for which to get the image.
            step: The pipeline step for which to get the image. If no image
                exists for this step, will fall back to the pipeline image for
                the same key.

        Returns:
            The image name or digest.
        """
        return self._get_item(component_key=component_key, step=step).image

    def get_settings_checksum(
        self, component_key: str, step: Optional[str] = None
    ) -> Optional[str]:
        """Get the settings checksum for a specific key.

        Args:
            component_key: The key for which to get the checksum.
            step: The pipeline step for which to get the checksum. If no
                image exists for this step, will fall back to the pipeline image
                for the same key.

        Returns:
            The settings checksum.
        """
        return self._get_item(
            component_key=component_key, step=step
        ).settings_checksum

    def _get_item(
        self, component_key: str, step: Optional[str] = None
    ) -> "BuildItem":
        """Get the item for a specific key.

        Args:
            component_key: The key for which to get the item.
            step: The pipeline step for which to get the item. If no item
                exists for this step, will fall back to the item for
                the same key.

        Raises:
            KeyError: If no item exists for the given key.

        Returns:
            The build item.
        """
        if step:
            try:
                combined_key = self.get_image_key(
                    component_key=component_key, step=step
                )
                return self.images[combined_key]
            except KeyError:
                pass

        try:
            return self.images[component_key]
        except KeyError:
            raise KeyError(
                f"Unable to find image for key {component_key}. Available keys: "
                f"{set(self.images)}."
            )

requires_code_download: bool property readonly

Whether the build requires code download.

Returns:

Type	Description
bool	Whether the build requires code download.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_image(self, component_key, step=None)

Get the image built for a specific key.

Parameters:

Name	Type	Description	Default
component_key	str	The key for which to get the image.	required
step	Optional[str]	The pipeline step for which to get the image. If no image exists for this step, will fall back to the pipeline image for the same key.	None

Returns:

Type	Description
str	The image name or digest.

Source code in zenml/models/v2/core/pipeline_build.py

def get_image(self, component_key: str, step: Optional[str] = None) -> str:
    """Get the image built for a specific key.

    Args:
        component_key: The key for which to get the image.
        step: The pipeline step for which to get the image. If no image
            exists for this step, will fall back to the pipeline image for
            the same key.

    Returns:
        The image name or digest.
    """
    return self._get_item(component_key=component_key, step=step).image

get_image_key(component_key, step=None) staticmethod

Get the image key.

Parameters:

Name	Type	Description	Default
component_key	str	The component key.	required
step	Optional[str]	The pipeline step for which the image was built.	None

Returns:

Type	Description
str	The image key.

Source code in zenml/models/v2/core/pipeline_build.py

@staticmethod
def get_image_key(component_key: str, step: Optional[str] = None) -> str:
    """Get the image key.

    Args:
        component_key: The component key.
        step: The pipeline step for which the image was built.

    Returns:
        The image key.
    """
    if step:
        return f"{step}.{component_key}"
    else:
        return component_key

get_settings_checksum(self, component_key, step=None)

Get the settings checksum for a specific key.

Parameters:

Name	Type	Description	Default
component_key	str	The key for which to get the checksum.	required
step	Optional[str]	The pipeline step for which to get the checksum. If no image exists for this step, will fall back to the pipeline image for the same key.	None

Returns:

Type	Description
Optional[str]	The settings checksum.

Source code in zenml/models/v2/core/pipeline_build.py

def get_settings_checksum(
    self, component_key: str, step: Optional[str] = None
) -> Optional[str]:
    """Get the settings checksum for a specific key.

    Args:
        component_key: The key for which to get the checksum.
        step: The pipeline step for which to get the checksum. If no
            image exists for this step, will fall back to the pipeline image
            for the same key.

    Returns:
        The settings checksum.
    """
    return self._get_item(
        component_key=component_key, step=step
    ).settings_checksum

PipelineBuildFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all pipeline builds.

Source code in zenml/models/v2/core/pipeline_build.py

class PipelineBuildFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all pipeline builds."""

    workspace_id: Union[UUID, str, None] = Field(
        description="Workspace for this pipeline build."
    )
    user_id: Union[UUID, str, None] = Field(
        description="User that produced this pipeline build."
    )
    pipeline_id: Union[UUID, str, None] = Field(
        description="Pipeline associated with the pipeline build.",
    )
    stack_id: Union[UUID, str, None] = Field(
        description="Stack used for the Pipeline Run"
    )
    is_local: Optional[bool] = Field(
        description="Whether the build images are stored in a container "
        "registry or locally.",
    )
    contains_code: Optional[bool] = Field(
        description="Whether any image of the build contains user code.",
    )
    zenml_version: Optional[str] = Field(
        description="The version of ZenML used for this build."
    )
    python_version: Optional[str] = Field(
        description="The Python version used for this build."
    )
    checksum: Optional[str] = Field(description="The build checksum.")

checksum: str pydantic-field

The build checksum.

contains_code: bool pydantic-field

Whether any image of the build contains user code.

is_local: bool pydantic-field

Whether the build images are stored in a container registry or locally.

pipeline_id: Union[uuid.UUID, str] pydantic-field

Pipeline associated with the pipeline build.

python_version: str pydantic-field

The Python version used for this build.

stack_id: Union[uuid.UUID, str] pydantic-field

Stack used for the Pipeline Run

user_id: Union[uuid.UUID, str] pydantic-field

User that produced this pipeline build.

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace for this pipeline build.

zenml_version: str pydantic-field

The version of ZenML used for this build.

PipelineBuildRequest (PipelineBuildBase, WorkspaceScopedRequest) pydantic-model

Request model for pipelines builds.

Source code in zenml/models/v2/core/pipeline_build.py

class PipelineBuildRequest(PipelineBuildBase, WorkspaceScopedRequest):
    """Request model for pipelines builds."""

    checksum: Optional[str] = Field(title="The build checksum.")

    stack: Optional[UUID] = Field(
        title="The stack that was used for this build."
    )
    pipeline: Optional[UUID] = Field(
        title="The pipeline that was used for this build."
    )
    template_deployment_id: Optional[UUID] = None

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineBuildResponse (WorkspaceScopedResponse[PipelineBuildResponseBody, PipelineBuildResponseMetadata, PipelineBuildResponseResources]) pydantic-model

Response model for pipeline builds.

Source code in zenml/models/v2/core/pipeline_build.py

class PipelineBuildResponse(
    WorkspaceScopedResponse[
        PipelineBuildResponseBody,
        PipelineBuildResponseMetadata,
        PipelineBuildResponseResources,
    ]
):
    """Response model for pipeline builds."""

    def get_hydrated_version(self) -> "PipelineBuildResponse":
        """Return the hydrated version of this pipeline build.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_build(self.id)

    # Helper methods
    def to_yaml(self) -> Dict[str, Any]:
        """Create a yaml representation of the pipeline build.

        Create a yaml representation of the pipeline build that can be used
        to create a PipelineBuildBase instance.

        Returns:
            The yaml representation of the pipeline build.
        """
        # Get the base attributes
        yaml_dict: Dict[str, Any] = json.loads(
            self.json(
                exclude={
                    "body",
                    "metadata",
                }
            )
        )
        images = json.loads(
            self.get_metadata().json(
                exclude={
                    "pipeline",
                    "stack",
                    "workspace",
                }
            )
        )
        yaml_dict.update(images)
        return yaml_dict

    @property
    def requires_code_download(self) -> bool:
        """Whether the build requires code download.

        Returns:
            Whether the build requires code download.
        """
        return any(
            item.requires_code_download for item in self.images.values()
        )

    @staticmethod
    def get_image_key(component_key: str, step: Optional[str] = None) -> str:
        """Get the image key.

        Args:
            component_key: The component key.
            step: The pipeline step for which the image was built.

        Returns:
            The image key.
        """
        if step:
            return f"{step}.{component_key}"
        else:
            return component_key

    def get_image(self, component_key: str, step: Optional[str] = None) -> str:
        """Get the image built for a specific key.

        Args:
            component_key: The key for which to get the image.
            step: The pipeline step for which to get the image. If no image
                exists for this step, will fall back to the pipeline image for
                the same key.

        Returns:
            The image name or digest.
        """
        return self._get_item(component_key=component_key, step=step).image

    def get_settings_checksum(
        self, component_key: str, step: Optional[str] = None
    ) -> Optional[str]:
        """Get the settings checksum for a specific key.

        Args:
            component_key: The key for which to get the checksum.
            step: The pipeline step for which to get the checksum. If no
                image exists for this step, will fall back to the pipeline image
                for the same key.

        Returns:
            The settings checksum.
        """
        return self._get_item(
            component_key=component_key, step=step
        ).settings_checksum

    def _get_item(
        self, component_key: str, step: Optional[str] = None
    ) -> "BuildItem":
        """Get the item for a specific key.

        Args:
            component_key: The key for which to get the item.
            step: The pipeline step for which to get the item. If no item
                exists for this step, will fall back to the item for
                the same key.

        Raises:
            KeyError: If no item exists for the given key.

        Returns:
            The build item.
        """
        if step:
            try:
                combined_key = self.get_image_key(
                    component_key=component_key, step=step
                )
                return self.images[combined_key]
            except KeyError:
                pass

        try:
            return self.images[component_key]
        except KeyError:
            raise KeyError(
                f"Unable to find image for key {component_key}. Available keys: "
                f"{set(self.images)}."
            )

    # Body and metadata properties
    @property
    def pipeline(self) -> Optional["PipelineResponse"]:
        """The `pipeline` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline

    @property
    def stack(self) -> Optional["StackResponse"]:
        """The `stack` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack

    @property
    def images(self) -> Dict[str, "BuildItem"]:
        """The `images` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().images

    @property
    def zenml_version(self) -> Optional[str]:
        """The `zenml_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().zenml_version

    @property
    def python_version(self) -> Optional[str]:
        """The `python_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().python_version

    @property
    def checksum(self) -> Optional[str]:
        """The `checksum` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().checksum

    @property
    def is_local(self) -> bool:
        """The `is_local` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().is_local

    @property
    def contains_code(self) -> bool:
        """The `contains_code` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().contains_code

    @property
    def template_deployment_id(self) -> Optional[UUID]:
        """The `template_deployment_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().template_deployment_id

checksum: Optional[str] property readonly

The checksum property.

Returns:

Type	Description
Optional[str]	the value of the property.

contains_code: bool property readonly

The contains_code property.

Returns:

Type	Description
bool	the value of the property.

images: Dict[str, BuildItem] property readonly

The images property.

Returns:

Type	Description
Dict[str, BuildItem]	the value of the property.

is_local: bool property readonly

The is_local property.

Returns:

Type	Description
bool	the value of the property.

pipeline: Optional[PipelineResponse] property readonly

The pipeline property.

Returns:

Type	Description
Optional[PipelineResponse]	the value of the property.

python_version: Optional[str] property readonly

The python_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

requires_code_download: bool property readonly

Whether the build requires code download.

Returns:

Type	Description
bool	Whether the build requires code download.

stack: Optional[StackResponse] property readonly

The stack property.

Returns:

Type	Description
Optional[StackResponse]	the value of the property.

template_deployment_id: Optional[uuid.UUID] property readonly

The template_deployment_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

zenml_version: Optional[str] property readonly

The zenml_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Return the hydrated version of this pipeline build.

Returns:

Type	Description
PipelineBuildResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/pipeline_build.py

def get_hydrated_version(self) -> "PipelineBuildResponse":
    """Return the hydrated version of this pipeline build.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_build(self.id)

get_image(self, component_key, step=None)

Get the image built for a specific key.

Parameters:

Name	Type	Description	Default
component_key	str	The key for which to get the image.	required
step	Optional[str]	The pipeline step for which to get the image. If no image exists for this step, will fall back to the pipeline image for the same key.	None

Returns:

Type	Description
str	The image name or digest.

Source code in zenml/models/v2/core/pipeline_build.py

def get_image(self, component_key: str, step: Optional[str] = None) -> str:
    """Get the image built for a specific key.

    Args:
        component_key: The key for which to get the image.
        step: The pipeline step for which to get the image. If no image
            exists for this step, will fall back to the pipeline image for
            the same key.

    Returns:
        The image name or digest.
    """
    return self._get_item(component_key=component_key, step=step).image

get_image_key(component_key, step=None) staticmethod

Get the image key.

Parameters:

Name	Type	Description	Default
component_key	str	The component key.	required
step	Optional[str]	The pipeline step for which the image was built.	None

Returns:

Type	Description
str	The image key.

Source code in zenml/models/v2/core/pipeline_build.py

@staticmethod
def get_image_key(component_key: str, step: Optional[str] = None) -> str:
    """Get the image key.

    Args:
        component_key: The component key.
        step: The pipeline step for which the image was built.

    Returns:
        The image key.
    """
    if step:
        return f"{step}.{component_key}"
    else:
        return component_key

get_settings_checksum(self, component_key, step=None)

Get the settings checksum for a specific key.

Parameters:

Name	Type	Description	Default
component_key	str	The key for which to get the checksum.	required
step	Optional[str]	The pipeline step for which to get the checksum. If no image exists for this step, will fall back to the pipeline image for the same key.	None

Returns:

Type	Description
Optional[str]	The settings checksum.

Source code in zenml/models/v2/core/pipeline_build.py

def get_settings_checksum(
    self, component_key: str, step: Optional[str] = None
) -> Optional[str]:
    """Get the settings checksum for a specific key.

    Args:
        component_key: The key for which to get the checksum.
        step: The pipeline step for which to get the checksum. If no
            image exists for this step, will fall back to the pipeline image
            for the same key.

    Returns:
        The settings checksum.
    """
    return self._get_item(
        component_key=component_key, step=step
    ).settings_checksum

to_yaml(self)

Create a yaml representation of the pipeline build.

Create a yaml representation of the pipeline build that can be used to create a PipelineBuildBase instance.

Returns:

Type	Description
Dict[str, Any]	The yaml representation of the pipeline build.

Source code in zenml/models/v2/core/pipeline_build.py

def to_yaml(self) -> Dict[str, Any]:
    """Create a yaml representation of the pipeline build.

    Create a yaml representation of the pipeline build that can be used
    to create a PipelineBuildBase instance.

    Returns:
        The yaml representation of the pipeline build.
    """
    # Get the base attributes
    yaml_dict: Dict[str, Any] = json.loads(
        self.json(
            exclude={
                "body",
                "metadata",
            }
        )
    )
    images = json.loads(
        self.get_metadata().json(
            exclude={
                "pipeline",
                "stack",
                "workspace",
            }
        )
    )
    yaml_dict.update(images)
    return yaml_dict

PipelineBuildResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for pipeline builds.

Source code in zenml/models/v2/core/pipeline_build.py

class PipelineBuildResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipeline builds."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineBuildResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for pipeline builds.

Source code in zenml/models/v2/core/pipeline_build.py

class PipelineBuildResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipeline builds."""

    pipeline: Optional["PipelineResponse"] = Field(
        default=None, title="The pipeline that was used for this build."
    )
    stack: Optional["StackResponse"] = Field(
        default=None, title="The stack that was used for this build."
    )
    images: Dict[str, "BuildItem"] = Field(
        default={}, title="The images of this build."
    )
    zenml_version: Optional[str] = Field(
        default=None, title="The version of ZenML used for this build."
    )
    python_version: Optional[str] = Field(
        default=None, title="The Python version used for this build."
    )
    checksum: Optional[str] = Field(default=None, title="The build checksum.")
    is_local: bool = Field(
        title="Whether the build images are stored in a container "
        "registry or locally.",
    )
    contains_code: bool = Field(
        title="Whether any image of the build contains user code.",
    )
    template_deployment_id: Optional[UUID] = None

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineBuildResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the pipeline build entity.

Source code in zenml/models/v2/core/pipeline_build.py

class PipelineBuildResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the pipeline build entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[PipelineBuildResponseBody, PipelineBuildResponseMetadata, PipelineBuildResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][PipelineBuildResponseBody, PipelineBuildResponseMetadata, PipelineBuildResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/pipeline_build.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

pipeline_deployment

Models representing pipeline deployments.

Page[TriggerResponse] (Page) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/pipeline_deployment.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST API server to unpack SecretStr
    # values correctly before sending them to the client.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value() if v else None
    }

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineDeploymentBase (BaseZenModel) pydantic-model

Base model for pipeline deployments.

Source code in zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentBase(BaseZenModel):
    """Base model for pipeline deployments."""

    run_name_template: str = Field(
        title="The run name template for runs created using this deployment.",
    )
    pipeline_configuration: PipelineConfiguration = Field(
        title="The pipeline configuration for this deployment."
    )
    step_configurations: Dict[str, Step] = Field(
        default={}, title="The step configurations for this deployment."
    )
    client_environment: Dict[str, str] = Field(
        default={}, title="The client environment for this deployment."
    )
    client_version: Optional[str] = Field(
        default=None,
        title="The version of the ZenML installation on the client side.",
    )
    server_version: Optional[str] = Field(
        default=None,
        title="The version of the ZenML installation on the server side.",
    )

    @property
    def requires_included_files(self) -> bool:
        """Whether the deployment requires included files.

        Returns:
            Whether the deployment requires included files.
        """
        return any(
            step.config.docker_settings.source_files == SourceFileMode.INCLUDE
            for step in self.step_configurations.values()
        )

    @property
    def requires_code_download(self) -> bool:
        """Whether the deployment requires downloading some code files.

        Returns:
            Whether the deployment requires downloading some code files.
        """
        return any(
            step.config.docker_settings.source_files == SourceFileMode.DOWNLOAD
            for step in self.step_configurations.values()
        )

requires_code_download: bool property readonly

Whether the deployment requires downloading some code files.

Returns:

Type	Description
bool	Whether the deployment requires downloading some code files.

requires_included_files: bool property readonly

Whether the deployment requires included files.

Returns:

Type	Description
bool	Whether the deployment requires included files.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineDeploymentFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all pipeline deployments.

Source code in zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all pipeline deployments."""

    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace for this deployment."
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User that created this deployment."
    )
    pipeline_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Pipeline associated with the deployment."
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Stack associated with the deployment."
    )
    build_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Build associated with the deployment."
    )
    schedule_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Schedule associated with the deployment."
    )

build_id: Union[uuid.UUID, str] pydantic-field

Build associated with the deployment.

pipeline_id: Union[uuid.UUID, str] pydantic-field

Pipeline associated with the deployment.

schedule_id: Union[uuid.UUID, str] pydantic-field

Schedule associated with the deployment.

stack_id: Union[uuid.UUID, str] pydantic-field

Stack associated with the deployment.

user_id: Union[uuid.UUID, str] pydantic-field

User that created this deployment.

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace for this deployment.

PipelineDeploymentRequest (PipelineDeploymentBase, WorkspaceScopedRequest) pydantic-model

Request model for pipeline deployments.

Source code in zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentRequest(
    PipelineDeploymentBase, WorkspaceScopedRequest
):
    """Request model for pipeline deployments."""

    stack: UUID = Field(title="The stack associated with the deployment.")
    pipeline: Optional[UUID] = Field(
        default=None, title="The pipeline associated with the deployment."
    )
    build: Optional[UUID] = Field(
        default=None, title="The build associated with the deployment."
    )
    schedule: Optional[UUID] = Field(
        default=None, title="The schedule associated with the deployment."
    )
    code_reference: Optional["CodeReferenceRequest"] = Field(
        default=None,
        title="The code reference associated with the deployment.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineDeploymentResponse (WorkspaceScopedResponse[PipelineDeploymentResponseBody, PipelineDeploymentResponseMetadata, PipelineDeploymentResponseResources]) pydantic-model

Response model for pipeline deployments.

Source code in zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentResponse(
    WorkspaceScopedResponse[
        PipelineDeploymentResponseBody,
        PipelineDeploymentResponseMetadata,
        PipelineDeploymentResponseResources,
    ]
):
    """Response model for pipeline deployments."""

    def get_hydrated_version(self) -> "PipelineDeploymentResponse":
        """Return the hydrated version of this pipeline deployment.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_deployment(self.id)

    # Body and metadata properties
    @property
    def run_name_template(self) -> str:
        """The `run_name_template` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_name_template

    @property
    def pipeline_configuration(self) -> PipelineConfiguration:
        """The `pipeline_configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_configuration

    @property
    def step_configurations(self) -> Dict[str, Step]:
        """The `step_configurations` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().step_configurations

    @property
    def client_environment(self) -> Dict[str, str]:
        """The `client_environment` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().client_environment

    @property
    def client_version(self) -> Optional[str]:
        """The `client_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().client_version

    @property
    def server_version(self) -> Optional[str]:
        """The `server_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().server_version

    @property
    def pipeline(self) -> Optional[PipelineResponse]:
        """The `pipeline` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline

    @property
    def stack(self) -> Optional[StackResponse]:
        """The `stack` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack

    @property
    def build(self) -> Optional[PipelineBuildResponse]:
        """The `build` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().build

    @property
    def schedule(self) -> Optional[ScheduleResponse]:
        """The `schedule` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().schedule

    @property
    def code_reference(self) -> Optional[CodeReferenceResponse]:
        """The `code_reference` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().code_reference

    @property
    def requires_code_download(self) -> bool:
        """Whether the deployment requires downloading some code files.

        Returns:
            Whether the deployment requires downloading some code files.
        """
        return any(
            step.config.docker_settings.source_files == SourceFileMode.DOWNLOAD
            for step in self.step_configurations.values()
        )

build: Optional[zenml.models.v2.core.pipeline_build.PipelineBuildResponse] property readonly

The build property.

Returns:

Type	Description
Optional[zenml.models.v2.core.pipeline_build.PipelineBuildResponse]	the value of the property.

client_environment: Dict[str, str] property readonly

The client_environment property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

client_version: Optional[str] property readonly

The client_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

code_reference: Optional[zenml.models.v2.core.code_reference.CodeReferenceResponse] property readonly

The code_reference property.

Returns:

Type	Description
Optional[zenml.models.v2.core.code_reference.CodeReferenceResponse]	the value of the property.

pipeline: Optional[zenml.models.v2.core.pipeline.PipelineResponse] property readonly

The pipeline property.

Returns:

Type	Description
Optional[zenml.models.v2.core.pipeline.PipelineResponse]	the value of the property.

pipeline_configuration: PipelineConfiguration property readonly

The pipeline_configuration property.

Returns:

Type	Description
PipelineConfiguration	the value of the property.

requires_code_download: bool property readonly

Whether the deployment requires downloading some code files.

Returns:

Type	Description
bool	Whether the deployment requires downloading some code files.

run_name_template: str property readonly

The run_name_template property.

Returns:

Type	Description
str	the value of the property.

schedule: Optional[zenml.models.v2.core.schedule.ScheduleResponse] property readonly

The schedule property.

Returns:

Type	Description
Optional[zenml.models.v2.core.schedule.ScheduleResponse]	the value of the property.

server_version: Optional[str] property readonly

The server_version property.

Returns:

Type	Description
Optional[str]	the value of the property.

stack: Optional[zenml.models.v2.core.stack.StackResponse] property readonly

The stack property.

Returns:

Type	Description
Optional[zenml.models.v2.core.stack.StackResponse]	the value of the property.

step_configurations: Dict[str, zenml.config.step_configurations.Step] property readonly

The step_configurations property.

Returns:

Type	Description
Dict[str, zenml.config.step_configurations.Step]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Return the hydrated version of this pipeline deployment.

Returns:

Type	Description
PipelineDeploymentResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/pipeline_deployment.py

def get_hydrated_version(self) -> "PipelineDeploymentResponse":
    """Return the hydrated version of this pipeline deployment.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_deployment(self.id)

PipelineDeploymentResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for pipeline deployments.

Source code in zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipeline deployments."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineDeploymentResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for pipeline deployments.

Source code in zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipeline deployments."""

    run_name_template: str = Field(
        title="The run name template for runs created using this deployment.",
    )
    pipeline_configuration: PipelineConfiguration = Field(
        title="The pipeline configuration for this deployment."
    )
    step_configurations: Dict[str, Step] = Field(
        default={}, title="The step configurations for this deployment."
    )
    client_environment: Dict[str, str] = Field(
        default={}, title="The client environment for this deployment."
    )
    client_version: Optional[str] = Field(
        title="The version of the ZenML installation on the client side."
    )
    server_version: Optional[str] = Field(
        title="The version of the ZenML installation on the server side."
    )
    pipeline: Optional[PipelineResponse] = Field(
        default=None, title="The pipeline associated with the deployment."
    )
    stack: Optional[StackResponse] = Field(
        default=None, title="The stack associated with the deployment."
    )
    build: Optional[PipelineBuildResponse] = Field(
        default=None,
        title="The pipeline build associated with the deployment.",
    )
    schedule: Optional[ScheduleResponse] = Field(
        default=None, title="The schedule associated with the deployment."
    )
    code_reference: Optional[CodeReferenceResponse] = Field(
        default=None,
        title="The code reference associated with the deployment.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineDeploymentResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the pipeline deployment entity.

Source code in zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the pipeline deployment entity."""

    triggers: TriggerPage = Field(  # type: ignore[valid-type]
        title="The triggers configured with this event source.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[PipelineDeploymentResponseBody, PipelineDeploymentResponseMetadata, PipelineDeploymentResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][PipelineDeploymentResponseBody, PipelineDeploymentResponseMetadata, PipelineDeploymentResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/pipeline_deployment.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

pipeline_run

Models representing pipeline runs.

PipelineRunFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all Workspaces.

