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Pigeon

zenml.integrations.pigeon

Initialization of the Pigeon integration.

Attributes

PIGEON = 'pigeon' module-attribute

PIGEON_ANNOTATOR_FLAVOR = 'pigeon' module-attribute

Classes

Flavor

Class for ZenML Flavors.

Attributes
config_class: Type[StackComponentConfig] abstractmethod property

Returns StackComponentConfig config class.

Returns:

Type Description
Type[StackComponentConfig]

The config class.
config_schema: Dict[str, Any] property

The config schema for a flavor.

Returns:

Type Description
Dict[str, Any]

The config schema.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[StackComponent] abstractmethod property

Implementation class for this flavor.

Returns:

Type Description
Type[StackComponent]

The implementation class for this flavor.
logo_url: Optional[str] property

A url to represent the flavor in the dashboard.

Returns:

Type Description
Optional[str]

The flavor logo.
name: str abstractmethod property

The flavor name.

Returns:

Type Description
str

The flavor name.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for service connectors.

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

Returns:

Type Description
Optional[ServiceConnectorRequirements]

Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]

connector is required for this flavor.
type: StackComponentType abstractmethod property

The stack component type.

Returns:

Type Description
StackComponentType

The stack component type.
Functions
from_model(flavor_model: FlavorResponse) -> Flavor classmethod

Loads a flavor from a model.

Parameters:

Name Type Description Default
flavor_model FlavorResponse

The model to load from.

 required

Raises:

Type Description
CustomFlavorImportError

If the custom flavor can't be imported.
ImportError

If the flavor can't be imported.

Returns:

Type Description
Flavor

The loaded flavor.
Source code in src/zenml/stack/flavor.py 
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@classmethod
def from_model(cls, flavor_model: FlavorResponse) -> "Flavor":
    """Loads a flavor from a model.

    Args:
        flavor_model: The model to load from.

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

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

Generate the doc urls for all inbuilt and integration flavors.

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

Returns:

Type Description
str

The complete url to the zenml documentation
Source code in src/zenml/stack/flavor.py 
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def generate_default_docs_url(self) -> str:
    """Generate the doc urls for all inbuilt and integration flavors.

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

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

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

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

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

Generate SDK docs url for a flavor.

Returns:

Type Description
str

The complete url to the zenml SDK docs
Source code in src/zenml/stack/flavor.py 
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def generate_default_sdk_docs_url(self) -> str:
    """Generate SDK docs url for a flavor.

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

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

    component_type = self.type.plural

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

        return (
            f"{base}/integration_code_docs"
            f"/integrations-{integration}/#{self.__module__}"
        )

    else:
        return (
            f"{base}/core_code_docs/core-{component_type}/"
            f"#{self.__module__}"
        )
to_model(integration: Optional[str] = None, is_custom: bool = True) -> FlavorRequest

Converts a flavor to a model.

Parameters:

Name Type Description Default
integration Optional[str]

The integration to use for the model.

 None
is_custom bool

Whether the flavor is a custom flavor.

 True

Returns:

Type Description
FlavorRequest

The model.
Source code in src/zenml/stack/flavor.py 
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def to_model(
    self,
    integration: Optional[str] = None,
    is_custom: bool = True,
) -> FlavorRequest:
    """Converts a flavor to a model.

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

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

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

Integration

Base class for integration in ZenML.

Functions
activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py 
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