Source code in zenml/models/v2/core/pipeline_run.py

class PipelineRunFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all Workspaces."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "unlisted",
        "code_repository_id",
        "build_id",
        "schedule_id",
        "stack_id",
        "pipeline_name",
    ]
    name: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline Run",
    )
    orchestrator_run_id: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline Run within the orchestrator",
    )
    pipeline_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Pipeline associated with the Pipeline Run"
    )
    pipeline_name: Optional[str] = Field(
        default=None,
        description="Name of the pipeline associated with the run",
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of the Pipeline Run"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User that created the Pipeline Run"
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Stack used for the Pipeline Run"
    )
    schedule_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Schedule that triggered the Pipeline Run"
    )
    build_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Build used for the Pipeline Run"
    )
    deployment_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Deployment used for the Pipeline Run"
    )
    code_repository_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Code repository used for the Pipeline Run"
    )
    status: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline Run",
    )
    start_time: Optional[Union[datetime, str]] = Field(
        default=None, description="Start time for this run"
    )
    end_time: Optional[Union[datetime, str]] = Field(
        default=None, description="End time for this run"
    )
    unlisted: Optional[bool] = None

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom filters.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters()

        from sqlalchemy import and_

        from zenml.zen_stores.schemas import (
            CodeReferenceSchema,
            PipelineBuildSchema,
            PipelineDeploymentSchema,
            PipelineRunSchema,
            PipelineSchema,
            ScheduleSchema,
            StackSchema,
        )

        if self.unlisted is not None:
            if self.unlisted is True:
                unlisted_filter = PipelineRunSchema.pipeline_id.is_(None)  # type: ignore[union-attr]
            else:
                unlisted_filter = PipelineRunSchema.pipeline_id.is_not(None)  # type: ignore[union-attr]
            custom_filters.append(unlisted_filter)

        if self.pipeline_name is not None:
            value, filter_operator = self._resolve_operator(self.pipeline_name)
            filter_ = StrFilter(
                operation=GenericFilterOps(filter_operator),
                column="name",
                value=value,
            )
            pipeline_name_filter = and_(  # type: ignore[type-var]
                PipelineRunSchema.pipeline_id == PipelineSchema.id,
                filter_.generate_query_conditions(PipelineSchema),
            )
            custom_filters.append(pipeline_name_filter)

        if self.code_repository_id:
            code_repo_filter = and_(  # type: ignore[type-var]
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.code_reference_id
                == CodeReferenceSchema.id,
                CodeReferenceSchema.code_repository_id
                == self.code_repository_id,
            )
            custom_filters.append(code_repo_filter)

        if self.stack_id:
            stack_filter = and_(  # type: ignore[type-var]
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.stack_id == StackSchema.id,
                StackSchema.id == self.stack_id,
            )
            custom_filters.append(stack_filter)

        if self.schedule_id:
            schedule_filter = and_(  # type: ignore[type-var]
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.schedule_id == ScheduleSchema.id,
                ScheduleSchema.id == self.schedule_id,
            )
            custom_filters.append(schedule_filter)

        if self.build_id:
            pipeline_build_filter = and_(  # type: ignore[type-var]
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.build_id == PipelineBuildSchema.id,
                PipelineBuildSchema.id == self.build_id,
            )
            custom_filters.append(pipeline_build_filter)

        return custom_filters

build_id: Union[uuid.UUID, str] pydantic-field

Build used for the Pipeline Run

code_repository_id: Union[uuid.UUID, str] pydantic-field

Code repository used for the Pipeline Run

deployment_id: Union[uuid.UUID, str] pydantic-field

Deployment used for the Pipeline Run

end_time: Union[datetime.datetime, str] pydantic-field

End time for this run

name: str pydantic-field

Name of the Pipeline Run

orchestrator_run_id: str pydantic-field

Name of the Pipeline Run within the orchestrator

pipeline_id: Union[uuid.UUID, str] pydantic-field

Pipeline associated with the Pipeline Run

pipeline_name: str pydantic-field

Name of the pipeline associated with the run

schedule_id: Union[uuid.UUID, str] pydantic-field

Schedule that triggered the Pipeline Run

stack_id: Union[uuid.UUID, str] pydantic-field

Stack used for the Pipeline Run

start_time: Union[datetime.datetime, str] pydantic-field

Start time for this run

status: str pydantic-field

Name of the Pipeline Run

user_id: Union[uuid.UUID, str] pydantic-field

User that created the Pipeline Run

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the Pipeline Run

get_custom_filters(self)

Get custom filters.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/core/pipeline_run.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom filters.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters()

    from sqlalchemy import and_

    from zenml.zen_stores.schemas import (
        CodeReferenceSchema,
        PipelineBuildSchema,
        PipelineDeploymentSchema,
        PipelineRunSchema,
        PipelineSchema,
        ScheduleSchema,
        StackSchema,
    )

    if self.unlisted is not None:
        if self.unlisted is True:
            unlisted_filter = PipelineRunSchema.pipeline_id.is_(None)  # type: ignore[union-attr]
        else:
            unlisted_filter = PipelineRunSchema.pipeline_id.is_not(None)  # type: ignore[union-attr]
        custom_filters.append(unlisted_filter)

    if self.pipeline_name is not None:
        value, filter_operator = self._resolve_operator(self.pipeline_name)
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        pipeline_name_filter = and_(  # type: ignore[type-var]
            PipelineRunSchema.pipeline_id == PipelineSchema.id,
            filter_.generate_query_conditions(PipelineSchema),
        )
        custom_filters.append(pipeline_name_filter)

    if self.code_repository_id:
        code_repo_filter = and_(  # type: ignore[type-var]
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.code_reference_id
            == CodeReferenceSchema.id,
            CodeReferenceSchema.code_repository_id
            == self.code_repository_id,
        )
        custom_filters.append(code_repo_filter)

    if self.stack_id:
        stack_filter = and_(  # type: ignore[type-var]
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            StackSchema.id == self.stack_id,
        )
        custom_filters.append(stack_filter)

    if self.schedule_id:
        schedule_filter = and_(  # type: ignore[type-var]
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.schedule_id == ScheduleSchema.id,
            ScheduleSchema.id == self.schedule_id,
        )
        custom_filters.append(schedule_filter)

    if self.build_id:
        pipeline_build_filter = and_(  # type: ignore[type-var]
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.build_id == PipelineBuildSchema.id,
            PipelineBuildSchema.id == self.build_id,
        )
        custom_filters.append(pipeline_build_filter)

    return custom_filters

PipelineRunRequest (WorkspaceScopedRequest) pydantic-model

Request model for pipeline runs.

Source code in zenml/models/v2/core/pipeline_run.py

class PipelineRunRequest(WorkspaceScopedRequest):
    """Request model for pipeline runs."""

    name: str = Field(
        title="The name of the pipeline run.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    deployment: UUID = Field(
        title="The deployment associated with the pipeline run."
    )
    pipeline: Optional[UUID] = Field(
        title="The pipeline associated with the pipeline run."
    )
    orchestrator_run_id: Optional[str] = Field(
        title="The orchestrator run ID.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    start_time: Optional[datetime] = Field(
        title="The start time of the pipeline run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the pipeline run.",
        default=None,
    )
    status: ExecutionStatus = Field(
        title="The status of the pipeline run.",
    )
    client_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the client that initiated this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    orchestrator_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the orchestrator that executed this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    trigger_execution_id: Optional[UUID] = Field(
        default=None,
        title="ID of the trigger execution that triggered this run.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineRunResponse (WorkspaceScopedResponse[PipelineRunResponseBody, PipelineRunResponseMetadata, PipelineRunResponseResources]) pydantic-model

Response model for pipeline runs.

Source code in zenml/models/v2/core/pipeline_run.py

class PipelineRunResponse(
    WorkspaceScopedResponse[
        PipelineRunResponseBody,
        PipelineRunResponseMetadata,
        PipelineRunResponseResources,
    ]
):
    """Response model for pipeline runs."""

    name: str = Field(
        title="The name of the pipeline run.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "PipelineRunResponse":
        """Get the hydrated version of this pipeline run.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_run(self.id)

    # Helper methods
    @property
    def artifact_versions(self) -> List["ArtifactVersionResponse"]:
        """Get all artifact versions that are outputs of steps of this run.

        Returns:
            All output artifact versions of this run (including cached ones).
        """
        from zenml.artifacts.utils import (
            get_artifacts_versions_of_pipeline_run,
        )

        return get_artifacts_versions_of_pipeline_run(self)

    @property
    def produced_artifact_versions(self) -> List["ArtifactVersionResponse"]:
        """Get all artifact versions produced during this pipeline run.

        Returns:
            A list of all artifact versions produced during this pipeline run.
        """
        from zenml.artifacts.utils import (
            get_artifacts_versions_of_pipeline_run,
        )

        return get_artifacts_versions_of_pipeline_run(self, only_produced=True)

    # Body and metadata properties
    @property
    def status(self) -> ExecutionStatus:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().status

    @property
    def stack(self) -> Optional["StackResponse"]:
        """The `stack` property.

        Returns:
            the value of the property.
        """
        return self.get_body().stack

    @property
    def pipeline(self) -> Optional["PipelineResponse"]:
        """The `pipeline` property.

        Returns:
            the value of the property.
        """
        return self.get_body().pipeline

    @property
    def build(self) -> Optional["PipelineBuildResponse"]:
        """The `build` property.

        Returns:
            the value of the property.
        """
        return self.get_body().build

    @property
    def schedule(self) -> Optional["ScheduleResponse"]:
        """The `schedule` property.

        Returns:
            the value of the property.
        """
        return self.get_body().schedule

    @property
    def trigger_execution(self) -> Optional["TriggerExecutionResponse"]:
        """The `trigger_execution` property.

        Returns:
            the value of the property.
        """
        return self.get_body().trigger_execution

    @property
    def code_reference(self) -> Optional["CodeReferenceResponse"]:
        """The `schedule` property.

        Returns:
            the value of the property.
        """
        return self.get_body().code_reference

    @property
    def deployment_id(self) -> Optional["UUID"]:
        """The `deployment_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().deployment_id

    @property
    def run_metadata(self) -> Dict[str, "RunMetadataResponse"]:
        """The `run_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    @property
    def steps(self) -> Dict[str, "StepRunResponse"]:
        """The `steps` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().steps

    @property
    def config(self) -> PipelineConfiguration:
        """The `config` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config

    @property
    def start_time(self) -> Optional[datetime]:
        """The `start_time` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().start_time

    @property
    def end_time(self) -> Optional[datetime]:
        """The `end_time` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().end_time

    @property
    def client_environment(self) -> Dict[str, str]:
        """The `client_environment` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().client_environment

    @property
    def orchestrator_environment(self) -> Dict[str, str]:
        """The `orchestrator_environment` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().orchestrator_environment

    @property
    def orchestrator_run_id(self) -> Optional[str]:
        """The `orchestrator_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().orchestrator_run_id

artifact_versions: List[ArtifactVersionResponse] property readonly

Get all artifact versions that are outputs of steps of this run.

Returns:

Type	Description
List[ArtifactVersionResponse]	All output artifact versions of this run (including cached ones).

build: Optional[PipelineBuildResponse] property readonly

The build property.

Returns:

Type	Description
Optional[PipelineBuildResponse]	the value of the property.

client_environment: Dict[str, str] property readonly

The client_environment property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

code_reference: Optional[CodeReferenceResponse] property readonly

The schedule property.

Returns:

Type	Description
Optional[CodeReferenceResponse]	the value of the property.

config: PipelineConfiguration property readonly

The config property.

Returns:

Type	Description
PipelineConfiguration	the value of the property.

deployment_id: Optional[UUID] property readonly

The deployment_id property.

Returns:

Type	Description
Optional[UUID]	the value of the property.

end_time: Optional[datetime.datetime] property readonly

The end_time property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

orchestrator_environment: Dict[str, str] property readonly

The orchestrator_environment property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

orchestrator_run_id: Optional[str] property readonly

The orchestrator_run_id property.

Returns:

Type	Description
Optional[str]	the value of the property.

pipeline: Optional[PipelineResponse] property readonly

The pipeline property.

Returns:

Type	Description
Optional[PipelineResponse]	the value of the property.

produced_artifact_versions: List[ArtifactVersionResponse] property readonly

Get all artifact versions produced during this pipeline run.

Returns:

Type	Description
List[ArtifactVersionResponse]	A list of all artifact versions produced during this pipeline run.

run_metadata: Dict[str, RunMetadataResponse] property readonly

The run_metadata property.

Returns:

Type	Description
Dict[str, RunMetadataResponse]	the value of the property.

schedule: Optional[ScheduleResponse] property readonly

The schedule property.

Returns:

Type	Description
Optional[ScheduleResponse]	the value of the property.

stack: Optional[StackResponse] property readonly

The stack property.

Returns:

Type	Description
Optional[StackResponse]	the value of the property.

start_time: Optional[datetime.datetime] property readonly

The start_time property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

status: ExecutionStatus property readonly

The status property.

Returns:

Type	Description
ExecutionStatus	the value of the property.

steps: Dict[str, StepRunResponse] property readonly

The steps property.

Returns:

Type	Description
Dict[str, StepRunResponse]	the value of the property.

trigger_execution: Optional[TriggerExecutionResponse] property readonly

The trigger_execution property.

Returns:

Type	Description
Optional[TriggerExecutionResponse]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this pipeline run.

Returns:

Type	Description
PipelineRunResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/pipeline_run.py

def get_hydrated_version(self) -> "PipelineRunResponse":
    """Get the hydrated version of this pipeline run.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_run(self.id)

PipelineRunResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for pipeline runs.

Source code in zenml/models/v2/core/pipeline_run.py

class PipelineRunResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipeline runs."""

    status: ExecutionStatus = Field(
        title="The status of the pipeline run.",
    )
    stack: Optional["StackResponse"] = Field(
        default=None, title="The stack that was used for this run."
    )
    pipeline: Optional["PipelineResponse"] = Field(
        default=None, title="The pipeline this run belongs to."
    )
    build: Optional["PipelineBuildResponse"] = Field(
        default=None, title="The pipeline build that was used for this run."
    )
    schedule: Optional["ScheduleResponse"] = Field(
        default=None, title="The schedule that was used for this run."
    )
    code_reference: Optional["CodeReferenceResponse"] = Field(
        default=None, title="The code reference that was used for this run."
    )
    deployment_id: Optional[UUID] = Field(
        default=None, title="The deployment that was used for this run."
    )
    trigger_execution: Optional["TriggerExecutionResponse"] = Field(
        default=None, title="The trigger execution that triggered this run."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineRunResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for pipeline runs.

Source code in zenml/models/v2/core/pipeline_run.py

class PipelineRunResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipeline runs."""

    run_metadata: Dict[str, "RunMetadataResponse"] = Field(
        default={},
        title="Metadata associated with this pipeline run.",
    )
    steps: Dict[str, "StepRunResponse"] = Field(
        default={}, title="The steps of this run."
    )
    config: PipelineConfiguration = Field(
        title="The pipeline configuration used for this pipeline run.",
    )
    start_time: Optional[datetime] = Field(
        title="The start time of the pipeline run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the pipeline run.",
        default=None,
    )
    client_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the client that initiated this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    orchestrator_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the orchestrator that executed this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    orchestrator_run_id: Optional[str] = Field(
        title="The orchestrator run ID.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineRunResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the pipeline run entity.

Source code in zenml/models/v2/core/pipeline_run.py

class PipelineRunResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the pipeline run entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

PipelineRunUpdate (BaseModel) pydantic-model

Pipeline run update model.

Source code in zenml/models/v2/core/pipeline_run.py

class PipelineRunUpdate(BaseModel):
    """Pipeline run update model."""

    status: Optional[ExecutionStatus] = None
    end_time: Optional[datetime] = None

WorkspaceScopedResponse[PipelineRunResponseBody, PipelineRunResponseMetadata, PipelineRunResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][PipelineRunResponseBody, PipelineRunResponseMetadata, PipelineRunResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/pipeline_run.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

run_metadata

Models representing run metadata.

LazyRunMetadataResponse (RunMetadataResponse) pydantic-model

Lazy run metadata response.

Used if the run metadata is accessed from the model in a pipeline context available only during pipeline compilation.

Source code in zenml/models/v2/core/run_metadata.py

class LazyRunMetadataResponse(RunMetadataResponse):
    """Lazy run metadata response.

    Used if the run metadata is accessed from the model in
    a pipeline context available only during pipeline compilation.
    """

    id: Optional[UUID] = None  # type: ignore[assignment]
    _lazy_load_artifact_name: Optional[str] = None
    _lazy_load_artifact_version: Optional[str] = None
    _lazy_load_metadata_name: Optional[str] = None
    _lazy_load_model: "Model"

    def get_body(self) -> None:  # type: ignore[override]
        """Protects from misuse of the lazy loader.

        Raises:
            RuntimeError: always
        """
        raise RuntimeError(
            "Cannot access run metadata body before pipeline runs."
        )

    def get_metadata(self) -> None:  # type: ignore[override]
        """Protects from misuse of the lazy loader.

        Raises:
            RuntimeError: always
        """
        raise RuntimeError(
            "Cannot access run metadata metadata before pipeline runs."
        )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_body(self)

Protects from misuse of the lazy loader.

Exceptions:

Type	Description
RuntimeError	always

Source code in zenml/models/v2/core/run_metadata.py

def get_body(self) -> None:  # type: ignore[override]
    """Protects from misuse of the lazy loader.

    Raises:
        RuntimeError: always
    """
    raise RuntimeError(
        "Cannot access run metadata body before pipeline runs."
    )

get_metadata(self)

Protects from misuse of the lazy loader.

Exceptions:

Type	Description
RuntimeError	always

Source code in zenml/models/v2/core/run_metadata.py

def get_metadata(self) -> None:  # type: ignore[override]
    """Protects from misuse of the lazy loader.

    Raises:
        RuntimeError: always
    """
    raise RuntimeError(
        "Cannot access run metadata metadata before pipeline runs."
    )

RunMetadataFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of run metadata.

Source code in zenml/models/v2/core/run_metadata.py

class RunMetadataFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of run metadata."""

    resource_id: Optional[Union[str, UUID]] = None
    resource_type: Optional[MetadataResourceTypes] = None
    stack_component_id: Optional[Union[str, UUID]] = None
    key: Optional[str] = None
    type: Optional[Union[str, MetadataTypeEnum]] = None

RunMetadataRequest (WorkspaceScopedRequest) pydantic-model

Request model for run metadata.

Source code in zenml/models/v2/core/run_metadata.py

class RunMetadataRequest(WorkspaceScopedRequest):
    """Request model for run metadata."""

    resource_id: UUID = Field(
        title="The ID of the resource that this metadata belongs to.",
    )
    resource_type: MetadataResourceTypes = Field(
        title="The type of the resource that this metadata belongs to.",
    )
    stack_component_id: Optional[UUID] = Field(
        title="The ID of the stack component that this metadata belongs to."
    )
    values: Dict[str, "MetadataType"] = Field(
        title="The metadata to be created.",
    )
    types: Dict[str, "MetadataTypeEnum"] = Field(
        title="The types of the metadata to be created.",
    )

    class Config:
        """Pydantic configuration."""

        smart_union = True

Config

Pydantic configuration.

Source code in zenml/models/v2/core/run_metadata.py

class Config:
    """Pydantic configuration."""

    smart_union = True

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

RunMetadataResponse (WorkspaceScopedResponse[RunMetadataResponseBody, RunMetadataResponseMetadata, RunMetadataResponseResources]) pydantic-model

Response model for run metadata.

Source code in zenml/models/v2/core/run_metadata.py

class RunMetadataResponse(
    WorkspaceScopedResponse[
        RunMetadataResponseBody,
        RunMetadataResponseMetadata,
        RunMetadataResponseResources,
    ]
):
    """Response model for run metadata."""

    def get_hydrated_version(self) -> "RunMetadataResponse":
        """Get the hydrated version of this run metadata.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_run_metadata(self.id)

    # Body and metadata properties
    @property
    def key(self) -> str:
        """The `key` property.

        Returns:
            the value of the property.
        """
        return self.get_body().key

    @property
    def value(self) -> MetadataType:
        """The `value` property.

        Returns:
            the value of the property.
        """
        return self.get_body().value

    @property
    def type(self) -> MetadataTypeEnum:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def resource_id(self) -> UUID:
        """The `resource_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().resource_id

    @property
    def resource_type(self) -> MetadataResourceTypes:
        """The `resource_type` property.

        Returns:
            the value of the property.
        """
        return MetadataResourceTypes(self.get_metadata().resource_type)

    @property
    def stack_component_id(self) -> Optional[UUID]:
        """The `stack_component_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack_component_id

key: str property readonly

The key property.

Returns:

Type	Description
str	the value of the property.

resource_id: UUID property readonly

The resource_id property.

Returns:

Type	Description
UUID	the value of the property.

resource_type: MetadataResourceTypes property readonly

The resource_type property.

Returns:

Type	Description
MetadataResourceTypes	the value of the property.

stack_component_id: Optional[uuid.UUID] property readonly

The stack_component_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

type: MetadataTypeEnum property readonly

The type property.

Returns:

Type	Description
MetadataTypeEnum	the value of the property.

value: Union[str, int, float, bool, Dict[Any, Any], List[Any], Set[Any], Tuple[Any, ...], zenml.metadata.metadata_types.Uri, zenml.metadata.metadata_types.Path, zenml.metadata.metadata_types.DType, zenml.metadata.metadata_types.StorageSize] property readonly

The value property.

Returns:

Type	Description
Union[str, int, float, bool, Dict[Any, Any], List[Any], Set[Any], Tuple[Any, ...], zenml.metadata.metadata_types.Uri, zenml.metadata.metadata_types.Path, zenml.metadata.metadata_types.DType, zenml.metadata.metadata_types.StorageSize]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this run metadata.

Returns:

Type	Description
RunMetadataResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/run_metadata.py

def get_hydrated_version(self) -> "RunMetadataResponse":
    """Get the hydrated version of this run metadata.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_run_metadata(self.id)

RunMetadataResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for run metadata.

Source code in zenml/models/v2/core/run_metadata.py

class RunMetadataResponseBody(WorkspaceScopedResponseBody):
    """Response body for run metadata."""

    key: str = Field(
        title="The key of the metadata.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    value: MetadataType = Field(
        title="The value of the metadata.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    type: MetadataTypeEnum = Field(
        title="The type of the metadata.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    class Config:
        """Pydantic configuration."""

        smart_union = True

Config

Pydantic configuration.

Source code in zenml/models/v2/core/run_metadata.py

class Config:
    """Pydantic configuration."""

    smart_union = True

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

RunMetadataResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for run metadata.

Source code in zenml/models/v2/core/run_metadata.py

class RunMetadataResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for run metadata."""

    resource_id: UUID = Field(
        title="The ID of the resource that this metadata belongs to.",
    )
    resource_type: MetadataResourceTypes = Field(
        title="The type of the resource that this metadata belongs to.",
    )
    stack_component_id: Optional[UUID] = Field(
        title="The ID of the stack component that this metadata belongs to."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

RunMetadataResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the run metadata entity.

Source code in zenml/models/v2/core/run_metadata.py

class RunMetadataResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the run metadata entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[RunMetadataResponseBody, RunMetadataResponseMetadata, RunMetadataResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][RunMetadataResponseBody, RunMetadataResponseMetadata, RunMetadataResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/run_metadata.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

schedule

Models representing schedules.

ScheduleFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all Users.

Source code in zenml/models/v2/core/schedule.py

class ScheduleFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all Users."""

    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace scope of the schedule."
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User that created the schedule"
    )
    pipeline_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Pipeline that the schedule is attached to."
    )
    orchestrator_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Orchestrator that the schedule is attached to.",
    )
    active: Optional[bool] = Field(
        default=None,
        description="If the schedule is active",
    )
    cron_expression: Optional[str] = Field(
        default=None,
        description="The cron expression, describing the schedule",
    )
    start_time: Optional[Union[datetime.datetime, str]] = Field(
        default=None, description="Start time"
    )
    end_time: Optional[Union[datetime.datetime, str]] = Field(
        default=None, description="End time"
    )
    interval_second: Optional[Optional[float]] = Field(
        default=None,
        description="The repetition interval in seconds",
    )
    catchup: Optional[bool] = Field(
        default=None,
        description="Whether or not the schedule is set to catchup past missed "
        "events",
    )
    name: Optional[str] = Field(
        default=None,
        description="Name of the schedule",
    )

active: bool pydantic-field

If the schedule is active

catchup: bool pydantic-field

Whether or not the schedule is set to catchup past missed events

cron_expression: str pydantic-field

The cron expression, describing the schedule

end_time: Union[datetime.datetime, str] pydantic-field

End time

interval_second: float pydantic-field

The repetition interval in seconds

name: str pydantic-field

Name of the schedule

orchestrator_id: Union[uuid.UUID, str] pydantic-field

Orchestrator that the schedule is attached to.

pipeline_id: Union[uuid.UUID, str] pydantic-field

Pipeline that the schedule is attached to.

start_time: Union[datetime.datetime, str] pydantic-field

Start time

user_id: Union[uuid.UUID, str] pydantic-field

User that created the schedule

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace scope of the schedule.

ScheduleRequest (WorkspaceScopedRequest) pydantic-model

Request model for schedules.

Source code in zenml/models/v2/core/schedule.py

class ScheduleRequest(WorkspaceScopedRequest):
    """Request model for schedules."""

    name: str
    active: bool

    cron_expression: Optional[str] = None
    start_time: Optional[datetime.datetime] = None
    end_time: Optional[datetime.datetime] = None
    interval_second: Optional[datetime.timedelta] = None
    catchup: bool = False

    orchestrator_id: Optional[UUID]
    pipeline_id: Optional[UUID]

    @root_validator
    def _ensure_cron_or_periodic_schedule_configured(
        cls, values: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Ensures that the cron expression or start time + interval are set.

        Args:
            values: All attributes of the schedule.

        Returns:
            All schedule attributes.

        Raises:
            ValueError: If no cron expression or start time + interval were
                provided.
        """
        cron_expression = values.get("cron_expression")
        periodic_schedule = values.get("start_time") and values.get(
            "interval_second"
        )

        if cron_expression and periodic_schedule:
            logger.warning(
                "This schedule was created with a cron expression as well as "
                "values for `start_time` and `interval_seconds`. The resulting "
                "behavior depends on the concrete orchestrator implementation "
                "but will usually ignore the interval and use the cron "
                "expression."
            )
            return values
        elif cron_expression or periodic_schedule:
            return values
        else:
            raise ValueError(
                "Either a cron expression or start time and interval seconds "
                "need to be set for a valid schedule."
            )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ScheduleResponse (WorkspaceScopedResponse[ScheduleResponseBody, ScheduleResponseMetadata, ScheduleResponseResources]) pydantic-model

Response model for schedules.

Source code in zenml/models/v2/core/schedule.py

class ScheduleResponse(
    WorkspaceScopedResponse[
        ScheduleResponseBody,
        ScheduleResponseMetadata,
        ScheduleResponseResources,
    ],
):
    """Response model for schedules."""

    name: str = Field(
        title="Name of this schedule.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ScheduleResponse":
        """Get the hydrated version of this schedule.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_schedule(self.id)

    # Helper methods
    @property
    def utc_start_time(self) -> Optional[str]:
        """Optional ISO-formatted string of the UTC start time.

        Returns:
            Optional ISO-formatted string of the UTC start time.
        """
        if not self.start_time:
            return None

        return self.start_time.astimezone(datetime.timezone.utc).isoformat()

    @property
    def utc_end_time(self) -> Optional[str]:
        """Optional ISO-formatted string of the UTC end time.

        Returns:
            Optional ISO-formatted string of the UTC end time.
        """
        if not self.end_time:
            return None

        return self.end_time.astimezone(datetime.timezone.utc).isoformat()

    # Body and metadata properties
    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def cron_expression(self) -> Optional[str]:
        """The `cron_expression` property.

        Returns:
            the value of the property.
        """
        return self.get_body().cron_expression

    @property
    def start_time(self) -> Optional[datetime.datetime]:
        """The `start_time` property.

        Returns:
            the value of the property.
        """
        return self.get_body().start_time

    @property
    def end_time(self) -> Optional[datetime.datetime]:
        """The `end_time` property.

        Returns:
            the value of the property.
        """
        return self.get_body().end_time

    @property
    def interval_second(self) -> Optional[datetime.timedelta]:
        """The `interval_second` property.

        Returns:
            the value of the property.
        """
        return self.get_body().interval_second

    @property
    def catchup(self) -> bool:
        """The `catchup` property.

        Returns:
            the value of the property.
        """
        return self.get_body().catchup

    @property
    def orchestrator_id(self) -> Optional[UUID]:
        """The `orchestrator_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().orchestrator_id

    @property
    def pipeline_id(self) -> Optional[UUID]:
        """The `pipeline_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_id

active: bool property readonly

The active property.

Returns:

Type	Description
bool	the value of the property.

catchup: bool property readonly

The catchup property.

Returns:

Type	Description
bool	the value of the property.

cron_expression: Optional[str] property readonly

The cron_expression property.

Returns:

Type	Description
Optional[str]	the value of the property.

end_time: Optional[datetime.datetime] property readonly

The end_time property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

interval_second: Optional[datetime.timedelta] property readonly

The interval_second property.

Returns:

Type	Description
Optional[datetime.timedelta]	the value of the property.

orchestrator_id: Optional[uuid.UUID] property readonly

The orchestrator_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

pipeline_id: Optional[uuid.UUID] property readonly

The pipeline_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

start_time: Optional[datetime.datetime] property readonly

The start_time property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

utc_end_time: Optional[str] property readonly

Optional ISO-formatted string of the UTC end time.

Returns:

Type	Description
Optional[str]	Optional ISO-formatted string of the UTC end time.

utc_start_time: Optional[str] property readonly

Optional ISO-formatted string of the UTC start time.

Returns:

Type	Description
Optional[str]	Optional ISO-formatted string of the UTC start time.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this schedule.

Returns:

Type	Description
ScheduleResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/schedule.py

def get_hydrated_version(self) -> "ScheduleResponse":
    """Get the hydrated version of this schedule.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_schedule(self.id)

ScheduleResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for schedules.

Source code in zenml/models/v2/core/schedule.py

class ScheduleResponseBody(WorkspaceScopedResponseBody):
    """Response body for schedules."""

    active: bool
    cron_expression: Optional[str] = None
    start_time: Optional[datetime.datetime] = None
    end_time: Optional[datetime.datetime] = None
    interval_second: Optional[datetime.timedelta] = None
    catchup: bool = False

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ScheduleResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for schedules.

Source code in zenml/models/v2/core/schedule.py

class ScheduleResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for schedules."""

    orchestrator_id: Optional[UUID]
    pipeline_id: Optional[UUID]

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ScheduleResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the schedule entity.

Source code in zenml/models/v2/core/schedule.py

class ScheduleResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the schedule entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ScheduleUpdate (ScheduleRequest) pydantic-model

Update model for schedules.

Source code in zenml/models/v2/core/schedule.py

@update_model
class ScheduleUpdate(ScheduleRequest):
    """Update model for schedules."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[ScheduleResponseBody, ScheduleResponseMetadata, ScheduleResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][ScheduleResponseBody, ScheduleResponseMetadata, ScheduleResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/schedule.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

secret

Models representing secrets.

SecretFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all Secrets.

Source code in zenml/models/v2/core/secret.py

class SecretFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all Secrets."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "values",
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the secret",
    )

    scope: Optional[Union[SecretScope, str]] = Field(
        default=None,
        description="Scope in which to filter secrets",
    )

    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of the Secret"
    )

    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User that created the Secret"
    )

    @staticmethod
    def _get_filtering_value(value: Optional[Any]) -> str:
        """Convert the value to a string that can be used for lexicographical filtering and sorting.

        Args:
            value: The value to convert.

        Returns:
            The value converted to string format that can be used for
            lexicographical sorting and filtering.
        """
        if value is None:
            return ""
        str_value = str(value)
        if isinstance(value, datetime):
            str_value = value.strftime("%Y-%m-%d %H:%M:%S")
        return str_value

    def secret_matches(self, secret: SecretResponse) -> bool:
        """Checks if a secret matches the filter criteria.

        Args:
            secret: The secret to check.

        Returns:
            True if the secret matches the filter criteria, False otherwise.
        """
        for filter in self.list_of_filters:
            column_value: Optional[Any] = None
            if filter.column == "workspace_id":
                column_value = secret.workspace.id
            elif filter.column == "user_id":
                column_value = secret.user.id if secret.user else None
            else:
                column_value = getattr(secret, filter.column)

            # Convert the values to strings for lexicographical comparison.
            str_column_value = self._get_filtering_value(column_value)
            str_filter_value = self._get_filtering_value(filter.value)

            # Compare the lexicographical values according to the operation.
            if filter.operation == GenericFilterOps.EQUALS:
                result = str_column_value == str_filter_value
            elif filter.operation == GenericFilterOps.CONTAINS:
                result = str_filter_value in str_column_value
            elif filter.operation == GenericFilterOps.STARTSWITH:
                result = str_column_value.startswith(str_filter_value)
            elif filter.operation == GenericFilterOps.ENDSWITH:
                result = str_column_value.endswith(str_filter_value)
            elif filter.operation == GenericFilterOps.GT:
                result = str_column_value > str_filter_value
            elif filter.operation == GenericFilterOps.GTE:
                result = str_column_value >= str_filter_value
            elif filter.operation == GenericFilterOps.LT:
                result = str_column_value < str_filter_value
            elif filter.operation == GenericFilterOps.LTE:
                result = str_column_value <= str_filter_value

            # Exit early if the result is False for AND, and True for OR
            if self.logical_operator == LogicalOperators.AND:
                if not result:
                    return False
            else:
                if result:
                    return True

        # If we get here, all filters have been checked and the result is
        # True for AND, and False for OR
        if self.logical_operator == LogicalOperators.AND:
            return True
        else:
            return False

    def sort_secrets(
        self, secrets: List[SecretResponse]
    ) -> List[SecretResponse]:
        """Sorts a list of secrets according to the filter criteria.

        Args:
            secrets: The list of secrets to sort.

        Returns:
            The sorted list of secrets.
        """
        column, sort_op = self.sorting_params
        sorted_secrets = sorted(
            secrets,
            key=lambda secret: self._get_filtering_value(
                getattr(secret, column)
            ),
            reverse=sort_op == SorterOps.DESCENDING,
        )

        return sorted_secrets

name: str pydantic-field

Name of the secret

scope: Union[zenml.enums.SecretScope, str] pydantic-field

Scope in which to filter secrets

user_id: Union[uuid.UUID, str] pydantic-field

User that created the Secret

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the Secret

secret_matches(self, secret)

Checks if a secret matches the filter criteria.

Parameters:

Name	Type	Description	Default
secret	SecretResponse	The secret to check.	required

Returns:

Type	Description
bool	True if the secret matches the filter criteria, False otherwise.

Source code in zenml/models/v2/core/secret.py

def secret_matches(self, secret: SecretResponse) -> bool:
    """Checks if a secret matches the filter criteria.

    Args:
        secret: The secret to check.

    Returns:
        True if the secret matches the filter criteria, False otherwise.
    """
    for filter in self.list_of_filters:
        column_value: Optional[Any] = None
        if filter.column == "workspace_id":
            column_value = secret.workspace.id
        elif filter.column == "user_id":
            column_value = secret.user.id if secret.user else None
        else:
            column_value = getattr(secret, filter.column)

        # Convert the values to strings for lexicographical comparison.
        str_column_value = self._get_filtering_value(column_value)
        str_filter_value = self._get_filtering_value(filter.value)

        # Compare the lexicographical values according to the operation.
        if filter.operation == GenericFilterOps.EQUALS:
            result = str_column_value == str_filter_value
        elif filter.operation == GenericFilterOps.CONTAINS:
            result = str_filter_value in str_column_value
        elif filter.operation == GenericFilterOps.STARTSWITH:
            result = str_column_value.startswith(str_filter_value)
        elif filter.operation == GenericFilterOps.ENDSWITH:
            result = str_column_value.endswith(str_filter_value)
        elif filter.operation == GenericFilterOps.GT:
            result = str_column_value > str_filter_value
        elif filter.operation == GenericFilterOps.GTE:
            result = str_column_value >= str_filter_value
        elif filter.operation == GenericFilterOps.LT:
            result = str_column_value < str_filter_value
        elif filter.operation == GenericFilterOps.LTE:
            result = str_column_value <= str_filter_value

        # Exit early if the result is False for AND, and True for OR
        if self.logical_operator == LogicalOperators.AND:
            if not result:
                return False
        else:
            if result:
                return True

    # If we get here, all filters have been checked and the result is
    # True for AND, and False for OR
    if self.logical_operator == LogicalOperators.AND:
        return True
    else:
        return False

sort_secrets(self, secrets)

Sorts a list of secrets according to the filter criteria.

Parameters:

Name	Type	Description	Default
secrets	List[zenml.models.v2.core.secret.SecretResponse]	The list of secrets to sort.	required

Returns:

Type	Description
List[zenml.models.v2.core.secret.SecretResponse]	The sorted list of secrets.

Source code in zenml/models/v2/core/secret.py

def sort_secrets(
    self, secrets: List[SecretResponse]
) -> List[SecretResponse]:
    """Sorts a list of secrets according to the filter criteria.

    Args:
        secrets: The list of secrets to sort.

    Returns:
        The sorted list of secrets.
    """
    column, sort_op = self.sorting_params
    sorted_secrets = sorted(
        secrets,
        key=lambda secret: self._get_filtering_value(
            getattr(secret, column)
        ),
        reverse=sort_op == SorterOps.DESCENDING,
    )

    return sorted_secrets

SecretRequest (WorkspaceScopedRequest) pydantic-model

Request models for secrets.

Source code in zenml/models/v2/core/secret.py

class SecretRequest(WorkspaceScopedRequest):
    """Request models for secrets."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["scope"]

    name: str = Field(
        title="The name of the secret.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    scope: SecretScope = Field(
        SecretScope.WORKSPACE, title="The scope of the secret."
    )
    values: Dict[str, Optional[SecretStr]] = Field(
        default_factory=dict, title="The values stored in this secret."
    )

    @property
    def secret_values(self) -> Dict[str, str]:
        """A dictionary with all un-obfuscated values stored in this secret.

        The values are returned as strings, not SecretStr. If a value is
        None, it is not included in the returned dictionary. This is to enable
        the use of None values in the update model to indicate that a secret
        value should be deleted.

        Returns:
            A dictionary containing the secret's values.
        """
        return {
            k: v.get_secret_value()
            for k, v in self.values.items()
            if v is not None
        }

secret_values: Dict[str, str] property readonly

A dictionary with all un-obfuscated values stored in this secret.

The values are returned as strings, not SecretStr. If a value is None, it is not included in the returned dictionary. This is to enable the use of None values in the update model to indicate that a secret value should be deleted.

Returns:

Type	Description
Dict[str, str]	A dictionary containing the secret's values.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

SecretResponse (WorkspaceScopedResponse[SecretResponseBody, SecretResponseMetadata, SecretResponseResources]) pydantic-model

Response model for secrets.

Source code in zenml/models/v2/core/secret.py

class SecretResponse(
    WorkspaceScopedResponse[
        SecretResponseBody, SecretResponseMetadata, SecretResponseResources
    ]
):
    """Response model for secrets."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["scope"]

    name: str = Field(
        title="The name of the secret.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "SecretResponse":
        """Get the hydrated version of this workspace.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_secret(self.id)

    # Body and metadata properties

    @property
    def scope(self) -> SecretScope:
        """The `scope` property.

        Returns:
            the value of the property.
        """
        return self.get_body().scope

    @property
    def values(self) -> Dict[str, Optional[SecretStr]]:
        """The `values` property.

        Returns:
            the value of the property.
        """
        return self.get_body().values

    # Helper methods
    @property
    def secret_values(self) -> Dict[str, str]:
        """A dictionary with all un-obfuscated values stored in this secret.

        The values are returned as strings, not SecretStr. If a value is
        None, it is not included in the returned dictionary. This is to enable
        the use of None values in the update model to indicate that a secret
        value should be deleted.

        Returns:
            A dictionary containing the secret's values.
        """
        return {
            k: v.get_secret_value()
            for k, v in self.values.items()
            if v is not None
        }

    @property
    def has_missing_values(self) -> bool:
        """Returns True if the secret has missing values (i.e. None).

        Values can be missing from a secret for example if the user retrieves a
        secret but does not have the permission to view the secret values.

        Returns:
            True if the secret has any values set to None.
        """
        return any(v is None for v in self.values.values())

    def add_secret(self, key: str, value: str) -> None:
        """Adds a secret value to the secret.

        Args:
            key: The key of the secret value.
            value: The secret value.
        """
        self.get_body().values[key] = SecretStr(value)

    def remove_secret(self, key: str) -> None:
        """Removes a secret value from the secret.

        Args:
            key: The key of the secret value.
        """
        del self.get_body().values[key]

    def remove_secrets(self) -> None:
        """Removes all secret values from the secret but keep the keys."""
        self.get_body().values = {k: None for k in self.values.keys()}

    def set_secrets(self, values: Dict[str, str]) -> None:
        """Sets the secret values of the secret.

        Args:
            values: The secret values to set.
        """
        self.get_body().values = {k: SecretStr(v) for k, v in values.items()}

has_missing_values: bool property readonly

Returns True if the secret has missing values (i.e. None).

Values can be missing from a secret for example if the user retrieves a secret but does not have the permission to view the secret values.

Returns:

Type	Description
bool	True if the secret has any values set to None.

scope: SecretScope property readonly

The scope property.

Returns:

Type	Description
SecretScope	the value of the property.

secret_values: Dict[str, str] property readonly

A dictionary with all un-obfuscated values stored in this secret.

The values are returned as strings, not SecretStr. If a value is None, it is not included in the returned dictionary. This is to enable the use of None values in the update model to indicate that a secret value should be deleted.

Returns:

Type	Description
Dict[str, str]	A dictionary containing the secret's values.

values: Dict[str, Optional[pydantic.types.SecretStr]] property readonly

The values property.

Returns:

Type	Description
Dict[str, Optional[pydantic.types.SecretStr]]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

add_secret(self, key, value)

Adds a secret value to the secret.

Parameters:

Name	Type	Description	Default
key	str	The key of the secret value.	required
value	str	The secret value.	required

Source code in zenml/models/v2/core/secret.py

def add_secret(self, key: str, value: str) -> None:
    """Adds a secret value to the secret.

    Args:
        key: The key of the secret value.
        value: The secret value.
    """
    self.get_body().values[key] = SecretStr(value)

get_hydrated_version(self)

Get the hydrated version of this workspace.

Returns:

Type	Description
SecretResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/secret.py

def get_hydrated_version(self) -> "SecretResponse":
    """Get the hydrated version of this workspace.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_secret(self.id)

remove_secret(self, key)

Removes a secret value from the secret.

Parameters:

Name	Type	Description	Default
key	str	The key of the secret value.	required

Source code in zenml/models/v2/core/secret.py

def remove_secret(self, key: str) -> None:
    """Removes a secret value from the secret.

    Args:
        key: The key of the secret value.
    """
    del self.get_body().values[key]

remove_secrets(self)

Removes all secret values from the secret but keep the keys.

Source code in zenml/models/v2/core/secret.py

def remove_secrets(self) -> None:
    """Removes all secret values from the secret but keep the keys."""
    self.get_body().values = {k: None for k in self.values.keys()}

set_secrets(self, values)

Sets the secret values of the secret.

Parameters:

Name	Type	Description	Default
values	Dict[str, str]	The secret values to set.	required

Source code in zenml/models/v2/core/secret.py

def set_secrets(self, values: Dict[str, str]) -> None:
    """Sets the secret values of the secret.

    Args:
        values: The secret values to set.
    """
    self.get_body().values = {k: SecretStr(v) for k, v in values.items()}

SecretResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for secrets.

Source code in zenml/models/v2/core/secret.py

class SecretResponseBody(WorkspaceScopedResponseBody):
    """Response body for secrets."""

    scope: SecretScope = Field(
        SecretScope.WORKSPACE, title="The scope of the secret."
    )
    values: Dict[str, Optional[SecretStr]] = Field(
        default_factory=dict, title="The values stored in this secret."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

SecretResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for secrets.

Source code in zenml/models/v2/core/secret.py

class SecretResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for secrets."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

SecretResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the secret entity.

Source code in zenml/models/v2/core/secret.py

class SecretResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the secret entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

SecretUpdate (SecretRequest) pydantic-model

Secret update model.

Source code in zenml/models/v2/core/secret.py

@update_model
class SecretUpdate(SecretRequest):
    """Secret update model."""

    scope: Optional[SecretScope] = Field(  # type: ignore[assignment]
        default=None, title="The scope of the secret."
    )

    def get_secret_values_update(self) -> Dict[str, Optional[str]]:
        """Returns a dictionary with the secret values to update.

        Returns:
            A dictionary with the secret values to update.
        """
        return {
            k: v.get_secret_value() if v is not None else None
            for k, v in self.values.items()
        }

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_secret_values_update(self)

Returns a dictionary with the secret values to update.

Returns:

Type	Description
Dict[str, Optional[str]]	A dictionary with the secret values to update.

Source code in zenml/models/v2/core/secret.py

def get_secret_values_update(self) -> Dict[str, Optional[str]]:
    """Returns a dictionary with the secret values to update.

    Returns:
        A dictionary with the secret values to update.
    """
    return {
        k: v.get_secret_value() if v is not None else None
        for k, v in self.values.items()
    }

WorkspaceScopedResponse[SecretResponseBody, SecretResponseMetadata, SecretResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][SecretResponseBody, SecretResponseMetadata, SecretResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/secret.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

service_account

Models representing service accounts.

BaseIdentifiedResponse[ServiceAccountResponseBody, ServiceAccountResponseMetadata, ServiceAccountResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][ServiceAccountResponseBody, ServiceAccountResponseMetadata, ServiceAccountResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/service_account.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceAccountFilter (BaseFilter) pydantic-model

Model to enable advanced filtering of service accounts.

Source code in zenml/models/v2/core/service_account.py

class ServiceAccountFilter(BaseFilter):
    """Model to enable advanced filtering of service accounts."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the user",
    )
    description: Optional[str] = Field(
        default=None,
        title="Filter by the service account description.",
    )
    active: Optional[Union[bool, str]] = Field(
        default=None,
        description="Whether the user is active",
    )

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Override to filter out user accounts from the query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)
        query = query.where(
            getattr(table, "is_service_account") == True  # noqa: E712
        )

        return query

active: Union[bool, str] pydantic-field

Whether the user is active

name: str pydantic-field

Name of the user

apply_filter(self, query, table)

Override to filter out user accounts from the query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/core/service_account.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to filter out user accounts from the query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)
    query = query.where(
        getattr(table, "is_service_account") == True  # noqa: E712
    )

    return query

ServiceAccountRequest (BaseRequest) pydantic-model

Request model for service accounts.

Source code in zenml/models/v2/core/service_account.py

class ServiceAccountRequest(BaseRequest):
    """Request model for service accounts."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "active",
    ]

    name: str = Field(
        title="The unique name for the service account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="A description of the service account.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    active: bool = Field(title="Whether the service account is active or not.")

    class Config:
        """Pydantic configuration class."""

        # Validate attributes when assigning them
        validate_assignment = True
        extra = "ignore"

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/service_account.py

class Config:
    """Pydantic configuration class."""

    # Validate attributes when assigning them
    validate_assignment = True
    extra = "ignore"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceAccountResponse (BaseIdentifiedResponse[ServiceAccountResponseBody, ServiceAccountResponseMetadata, ServiceAccountResponseResources]) pydantic-model

Response model for service accounts.

Source code in zenml/models/v2/core/service_account.py

class ServiceAccountResponse(
    BaseIdentifiedResponse[
        ServiceAccountResponseBody,
        ServiceAccountResponseMetadata,
        ServiceAccountResponseResources,
    ]
):
    """Response model for service accounts."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "active",
    ]

    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ServiceAccountResponse":
        """Get the hydrated version of this service account.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_service_account(self.id)

    def to_user_model(self) -> "UserResponse":
        """Converts the service account to a user model.

        For now, a lot of code still relies on the active user and resource
        owners being a UserResponse object, which is a superset of the
        ServiceAccountResponse object. We need this method to convert the
        service account to a user.

        Returns:
            The user model.
        """
        from zenml.models.v2.core.user import (
            UserResponse,
            UserResponseBody,
            UserResponseMetadata,
        )

        return UserResponse(
            id=self.id,
            name=self.name,
            body=UserResponseBody(
                active=self.active,
                is_service_account=True,
                email_opted_in=False,
                created=self.created,
                updated=self.updated,
            ),
            metadata=UserResponseMetadata(
                description=self.description,
            ),
        )

    # Body and metadata properties
    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

active: bool property readonly

The active property.

Returns:

Type	Description
bool	the value of the property.

description: str property readonly

The description property.

Returns:

Type	Description
str	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this service account.

Returns:

Type	Description
ServiceAccountResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/service_account.py

def get_hydrated_version(self) -> "ServiceAccountResponse":
    """Get the hydrated version of this service account.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_service_account(self.id)

to_user_model(self)

Converts the service account to a user model.

For now, a lot of code still relies on the active user and resource owners being a UserResponse object, which is a superset of the ServiceAccountResponse object. We need this method to convert the service account to a user.

Returns:

Type	Description
UserResponse	The user model.

Source code in zenml/models/v2/core/service_account.py

def to_user_model(self) -> "UserResponse":
    """Converts the service account to a user model.

    For now, a lot of code still relies on the active user and resource
    owners being a UserResponse object, which is a superset of the
    ServiceAccountResponse object. We need this method to convert the
    service account to a user.

    Returns:
        The user model.
    """
    from zenml.models.v2.core.user import (
        UserResponse,
        UserResponseBody,
        UserResponseMetadata,
    )

    return UserResponse(
        id=self.id,
        name=self.name,
        body=UserResponseBody(
            active=self.active,
            is_service_account=True,
            email_opted_in=False,
            created=self.created,
            updated=self.updated,
        ),
        metadata=UserResponseMetadata(
            description=self.description,
        ),
    )

ServiceAccountResponseBody (BaseDatedResponseBody) pydantic-model

Response body for service accounts.

Source code in zenml/models/v2/core/service_account.py

class ServiceAccountResponseBody(BaseDatedResponseBody):
    """Response body for service accounts."""

    active: bool = Field(default=False, title="Whether the account is active.")

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceAccountResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for service accounts.

Source code in zenml/models/v2/core/service_account.py

class ServiceAccountResponseMetadata(BaseResponseMetadata):
    """Response metadata for service accounts."""

    description: str = Field(
        default="",
        title="A description of the service account.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceAccountResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the service account entity.

Source code in zenml/models/v2/core/service_account.py

class ServiceAccountResponseResources(BaseResponseResources):
    """Class for all resource models associated with the service account entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceAccountUpdate (ServiceAccountRequest) pydantic-model

Update model for service accounts.

Source code in zenml/models/v2/core/service_account.py

@update_model
class ServiceAccountUpdate(ServiceAccountRequest):
    """Update model for service accounts."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

service_connector

Models representing service connectors.

ServiceConnectorFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of service connectors.

Source code in zenml/models/v2/core/service_connector.py

class ServiceConnectorFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of service connectors."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "scope_type",
        "resource_type",
        "labels_str",
        "labels",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        "scope_type",
        "labels_str",
        "labels",
    ]
    scope_type: Optional[str] = Field(
        default=None,
        description="The type to scope this query to.",
    )

    name: Optional[str] = Field(
        default=None,
        description="The name to filter by",
    )
    connector_type: Optional[str] = Field(
        default=None,
        description="The type of service connector to filter by",
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace to filter by"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User to filter by"
    )
    auth_method: Optional[str] = Field(
        default=None,
        title="Filter by the authentication method configured for the "
        "connector",
    )
    resource_type: Optional[str] = Field(
        default=None,
        title="Filter by the type of resource that the connector can be used "
        "to access",
    )
    resource_id: Optional[str] = Field(
        default=None,
        title="Filter by the ID of the resource instance that the connector "
        "is configured to access",
    )
    labels_str: Optional[str] = Field(
        default=None,
        title="Filter by one or more labels. This field can be either a JSON "
        "formatted dictionary of label names and values, where the values are "
        'optional and can be set to None (e.g. `{"label1":"value1", "label2": '
        "null}` ), or a comma-separated list of label names and values (e.g "
        "`label1=value1,label2=`. If a label name is specified without a "
        "value, the filter will match all service connectors that have that "
        "label present, regardless of value.",
    )
    secret_id: Optional[Union[UUID, str]] = Field(
        default=None,
        title="Filter by the ID of the secret that contains the service "
        "connector's credentials",
    )

    # Use this internally to configure and access the labels as a dictionary
    labels: Optional[Dict[str, Optional[str]]] = Field(
        default=None,
        title="The labels to filter by, as a dictionary",
    )

    @root_validator
    def validate_labels(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Parse the labels string into a label dictionary and vice-versa.

        Args:
            values: The values to validate.

        Returns:
            The validated values.
        """
        labels_str = values.get("labels_str")
        labels = values.get("labels")
        if labels_str is not None:
            try:
                values["labels"] = json.loads(labels_str)
            except json.JSONDecodeError:
                # Interpret as comma-separated values instead
                values["labels"] = {
                    label.split("=", 1)[0]: label.split("=", 1)[1]
                    if "=" in label
                    else None
                    for label in labels_str.split(",")
                }
        elif labels is not None:
            values["labels_str"] = json.dumps(values["labels"])

        return values

    class Config:
        """Pydantic config class."""

        # Exclude the labels field from the serialized response
        # (it is only used internally). The labels_str field is a string
        # representation of the labels that can be used in the API.
        exclude = ["labels"]

connector_type: str pydantic-field

The type of service connector to filter by

name: str pydantic-field

The name to filter by

scope_type: str pydantic-field

The type to scope this query to.

user_id: Union[uuid.UUID, str] pydantic-field

User to filter by

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace to filter by

Config

Pydantic config class.

Source code in zenml/models/v2/core/service_connector.py

class Config:
    """Pydantic config class."""

    # Exclude the labels field from the serialized response
    # (it is only used internally). The labels_str field is a string
    # representation of the labels that can be used in the API.
    exclude = ["labels"]

validate_labels(values) classmethod

Parse the labels string into a label dictionary and vice-versa.

Parameters:

Name	Type	Description	Default
values	Dict[str, Any]	The values to validate.	required

Returns:

Type	Description
Dict[str, Any]	The validated values.

Source code in zenml/models/v2/core/service_connector.py

@root_validator
def validate_labels(cls, values: Dict[str, Any]) -> Dict[str, Any]:
    """Parse the labels string into a label dictionary and vice-versa.

    Args:
        values: The values to validate.

    Returns:
        The validated values.
    """
    labels_str = values.get("labels_str")
    labels = values.get("labels")
    if labels_str is not None:
        try:
            values["labels"] = json.loads(labels_str)
        except json.JSONDecodeError:
            # Interpret as comma-separated values instead
            values["labels"] = {
                label.split("=", 1)[0]: label.split("=", 1)[1]
                if "=" in label
                else None
                for label in labels_str.split(",")
            }
    elif labels is not None:
        values["labels_str"] = json.dumps(values["labels"])

    return values

ServiceConnectorRequest (WorkspaceScopedRequest) pydantic-model

Request model for service connectors.

Source code in zenml/models/v2/core/service_connector.py

class ServiceConnectorRequest(WorkspaceScopedRequest):
    """Request model for service connectors."""

    name: str = Field(
        title="The service connector name.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_type: Union[str, "ServiceConnectorTypeModel"] = Field(
        title="The type of service connector.",
    )
    description: str = Field(
        default="",
        title="The service connector instance description.",
    )
    auth_method: str = Field(
        title="The authentication method that the connector instance uses to "
        "access the resources.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    resource_types: List[str] = Field(
        default_factory=list,
        title="The type(s) of resource that the connector instance can be used "
        "to gain access to.",
    )
    resource_id: Optional[str] = Field(
        default=None,
        title="Uniquely identifies a specific resource instance that the "
        "connector instance can be used to access. If omitted, the connector "
        "instance can be used to access any and all resource instances that "
        "the authentication method and resource type(s) allow.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    supports_instances: bool = Field(
        default=False,
        title="Indicates whether the connector instance can be used to access "
        "multiple instances of the configured resource type.",
    )
    expires_at: Optional[datetime] = Field(
        default=None,
        title="Time when the authentication credentials configured for the "
        "connector expire. If omitted, the credentials do not expire.",
    )
    expires_skew_tolerance: Optional[int] = Field(
        default=None,
        title="The number of seconds of tolerance to apply when checking "
        "whether the authentication credentials configured for the connector "
        "have expired. If omitted, no tolerance is applied.",
    )
    expiration_seconds: Optional[int] = Field(
        default=None,
        title="The duration, in seconds, that the temporary credentials "
        "generated by this connector should remain valid. Only applicable for "
        "connectors and authentication methods that involve generating "
        "temporary credentials from the ones configured in the connector.",
    )
    configuration: Dict[str, Any] = Field(
        default_factory=dict,
        title="The service connector configuration, not including secrets.",
    )
    secrets: Dict[str, Optional[SecretStr]] = Field(
        default_factory=dict,
        title="The service connector secrets.",
    )
    labels: Dict[str, str] = Field(
        default_factory=dict,
        title="Service connector labels.",
    )

    # Analytics
    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "connector_type",
        "auth_method",
        "resource_types",
    ]

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Format the resource types in the analytics metadata.

        Returns:
            Dict of analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        if len(self.resource_types) == 1:
            metadata["resource_types"] = self.resource_types[0]
        else:
            metadata["resource_types"] = ", ".join(self.resource_types)
        metadata["connector_type"] = self.type
        return metadata

    # Helper methods
    @property
    def type(self) -> str:
        """Get the connector type.

        Returns:
            The connector type.
        """
        if isinstance(self.connector_type, str):
            return self.connector_type
        return self.connector_type.connector_type

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return self.connector_type.emojified_connector_type

        return self.connector_type

    @property
    def emojified_resource_types(self) -> List[str]:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
                for resource_type in self.resource_types
            ]

        return self.resource_types

    def validate_and_configure_resources(
        self,
        connector_type: "ServiceConnectorTypeModel",
        resource_types: Optional[Union[str, List[str]]] = None,
        resource_id: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
    ) -> None:
        """Validate and configure the resources that the connector can be used to access.

        Args:
            connector_type: The connector type specification used to validate
                the connector configuration.
            resource_types: The type(s) of resource that the connector instance
                can be used to access. If omitted, a multi-type connector is
                configured.
            resource_id: Uniquely identifies a specific resource instance that
                the connector instance can be used to access.
            configuration: The connector configuration.
            secrets: The connector secrets.
        """
        _validate_and_configure_resources(
            connector=self,
            connector_type=connector_type,
            resource_types=resource_types,
            resource_id=resource_id,
            configuration=configuration,
            secrets=secrets,
        )

emojified_connector_type: str property readonly

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

emojified_resource_types: List[str] property readonly

Get the emojified connector type.

Returns:

Type	Description
List[str]	The emojified connector type.

type: str property readonly

Get the connector type.

Returns:

Type	Description
str	The connector type.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_analytics_metadata(self)

Format the resource types in the analytics metadata.

Returns:

Type	Description
Dict[str, Any]	Dict of analytics metadata.

Source code in zenml/models/v2/core/service_connector.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Format the resource types in the analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    if len(self.resource_types) == 1:
        metadata["resource_types"] = self.resource_types[0]
    else:
        metadata["resource_types"] = ", ".join(self.resource_types)
    metadata["connector_type"] = self.type
    return metadata

validate_and_configure_resources(self, connector_type, resource_types=None, resource_id=None, configuration=None, secrets=None)

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
connector_type	ServiceConnectorTypeModel	The connector type specification used to validate the connector configuration.	required
resource_types	Union[str, List[str]]	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	None
resource_id	Optional[str]	Uniquely identifies a specific resource instance that the connector instance can be used to access.	None
configuration	Optional[Dict[str, Any]]	The connector configuration.	None
secrets	Optional[Dict[str, Optional[pydantic.types.SecretStr]]]	The connector secrets.	None

Source code in zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

ServiceConnectorResponse (WorkspaceScopedResponse[ServiceConnectorResponseBody, ServiceConnectorResponseMetadata, ServiceConnectorResponseResources]) pydantic-model

Response model for service connectors.

Source code in zenml/models/v2/core/service_connector.py

class ServiceConnectorResponse(
    WorkspaceScopedResponse[
        ServiceConnectorResponseBody,
        ServiceConnectorResponseMetadata,
        ServiceConnectorResponseResources,
    ]
):
    """Response model for service connectors."""

    name: str = Field(
        title="The service connector name.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ServiceConnectorResponse":
        """Get the hydrated version of this service connector.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_service_connector(self.id)

    # Helper methods
    @property
    def type(self) -> str:
        """Get the connector type.

        Returns:
            The connector type.
        """
        if isinstance(self.connector_type, str):
            return self.connector_type
        return self.connector_type.connector_type

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return self.connector_type.emojified_connector_type

        return self.connector_type

    @property
    def emojified_resource_types(self) -> List[str]:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
                for resource_type in self.resource_types
            ]

        return self.resource_types

    @property
    def is_multi_type(self) -> bool:
        """Checks if the connector is multi-type.

        A multi-type connector can be used to access multiple types of
        resources.

        Returns:
            True if the connector is multi-type, False otherwise.
        """
        return len(self.resource_types) > 1

    @property
    def is_multi_instance(self) -> bool:
        """Checks if the connector is multi-instance.

        A multi-instance connector is configured to access multiple instances
        of the configured resource type.

        Returns:
            True if the connector is multi-instance, False otherwise.
        """
        return (
            not self.is_multi_type
            and self.supports_instances
            and not self.resource_id
        )

    @property
    def is_single_instance(self) -> bool:
        """Checks if the connector is single-instance.

        A single-instance connector is configured to access only a single
        instance of the configured resource type or does not support multiple
        resource instances.

        Returns:
            True if the connector is single-instance, False otherwise.
        """
        return not self.is_multi_type and not self.is_multi_instance

    @property
    def full_configuration(self) -> Dict[str, str]:
        """Get the full connector configuration, including secrets.

        Returns:
            The full connector configuration, including secrets.
        """
        config = self.configuration.copy()
        config.update(
            {k: v.get_secret_value() for k, v in self.secrets.items() if v}
        )
        return config

    def set_connector_type(
        self, value: Union[str, "ServiceConnectorTypeModel"]
    ) -> None:
        """Auxiliary method to set the connector type.

        Args:
            value: the new value for the connector type.
        """
        self.get_body().connector_type = value

    def validate_and_configure_resources(
        self,
        connector_type: "ServiceConnectorTypeModel",
        resource_types: Optional[Union[str, List[str]]] = None,
        resource_id: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
    ) -> None:
        """Validate and configure the resources that the connector can be used to access.

        Args:
            connector_type: The connector type specification used to validate
                the connector configuration.
            resource_types: The type(s) of resource that the connector instance
                can be used to access. If omitted, a multi-type connector is
                configured.
            resource_id: Uniquely identifies a specific resource instance that
                the connector instance can be used to access.
            configuration: The connector configuration.
            secrets: The connector secrets.
        """
        _validate_and_configure_resources(
            connector=self,
            connector_type=connector_type,
            resource_types=resource_types,
            resource_id=resource_id,
            configuration=configuration,
            secrets=secrets,
        )

    # Body and metadata properties
    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_body().description

    @property
    def connector_type(self) -> Union[str, "ServiceConnectorTypeModel"]:
        """The `connector_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().connector_type

    @property
    def auth_method(self) -> str:
        """The `auth_method` property.

        Returns:
            the value of the property.
        """
        return self.get_body().auth_method

    @property
    def resource_types(self) -> List[str]:
        """The `resource_types` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_types

    @property
    def resource_id(self) -> Optional[str]:
        """The `resource_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_id

    @property
    def supports_instances(self) -> bool:
        """The `supports_instances` property.

        Returns:
            the value of the property.
        """
        return self.get_body().supports_instances

    @property
    def expires_at(self) -> Optional[datetime]:
        """The `expires_at` property.

        Returns:
            the value of the property.
        """
        return self.get_body().expires_at

    @property
    def expires_skew_tolerance(self) -> Optional[int]:
        """The `expires_skew_tolerance` property.

        Returns:
            the value of the property.
        """
        return self.get_body().expires_skew_tolerance

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    @property
    def secret_id(self) -> Optional[UUID]:
        """The `secret_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().secret_id

    @property
    def expiration_seconds(self) -> Optional[int]:
        """The `expiration_seconds` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().expiration_seconds

    @property
    def secrets(self) -> Dict[str, Optional[SecretStr]]:
        """The `secrets` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().secrets

    @property
    def labels(self) -> Dict[str, str]:
        """The `labels` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().labels

auth_method: str property readonly

The auth_method property.

Returns:

Type	Description
str	the value of the property.

configuration: Dict[str, Any] property readonly

The configuration property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

connector_type: Union[str, ServiceConnectorTypeModel] property readonly

The connector_type property.

Returns:

Type	Description
Union[str, ServiceConnectorTypeModel]	the value of the property.

description: str property readonly

The description property.

Returns:

Type	Description
str	the value of the property.

emojified_connector_type: str property readonly

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

emojified_resource_types: List[str] property readonly

Get the emojified connector type.

Returns:

Type	Description
List[str]	The emojified connector type.

expiration_seconds: Optional[int] property readonly

The expiration_seconds property.

Returns:

Type	Description
Optional[int]	the value of the property.

expires_at: Optional[datetime.datetime] property readonly

The expires_at property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

expires_skew_tolerance: Optional[int] property readonly

The expires_skew_tolerance property.

Returns:

Type	Description
Optional[int]	the value of the property.

full_configuration: Dict[str, str] property readonly

Get the full connector configuration, including secrets.

Returns:

Type	Description
Dict[str, str]	The full connector configuration, including secrets.

is_multi_instance: bool property readonly

Checks if the connector is multi-instance.

A multi-instance connector is configured to access multiple instances of the configured resource type.

Returns:

Type	Description
bool	True if the connector is multi-instance, False otherwise.

is_multi_type: bool property readonly

Checks if the connector is multi-type.

A multi-type connector can be used to access multiple types of resources.

Returns:

Type	Description
bool	True if the connector is multi-type, False otherwise.

is_single_instance: bool property readonly

Checks if the connector is single-instance.

A single-instance connector is configured to access only a single instance of the configured resource type or does not support multiple resource instances.

Returns:

Type	Description
bool	True if the connector is single-instance, False otherwise.

labels: Dict[str, str] property readonly

The labels property.

Returns:

Type	Description
Dict[str, str]	the value of the property.

resource_id: Optional[str] property readonly

The resource_id property.

Returns:

Type	Description
Optional[str]	the value of the property.

resource_types: List[str] property readonly

The resource_types property.

Returns:

Type	Description
List[str]	the value of the property.

secret_id: Optional[uuid.UUID] property readonly

The secret_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

secrets: Dict[str, Optional[pydantic.types.SecretStr]] property readonly

The secrets property.

Returns:

Type	Description
Dict[str, Optional[pydantic.types.SecretStr]]	the value of the property.

supports_instances: bool property readonly

The supports_instances property.

Returns:

Type	Description
bool	the value of the property.

type: str property readonly

Get the connector type.

Returns:

Type	Description
str	The connector type.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this service connector.

Returns:

Type	Description
ServiceConnectorResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/service_connector.py

def get_hydrated_version(self) -> "ServiceConnectorResponse":
    """Get the hydrated version of this service connector.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_service_connector(self.id)

set_connector_type(self, value)

Auxiliary method to set the connector type.

Parameters:

Name	Type	Description	Default
value	Union[str, ServiceConnectorTypeModel]	the new value for the connector type.	required

Source code in zenml/models/v2/core/service_connector.py

def set_connector_type(
    self, value: Union[str, "ServiceConnectorTypeModel"]
) -> None:
    """Auxiliary method to set the connector type.

    Args:
        value: the new value for the connector type.
    """
    self.get_body().connector_type = value

validate_and_configure_resources(self, connector_type, resource_types=None, resource_id=None, configuration=None, secrets=None)

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
connector_type	ServiceConnectorTypeModel	The connector type specification used to validate the connector configuration.	required
resource_types	Union[str, List[str]]	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	None
resource_id	Optional[str]	Uniquely identifies a specific resource instance that the connector instance can be used to access.	None
configuration	Optional[Dict[str, Any]]	The connector configuration.	None
secrets	Optional[Dict[str, Optional[pydantic.types.SecretStr]]]	The connector secrets.	None

Source code in zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

ServiceConnectorResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for service connectors.

Source code in zenml/models/v2/core/service_connector.py

class ServiceConnectorResponseBody(WorkspaceScopedResponseBody):
    """Response body for service connectors."""

    description: str = Field(
        default="",
        title="The service connector instance description.",
    )
    connector_type: Union[str, "ServiceConnectorTypeModel"] = Field(
        title="The type of service connector.",
    )
    auth_method: str = Field(
        title="The authentication method that the connector instance uses to "
        "access the resources.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    resource_types: List[str] = Field(
        default_factory=list,
        title="The type(s) of resource that the connector instance can be used "
        "to gain access to.",
    )
    resource_id: Optional[str] = Field(
        default=None,
        title="Uniquely identifies a specific resource instance that the "
        "connector instance can be used to access. If omitted, the connector "
        "instance can be used to access any and all resource instances that "
        "the authentication method and resource type(s) allow.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    supports_instances: bool = Field(
        default=False,
        title="Indicates whether the connector instance can be used to access "
        "multiple instances of the configured resource type.",
    )
    expires_at: Optional[datetime] = Field(
        default=None,
        title="Time when the authentication credentials configured for the "
        "connector expire. If omitted, the credentials do not expire.",
    )
    expires_skew_tolerance: Optional[int] = Field(
        default=None,
        title="The number of seconds of tolerance to apply when checking "
        "whether the authentication credentials configured for the connector "
        "have expired. If omitted, no tolerance is applied.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceConnectorResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for service connectors.

Source code in zenml/models/v2/core/service_connector.py

class ServiceConnectorResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for service connectors."""

    configuration: Dict[str, Any] = Field(
        default_factory=dict,
        title="The service connector configuration, not including secrets.",
    )
    secret_id: Optional[UUID] = Field(
        default=None,
        title="The ID of the secret that contains the service connector "
        "secret configuration values.",
    )
    expiration_seconds: Optional[int] = Field(
        default=None,
        title="The duration, in seconds, that the temporary credentials "
        "generated by this connector should remain valid. Only applicable for "
        "connectors and authentication methods that involve generating "
        "temporary credentials from the ones configured in the connector.",
    )
    secrets: Dict[str, Optional[SecretStr]] = Field(
        default_factory=dict,
        title="The service connector secrets.",
    )
    labels: Dict[str, str] = Field(
        default_factory=dict,
        title="Service connector labels.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceConnectorResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the service connector entity.

Source code in zenml/models/v2/core/service_connector.py

class ServiceConnectorResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the service connector entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

ServiceConnectorUpdate (ServiceConnectorRequest) pydantic-model

Model used for service connector updates.

Most fields in the update model are optional and will not be updated if omitted. However, the following fields are "special" and leaving them out will also cause the corresponding value to be removed from the service connector in the database:

• the resource_id field
• the expiration_seconds field

In addition to the above exceptions, the following rules apply:

• the configuration and secrets fields together represent a full valid configuration update, not just a partial update. If either is set (i.e. not None) in the update, their values are merged together and will replace the existing configuration and secrets values.
• the secret_id field value in the update is ignored, given that secrets are managed internally by the ZenML store.
• the labels field is also a full labels update: if set (i.e. not None), all existing labels are removed and replaced by the new labels in the update.

NOTE: the attributes here override the ones in the base class, so they have a None default value.

Source code in zenml/models/v2/core/service_connector.py

@update_model
class ServiceConnectorUpdate(ServiceConnectorRequest):
    """Model used for service connector updates.

    Most fields in the update model are optional and will not be updated if
    omitted. However, the following fields are "special" and leaving them out
    will also cause the corresponding value to be removed from the service
    connector in the database:

    * the `resource_id` field
    * the `expiration_seconds` field

    In addition to the above exceptions, the following rules apply:

    * the `configuration` and `secrets` fields together represent a full
    valid configuration update, not just a partial update. If either is
    set (i.e. not None) in the update, their values are merged together and
    will replace the existing configuration and secrets values.
    * the `secret_id` field value in the update is ignored, given that
    secrets are managed internally by the ZenML store.
    * the `labels` field is also a full labels update: if set (i.e. not
    `None`), all existing labels are removed and replaced by the new labels
    in the update.

    NOTE: the attributes here override the ones in the base class, so they
    have a None default value.
    """

    resource_types: Optional[List[str]] = Field(  # type: ignore[assignment]
        default=None,
        title="The type(s) of resource that the connector instance can be used "
        "to gain access to.",
    )
    configuration: Optional[Dict[str, Any]] = Field(  # type: ignore[assignment]
        default=None,
        title="The service connector configuration, not including secrets.",
    )
    secrets: Optional[Dict[str, Optional[SecretStr]]] = Field(  # type: ignore[assignment]
        default=None,
        title="The service connector secrets.",
    )
    labels: Optional[Dict[str, str]] = Field(  # type: ignore[assignment]
        default=None,
        title="Service connector labels.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[ServiceConnectorResponseBody, ServiceConnectorResponseMetadata, ServiceConnectorResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][ServiceConnectorResponseBody, ServiceConnectorResponseMetadata, ServiceConnectorResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/service_connector.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

stack

Models representing stacks.

InternalStackRequest (StackRequest) pydantic-model

Internal stack request model.

Source code in zenml/models/v2/core/stack.py

@server_owned_request_model
class InternalStackRequest(StackRequest):
    """Internal stack request model."""

    pass

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StackFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all StackModels.

The Stack Model needs additional scoping. As such the _scope_user field can be set to the user that is doing the filtering. The generate_filter() method of the baseclass is overwritten to include the scoping.

Source code in zenml/models/v2/core/stack.py

class StackFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all StackModels.

    The Stack Model needs additional scoping. As such the `_scope_user` field
    can be set to the user that is doing the filtering. The
    `generate_filter()` method of the baseclass is overwritten to include the
    scoping.
    """

    # `component_id` refers to a relationship through a link-table
    #  rather than a field in the db, hence it needs to be handled
    #  explicitly
    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "component_id",  # This is a relationship, not a field
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the stack",
    )
    description: Optional[str] = Field(
        default=None, description="Description of the stack"
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of the stack"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User of the stack"
    )
    component_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Component in the stack"
    )

    def get_custom_filters(
        self,
    ) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
        """Get custom filters.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters()

        from zenml.zen_stores.schemas.stack_schemas import (
            StackCompositionSchema,
            StackSchema,
        )

        if self.component_id:
            component_id_filter = and_(  # type: ignore[type-var]
                StackCompositionSchema.stack_id == StackSchema.id,
                StackCompositionSchema.component_id == self.component_id,
            )
            custom_filters.append(component_id_filter)

        return custom_filters

component_id: Union[uuid.UUID, str] pydantic-field

Component in the stack

description: str pydantic-field

Description of the stack

name: str pydantic-field

Name of the stack

user_id: Union[uuid.UUID, str] pydantic-field

User of the stack

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of the stack

get_custom_filters(self)

Get custom filters.

Returns:

Type	Description
List[Union[BinaryExpression[Any], BooleanClauseList[Any]]]	A list of custom filters.

Source code in zenml/models/v2/core/stack.py

def get_custom_filters(
    self,
) -> List[Union["BinaryExpression[Any]", "BooleanClauseList[Any]"]]:
    """Get custom filters.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters()

    from zenml.zen_stores.schemas.stack_schemas import (
        StackCompositionSchema,
        StackSchema,
    )

    if self.component_id:
        component_id_filter = and_(  # type: ignore[type-var]
            StackCompositionSchema.stack_id == StackSchema.id,
            StackCompositionSchema.component_id == self.component_id,
        )
        custom_filters.append(component_id_filter)

    return custom_filters

StackRequest (WorkspaceScopedRequest) pydantic-model

Request model for stacks.

Source code in zenml/models/v2/core/stack.py

class StackRequest(WorkspaceScopedRequest):
    """Request model for stacks."""

    name: str = Field(
        title="The name of the stack.", max_length=STR_FIELD_MAX_LENGTH
    )
    description: str = Field(
        default="",
        title="The description of the stack",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    stack_spec_path: Optional[str] = Field(
        default=None,
        title="The path to the stack spec used for mlstacks deployments.",
    )
    components: Optional[Dict[StackComponentType, List[UUID]]] = Field(
        default=None,
        title="A mapping of stack component types to the actual"
        "instances of components of this type.",
    )

    @property
    def is_valid(self) -> bool:
        """Check if the stack is valid.

        Returns:
            True if the stack is valid, False otherwise.
        """
        if not self.components:
            return False
        return (
            StackComponentType.ARTIFACT_STORE in self.components
            and StackComponentType.ORCHESTRATOR in self.components
        )

is_valid: bool property readonly

Check if the stack is valid.

Returns:

Type	Description
bool	True if the stack is valid, False otherwise.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StackResponse (WorkspaceScopedResponse[StackResponseBody, StackResponseMetadata, StackResponseResources]) pydantic-model

Response model for stacks.

Source code in zenml/models/v2/core/stack.py

class StackResponse(
    WorkspaceScopedResponse[
        StackResponseBody, StackResponseMetadata, StackResponseResources
    ]
):
    """Response model for stacks."""

    name: str = Field(
        title="The name of the stack.", max_length=STR_FIELD_MAX_LENGTH
    )

    def get_hydrated_version(self) -> "StackResponse":
        """Get the hydrated version of this stack.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_stack(self.id)

    # Helper methods
    @property
    def is_valid(self) -> bool:
        """Check if the stack is valid.

        Returns:
            True if the stack is valid, False otherwise.
        """
        return (
            StackComponentType.ARTIFACT_STORE in self.components
            and StackComponentType.ORCHESTRATOR in self.components
        )

    def to_yaml(self) -> Dict[str, Any]:
        """Create yaml representation of the Stack Model.

        Returns:
            The yaml representation of the Stack Model.
        """
        component_data = {}
        for component_type, components_list in self.components.items():
            component = components_list[0]
            component_dict = dict(
                name=component.name,
                type=str(component.type),
                flavor=component.flavor,
            )
            configuration = json.loads(
                component.get_metadata().json(include={"configuration"})
            )
            component_dict.update(configuration)

            component_data[component_type.value] = component_dict

        # write zenml version and stack dict to YAML
        yaml_data = {
            "stack_name": self.name,
            "components": component_data,
        }

        return yaml_data

    # Analytics
    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Add the stack components to the stack analytics metadata.

        Returns:
            Dict of analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        metadata.update({ct: c[0].flavor for ct, c in self.components.items()})
        return metadata

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def stack_spec_path(self) -> Optional[str]:
        """The `stack_spec_path` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack_spec_path

    @property
    def components(self) -> Dict[StackComponentType, List[ComponentResponse]]:
        """The `components` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().components

components: Dict[zenml.enums.StackComponentType, List[zenml.models.v2.core.component.ComponentResponse]] property readonly

The components property.

Returns:

Type	Description
Dict[zenml.enums.StackComponentType, List[zenml.models.v2.core.component.ComponentResponse]]	the value of the property.

description: Optional[str] property readonly

The description property.

Returns:

Type	Description
Optional[str]	the value of the property.

is_valid: bool property readonly

Check if the stack is valid.

Returns:

Type	Description
bool	True if the stack is valid, False otherwise.

stack_spec_path: Optional[str] property readonly

The stack_spec_path property.

Returns:

Type	Description
Optional[str]	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_analytics_metadata(self)

Add the stack components to the stack analytics metadata.

Returns:

Type	Description
Dict[str, Any]	Dict of analytics metadata.

Source code in zenml/models/v2/core/stack.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Add the stack components to the stack analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata.update({ct: c[0].flavor for ct, c in self.components.items()})
    return metadata

get_hydrated_version(self)

Get the hydrated version of this stack.

Returns:

Type	Description
StackResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/stack.py

def get_hydrated_version(self) -> "StackResponse":
    """Get the hydrated version of this stack.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_stack(self.id)

to_yaml(self)

Create yaml representation of the Stack Model.

Returns:

Type	Description
Dict[str, Any]	The yaml representation of the Stack Model.

Source code in zenml/models/v2/core/stack.py

def to_yaml(self) -> Dict[str, Any]:
    """Create yaml representation of the Stack Model.

    Returns:
        The yaml representation of the Stack Model.
    """
    component_data = {}
    for component_type, components_list in self.components.items():
        component = components_list[0]
        component_dict = dict(
            name=component.name,
            type=str(component.type),
            flavor=component.flavor,
        )
        configuration = json.loads(
            component.get_metadata().json(include={"configuration"})
        )
        component_dict.update(configuration)

        component_data[component_type.value] = component_dict

    # write zenml version and stack dict to YAML
    yaml_data = {
        "stack_name": self.name,
        "components": component_data,
    }

    return yaml_data

StackResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for stacks.

Source code in zenml/models/v2/core/stack.py

class StackResponseBody(WorkspaceScopedResponseBody):
    """Response body for stacks."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StackResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for stacks.

Source code in zenml/models/v2/core/stack.py

class StackResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for stacks."""

    components: Dict[StackComponentType, List[ComponentResponse]] = Field(
        title="A mapping of stack component types to the actual"
        "instances of components of this type."
    )
    description: Optional[str] = Field(
        default="",
        title="The description of the stack",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    stack_spec_path: Optional[str] = Field(
        default=None,
        title="The path to the stack spec used for mlstacks deployments.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StackResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the stack entity.

Source code in zenml/models/v2/core/stack.py

class StackResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the stack entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StackUpdate (StackRequest) pydantic-model

Update model for stacks.

Source code in zenml/models/v2/core/stack.py

@update_model
class StackUpdate(StackRequest):
    """Update model for stacks."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceScopedResponse[StackResponseBody, StackResponseMetadata, StackResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][StackResponseBody, StackResponseMetadata, StackResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/stack.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

step_run

Models representing steps runs.

StepRunFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of step runs.

Source code in zenml/models/v2/core/step_run.py

class StepRunFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of step runs."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the step run",
    )
    code_hash: Optional[str] = Field(
        default=None,
        description="Code hash for this step run",
    )
    cache_key: Optional[str] = Field(
        default=None,
        description="Cache key for this step run",
    )
    status: Optional[str] = Field(
        default=None,
        description="Status of the Step Run",
    )
    start_time: Optional[Union[datetime, str]] = Field(
        default=None, description="Start time for this run"
    )
    end_time: Optional[Union[datetime, str]] = Field(
        default=None, description="End time for this run"
    )
    pipeline_run_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Pipeline run of this step run"
    )
    original_step_run_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Original id for this step run"
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None, description="User that produced this step run"
    )
    workspace_id: Optional[Union[UUID, str]] = Field(
        default=None, description="Workspace of this step run"
    )

cache_key: str pydantic-field

Cache key for this step run

code_hash: str pydantic-field

Code hash for this step run

end_time: Union[datetime.datetime, str] pydantic-field

End time for this run

name: str pydantic-field

Name of the step run

original_step_run_id: Union[uuid.UUID, str] pydantic-field

Original id for this step run

pipeline_run_id: Union[uuid.UUID, str] pydantic-field

Pipeline run of this step run

start_time: Union[datetime.datetime, str] pydantic-field

Start time for this run

status: str pydantic-field

Status of the Step Run

user_id: Union[uuid.UUID, str] pydantic-field

User that produced this step run

workspace_id: Union[uuid.UUID, str] pydantic-field

Workspace of this step run

StepRunRequest (WorkspaceScopedRequest) pydantic-model

Request model for step runs.

Source code in zenml/models/v2/core/step_run.py

class StepRunRequest(WorkspaceScopedRequest):
    """Request model for step runs."""

    name: str = Field(
        title="The name of the pipeline run step.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    start_time: Optional[datetime] = Field(
        title="The start time of the step run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the step run.",
        default=None,
    )
    status: ExecutionStatus = Field(title="The status of the step.")
    cache_key: Optional[str] = Field(
        title="The cache key of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    code_hash: Optional[str] = Field(
        title="The code hash of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    docstring: Optional[str] = Field(
        title="The docstring of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    source_code: Optional[str] = Field(
        title="The source code of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    pipeline_run_id: UUID = Field(
        title="The ID of the pipeline run that this step run belongs to.",
    )
    original_step_run_id: Optional[UUID] = Field(
        title="The ID of the original step run if this step was cached.",
        default=None,
    )
    parent_step_ids: List[UUID] = Field(
        title="The IDs of the parent steps of this step run.",
        default_factory=list,
    )
    inputs: Dict[str, UUID] = Field(
        title="The IDs of the input artifact versions of the step run.",
        default={},
    )
    outputs: Dict[str, UUID] = Field(
        title="The IDs of the output artifact versions of the step run.",
        default={},
    )
    logs: Optional["LogsRequest"] = Field(
        title="Logs associated with this step run.",
        default=None,
    )
    deployment: UUID = Field(
        title="The deployment associated with the step run."
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StepRunResponse (WorkspaceScopedResponse[StepRunResponseBody, StepRunResponseMetadata, StepRunResponseResources]) pydantic-model

Response model for step runs.

Source code in zenml/models/v2/core/step_run.py

class StepRunResponse(
    WorkspaceScopedResponse[
        StepRunResponseBody, StepRunResponseMetadata, StepRunResponseResources
    ]
):
    """Response model for step runs."""

    name: str = Field(
        title="The name of the pipeline run step.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "StepRunResponse":
        """Get the hydrated version of this step run.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_run_step(self.id)

    # Helper properties
    @property
    def input(self) -> "ArtifactVersionResponse":
        """Returns the input artifact that was used to run this step.

        Returns:
            The input artifact.

        Raises:
            ValueError: If there were zero or multiple inputs to this step.
        """
        if not self.inputs:
            raise ValueError(f"Step {self.name} has no inputs.")
        if len(self.inputs) > 1:
            raise ValueError(
                f"Step {self.name} has multiple inputs, so `Step.input` is "
                "ambiguous. Please use `Step.inputs` instead."
            )
        return next(iter(self.inputs.values()))

    @property
    def output(self) -> "ArtifactVersionResponse":
        """Returns the output artifact that was written by this step.

        Returns:
            The output artifact.

        Raises:
            ValueError: If there were zero or multiple step outputs.
        """
        if not self.outputs:
            raise ValueError(f"Step {self.name} has no outputs.")
        if len(self.outputs) > 1:
            raise ValueError(
                f"Step {self.name} has multiple outputs, so `Step.output` is "
                "ambiguous. Please use `Step.outputs` instead."
            )
        return next(iter(self.outputs.values()))

    # Body and metadata properties
    @property
    def status(self) -> ExecutionStatus:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().status

    @property
    def inputs(self) -> Dict[str, "ArtifactVersionResponse"]:
        """The `inputs` property.

        Returns:
            the value of the property.
        """
        return self.get_body().inputs

    @property
    def outputs(self) -> Dict[str, "ArtifactVersionResponse"]:
        """The `outputs` property.

        Returns:
            the value of the property.
        """
        return self.get_body().outputs

    @property
    def config(self) -> "StepConfiguration":
        """The `config` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config

    @property
    def spec(self) -> "StepSpec":
        """The `spec` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().spec

    @property
    def cache_key(self) -> Optional[str]:
        """The `cache_key` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().cache_key

    @property
    def code_hash(self) -> Optional[str]:
        """The `code_hash` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().code_hash

    @property
    def docstring(self) -> Optional[str]:
        """The `docstring` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().docstring

    @property
    def source_code(self) -> Optional[str]:
        """The `source_code` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().source_code

    @property
    def start_time(self) -> Optional[datetime]:
        """The `start_time` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().start_time

    @property
    def end_time(self) -> Optional[datetime]:
        """The `end_time` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().end_time

    @property
    def logs(self) -> Optional["LogsResponse"]:
        """The `logs` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().logs

    @property
    def deployment_id(self) -> UUID:
        """The `deployment_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().deployment_id

    @property
    def pipeline_run_id(self) -> UUID:
        """The `pipeline_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_run_id

    @property
    def original_step_run_id(self) -> Optional[UUID]:
        """The `original_step_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().original_step_run_id

    @property
    def parent_step_ids(self) -> List[UUID]:
        """The `parent_step_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().parent_step_ids

    @property
    def run_metadata(self) -> Dict[str, "RunMetadataResponse"]:
        """The `run_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

cache_key: Optional[str] property readonly

The cache_key property.

Returns:

Type	Description
Optional[str]	the value of the property.

code_hash: Optional[str] property readonly

The code_hash property.

Returns:

Type	Description
Optional[str]	the value of the property.

config: StepConfiguration property readonly

The config property.

Returns:

Type	Description
StepConfiguration	the value of the property.

deployment_id: UUID property readonly

The deployment_id property.

Returns:

Type	Description
UUID	the value of the property.

docstring: Optional[str] property readonly

The docstring property.

Returns:

Type	Description
Optional[str]	the value of the property.

end_time: Optional[datetime.datetime] property readonly

The end_time property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

input: ArtifactVersionResponse property readonly

Returns the input artifact that was used to run this step.

Returns:

Type	Description
ArtifactVersionResponse	The input artifact.

Exceptions:

Type	Description
ValueError	If there were zero or multiple inputs to this step.

inputs: Dict[str, ArtifactVersionResponse] property readonly

The inputs property.

Returns:

Type	Description
Dict[str, ArtifactVersionResponse]	the value of the property.

logs: Optional[LogsResponse] property readonly

The logs property.

Returns:

Type	Description
Optional[LogsResponse]	the value of the property.

original_step_run_id: Optional[uuid.UUID] property readonly

The original_step_run_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

output: ArtifactVersionResponse property readonly

Returns the output artifact that was written by this step.

Returns:

Type	Description
ArtifactVersionResponse	The output artifact.

Exceptions:

Type	Description
ValueError	If there were zero or multiple step outputs.

outputs: Dict[str, ArtifactVersionResponse] property readonly

The outputs property.

Returns:

Type	Description
Dict[str, ArtifactVersionResponse]	the value of the property.

parent_step_ids: List[uuid.UUID] property readonly

The parent_step_ids property.

Returns:

Type	Description
List[uuid.UUID]	the value of the property.

pipeline_run_id: UUID property readonly

The pipeline_run_id property.

Returns:

Type	Description
UUID	the value of the property.

run_metadata: Dict[str, RunMetadataResponse] property readonly

The run_metadata property.

Returns:

Type	Description
Dict[str, RunMetadataResponse]	the value of the property.

source_code: Optional[str] property readonly

The source_code property.

Returns:

Type	Description
Optional[str]	the value of the property.

spec: StepSpec property readonly

The spec property.

Returns:

Type	Description
StepSpec	the value of the property.

start_time: Optional[datetime.datetime] property readonly

The start_time property.

Returns:

Type	Description
Optional[datetime.datetime]	the value of the property.

status: ExecutionStatus property readonly

The status property.

Returns:

Type	Description
ExecutionStatus	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this step run.

Returns:

Type	Description
StepRunResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/step_run.py

def get_hydrated_version(self) -> "StepRunResponse":
    """Get the hydrated version of this step run.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_run_step(self.id)

StepRunResponseBody (WorkspaceScopedResponseBody) pydantic-model

Response body for step runs.

Source code in zenml/models/v2/core/step_run.py

class StepRunResponseBody(WorkspaceScopedResponseBody):
    """Response body for step runs."""

    status: ExecutionStatus = Field(title="The status of the step.")
    inputs: Dict[str, "ArtifactVersionResponse"] = Field(
        title="The input artifact versions of the step run.",
        default={},
    )
    outputs: Dict[str, "ArtifactVersionResponse"] = Field(
        title="The output artifact versions of the step run.",
        default={},
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StepRunResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for step runs.

Source code in zenml/models/v2/core/step_run.py

class StepRunResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for step runs."""

    # Configuration
    config: "StepConfiguration" = Field(title="The configuration of the step.")
    spec: "StepSpec" = Field(title="The spec of the step.")

    # Code related fields
    cache_key: Optional[str] = Field(
        title="The cache key of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    code_hash: Optional[str] = Field(
        title="The code hash of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    docstring: Optional[str] = Field(
        title="The docstring of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    source_code: Optional[str] = Field(
        title="The source code of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

    # Timestamps
    start_time: Optional[datetime] = Field(
        title="The start time of the step run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the step run.",
        default=None,
    )

    # References
    logs: Optional["LogsResponse"] = Field(
        title="Logs associated with this step run.",
        default=None,
    )
    deployment_id: UUID = Field(
        title="The deployment associated with the step run."
    )
    pipeline_run_id: UUID = Field(
        title="The ID of the pipeline run that this step run belongs to.",
    )
    original_step_run_id: Optional[UUID] = Field(
        title="The ID of the original step run if this step was cached.",
        default=None,
    )
    parent_step_ids: List[UUID] = Field(
        title="The IDs of the parent steps of this step run.",
        default_factory=list,
    )
    run_metadata: Dict[str, "RunMetadataResponse"] = Field(
        title="Metadata associated with this step run.",
        default={},
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StepRunResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the step run entity.

Source code in zenml/models/v2/core/step_run.py

class StepRunResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the step run entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

StepRunUpdate (BaseModel) pydantic-model

Update model for step runs.

Source code in zenml/models/v2/core/step_run.py

class StepRunUpdate(BaseModel):
    """Update model for step runs."""

    outputs: Dict[str, UUID] = Field(
        title="The IDs of the output artifact versions of the step run.",
        default={},
    )
    saved_artifact_versions: Dict[str, UUID] = Field(
        title="The IDs of artifact versions that were saved by this step run.",
        default={},
    )
    loaded_artifact_versions: Dict[str, UUID] = Field(
        title="The IDs of artifact versions that were loaded by this step run.",
        default={},
    )
    status: Optional[ExecutionStatus] = Field(
        title="The status of the step.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the step run.",
        default=None,
    )

WorkspaceScopedResponse[StepRunResponseBody, StepRunResponseMetadata, StepRunResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][StepRunResponseBody, StepRunResponseMetadata, StepRunResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/step_run.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

tag

Models representing tags.

BaseIdentifiedResponse[TagResponseBody, BaseResponseMetadata, TagResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][TagResponseBody, BaseResponseMetadata, TagResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/tag.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagFilter (BaseFilter) pydantic-model

Model to enable advanced filtering of all tags.

Source code in zenml/models/v2/core/tag.py

class TagFilter(BaseFilter):
    """Model to enable advanced filtering of all tags."""

    name: Optional[str]
    color: Optional[ColorVariants]

TagRequest (BaseRequest) pydantic-model

Request model for tags.

Source code in zenml/models/v2/core/tag.py

class TagRequest(BaseRequest):
    """Request model for tags."""

    name: str = Field(
        description="The unique title of the tag.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    color: ColorVariants = Field(
        description="The color variant assigned to the tag.",
        default_factory=lambda: random.choice(list(ColorVariants)),
    )

color: ColorVariants pydantic-field

The color variant assigned to the tag.

name: ConstrainedStrValue pydantic-field required

The unique title of the tag.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagResponse (BaseIdentifiedResponse[TagResponseBody, BaseResponseMetadata, TagResponseResources]) pydantic-model

Response model for tags.

Source code in zenml/models/v2/core/tag.py

class TagResponse(
    BaseIdentifiedResponse[
        TagResponseBody, BaseResponseMetadata, TagResponseResources
    ]
):
    """Response model for tags."""

    name: str = Field(
        description="The unique title of the tag.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "TagResponse":
        """Get the hydrated version of this tag.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_tag(self.id)

    @property
    def color(self) -> ColorVariants:
        """The `color` property.

        Returns:
            the value of the property.
        """
        return self.get_body().color

    @property
    def tagged_count(self) -> int:
        """The `tagged_count` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tagged_count

color: ColorVariants property readonly

The color property.

Returns:

Type	Description
ColorVariants	the value of the property.

name: ConstrainedStrValue pydantic-field required

The unique title of the tag.

tagged_count: int property readonly

The tagged_count property.

Returns:

Type	Description
int	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this tag.

Returns:

Type	Description
TagResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/tag.py

def get_hydrated_version(self) -> "TagResponse":
    """Get the hydrated version of this tag.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_tag(self.id)

TagResponseBody (BaseDatedResponseBody) pydantic-model

Response body for tags.

Source code in zenml/models/v2/core/tag.py

class TagResponseBody(BaseDatedResponseBody):
    """Response body for tags."""

    color: ColorVariants = Field(
        description="The color variant assigned to the tag.",
        default_factory=lambda: random.choice(list(ColorVariants)),
    )
    tagged_count: int = Field(
        description="The count of resources tagged with this tag."
    )

color: ColorVariants pydantic-field

The color variant assigned to the tag.

tagged_count: int pydantic-field required

The count of resources tagged with this tag.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the tag entity.

Source code in zenml/models/v2/core/tag.py

class TagResponseResources(BaseResponseResources):
    """Class for all resource models associated with the tag entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagUpdate (BaseModel) pydantic-model

Update model for tags.

Source code in zenml/models/v2/core/tag.py

class TagUpdate(BaseModel):
    """Update model for tags."""

    name: Optional[str]
    color: Optional[ColorVariants]

tag_resource

Models representing the link between tags and resources.

BaseIdentifiedResponse[TagResourceResponseBody, BaseResponseMetadata, TagResourceResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][TagResourceResponseBody, BaseResponseMetadata, TagResourceResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/tag_resource.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagResourceRequest (BaseRequest) pydantic-model

Request model for links between tags and resources.

Source code in zenml/models/v2/core/tag_resource.py

class TagResourceRequest(BaseRequest):
    """Request model for links between tags and resources."""

    tag_id: UUID
    resource_id: UUID
    resource_type: TaggableResourceTypes

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagResourceResponse (BaseIdentifiedResponse[TagResourceResponseBody, BaseResponseMetadata, TagResourceResponseResources]) pydantic-model

Response model for the links between tags and resources.

Source code in zenml/models/v2/core/tag_resource.py

class TagResourceResponse(
    BaseIdentifiedResponse[
        TagResourceResponseBody,
        BaseResponseMetadata,
        TagResourceResponseResources,
    ]
):
    """Response model for the links between tags and resources."""

    @property
    def tag_id(self) -> UUID:
        """The `tag_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tag_id

    @property
    def resource_id(self) -> UUID:
        """The `resource_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_id

    @property
    def resource_type(self) -> TaggableResourceTypes:
        """The `resource_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_type

resource_id: UUID property readonly

The resource_id property.

Returns:

Type	Description
UUID	the value of the property.

resource_type: TaggableResourceTypes property readonly

The resource_type property.

Returns:

Type	Description
TaggableResourceTypes	the value of the property.

tag_id: UUID property readonly

The tag_id property.

Returns:

Type	Description
UUID	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagResourceResponseBody (BaseDatedResponseBody) pydantic-model

Response body for the links between tags and resources.

Source code in zenml/models/v2/core/tag_resource.py

class TagResourceResponseBody(BaseDatedResponseBody):
    """Response body for the links between tags and resources."""

    tag_id: UUID
    resource_id: UUID
    resource_type: TaggableResourceTypes

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TagResourceResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the tag resource entity.

Source code in zenml/models/v2/core/tag_resource.py

class TagResourceResponseResources(BaseResponseResources):
    """Class for all resource models associated with the tag resource entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

trigger

Collection of all models concerning triggers.

TriggerBase (BaseModel) pydantic-model

Base model for triggers.

Source code in zenml/models/v2/core/trigger.py

class TriggerBase(BaseModel):
    """Base model for triggers."""

    name: str = Field(
        title="The name of the Trigger.", max_length=STR_FIELD_MAX_LENGTH
    )
    description: str = Field(
        default="",
        title="The description of the trigger",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    event_source_id: UUID = Field(
        title="The event source that activates this trigger.",
    )
    event_filter: Dict[str, Any] = Field(
        title="Filter applied to events that activate this trigger.",
    )

    action: Dict[str, Any] = Field(
        title="The configuration for the action that is executed by this "
        "trigger.",
    )
    action_flavor: str = Field(
        title="The flavor of the action that is executed by this trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    action_subtype: PluginSubType = Field(
        title="The subtype of the action that is executed by this trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    service_account_id: UUID = Field(
        title="The service account that is used to execute the action.",
    )
    auth_window: Optional[int] = Field(
        default=None,
        title="The time window in minutes for which the service account is "
        "authorized to execute the action. Set this to 0 to authorize the "
        "service account indefinitely (not recommended). If not set, a "
        "default value defined for each individual action type is used.",
    )

TriggerFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all TriggerModels.

Source code in zenml/models/v2/core/trigger.py

class TriggerFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all TriggerModels."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the trigger",
    )
    event_source_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="By the event source this trigger is attached to.",
    )
    is_active: Optional[bool] = Field(
        default=None,
        description="Whether the trigger is active.",
    )
    action_flavor: Optional[str] = Field(
        default=None,
        title="The flavor of the action that is executed by this trigger.",
    )
    action_subtype: Optional[str] = Field(
        default=None,
        title="The subtype of the action that is executed by this trigger.",
    )
    # TODO: Ignore these in normal filter and handle in sqlzenstore
    resource_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="By the resource this trigger references.",
    )
    resource_type: Optional[str] = Field(
        default=None,
        description="By the resource type this trigger references.",
    )

event_source_id: Union[uuid.UUID, str] pydantic-field

By the event source this trigger is attached to.

is_active: bool pydantic-field

Whether the trigger is active.

name: str pydantic-field

Name of the trigger

resource_id: Union[uuid.UUID, str] pydantic-field

By the resource this trigger references.

resource_type: str pydantic-field

By the resource type this trigger references.

TriggerRequest (TriggerBase, WorkspaceScopedRequest) pydantic-model

Model for creating a new Trigger.

Source code in zenml/models/v2/core/trigger.py

class TriggerRequest(TriggerBase, WorkspaceScopedRequest):
    """Model for creating a new Trigger."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerResponse (WorkspaceScopedResponse[TriggerResponseBody, TriggerResponseMetadata, TriggerResponseResources]) pydantic-model

Response model for models.

Source code in zenml/models/v2/core/trigger.py

class TriggerResponse(
    WorkspaceScopedResponse[
        TriggerResponseBody, TriggerResponseMetadata, TriggerResponseResources
    ]
):
    """Response model for models."""

    name: str = Field(
        title="The name of the model",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "TriggerResponse":
        """Get the hydrated version of this trigger.

        Returns:
            An instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_trigger(self.id)

    @property
    def event_source_flavor(self) -> str:
        """The `event_source_flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().event_source_flavor

    @property
    def action_flavor(self) -> str:
        """The `action_flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().action_flavor

    @property
    def action_subtype(self) -> PluginSubType:
        """The `action_subtype` property.

        Returns:
            the value of the property.
        """
        return self.get_body().action_subtype

    @property
    def is_active(self) -> bool:
        """The `is_active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_active

    @property
    def event_filter(self) -> Dict[str, Any]:
        """The `event_filter` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().event_filter

    @property
    def action(self) -> Dict[str, Any]:
        """The `action` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().action

    def set_action(self, action: Dict[str, Any]) -> None:
        """Set the `action` property.

        Args:
            action: The value to set.
        """
        self.get_metadata().action = action

    @property
    def event_source(self) -> "EventSourceResponse":
        """The `event_source` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().event_source

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def service_account(self) -> UserResponse:
        """The `service_account` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().service_account

    @property
    def auth_window(self) -> int:
        """The `auth_window` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().auth_window

action: Dict[str, Any] property readonly

The action property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

action_flavor: str property readonly

The action_flavor property.

Returns:

Type	Description
str	the value of the property.

action_subtype: PluginSubType property readonly

The action_subtype property.

Returns:

Type	Description
PluginSubType	the value of the property.

auth_window: int property readonly

The auth_window property.

Returns:

Type	Description
int	the value of the property.

description: str property readonly

The description property.

Returns:

Type	Description
str	the value of the property.

event_filter: Dict[str, Any] property readonly

The event_filter property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

event_source: EventSourceResponse property readonly

The event_source property.

Returns:

Type	Description
EventSourceResponse	the value of the property.

event_source_flavor: str property readonly

The event_source_flavor property.

Returns:

Type	Description
str	the value of the property.

is_active: bool property readonly

The is_active property.

Returns:

Type	Description
bool	the value of the property.

service_account: UserResponse property readonly

The service_account property.

Returns:

Type	Description
UserResponse	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this trigger.

Returns:

Type	Description
TriggerResponse	An instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/trigger.py

def get_hydrated_version(self) -> "TriggerResponse":
    """Get the hydrated version of this trigger.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_trigger(self.id)

set_action(self, action)

Set the action property.

Parameters:

Name	Type	Description	Default
action	Dict[str, Any]	The value to set.	required

Source code in zenml/models/v2/core/trigger.py

def set_action(self, action: Dict[str, Any]) -> None:
    """Set the `action` property.

    Args:
        action: The value to set.
    """
    self.get_metadata().action = action

TriggerResponseBody (WorkspaceScopedResponseBody) pydantic-model

ResponseBody for triggers.

Source code in zenml/models/v2/core/trigger.py

class TriggerResponseBody(WorkspaceScopedResponseBody):
    """ResponseBody for triggers."""

    event_source_flavor: str = Field(
        title="The flavor of the event source that activates this trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    action_flavor: str = Field(
        title="The flavor of the action that is executed by this trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    action_subtype: PluginSubType = Field(
        title="The subtype of the action that is executed by this trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    is_active: bool = Field(
        title="Whether the trigger is active.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerResponseMetadata (WorkspaceScopedResponseMetadata) pydantic-model

Response metadata for triggers.

Source code in zenml/models/v2/core/trigger.py

class TriggerResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for triggers."""

    event_filter: Dict[str, Any] = Field(
        title="The event that activates this trigger.",
    )
    action: Dict[str, Any] = Field(
        title="The action that is executed by this trigger.",
    )
    description: str = Field(
        default="",
        title="The description of the trigger",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    auth_window: int = Field(
        title="The time window in minutes for which the service account is "
        "authorized to execute the action. Set this to 0 to authorize the "
        "service account indefinitely (not recommended). If not set, a "
        "default value defined for each individual action type is used.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerResponseResources (WorkspaceScopedResponseResources) pydantic-model

Class for all resource models associated with the trigger entity.

Source code in zenml/models/v2/core/trigger.py

class TriggerResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the trigger entity."""

    event_source: "EventSourceResponse" = Field(
        title="The event source that activates this trigger.",
    )
    service_account: UserResponse = Field(
        title="The service account that is used to execute the action.",
    )
    executions: Page[TriggerExecutionResponse] = Field(
        title="The executions of this trigger.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerUpdate (BaseZenModel) pydantic-model

Update model for triggers.

Source code in zenml/models/v2/core/trigger.py

class TriggerUpdate(BaseZenModel):
    """Update model for triggers."""

    name: Optional[str] = Field(
        default=None,
        title="The new name for the Trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The new description for the trigger",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    event_filter: Optional[Dict[str, Any]] = Field(
        default=None,
        title="New filter applied to events that activate this trigger.",
    )
    action: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The new configuration for the action that is executed by this "
        "trigger.",
    )
    is_active: Optional[bool] = Field(
        default=None,
        title="The new status of the trigger.",
    )
    service_account_id: Optional[UUID] = Field(
        default=None,
        title="The service account that is used to execute the action.",
    )
    auth_window: Optional[int] = Field(
        default=None,
        title="The time window in minutes for which the service account is "
        "authorized to execute the action. Set this to 0 to authorize the "
        "service account indefinitely (not recommended). If not set, a "
        "default value defined for each individual action type is used.",
    )

    @classmethod
    def from_response(cls, response: "TriggerResponse") -> "TriggerUpdate":
        """Create an update model from a response model.

        Args:
            response: The response model to create the update model from.

        Returns:
            The update model.
        """
        return TriggerUpdate(
            name=response.name,
            description=response.description,
            action=copy.deepcopy(response.action),
            event_filter=copy.deepcopy(response.event_filter),
            is_active=response.is_active,
            service_account_id=response.get_resources().service_account.id,
        )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

from_response(response) classmethod

Create an update model from a response model.

Parameters:

Name	Type	Description	Default
response	TriggerResponse	The response model to create the update model from.	required

Returns:

Type	Description
TriggerUpdate	The update model.

Source code in zenml/models/v2/core/trigger.py

@classmethod
def from_response(cls, response: "TriggerResponse") -> "TriggerUpdate":
    """Create an update model from a response model.

    Args:
        response: The response model to create the update model from.

    Returns:
        The update model.
    """
    return TriggerUpdate(
        name=response.name,
        description=response.description,
        action=copy.deepcopy(response.action),
        event_filter=copy.deepcopy(response.event_filter),
        is_active=response.is_active,
        service_account_id=response.get_resources().service_account.id,
    )

WorkspaceScopedResponse[TriggerResponseBody, TriggerResponseMetadata, TriggerResponseResources] (WorkspaceScopedResponse, UserScopedResponse[WorkspaceBody, WorkspaceMetadata, WorkspaceResources][TriggerResponseBody, TriggerResponseMetadata, TriggerResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/trigger.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

trigger_execution

Collection of all models concerning trigger executions.

BaseIdentifiedResponse[TriggerExecutionResponseBody, TriggerExecutionResponseMetadata, TriggerExecutionResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][TriggerExecutionResponseBody, TriggerExecutionResponseMetadata, TriggerExecutionResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/trigger_execution.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerExecutionFilter (WorkspaceScopedFilter) pydantic-model

Model to enable advanced filtering of all trigger executions.

Source code in zenml/models/v2/core/trigger_execution.py

class TriggerExecutionFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all trigger executions."""

    trigger_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="ID of the trigger of the execution.",
    )

trigger_id: Union[uuid.UUID, str] pydantic-field

ID of the trigger of the execution.

TriggerExecutionRequest (BaseRequest) pydantic-model

Model for creating a new Trigger execution.

Source code in zenml/models/v2/core/trigger_execution.py

class TriggerExecutionRequest(BaseRequest):
    """Model for creating a new Trigger execution."""

    trigger: UUID
    event_metadata: Dict[str, Any] = {}

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerExecutionResponse (BaseIdentifiedResponse[TriggerExecutionResponseBody, TriggerExecutionResponseMetadata, TriggerExecutionResponseResources]) pydantic-model

Response model for trigger executions.

Source code in zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponse(
    BaseIdentifiedResponse[
        TriggerExecutionResponseBody,
        TriggerExecutionResponseMetadata,
        TriggerExecutionResponseResources,
    ]
):
    """Response model for trigger executions."""

    def get_hydrated_version(self) -> "TriggerExecutionResponse":
        """Get the hydrated version of this trigger execution.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_trigger_execution(self.id)

    # Body and metadata properties

    @property
    def trigger(self) -> "TriggerResponse":
        """The `trigger` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().trigger

    @property
    def event_metadata(self) -> Dict[str, Any]:
        """The `event_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().event_metadata

event_metadata: Dict[str, Any] property readonly

The event_metadata property.

Returns:

Type	Description
Dict[str, Any]	the value of the property.

trigger: TriggerResponse property readonly

The trigger property.

Returns:

Type	Description
TriggerResponse	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this trigger execution.

Returns:

Type	Description
TriggerExecutionResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/trigger_execution.py

def get_hydrated_version(self) -> "TriggerExecutionResponse":
    """Get the hydrated version of this trigger execution.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_trigger_execution(self.id)

TriggerExecutionResponseBody (BaseDatedResponseBody) pydantic-model

Response body for trigger executions.

Source code in zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponseBody(BaseDatedResponseBody):
    """Response body for trigger executions."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerExecutionResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for trigger executions.

Source code in zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponseMetadata(BaseResponseMetadata):
    """Response metadata for trigger executions."""

    event_metadata: Dict[str, Any] = {}

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

TriggerExecutionResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the trigger entity.

Source code in zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponseResources(BaseResponseResources):
    """Class for all resource models associated with the trigger entity."""

    trigger: "TriggerResponse" = Field(
        title="The event source that activates this trigger.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

user

Models representing users.

BaseIdentifiedResponse[UserResponseBody, UserResponseMetadata, UserResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][UserResponseBody, UserResponseMetadata, UserResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/user.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserFilter (BaseFilter) pydantic-model

Model to enable advanced filtering of all Users.

Source code in zenml/models/v2/core/user.py

class UserFilter(BaseFilter):
    """Model to enable advanced filtering of all Users."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the user",
    )
    full_name: Optional[str] = Field(
        default=None,
        description="Full Name of the user",
    )
    email: Optional[str] = Field(
        default=None,
        description="Email of the user",
    )
    active: Optional[Union[bool, str]] = Field(
        default=None,
        description="Whether the user is active",
    )
    email_opted_in: Optional[Union[bool, str]] = Field(
        default=None,
        description="Whether the user has opted in to emails",
    )
    external_user_id: Optional[Union[UUID, str]] = Field(
        default=None,
        title="The external user ID associated with the account.",
    )

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Override to filter out service accounts from the query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)
        query = query.where(
            getattr(table, "is_service_account") != True  # noqa: E712
        )

        return query

active: Union[bool, str] pydantic-field

Whether the user is active

email: str pydantic-field

Email of the user

email_opted_in: Union[bool, str] pydantic-field

Whether the user has opted in to emails

full_name: str pydantic-field

Full Name of the user

name: str pydantic-field

Name of the user

apply_filter(self, query, table)

Override to filter out service accounts from the query.

Parameters:

Name	Type	Description	Default
query	~AnyQuery	The query to which to apply the filter.	required
table	Type[AnySchema]	The query table.	required

Returns:

Type	Description
~AnyQuery	The query with filter applied.

Source code in zenml/models/v2/core/user.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to filter out service accounts from the query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)
    query = query.where(
        getattr(table, "is_service_account") != True  # noqa: E712
    )

    return query

UserRequest (BaseRequest) pydantic-model

Request model for users.

Source code in zenml/models/v2/core/user.py

class UserRequest(BaseRequest):
    """Request model for users."""

    # Analytics fields for user request models
    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "full_name",
        "active",
        "email_opted_in",
    ]

    # Fields
    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    full_name: str = Field(
        default="",
        title="The full name for the account owner. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    email: Optional[str] = Field(
        default=None,
        title="The email address associated with the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    email_opted_in: Optional[bool] = Field(
        default=None,
        title="Whether the user agreed to share their email. Only relevant for "
        "user accounts",
        description="`null` if not answered, `true` if agreed, "
        "`false` if skipped.",
    )
    hub_token: Optional[str] = Field(
        default=None,
        title="JWT Token for the connected Hub account. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    password: Optional[str] = Field(
        default=None,
        title="A password for the user.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    activation_token: Optional[str] = Field(
        default=None, max_length=STR_FIELD_MAX_LENGTH
    )
    external_user_id: Optional[UUID] = Field(
        default=None,
        title="The external user ID associated with the account.",
    )
    active: bool = Field(default=False, title="Whether the account is active.")

    class Config:
        """Pydantic configuration class."""

        # Validate attributes when assigning them
        validate_assignment = True

        # Forbid extra attributes to prevent unexpected behavior
        extra = "forbid"
        underscore_attrs_are_private = True

    @classmethod
    def _get_crypt_context(cls) -> "CryptContext":
        """Returns the password encryption context.

        Returns:
            The password encryption context.
        """
        from passlib.context import CryptContext

        return CryptContext(schemes=["bcrypt"], deprecated="auto")

    @classmethod
    def _create_hashed_secret(cls, secret: Optional[str]) -> Optional[str]:
        """Hashes the input secret and returns the hash value.

        Only applied if supplied and if not already hashed.

        Args:
            secret: The secret value to hash.

        Returns:
            The secret hash value, or None if no secret was supplied.
        """
        if secret is None:
            return None
        pwd_context = cls._get_crypt_context()
        return pwd_context.hash(secret)

    def create_hashed_password(self) -> Optional[str]:
        """Hashes the password.

        Returns:
            The hashed password.
        """
        return self._create_hashed_secret(self.password)

    def create_hashed_activation_token(self) -> Optional[str]:
        """Hashes the activation token.

        Returns:
            The hashed activation token.
        """
        return self._create_hashed_secret(self.activation_token)

    def generate_activation_token(self) -> str:
        """Generates and stores a new activation token.

        Returns:
            The generated activation token.
        """
        self.activation_token = token_hex(32)
        return self.activation_token

email_opted_in: bool pydantic-field

null if not answered, true if agreed, false if skipped.

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/user.py

class Config:
    """Pydantic configuration class."""

    # Validate attributes when assigning them
    validate_assignment = True

    # Forbid extra attributes to prevent unexpected behavior
    extra = "forbid"
    underscore_attrs_are_private = True

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

create_hashed_activation_token(self)

Hashes the activation token.

Returns:

Type	Description
Optional[str]	The hashed activation token.

Source code in zenml/models/v2/core/user.py

def create_hashed_activation_token(self) -> Optional[str]:
    """Hashes the activation token.

    Returns:
        The hashed activation token.
    """
    return self._create_hashed_secret(self.activation_token)

create_hashed_password(self)

Hashes the password.

Returns:

Type	Description
Optional[str]	The hashed password.

Source code in zenml/models/v2/core/user.py

def create_hashed_password(self) -> Optional[str]:
    """Hashes the password.

    Returns:
        The hashed password.
    """
    return self._create_hashed_secret(self.password)

generate_activation_token(self)

Generates and stores a new activation token.

Returns:

Type	Description
str	The generated activation token.

Source code in zenml/models/v2/core/user.py

def generate_activation_token(self) -> str:
    """Generates and stores a new activation token.

    Returns:
        The generated activation token.
    """
    self.activation_token = token_hex(32)
    return self.activation_token

UserResponse (BaseIdentifiedResponse[UserResponseBody, UserResponseMetadata, UserResponseResources]) pydantic-model

Response model for user and service accounts.

This returns the activation_token that is required for the user-invitation-flow of the frontend. The email is returned optionally as well for use by the analytics on the client-side.

Source code in zenml/models/v2/core/user.py

class UserResponse(
    BaseIdentifiedResponse[
        UserResponseBody, UserResponseMetadata, UserResponseResources
    ]
):
    """Response model for user and service accounts.

    This returns the activation_token that is required for the
    user-invitation-flow of the frontend. The email is returned optionally as
    well for use by the analytics on the client-side.
    """

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "full_name",
        "active",
        "email_opted_in",
        "is_service_account",
    ]

    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "UserResponse":
        """Get the hydrated version of this user.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_user(self.id)

    # Body and metadata properties
    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def activation_token(self) -> Optional[str]:
        """The `activation_token` property.

        Returns:
            the value of the property.
        """
        return self.get_body().activation_token

    @property
    def full_name(self) -> str:
        """The `full_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().full_name

    @property
    def email_opted_in(self) -> Optional[bool]:
        """The `email_opted_in` property.

        Returns:
            the value of the property.
        """
        return self.get_body().email_opted_in

    @property
    def is_service_account(self) -> bool:
        """The `is_service_account` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_service_account

    @property
    def email(self) -> Optional[str]:
        """The `email` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().email

    @property
    def hub_token(self) -> Optional[str]:
        """The `hub_token` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().hub_token

    @property
    def external_user_id(self) -> Optional[UUID]:
        """The `external_user_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().external_user_id

    # Helper methods
    @classmethod
    def _get_crypt_context(cls) -> "CryptContext":
        """Returns the password encryption context.

        Returns:
            The password encryption context.
        """
        from passlib.context import CryptContext

        return CryptContext(schemes=["bcrypt"], deprecated="auto")

activation_token: Optional[str] property readonly

The activation_token property.

Returns:

Type	Description
Optional[str]	the value of the property.

active: bool property readonly

The active property.

Returns:

Type	Description
bool	the value of the property.

email: Optional[str] property readonly

The email property.

Returns:

Type	Description
Optional[str]	the value of the property.

email_opted_in: Optional[bool] property readonly

The email_opted_in property.

Returns:

Type	Description
Optional[bool]	the value of the property.

external_user_id: Optional[uuid.UUID] property readonly

The external_user_id property.

Returns:

Type	Description
Optional[uuid.UUID]	the value of the property.

full_name: str property readonly

The full_name property.

Returns:

Type	Description
str	the value of the property.

hub_token: Optional[str] property readonly

The hub_token property.

Returns:

Type	Description
Optional[str]	the value of the property.

is_service_account: bool property readonly

The is_service_account property.

Returns:

Type	Description
bool	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this user.

Returns:

Type	Description
UserResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/user.py

def get_hydrated_version(self) -> "UserResponse":
    """Get the hydrated version of this user.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_user(self.id)

UserResponseBody (BaseDatedResponseBody) pydantic-model

Response body for users.

Source code in zenml/models/v2/core/user.py

class UserResponseBody(BaseDatedResponseBody):
    """Response body for users."""

    active: bool = Field(default=False, title="Whether the account is active.")
    activation_token: Optional[str] = Field(
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
        title="The activation token for the user. Only relevant for user "
        "accounts.",
    )
    full_name: str = Field(
        default="",
        title="The full name for the account owner. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    email_opted_in: Optional[bool] = Field(
        default=None,
        title="Whether the user agreed to share their email. Only relevant for "
        "user accounts",
        description="`null` if not answered, `true` if agreed, "
        "`false` if skipped.",
    )
    is_service_account: bool = Field(
        title="Indicates whether this is a service account or a user account."
    )

email_opted_in: bool pydantic-field

null if not answered, true if agreed, false if skipped.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for users.

Source code in zenml/models/v2/core/user.py

class UserResponseMetadata(BaseResponseMetadata):
    """Response metadata for users."""

    email: Optional[str] = Field(
        default="",
        title="The email address associated with the account. Only relevant "
        "for user accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    hub_token: Optional[str] = Field(
        default=None,
        title="JWT Token for the connected Hub account. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    external_user_id: Optional[UUID] = Field(
        default=None,
        title="The external user ID associated with the account. Only relevant "
        "for user accounts.",
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the user entity.

Source code in zenml/models/v2/core/user.py

class UserResponseResources(BaseResponseResources):
    """Class for all resource models associated with the user entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

UserUpdate (UserRequest) pydantic-model

Update model for users.

Source code in zenml/models/v2/core/user.py

@update_model
class UserUpdate(UserRequest):
    """Update model for users."""

    @root_validator
    def user_email_updates(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Validate that the UserUpdateModel conforms to the email-opt-in-flow.

        Args:
            values: The values to validate.

        Returns:
            The validated values.

        Raises:
            ValueError: If the email was not provided when the email_opted_in
                field was set to True.
        """
        # When someone sets the email, or updates the email and hasn't
        #  before explicitly opted out, they are opted in
        if values["email"] is not None:
            if values["email_opted_in"] is None:
                values["email_opted_in"] = True

        # It should not be possible to do opt in without an email
        if values["email_opted_in"] is True:
            if values["email"] is None:
                raise ValueError(
                    "Please provide an email, when you are opting-in with "
                    "your email."
                )
        return values

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

user_email_updates(values) classmethod

Validate that the UserUpdateModel conforms to the email-opt-in-flow.

Parameters:

Name	Type	Description	Default
values	Dict[str, Any]	The values to validate.	required

Returns:

Type	Description
Dict[str, Any]	The validated values.

Exceptions:

Type	Description
ValueError	If the email was not provided when the email_opted_in field was set to True.

Source code in zenml/models/v2/core/user.py

@root_validator
def user_email_updates(cls, values: Dict[str, Any]) -> Dict[str, Any]:
    """Validate that the UserUpdateModel conforms to the email-opt-in-flow.

    Args:
        values: The values to validate.

    Returns:
        The validated values.

    Raises:
        ValueError: If the email was not provided when the email_opted_in
            field was set to True.
    """
    # When someone sets the email, or updates the email and hasn't
    #  before explicitly opted out, they are opted in
    if values["email"] is not None:
        if values["email_opted_in"] is None:
            values["email_opted_in"] = True

    # It should not be possible to do opt in without an email
    if values["email_opted_in"] is True:
        if values["email"] is None:
            raise ValueError(
                "Please provide an email, when you are opting-in with "
                "your email."
            )
    return values

workspace

Models representing workspaces.

BaseIdentifiedResponse[WorkspaceResponseBody, WorkspaceResponseMetadata, WorkspaceResponseResources] (BaseIdentifiedResponse, BaseResponse[AnyDatedBody, AnyMetadata, AnyResources][WorkspaceResponseBody, WorkspaceResponseMetadata, WorkspaceResponseResources]) pydantic-model

Config

Pydantic configuration class.

Source code in zenml/models/v2/core/workspace.py

class Config:
    """Pydantic configuration class."""

    # This is needed to allow the REST client and server to unpack SecretStr
    # values correctly.
    json_encoders = {
        SecretStr: lambda v: v.get_secret_value()
        if v is not None
        else None
    }

    # Allow extras on all models to support forwards and backwards
    # compatibility (e.g. new fields in newer versions of ZenML servers
    # are allowed to be present in older versions of ZenML clients and
    # vice versa).
    extra = "allow"

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceFilter (BaseFilter) pydantic-model

Model to enable advanced filtering of all Workspaces.

Source code in zenml/models/v2/core/workspace.py

class WorkspaceFilter(BaseFilter):
    """Model to enable advanced filtering of all Workspaces."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the workspace",
    )

name: str pydantic-field

Name of the workspace

WorkspaceRequest (BaseRequest) pydantic-model

Request model for workspaces.

Source code in zenml/models/v2/core/workspace.py

class WorkspaceRequest(BaseRequest):
    """Request model for workspaces."""

    name: str = Field(
        title="The unique name of the workspace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: str = Field(
        default="",
        title="The description of the workspace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceResponse (BaseIdentifiedResponse[WorkspaceResponseBody, WorkspaceResponseMetadata, WorkspaceResponseResources]) pydantic-model

Response model for workspaces.

Source code in zenml/models/v2/core/workspace.py

class WorkspaceResponse(
    BaseIdentifiedResponse[
        WorkspaceResponseBody,
        WorkspaceResponseMetadata,
        WorkspaceResponseResources,
    ]
):
    """Response model for workspaces."""

    name: str = Field(
        title="The unique name of the workspace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "WorkspaceResponse":
        """Get the hydrated version of this workspace.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_workspace(self.id)

    # Body and metadata properties
    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

description: str property readonly

The description property.

Returns:

Type	Description
str	the value of the property.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hydrated_version(self)

Get the hydrated version of this workspace.

Returns:

Type	Description
WorkspaceResponse	an instance of the same entity with the metadata field attached.

Source code in zenml/models/v2/core/workspace.py

def get_hydrated_version(self) -> "WorkspaceResponse":
    """Get the hydrated version of this workspace.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_workspace(self.id)

WorkspaceResponseBody (BaseDatedResponseBody) pydantic-model

Response body for workspaces.

Source code in zenml/models/v2/core/workspace.py

class WorkspaceResponseBody(BaseDatedResponseBody):
    """Response body for workspaces."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceResponseMetadata (BaseResponseMetadata) pydantic-model

Response metadata for workspaces.

Source code in zenml/models/v2/core/workspace.py

class WorkspaceResponseMetadata(BaseResponseMetadata):
    """Response metadata for workspaces."""

    description: str = Field(
        default="",
        title="The description of the workspace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceResponseResources (BaseResponseResources) pydantic-model

Class for all resource models associated with the workspace entity.

Source code in zenml/models/v2/core/workspace.py

class WorkspaceResponseResources(BaseResponseResources):
    """Class for all resource models associated with the workspace entity."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

WorkspaceUpdate (WorkspaceRequest) pydantic-model

Update model for workspaces.

Source code in zenml/models/v2/core/workspace.py

@update_model
class WorkspaceUpdate(WorkspaceRequest):
    """Update model for workspaces."""

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

misc special

auth_models

Models representing OAuth2 requests and responses.

OAuthDeviceAuthorizationRequest (BaseModel) pydantic-model

OAuth2 device authorization grant request.

Source code in zenml/models/v2/misc/auth_models.py

class OAuthDeviceAuthorizationRequest(BaseModel):
    """OAuth2 device authorization grant request."""

    client_id: UUID

OAuthDeviceAuthorizationResponse (BaseModel) pydantic-model

OAuth2 device authorization grant response.

Source code in zenml/models/v2/misc/auth_models.py

class OAuthDeviceAuthorizationResponse(BaseModel):
    """OAuth2 device authorization grant response."""

    device_code: str
    user_code: str
    verification_uri: str
    verification_uri_complete: Optional[str] = None
    expires_in: int
    interval: int

OAuthDeviceTokenRequest (BaseModel) pydantic-model

OAuth2 device authorization grant request.

Source code in zenml/models/v2/misc/auth_models.py

class OAuthDeviceTokenRequest(BaseModel):
    """OAuth2 device authorization grant request."""

    grant_type: str = OAuthGrantTypes.OAUTH_DEVICE_CODE
    client_id: UUID
    device_code: str

OAuthDeviceUserAgentHeader (BaseModel) pydantic-model

OAuth2 device user agent header.

Source code in zenml/models/v2/misc/auth_models.py

class OAuthDeviceUserAgentHeader(BaseModel):
    """OAuth2 device user agent header."""

    hostname: Optional[str] = None
    os: Optional[str] = None
    python_version: Optional[str] = None
    zenml_version: Optional[str] = None

    @classmethod
    def decode(cls, header_str: str) -> "OAuthDeviceUserAgentHeader":
        """Decode the user agent header.

        Args:
            header_str: The user agent header string value.

        Returns:
            The decoded user agent header.
        """
        header = cls()
        properties = header_str.strip().split(" ")
        for property in properties:
            try:
                key, value = property.split("/", maxsplit=1)
            except ValueError:
                continue
            if key == "Host":
                header.hostname = value
            elif key == "ZenML":
                header.zenml_version = value
            elif key == "Python":
                header.python_version = value
            elif key == "OS":
                header.os = value
        return header

    def encode(self) -> str:
        """Encode the user agent header.

        Returns:
            The encoded user agent header.
        """
        return (
            f"Host/{self.hostname} "
            f"ZenML/{self.zenml_version} "
            f"Python/{self.python_version} "
            f"OS/{self.os}"
        )

decode(header_str) classmethod

Decode the user agent header.

Parameters:

Name	Type	Description	Default
header_str	str	The user agent header string value.	required

Returns:

Type	Description
OAuthDeviceUserAgentHeader	The decoded user agent header.

Source code in zenml/models/v2/misc/auth_models.py

@classmethod
def decode(cls, header_str: str) -> "OAuthDeviceUserAgentHeader":
    """Decode the user agent header.

    Args:
        header_str: The user agent header string value.

    Returns:
        The decoded user agent header.
    """
    header = cls()
    properties = header_str.strip().split(" ")
    for property in properties:
        try:
            key, value = property.split("/", maxsplit=1)
        except ValueError:
            continue
        if key == "Host":
            header.hostname = value
        elif key == "ZenML":
            header.zenml_version = value
        elif key == "Python":
            header.python_version = value
        elif key == "OS":
            header.os = value
    return header

encode(self)

Encode the user agent header.

Returns:

Type	Description
str	The encoded user agent header.

Source code in zenml/models/v2/misc/auth_models.py

def encode(self) -> str:
    """Encode the user agent header.

    Returns:
        The encoded user agent header.
    """
    return (
        f"Host/{self.hostname} "
        f"ZenML/{self.zenml_version} "
        f"Python/{self.python_version} "
        f"OS/{self.os}"
    )

OAuthDeviceVerificationRequest (BaseModel) pydantic-model

OAuth2 device authorization verification request.

Source code in zenml/models/v2/misc/auth_models.py

class OAuthDeviceVerificationRequest(BaseModel):
    """OAuth2 device authorization verification request."""

    user_code: str
    trusted_device: bool = False

OAuthRedirectResponse (BaseModel) pydantic-model

Redirect response.

Source code in zenml/models/v2/misc/auth_models.py

class OAuthRedirectResponse(BaseModel):
    """Redirect response."""

    authorization_url: str

OAuthTokenResponse (BaseModel) pydantic-model

OAuth2 device authorization token response.

Source code in zenml/models/v2/misc/auth_models.py

class OAuthTokenResponse(BaseModel):
    """OAuth2 device authorization token response."""

    access_token: str
    token_type: str
    expires_in: Optional[int] = None
    refresh_token: Optional[str] = None
    scope: Optional[str] = None

build_item

Model definition for pipeline build item.

BuildItem (BaseModel) pydantic-model

Pipeline build item.

Attributes:

Name	Type	Description
image	str	The image name or digest.
dockerfile	Optional[str]	The contents of the Dockerfile used to build the image.
requirements	Optional[str]	The pip requirements installed in the image. This is a string consisting of multiple concatenated requirements.txt files.
settings_checksum	Optional[str]	Checksum of the settings used for the build.
contains_code	bool	Whether the image contains user files.
requires_code_download	bool	Whether the image needs to download files.

Source code in zenml/models/v2/misc/build_item.py

class BuildItem(BaseModel):
    """Pipeline build item.

    Attributes:
        image: The image name or digest.
        dockerfile: The contents of the Dockerfile used to build the image.
        requirements: The pip requirements installed in the image. This is a
            string consisting of multiple concatenated requirements.txt files.
        settings_checksum: Checksum of the settings used for the build.
        contains_code: Whether the image contains user files.
        requires_code_download: Whether the image needs to download files.
    """

    image: str = Field(title="The image name or digest.")
    dockerfile: Optional[str] = Field(
        title="The dockerfile used to build the image."
    )
    requirements: Optional[str] = Field(
        title="The pip requirements installed in the image."
    )
    settings_checksum: Optional[str] = Field(
        title="The checksum of the build settings."
    )
    contains_code: bool = Field(
        default=True, title="Whether the image contains user files."
    )
    requires_code_download: bool = Field(
        default=False, title="Whether the image needs to download files."
    )

external_user

Models representing users.

ExternalUserModel (BaseModel) pydantic-model

External user model.

Source code in zenml/models/v2/misc/external_user.py

class ExternalUserModel(BaseModel):
    """External user model."""

    id: UUID
    email: str
    name: Optional[str] = None

    class Config:
        """Pydantic configuration."""

        # ignore arbitrary fields
        extra = "ignore"

Config

Pydantic configuration.

Source code in zenml/models/v2/misc/external_user.py

class Config:
    """Pydantic configuration."""

    # ignore arbitrary fields
    extra = "ignore"

hub_plugin_models

Models representing ZenML Hub plugins.

HubPluginBaseModel (BaseModel) pydantic-model

Base model for a ZenML Hub plugin.

Source code in zenml/models/v2/misc/hub_plugin_models.py

class HubPluginBaseModel(BaseModel):
    """Base model for a ZenML Hub plugin."""

    name: str
    description: Optional[str]
    version: Optional[str]
    release_notes: Optional[str]
    repository_url: str
    repository_subdirectory: Optional[str]
    repository_branch: Optional[str]
    repository_commit: Optional[str]
    tags: Optional[List[str]]
    logo_url: Optional[str]

HubPluginRequestModel (HubPluginBaseModel) pydantic-model

Request model for a ZenML Hub plugin.

Source code in zenml/models/v2/misc/hub_plugin_models.py

class HubPluginRequestModel(HubPluginBaseModel):
    """Request model for a ZenML Hub plugin."""

HubPluginResponseModel (HubPluginBaseModel) pydantic-model

Response model for a ZenML Hub plugin.

Source code in zenml/models/v2/misc/hub_plugin_models.py

class HubPluginResponseModel(HubPluginBaseModel):
    """Response model for a ZenML Hub plugin."""

    id: UUID
    status: PluginStatus
    author: str
    version: str
    index_url: Optional[str] = None
    package_name: Optional[str] = None
    requirements: Optional[List[str]] = None
    build_logs: Optional[str] = None
    created: datetime
    updated: datetime

HubUserResponseModel (BaseModel) pydantic-model

Model for a ZenML Hub user.

Source code in zenml/models/v2/misc/hub_plugin_models.py

class HubUserResponseModel(BaseModel):
    """Model for a ZenML Hub user."""

    id: UUID
    email: str
    username: Optional[str]

PluginStatus (StrEnum)

Enum that represents the status of a plugin.

• PENDING: Plugin is being built
• FAILED: Plugin build failed
• AVAILABLE: Plugin is available for installation
• YANKED: Plugin was yanked and is no longer available

Source code in zenml/models/v2/misc/hub_plugin_models.py

class PluginStatus(StrEnum):
    """Enum that represents the status of a plugin.

    - PENDING: Plugin is being built
    - FAILED: Plugin build failed
    - AVAILABLE: Plugin is available for installation
    - YANKED: Plugin was yanked and is no longer available
    """

    PENDING = "pending"
    FAILED = "failed"
    AVAILABLE = "available"
    YANKED = "yanked"

loaded_visualization

Model representing loaded visualizations.

LoadedVisualization (BaseModel) pydantic-model

Model for loaded visualizations.

Source code in zenml/models/v2/misc/loaded_visualization.py

class LoadedVisualization(BaseModel):
    """Model for loaded visualizations."""

    type: VisualizationType
    value: Union[str, bytes]

server_models

Model definitions for ZenML servers.

ServerDatabaseType (StrEnum)

Enum for server database types.

Source code in zenml/models/v2/misc/server_models.py

class ServerDatabaseType(StrEnum):
    """Enum for server database types."""

    SQLITE = "sqlite"
    MYSQL = "mysql"
    OTHER = "other"

ServerDeploymentType (StrEnum)

Enum for server deployment types.

Source code in zenml/models/v2/misc/server_models.py

class ServerDeploymentType(StrEnum):
    """Enum for server deployment types."""

    LOCAL = "local"
    DOCKER = "docker"
    KUBERNETES = "kubernetes"
    AWS = "aws"
    GCP = "gcp"
    AZURE = "azure"
    ALPHA = "alpha"
    OTHER = "other"
    HF_SPACES = "hf_spaces"
    SANDBOX = "sandbox"
    CLOUD = "cloud"

ServerModel (BaseModel) pydantic-model

Domain model for ZenML servers.

Source code in zenml/models/v2/misc/server_models.py

class ServerModel(BaseModel):
    """Domain model for ZenML servers."""

    id: UUID = Field(default_factory=uuid4, title="The unique server id.")

    version: str = Field(
        title="The ZenML version that the server is running.",
    )

    debug: bool = Field(
        False, title="Flag to indicate whether ZenML is running on debug mode."
    )

    deployment_type: ServerDeploymentType = Field(
        ServerDeploymentType.OTHER,
        title="The ZenML server deployment type.",
    )
    database_type: ServerDatabaseType = Field(
        ServerDatabaseType.OTHER,
        title="The database type that the server is using.",
    )
    secrets_store_type: SecretsStoreType = Field(
        SecretsStoreType.NONE,
        title="The type of secrets store that the server is using.",
    )
    auth_scheme: AuthScheme = Field(
        title="The authentication scheme that the server is using.",
    )
    base_url: str = Field(
        "",
        title="The Base URL of the server.",
    )
    metadata: Dict[str, str] = Field(
        {},
        title="The metadata associated with the server.",
    )

    def is_local(self) -> bool:
        """Return whether the server is running locally.

        Returns:
            True if the server is running locally, False otherwise.
        """
        from zenml.config.global_config import GlobalConfiguration

        # Local ZenML servers are identifiable by the fact that their
        # server ID is the same as the local client (user) ID.
        return self.id == GlobalConfiguration().user_id

is_local(self)

Return whether the server is running locally.

Returns:

Type	Description
bool	True if the server is running locally, False otherwise.

Source code in zenml/models/v2/misc/server_models.py

def is_local(self) -> bool:
    """Return whether the server is running locally.

    Returns:
        True if the server is running locally, False otherwise.
    """
    from zenml.config.global_config import GlobalConfiguration

    # Local ZenML servers are identifiable by the fact that their
    # server ID is the same as the local client (user) ID.
    return self.id == GlobalConfiguration().user_id

service_connector_type

Model definitions for ZenML service connectors.

AuthenticationMethodModel (BaseModel) pydantic-model

Authentication method specification.

Describes the schema for the configuration and secrets that need to be provided to configure an authentication method.

Source code in zenml/models/v2/misc/service_connector_type.py

class AuthenticationMethodModel(BaseModel):
    """Authentication method specification.

    Describes the schema for the configuration and secrets that need to be
    provided to configure an authentication method.
    """

    name: str = Field(
        title="User readable name for the authentication method.",
    )
    auth_method: str = Field(
        title="The name of the authentication method.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: str = Field(
        default="",
        title="A description of the authentication method.",
    )
    config_schema: Dict[str, Any] = Field(
        default_factory=dict,
        title="The JSON schema of the configuration for this authentication "
        "method.",
    )
    min_expiration_seconds: Optional[int] = Field(
        default=None,
        title="The minimum number of seconds that the authentication "
        "session can be configured to be valid for. Set to None for "
        "authentication sessions and long-lived credentials that don't expire.",
    )
    max_expiration_seconds: Optional[int] = Field(
        default=None,
        title="The maximum number of seconds that the authentication "
        "session can be configured to be valid for. Set to None for "
        "authentication sessions and long-lived credentials that don't expire.",
    )
    default_expiration_seconds: Optional[int] = Field(
        default=None,
        title="The default number of seconds that the authentication "
        "session is valid for. Set to None for authentication sessions and "
        "long-lived credentials that don't expire.",
    )
    _config_class: Optional[Type[BaseModel]] = None

    def __init__(
        self, config_class: Optional[Type[BaseModel]] = None, **values: Any
    ):
        """Initialize the authentication method.

        Args:
            config_class: The configuration class for the authentication
                method.
            **values: The data to initialize the authentication method with.
        """
        if config_class:
            values["config_schema"] = json.loads(config_class.schema_json())
        super().__init__(**values)
        self._config_class = config_class

    @property
    def config_class(self) -> Optional[Type[BaseModel]]:
        """Get the configuration class for the authentication method.

        Returns:
            The configuration class for the authentication method.
        """
        return self._config_class

    def supports_temporary_credentials(self) -> bool:
        """Check if the authentication method supports temporary credentials.

        Returns:
            True if the authentication method supports temporary credentials,
            False otherwise.
        """
        return (
            self.min_expiration_seconds is not None
            or self.max_expiration_seconds is not None
            or self.default_expiration_seconds is not None
        )

    def validate_expiration(
        self, expiration_seconds: Optional[int]
    ) -> Optional[int]:
        """Validate the expiration time.

        Args:
            expiration_seconds: The expiration time in seconds. If None, the
                default expiration time is used, if applicable.

        Returns:
            The expiration time in seconds or None if not applicable.

        Raises:
            ValueError: If the expiration time is not valid.
        """
        if not self.supports_temporary_credentials():
            if expiration_seconds is not None:
                # Expiration is not supported
                raise ValueError(
                    "Expiration time is not supported for this authentication "
                    f"method but a value was provided: {expiration_seconds}"
                )

            return None

        expiration_seconds = (
            expiration_seconds or self.default_expiration_seconds
        )

        if expiration_seconds is None:
            return None

        if self.min_expiration_seconds is not None:
            if expiration_seconds < self.min_expiration_seconds:
                raise ValueError(
                    f"Expiration time must be at least "
                    f"{self.min_expiration_seconds} seconds."
                )

        if self.max_expiration_seconds is not None:
            if expiration_seconds > self.max_expiration_seconds:
                raise ValueError(
                    f"Expiration time must be at most "
                    f"{self.max_expiration_seconds} seconds."
                )

        return expiration_seconds

    class Config:
        """Pydantic config class."""

        underscore_attrs_are_private = True

config_class: Optional[Type[pydantic.main.BaseModel]] property readonly

Get the configuration class for the authentication method.

Returns:

Type	Description
Optional[Type[pydantic.main.BaseModel]]	The configuration class for the authentication method.

Config

Pydantic config class.

Source code in zenml/models/v2/misc/service_connector_type.py

class Config:
    """Pydantic config class."""

    underscore_attrs_are_private = True

__init__(self, config_class=None, **values) special

Initialize the authentication method.

Parameters:

Name	Type	Description	Default
config_class	Optional[Type[pydantic.main.BaseModel]]	The configuration class for the authentication method.	None
**values	Any	The data to initialize the authentication method with.	{}

Source code in zenml/models/v2/misc/service_connector_type.py

def __init__(
    self, config_class: Optional[Type[BaseModel]] = None, **values: Any
):
    """Initialize the authentication method.

    Args:
        config_class: The configuration class for the authentication
            method.
        **values: The data to initialize the authentication method with.
    """
    if config_class:
        values["config_schema"] = json.loads(config_class.schema_json())
    super().__init__(**values)
    self._config_class = config_class

supports_temporary_credentials(self)

Check if the authentication method supports temporary credentials.

Returns:

Type	Description
bool	True if the authentication method supports temporary credentials, False otherwise.

Source code in zenml/models/v2/misc/service_connector_type.py

def supports_temporary_credentials(self) -> bool:
    """Check if the authentication method supports temporary credentials.

    Returns:
        True if the authentication method supports temporary credentials,
        False otherwise.
    """
    return (
        self.min_expiration_seconds is not None
        or self.max_expiration_seconds is not None
        or self.default_expiration_seconds is not None
    )

validate_expiration(self, expiration_seconds)

Validate the expiration time.

Parameters:

Name	Type	Description	Default
expiration_seconds	Optional[int]	The expiration time in seconds. If None, the default expiration time is used, if applicable.	required

Returns:

Type	Description
Optional[int]	The expiration time in seconds or None if not applicable.

Exceptions:

Type	Description
ValueError	If the expiration time is not valid.

Source code in zenml/models/v2/misc/service_connector_type.py

def validate_expiration(
    self, expiration_seconds: Optional[int]
) -> Optional[int]:
    """Validate the expiration time.

    Args:
        expiration_seconds: The expiration time in seconds. If None, the
            default expiration time is used, if applicable.

    Returns:
        The expiration time in seconds or None if not applicable.

    Raises:
        ValueError: If the expiration time is not valid.
    """
    if not self.supports_temporary_credentials():
        if expiration_seconds is not None:
            # Expiration is not supported
            raise ValueError(
                "Expiration time is not supported for this authentication "
                f"method but a value was provided: {expiration_seconds}"
            )

        return None

    expiration_seconds = (
        expiration_seconds or self.default_expiration_seconds
    )

    if expiration_seconds is None:
        return None

    if self.min_expiration_seconds is not None:
        if expiration_seconds < self.min_expiration_seconds:
            raise ValueError(
                f"Expiration time must be at least "
                f"{self.min_expiration_seconds} seconds."
            )

    if self.max_expiration_seconds is not None:
        if expiration_seconds > self.max_expiration_seconds:
            raise ValueError(
                f"Expiration time must be at most "
                f"{self.max_expiration_seconds} seconds."
            )

    return expiration_seconds

ResourceTypeModel (BaseModel) pydantic-model

Resource type specification.

Describes the authentication methods and resource instantiation model for one or more resource types.

Source code in zenml/models/v2/misc/service_connector_type.py

class ResourceTypeModel(BaseModel):
    """Resource type specification.

    Describes the authentication methods and resource instantiation model for
    one or more resource types.
    """

    name: str = Field(
        title="User readable name for the resource type.",
    )
    resource_type: str = Field(
        title="Resource type identifier.",
    )
    description: str = Field(
        default="",
        title="A description of the resource type.",
    )
    auth_methods: List[str] = Field(
        title="The list of authentication methods that can be used to access "
        "resources of this type.",
    )
    supports_instances: bool = Field(
        default=False,
        title="Specifies if a single connector instance can be used to access "
        "multiple instances of this resource type. If set to True, the "
        "connector is able to provide a list of resource IDs identifying all "
        "the resources that it can access and a resource ID needs to be "
        "explicitly configured or supplied when access to a resource is "
        "requested. If set to False, a connector instance is only able to "
        "access a single resource and a resource ID is not required to access "
        "the resource.",
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to a png,"
        "svg or jpg file can be attached.",
    )
    emoji: Optional[str] = Field(
        default=None,
        title="Optionally, a python-rich emoji can be attached.",
    )

    @property
    def emojified_resource_type(self) -> str:
        """Get the emojified resource type.

        Returns:
            The emojified resource type.
        """
        if not self.emoji:
            return self.resource_type
        return f"{self.emoji} {self.resource_type}"

emojified_resource_type: str property readonly

Get the emojified resource type.

Returns:

Type	Description
str	The emojified resource type.

ServiceConnectorRequirements (BaseModel) pydantic-model

Service connector requirements.

Describes requirements that a service connector consumer has for a service connector instance that it needs in order to access a resource.

Attributes:

Name	Type	Description
connector_type	Optional[str]	The type of service connector that is required. If omitted, any service connector type can be used.
resource_type	str	The type of resource that the service connector instance must be able to access.
resource_id_attr	Optional[str]	The name of an attribute in the stack component configuration that contains the resource ID of the resource that the service connector instance must be able to access.

Source code in zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorRequirements(BaseModel):
    """Service connector requirements.

    Describes requirements that a service connector consumer has for a
    service connector instance that it needs in order to access a resource.

    Attributes:
        connector_type: The type of service connector that is required. If
            omitted, any service connector type can be used.
        resource_type: The type of resource that the service connector instance
            must be able to access.
        resource_id_attr: The name of an attribute in the stack component
            configuration that contains the resource ID of the resource that
            the service connector instance must be able to access.
    """

    connector_type: Optional[str] = None
    resource_type: str
    resource_id_attr: Optional[str] = None

    def is_satisfied_by(
        self,
        connector: Union[
            "ServiceConnectorResponse", "ServiceConnectorRequest"
        ],
        component: Union["ComponentResponse", "ComponentBase"],
    ) -> Tuple[bool, str]:
        """Check if the requirements are satisfied by a connector.

        Args:
            connector: The connector to check.
            component: The stack component that the connector is associated
                with.

        Returns:
            True if the requirements are satisfied, False otherwise, and a
            message describing the reason for the failure.
        """
        if self.connector_type and self.connector_type != connector.type:
            return (
                False,
                f"connector type '{connector.type}' does not match the "
                f"'{self.connector_type}' connector type specified in the "
                "stack component requirements",
            )
        if self.resource_type not in connector.resource_types:
            return False, (
                f"connector does not provide the '{self.resource_type}' "
                "resource type specified in the stack component requirements. "
                "Only the following resource types are supported: "
                f"{', '.join(connector.resource_types)}"
            )
        if self.resource_id_attr:
            resource_id = component.configuration.get(self.resource_id_attr)
            if not resource_id:
                return (
                    False,
                    f"the '{self.resource_id_attr}' stack component "
                    f"configuration attribute plays the role of resource "
                    f"identifier, but the stack component does not contain a "
                    f"'{self.resource_id_attr}' attribute. Please add the "
                    f"'{self.resource_id_attr}' attribute to the stack "
                    "component configuration and try again.",
                )

        return True, ""

is_satisfied_by(self, connector, component)

Check if the requirements are satisfied by a connector.

Parameters:

Name	Type	Description	Default
connector	Union[ServiceConnectorResponse, ServiceConnectorRequest]	The connector to check.	required
component	Union[ComponentResponse, ComponentBase]	The stack component that the connector is associated with.	required

Returns:

Type	Description
Tuple[bool, str]	True if the requirements are satisfied, False otherwise, and a message describing the reason for the failure.

Source code in zenml/models/v2/misc/service_connector_type.py

def is_satisfied_by(
    self,
    connector: Union[
        "ServiceConnectorResponse", "ServiceConnectorRequest"
    ],
    component: Union["ComponentResponse", "ComponentBase"],
) -> Tuple[bool, str]:
    """Check if the requirements are satisfied by a connector.

    Args:
        connector: The connector to check.
        component: The stack component that the connector is associated
            with.

    Returns:
        True if the requirements are satisfied, False otherwise, and a
        message describing the reason for the failure.
    """
    if self.connector_type and self.connector_type != connector.type:
        return (
            False,
            f"connector type '{connector.type}' does not match the "
            f"'{self.connector_type}' connector type specified in the "
            "stack component requirements",
        )
    if self.resource_type not in connector.resource_types:
        return False, (
            f"connector does not provide the '{self.resource_type}' "
            "resource type specified in the stack component requirements. "
            "Only the following resource types are supported: "
            f"{', '.join(connector.resource_types)}"
        )
    if self.resource_id_attr:
        resource_id = component.configuration.get(self.resource_id_attr)
        if not resource_id:
            return (
                False,
                f"the '{self.resource_id_attr}' stack component "
                f"configuration attribute plays the role of resource "
                f"identifier, but the stack component does not contain a "
                f"'{self.resource_id_attr}' attribute. Please add the "
                f"'{self.resource_id_attr}' attribute to the stack "
                "component configuration and try again.",
            )

    return True, ""

ServiceConnectorResourcesModel (BaseModel) pydantic-model

Service connector resources list.

Lists the resource types and resource instances that a service connector can provide access to.

Source code in zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorResourcesModel(BaseModel):
    """Service connector resources list.

    Lists the resource types and resource instances that a service connector
    can provide access to.
    """

    id: Optional[UUID] = Field(
        default=None,
        title="The ID of the service connector instance providing this "
        "resource.",
    )

    name: Optional[str] = Field(
        default=None,
        title="The name of the service connector instance providing this "
        "resource.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    connector_type: Union[str, "ServiceConnectorTypeModel"] = Field(
        title="The type of service connector.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    resources: List[ServiceConnectorTypedResourcesModel] = Field(
        default_factory=list,
        title="The list of resources that the service connector instance can "
        "give access to. Contains one entry for every resource type "
        "that the connector is configured for.",
    )

    error: Optional[str] = Field(
        default=None,
        title="A global error message describing why the service connector "
        "instance could not authenticate to the remote service.",
    )

    @property
    def resources_dict(self) -> Dict[str, ServiceConnectorTypedResourcesModel]:
        """Get the resources as a dictionary indexed by resource type.

        Returns:
            The resources as a dictionary indexed by resource type.
        """
        return {
            resource.resource_type: resource for resource in self.resources
        }

    @property
    def resource_types(self) -> List[str]:
        """Get the resource types.

        Returns:
            The resource types.
        """
        return [resource.resource_type for resource in self.resources]

    def set_error(
        self, error: str, resource_type: Optional[str] = None
    ) -> None:
        """Set a global error message or an error for a single resource type.

        Args:
            error: The error message.
            resource_type: The resource type to set the error message for. If
                omitted, or if there is only one resource type involved, the
                error message is (also) set globally.

        Raises:
            KeyError: If the resource type is not found in the resources list.
        """
        if resource_type:
            resource = self.resources_dict.get(resource_type)
            if not resource:
                raise KeyError(
                    f"resource type '{resource_type}' not found in "
                    "service connector resources list"
                )
            resource.error = error
            resource.resource_ids = None
            if len(self.resources) == 1:
                # If there is only one resource type involved, set the global
                # error message as well.
                self.error = error
        else:
            self.error = error
            for resource in self.resources:
                resource.error = error
                resource.resource_ids = None

    def set_resource_ids(
        self, resource_type: str, resource_ids: List[str]
    ) -> None:
        """Set the resource IDs for a resource type.

        Args:
            resource_type: The resource type to set the resource IDs for.
            resource_ids: The resource IDs to set.

        Raises:
            KeyError: If the resource type is not found in the resources list.
        """
        resource = self.resources_dict.get(resource_type)
        if not resource:
            raise KeyError(
                f"resource type '{resource_type}' not found in "
                "service connector resources list"
            )
        resource.resource_ids = resource_ids
        resource.error = None

    @property
    def type(self) -> str:
        """Get the connector type.

        Returns:
            The connector type.
        """
        if isinstance(self.connector_type, str):
            return self.connector_type
        return self.connector_type.connector_type

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return self.connector_type.emojified_connector_type

        return self.connector_type

    def get_emojified_resource_types(
        self, resource_type: Optional[str] = None
    ) -> List[str]:
        """Get the emojified resource type.

        Args:
            resource_type: The resource type to get the emojified resource type
                for. If omitted, the emojified resource type for all resource
                types is returned.


        Returns:
            The list of emojified resource types.
        """
        if not isinstance(self.connector_type, str):
            if resource_type:
                return [
                    self.connector_type.resource_type_dict[
                        resource_type
                    ].emojified_resource_type
                ]
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
                for resource_type in self.resources_dict.keys()
            ]
        if resource_type:
            return [resource_type]
        return list(self.resources_dict.keys())

    def get_default_resource_id(self) -> Optional[str]:
        """Get the default resource ID, if included in the resource list.

        The default resource ID is a resource ID supplied by the connector
        implementation only for resource types that do not support multiple
        instances.

        Returns:
            The default resource ID, or None if no resource ID is set.
        """
        if len(self.resources) != 1:
            # multi-type connectors do not have a default resource ID
            return None

        if isinstance(self.connector_type, str):
            # can't determine default resource ID for unknown connector types
            return None

        resource_type_spec = self.connector_type.resource_type_dict[
            self.resources[0].resource_type
        ]
        if resource_type_spec.supports_instances:
            # resource types that support multiple instances do not have a
            # default resource ID
            return None

        resource_ids = self.resources[0].resource_ids

        if not resource_ids or len(resource_ids) != 1:
            return None

        return resource_ids[0]

    @classmethod
    def from_connector_model(
        cls,
        connector_model: "ServiceConnectorResponse",
        resource_type: Optional[str] = None,
    ) -> "ServiceConnectorResourcesModel":
        """Initialize a resource model from a connector model.

        Args:
            connector_model: The connector model.
            resource_type: The resource type to set on the resource model. If
                omitted, the resource type is set according to the connector
                model.

        Returns:
            A resource list model instance.
        """
        resources = cls(
            id=connector_model.id,
            name=connector_model.name,
            connector_type=connector_model.type,
        )

        resource_types = resource_type or connector_model.resource_types
        for resource_type in resource_types:
            resources.resources.append(
                ServiceConnectorTypedResourcesModel(
                    resource_type=resource_type,
                    resource_ids=[connector_model.resource_id]
                    if connector_model.resource_id
                    else None,
                )
            )

        return resources

emojified_connector_type: str property readonly

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

resource_types: List[str] property readonly

Get the resource types.

Returns:

Type	Description
List[str]	The resource types.

resources_dict: Dict[str, zenml.models.v2.misc.service_connector_type.ServiceConnectorTypedResourcesModel] property readonly

Get the resources as a dictionary indexed by resource type.

Returns:

Type	Description
Dict[str, zenml.models.v2.misc.service_connector_type.ServiceConnectorTypedResourcesModel]	The resources as a dictionary indexed by resource type.

type: str property readonly

Get the connector type.

Returns:

Type	Description
str	The connector type.

from_connector_model(connector_model, resource_type=None) classmethod

Initialize a resource model from a connector model.

Parameters:

Name	Type	Description	Default
connector_model	ServiceConnectorResponse	The connector model.	required
resource_type	Optional[str]	The resource type to set on the resource model. If omitted, the resource type is set according to the connector model.	None

Returns:

Type	Description
ServiceConnectorResourcesModel	A resource list model instance.

Source code in zenml/models/v2/misc/service_connector_type.py

@classmethod
def from_connector_model(
    cls,
    connector_model: "ServiceConnectorResponse",
    resource_type: Optional[str] = None,
) -> "ServiceConnectorResourcesModel":
    """Initialize a resource model from a connector model.

    Args:
        connector_model: The connector model.
        resource_type: The resource type to set on the resource model. If
            omitted, the resource type is set according to the connector
            model.

    Returns:
        A resource list model instance.
    """
    resources = cls(
        id=connector_model.id,
        name=connector_model.name,
        connector_type=connector_model.type,
    )

    resource_types = resource_type or connector_model.resource_types
    for resource_type in resource_types:
        resources.resources.append(
            ServiceConnectorTypedResourcesModel(
                resource_type=resource_type,
                resource_ids=[connector_model.resource_id]
                if connector_model.resource_id
                else None,
            )
        )

    return resources

get_default_resource_id(self)

Get the default resource ID, if included in the resource list.

The default resource ID is a resource ID supplied by the connector implementation only for resource types that do not support multiple instances.

Returns:

Type	Description
Optional[str]	The default resource ID, or None if no resource ID is set.

Source code in zenml/models/v2/misc/service_connector_type.py

def get_default_resource_id(self) -> Optional[str]:
    """Get the default resource ID, if included in the resource list.

    The default resource ID is a resource ID supplied by the connector
    implementation only for resource types that do not support multiple
    instances.

    Returns:
        The default resource ID, or None if no resource ID is set.
    """
    if len(self.resources) != 1:
        # multi-type connectors do not have a default resource ID
        return None

    if isinstance(self.connector_type, str):
        # can't determine default resource ID for unknown connector types
        return None

    resource_type_spec = self.connector_type.resource_type_dict[
        self.resources[0].resource_type
    ]
    if resource_type_spec.supports_instances:
        # resource types that support multiple instances do not have a
        # default resource ID
        return None

    resource_ids = self.resources[0].resource_ids

    if not resource_ids or len(resource_ids) != 1:
        return None

    return resource_ids[0]

get_emojified_resource_types(self, resource_type=None)

Get the emojified resource type.

Parameters:

Name	Type	Description	Default
resource_type	Optional[str]	The resource type to get the emojified resource type for. If omitted, the emojified resource type for all resource types is returned.	None

Returns:

Type	Description
List[str]	The list of emojified resource types.

Source code in zenml/models/v2/misc/service_connector_type.py

def get_emojified_resource_types(
    self, resource_type: Optional[str] = None
) -> List[str]:
    """Get the emojified resource type.

    Args:
        resource_type: The resource type to get the emojified resource type
            for. If omitted, the emojified resource type for all resource
            types is returned.


    Returns:
        The list of emojified resource types.
    """
    if not isinstance(self.connector_type, str):
        if resource_type:
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
            ]
        return [
            self.connector_type.resource_type_dict[
                resource_type
            ].emojified_resource_type
            for resource_type in self.resources_dict.keys()
        ]
    if resource_type:
        return [resource_type]
    return list(self.resources_dict.keys())

set_error(self, error, resource_type=None)

Set a global error message or an error for a single resource type.

Parameters:

Name	Type	Description	Default
error	str	The error message.	required
resource_type	Optional[str]	The resource type to set the error message for. If omitted, or if there is only one resource type involved, the error message is (also) set globally.	None

Exceptions:

Type	Description
KeyError	If the resource type is not found in the resources list.

Source code in zenml/models/v2/misc/service_connector_type.py

def set_error(
    self, error: str, resource_type: Optional[str] = None
) -> None:
    """Set a global error message or an error for a single resource type.

    Args:
        error: The error message.
        resource_type: The resource type to set the error message for. If
            omitted, or if there is only one resource type involved, the
            error message is (also) set globally.

    Raises:
        KeyError: If the resource type is not found in the resources list.
    """
    if resource_type:
        resource = self.resources_dict.get(resource_type)
        if not resource:
            raise KeyError(
                f"resource type '{resource_type}' not found in "
                "service connector resources list"
            )
        resource.error = error
        resource.resource_ids = None
        if len(self.resources) == 1:
            # If there is only one resource type involved, set the global
            # error message as well.
            self.error = error
    else:
        self.error = error
        for resource in self.resources:
            resource.error = error
            resource.resource_ids = None

set_resource_ids(self, resource_type, resource_ids)

Set the resource IDs for a resource type.

Parameters:

Name	Type	Description	Default
resource_type	str	The resource type to set the resource IDs for.	required
resource_ids	List[str]	The resource IDs to set.	required

Exceptions:

Type	Description
KeyError	If the resource type is not found in the resources list.

Source code in zenml/models/v2/misc/service_connector_type.py

def set_resource_ids(
    self, resource_type: str, resource_ids: List[str]
) -> None:
    """Set the resource IDs for a resource type.

    Args:
        resource_type: The resource type to set the resource IDs for.
        resource_ids: The resource IDs to set.

    Raises:
        KeyError: If the resource type is not found in the resources list.
    """
    resource = self.resources_dict.get(resource_type)
    if not resource:
        raise KeyError(
            f"resource type '{resource_type}' not found in "
            "service connector resources list"
        )
    resource.resource_ids = resource_ids
    resource.error = None

ServiceConnectorTypeModel (BaseModel) pydantic-model

Service connector type specification.

Describes the types of resources to which the service connector can be used to gain access and the authentication methods that are supported by the service connector.

The connector type, resource types, resource IDs and authentication methods can all be used as search criteria to lookup and filter service connector instances that are compatible with the requirements of a consumer (e.g. a stack component).

Source code in zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorTypeModel(BaseModel):
    """Service connector type specification.

    Describes the types of resources to which the service connector can be used
    to gain access and the authentication methods that are supported by the
    service connector.

    The connector type, resource types, resource IDs and authentication
    methods can all be used as search criteria to lookup and filter service
    connector instances that are compatible with the requirements of a consumer
    (e.g. a stack component).
    """

    name: str = Field(
        title="User readable name for the service connector type.",
    )
    connector_type: str = Field(
        title="The type of service connector. It can be used to represent a "
        "generic resource (e.g. Docker, Kubernetes) or a group of different "
        "resources accessible through a common interface or point of access "
        "and authentication (e.g. a cloud provider or a platform).",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: str = Field(
        default="",
        title="A description of the service connector.",
    )
    resource_types: List[ResourceTypeModel] = Field(
        title="A list of resource types that the connector can be used to "
        "access.",
    )
    auth_methods: List[AuthenticationMethodModel] = Field(
        title="A list of specifications describing the authentication "
        "methods that are supported by the service connector, along with the "
        "configuration and secrets attributes that need to be configured for "
        "them.",
    )
    supports_auto_configuration: bool = Field(
        default=False,
        title="Models if the connector can be configured automatically based "
        "on information extracted from a local environment.",
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to a png,"
        "svg or jpg can be attached.",
    )
    emoji: Optional[str] = Field(
        default=None,
        title="Optionally, a python-rich emoji can be attached.",
    )
    docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to docs, within docs.zenml.io.",
    )
    sdk_docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to SDK docs,"
        "within sdkdocs.zenml.io.",
    )
    local: bool = Field(
        default=True,
        title="If True, the service connector is available locally.",
    )
    remote: bool = Field(
        default=False,
        title="If True, the service connector is available remotely.",
    )
    _connector_class: Optional[Type["ServiceConnector"]] = None

    @property
    def connector_class(self) -> Optional[Type["ServiceConnector"]]:
        """Get the service connector class.

        Returns:
            The service connector class.
        """
        return self._connector_class

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not self.emoji:
            return self.connector_type
        return f"{self.emoji} {self.connector_type}"

    @property
    def emojified_resource_types(self) -> List[str]:
        """Get the emojified connector types.

        Returns:
            The emojified connector types.
        """
        return [
            resource_type.emojified_resource_type
            for resource_type in self.resource_types
        ]

    def set_connector_class(
        self, connector_class: Type["ServiceConnector"]
    ) -> None:
        """Set the service connector class.

        Args:
            connector_class: The service connector class.
        """
        self._connector_class = connector_class

    @validator("resource_types")
    def validate_resource_types(
        cls, values: List[ResourceTypeModel]
    ) -> List[ResourceTypeModel]:
        """Validate that the resource types are unique.

        Args:
            values: The list of resource types.

        Returns:
            The list of resource types.

        Raises:
            ValueError: If two or more resource type specifications list the
                same resource type.
        """
        # Gather all resource types from the list of resource type
        # specifications.
        resource_types = [r.resource_type for r in values]
        if len(resource_types) != len(set(resource_types)):
            raise ValueError(
                "Two or more resource type specifications must not list "
                "the same resource type."
            )

        return values

    @validator("auth_methods")
    def validate_auth_methods(
        cls, values: List[AuthenticationMethodModel]
    ) -> List[AuthenticationMethodModel]:
        """Validate that the authentication methods are unique.

        Args:
            values: The list of authentication methods.

        Returns:
            The list of authentication methods.

        Raises:
            ValueError: If two or more authentication method specifications
                share the same authentication method value.
        """
        # Gather all auth methods from the list of auth method
        # specifications.
        auth_methods = [a.auth_method for a in values]
        if len(auth_methods) != len(set(auth_methods)):
            raise ValueError(
                "Two or more authentication method specifications must not "
                "share the same authentication method value."
            )

        return values

    @property
    def resource_type_dict(
        self,
    ) -> Dict[str, ResourceTypeModel]:
        """Returns a map of resource types to resource type specifications.

        Returns:
            A map of resource types to resource type specifications.
        """
        return {r.resource_type: r for r in self.resource_types}

    @property
    def auth_method_dict(
        self,
    ) -> Dict[str, AuthenticationMethodModel]:
        """Returns a map of authentication methods to authentication method specifications.

        Returns:
            A map of authentication methods to authentication method
            specifications.
        """
        return {a.auth_method: a for a in self.auth_methods}

    def find_resource_specifications(
        self,
        auth_method: str,
        resource_type: Optional[str] = None,
    ) -> Tuple[AuthenticationMethodModel, Optional[ResourceTypeModel]]:
        """Find the specifications for a configurable resource.

        Validate the supplied connector configuration parameters against the
        connector specification and return the matching authentication method
        specification and resource specification.

        Args:
            auth_method: The name of the authentication method.
            resource_type: The type of resource being configured.

        Returns:
            The authentication method specification and resource specification
            for the specified authentication method and resource type.

        Raises:
            KeyError: If the authentication method is not supported by the
                connector for the specified resource type and ID.
        """
        # Verify the authentication method
        auth_method_dict = self.auth_method_dict
        if auth_method in auth_method_dict:
            # A match was found for the authentication method
            auth_method_spec = auth_method_dict[auth_method]
        else:
            # No match was found for the authentication method
            raise KeyError(
                f"connector type '{self.connector_type}' does not support the "
                f"'{auth_method}' authentication method. Supported "
                f"authentication methods are: {list(auth_method_dict.keys())}."
            )

        if resource_type is None:
            # No resource type was specified, so no resource type
            # specification can be returned.
            return auth_method_spec, None

        # Verify the resource type
        resource_type_dict = self.resource_type_dict
        if resource_type in resource_type_dict:
            resource_type_spec = resource_type_dict[resource_type]
        else:
            raise KeyError(
                f"connector type '{self.connector_type}' does not support "
                f"resource type '{resource_type}'. Supported resource types "
                f"are: {list(resource_type_dict.keys())}."
            )

        if auth_method not in resource_type_spec.auth_methods:
            raise KeyError(
                f"the '{self.connector_type}' connector type does not support "
                f"the '{auth_method}' authentication method for the "
                f"'{resource_type}' resource type. Supported authentication "
                f"methods are: {resource_type_spec.auth_methods}."
            )

        return auth_method_spec, resource_type_spec

    class Config:
        """Pydantic config class."""

        underscore_attrs_are_private = True

auth_method_dict: Dict[str, zenml.models.v2.misc.service_connector_type.AuthenticationMethodModel] property readonly

Returns a map of authentication methods to authentication method specifications.

Returns:

Type	Description
Dict[str, zenml.models.v2.misc.service_connector_type.AuthenticationMethodModel]	A map of authentication methods to authentication method specifications.

connector_class: Optional[Type[ServiceConnector]] property readonly

Get the service connector class.

Returns:

Type	Description
Optional[Type[ServiceConnector]]	The service connector class.

emojified_connector_type: str property readonly

Get the emojified connector type.

Returns:

Type	Description
str	The emojified connector type.

emojified_resource_types: List[str] property readonly

Get the emojified connector types.

Returns:

Type	Description
List[str]	The emojified connector types.

resource_type_dict: Dict[str, zenml.models.v2.misc.service_connector_type.ResourceTypeModel] property readonly

Returns a map of resource types to resource type specifications.

Returns:

Type	Description
Dict[str, zenml.models.v2.misc.service_connector_type.ResourceTypeModel]	A map of resource types to resource type specifications.

Config

Pydantic config class.

Source code in zenml/models/v2/misc/service_connector_type.py

class Config:
    """Pydantic config class."""

    underscore_attrs_are_private = True

find_resource_specifications(self, auth_method, resource_type=None)

Find the specifications for a configurable resource.

Validate the supplied connector configuration parameters against the connector specification and return the matching authentication method specification and resource specification.

Parameters:

Name	Type	Description	Default
auth_method	str	The name of the authentication method.	required
resource_type	Optional[str]	The type of resource being configured.	None

Returns:

Type	Description
Tuple[zenml.models.v2.misc.service_connector_type.AuthenticationMethodModel, Optional[zenml.models.v2.misc.service_connector_type.ResourceTypeModel]]	The authentication method specification and resource specification for the specified authentication method and resource type.

Exceptions:

Type	Description
KeyError	If the authentication method is not supported by the connector for the specified resource type and ID.

Source code in zenml/models/v2/misc/service_connector_type.py

def find_resource_specifications(
    self,
    auth_method: str,
    resource_type: Optional[str] = None,
) -> Tuple[AuthenticationMethodModel, Optional[ResourceTypeModel]]:
    """Find the specifications for a configurable resource.

    Validate the supplied connector configuration parameters against the
    connector specification and return the matching authentication method
    specification and resource specification.

    Args:
        auth_method: The name of the authentication method.
        resource_type: The type of resource being configured.

    Returns:
        The authentication method specification and resource specification
        for the specified authentication method and resource type.

    Raises:
        KeyError: If the authentication method is not supported by the
            connector for the specified resource type and ID.
    """
    # Verify the authentication method
    auth_method_dict = self.auth_method_dict
    if auth_method in auth_method_dict:
        # A match was found for the authentication method
        auth_method_spec = auth_method_dict[auth_method]
    else:
        # No match was found for the authentication method
        raise KeyError(
            f"connector type '{self.connector_type}' does not support the "
            f"'{auth_method}' authentication method. Supported "
            f"authentication methods are: {list(auth_method_dict.keys())}."
        )

    if resource_type is None:
        # No resource type was specified, so no resource type
        # specification can be returned.
        return auth_method_spec, None

    # Verify the resource type
    resource_type_dict = self.resource_type_dict
    if resource_type in resource_type_dict:
        resource_type_spec = resource_type_dict[resource_type]
    else:
        raise KeyError(
            f"connector type '{self.connector_type}' does not support "
            f"resource type '{resource_type}'. Supported resource types "
            f"are: {list(resource_type_dict.keys())}."
        )

    if auth_method not in resource_type_spec.auth_methods:
        raise KeyError(
            f"the '{self.connector_type}' connector type does not support "
            f"the '{auth_method}' authentication method for the "
            f"'{resource_type}' resource type. Supported authentication "
            f"methods are: {resource_type_spec.auth_methods}."
        )

    return auth_method_spec, resource_type_spec

set_connector_class(self, connector_class)

Set the service connector class.

Parameters:

Name	Type	Description	Default
connector_class	Type[ServiceConnector]	The service connector class.	required

Source code in zenml/models/v2/misc/service_connector_type.py

def set_connector_class(
    self, connector_class: Type["ServiceConnector"]
) -> None:
    """Set the service connector class.

    Args:
        connector_class: The service connector class.
    """
    self._connector_class = connector_class

validate_auth_methods(values) classmethod

Validate that the authentication methods are unique.

Parameters:

Name	Type	Description	Default
values	List[zenml.models.v2.misc.service_connector_type.AuthenticationMethodModel]	The list of authentication methods.	required

Returns:

Type	Description
List[zenml.models.v2.misc.service_connector_type.AuthenticationMethodModel]	The list of authentication methods.

Exceptions:

Type	Description
ValueError	If two or more authentication method specifications share the same authentication method value.

Source code in zenml/models/v2/misc/service_connector_type.py

@validator("auth_methods")
def validate_auth_methods(
    cls, values: List[AuthenticationMethodModel]
) -> List[AuthenticationMethodModel]:
    """Validate that the authentication methods are unique.

    Args:
        values: The list of authentication methods.

    Returns:
        The list of authentication methods.

    Raises:
        ValueError: If two or more authentication method specifications
            share the same authentication method value.
    """
    # Gather all auth methods from the list of auth method
    # specifications.
    auth_methods = [a.auth_method for a in values]
    if len(auth_methods) != len(set(auth_methods)):
        raise ValueError(
            "Two or more authentication method specifications must not "
            "share the same authentication method value."
        )

    return values

validate_resource_types(values) classmethod

Validate that the resource types are unique.

Parameters:

Name	Type	Description	Default
values	List[zenml.models.v2.misc.service_connector_type.ResourceTypeModel]	The list of resource types.	required

Returns:

Type	Description
List[zenml.models.v2.misc.service_connector_type.ResourceTypeModel]	The list of resource types.

Exceptions:

Type	Description
ValueError	If two or more resource type specifications list the same resource type.

Source code in zenml/models/v2/misc/service_connector_type.py

@validator("resource_types")
def validate_resource_types(
    cls, values: List[ResourceTypeModel]
) -> List[ResourceTypeModel]:
    """Validate that the resource types are unique.

    Args:
        values: The list of resource types.

    Returns:
        The list of resource types.

    Raises:
        ValueError: If two or more resource type specifications list the
            same resource type.
    """
    # Gather all resource types from the list of resource type
    # specifications.
    resource_types = [r.resource_type for r in values]
    if len(resource_types) != len(set(resource_types)):
        raise ValueError(
            "Two or more resource type specifications must not list "
            "the same resource type."
        )

    return values

ServiceConnectorTypedResourcesModel (BaseModel) pydantic-model

Service connector typed resources list.

Lists the resource instances that a service connector can provide access to.

Source code in zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorTypedResourcesModel(BaseModel):
    """Service connector typed resources list.

    Lists the resource instances that a service connector can provide
    access to.
    """

    resource_type: str = Field(
        title="The type of resource that the service connector instance can "
        "be used to access.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    resource_ids: Optional[List[str]] = Field(
        default=None,
        title="The resource IDs of all resource instances that the service "
        "connector instance can be used to access. Omitted (set to None) for "
        "multi-type service connectors that didn't explicitly request to "
        "fetch resources for all resource types. Also omitted if an error "
        "occurred while listing the resource instances or if no resources are "
        "listed due to authorization issues or lack of permissions (in both "
        "cases the 'error' field is set to an error message). For resource "
        "types that do not support multiple instances, a single resource ID is "
        "listed.",
    )

    error: Optional[str] = Field(
        default=None,
        title="An error message describing why the service connector instance "
        "could not list the resources that it is configured to access.",
    )

user_auth

Model definition for auth users.

UserAuthModel (BaseZenModel) pydantic-model

Authentication Model for the User.

This model is only used server-side. The server endpoints can use this model to authenticate the user credentials (Token, Password).

Source code in zenml/models/v2/misc/user_auth.py

class UserAuthModel(BaseZenModel):
    """Authentication Model for the User.

    This model is only used server-side. The server endpoints can use this model
    to authenticate the user credentials (Token, Password).
    """

    id: UUID = Field(title="The unique resource id.")

    created: datetime = Field(title="Time when this resource was created.")
    updated: datetime = Field(
        title="Time when this resource was last updated."
    )

    active: bool = Field(default=False, title="Active account.")
    is_service_account: bool = Field(
        title="Indicates whether this is a service account or a regular user "
        "account."
    )

    activation_token: Optional[SecretStr] = Field(default=None, exclude=True)
    password: Optional[SecretStr] = Field(default=None, exclude=True)
    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    full_name: str = Field(
        default="",
        title="The full name for the account owner. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    email_opted_in: Optional[bool] = Field(
        default=None,
        title="Whether the user agreed to share their email. Only relevant for "
        "user accounts",
        description="`null` if not answered, `true` if agreed, "
        "`false` if skipped.",
    )

    hub_token: Optional[str] = Field(
        default=None,
        title="JWT Token for the connected Hub account. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    @classmethod
    def _get_crypt_context(cls) -> "CryptContext":
        """Returns the password encryption context.

        Returns:
            The password encryption context.
        """
        from passlib.context import CryptContext

        return CryptContext(schemes=["bcrypt"], deprecated="auto")

    @classmethod
    def _is_hashed_secret(cls, secret: SecretStr) -> bool:
        """Checks if a secret value is already hashed.

        Args:
            secret: The secret value to check.

        Returns:
            True if the secret value is hashed, otherwise False.
        """
        return (
            re.match(r"^\$2[ayb]\$.{56}$", secret.get_secret_value())
            is not None
        )

    @classmethod
    def _get_hashed_secret(cls, secret: Optional[SecretStr]) -> Optional[str]:
        """Hashes the input secret and returns the hash value.

        Only applied if supplied and if not already hashed.

        Args:
            secret: The secret value to hash.

        Returns:
            The secret hash value, or None if no secret was supplied.
        """
        if secret is None:
            return None
        if cls._is_hashed_secret(secret):
            return secret.get_secret_value()
        pwd_context = cls._get_crypt_context()
        return pwd_context.hash(secret.get_secret_value())

    def get_password(self) -> Optional[str]:
        """Get the password.

        Returns:
            The password as a plain string, if it exists.
        """
        if self.password is None:
            return None
        return self.password.get_secret_value()

    def get_hashed_password(self) -> Optional[str]:
        """Returns the hashed password, if configured.

        Returns:
            The hashed password.
        """
        return self._get_hashed_secret(self.password)

    def get_hashed_activation_token(self) -> Optional[str]:
        """Returns the hashed activation token, if configured.

        Returns:
            The hashed activation token.
        """
        return self._get_hashed_secret(self.activation_token)

    @classmethod
    def verify_password(
        cls, plain_password: str, user: Optional["UserAuthModel"] = None
    ) -> bool:
        """Verifies a given plain password against the stored password.

        Args:
            plain_password: Input password to be verified.
            user: User for which the password is to be verified.

        Returns:
            True if the passwords match.
        """
        # even when the user or password is not set, we still want to execute
        # the password hash verification to protect against response discrepancy
        # attacks (https://cwe.mitre.org/data/definitions/204.html)
        password_hash: Optional[str] = None
        if (
            user is not None
            # Disable password verification for service accounts as an extra
            # security measure. Service accounts should only be used with API
            # keys.
            and not user.is_service_account
            and user.password is not None
        ):  # and user.active:
            password_hash = user.get_hashed_password()
        pwd_context = cls._get_crypt_context()
        return pwd_context.verify(plain_password, password_hash)

    @classmethod
    def verify_activation_token(
        cls, activation_token: str, user: Optional["UserAuthModel"] = None
    ) -> bool:
        """Verifies a given activation token against the stored token.

        Args:
            activation_token: Input activation token to be verified.
            user: User for which the activation token is to be verified.

        Returns:
            True if the token is valid.
        """
        # even when the user or token is not set, we still want to execute the
        # token hash verification to protect against response discrepancy
        # attacks (https://cwe.mitre.org/data/definitions/204.html)
        token_hash: str = ""
        if (
            user is not None
            # Disable activation tokens for service accounts as an extra
            # security measure. Service accounts should only be used with API
            # keys.
            and not user.is_service_account
            and user.activation_token is not None
            and not user.active
        ):
            token_hash = user.get_hashed_activation_token() or ""
        pwd_context = cls._get_crypt_context()
        return pwd_context.verify(activation_token, token_hash)

email_opted_in: bool pydantic-field

null if not answered, true if agreed, false if skipped.

__json_encoder__(obj) special staticmethod

partial(func, args, *keywords) - new function with partial application of the given arguments and keywords.

get_hashed_activation_token(self)

Returns the hashed activation token, if configured.

Returns:

Type	Description
Optional[str]	The hashed activation token.

Source code in zenml/models/v2/misc/user_auth.py

def get_hashed_activation_token(self) -> Optional[str]:
    """Returns the hashed activation token, if configured.

    Returns:
        The hashed activation token.
    """
    return self._get_hashed_secret(self.activation_token)

get_hashed_password(self)

Returns the hashed password, if configured.

Returns:

Type	Description
Optional[str]	The hashed password.

Source code in zenml/models/v2/misc/user_auth.py

def get_hashed_password(self) -> Optional[str]:
    """Returns the hashed password, if configured.

    Returns:
        The hashed password.
    """
    return self._get_hashed_secret(self.password)

get_password(self)

Get the password.

Returns:

Type	Description
Optional[str]	The password as a plain string, if it exists.

Source code in zenml/models/v2/misc/user_auth.py

def get_password(self) -> Optional[str]:
    """Get the password.

    Returns:
        The password as a plain string, if it exists.
    """
    if self.password is None:
        return None
    return self.password.get_secret_value()

verify_activation_token(activation_token, user=None) classmethod

Verifies a given activation token against the stored token.

Parameters:

Name	Type	Description	Default
activation_token	str	Input activation token to be verified.	required
user	Optional[UserAuthModel]	User for which the activation token is to be verified.	None

Returns:

Type	Description
bool	True if the token is valid.

Source code in zenml/models/v2/misc/user_auth.py

@classmethod
def verify_activation_token(
    cls, activation_token: str, user: Optional["UserAuthModel"] = None
) -> bool:
    """Verifies a given activation token against the stored token.

    Args:
        activation_token: Input activation token to be verified.
        user: User for which the activation token is to be verified.

    Returns:
        True if the token is valid.
    """
    # even when the user or token is not set, we still want to execute the
    # token hash verification to protect against response discrepancy
    # attacks (https://cwe.mitre.org/data/definitions/204.html)
    token_hash: str = ""
    if (
        user is not None
        # Disable activation tokens for service accounts as an extra
        # security measure. Service accounts should only be used with API
        # keys.
        and not user.is_service_account
        and user.activation_token is not None
        and not user.active
    ):
        token_hash = user.get_hashed_activation_token() or ""
    pwd_context = cls._get_crypt_context()
    return pwd_context.verify(activation_token, token_hash)

verify_password(plain_password, user=None) classmethod

Verifies a given plain password against the stored password.

Parameters:

Name	Type	Description	Default
plain_password	str	Input password to be verified.	required
user	Optional[UserAuthModel]	User for which the password is to be verified.	None

Returns:

Type	Description
bool	True if the passwords match.

Source code in zenml/models/v2/misc/user_auth.py

@classmethod
def verify_password(
    cls, plain_password: str, user: Optional["UserAuthModel"] = None
) -> bool:
    """Verifies a given plain password against the stored password.

    Args:
        plain_password: Input password to be verified.
        user: User for which the password is to be verified.

    Returns:
        True if the passwords match.
    """
    # even when the user or password is not set, we still want to execute
    # the password hash verification to protect against response discrepancy
    # attacks (https://cwe.mitre.org/data/definitions/204.html)
    password_hash: Optional[str] = None
    if (
        user is not None
        # Disable password verification for service accounts as an extra
        # security measure. Service accounts should only be used with API
        # keys.
        and not user.is_service_account
        and user.password is not None
    ):  # and user.active:
        password_hash = user.get_hashed_password()
    pwd_context = cls._get_crypt_context()
    return pwd_context.verify(plain_password, password_hash)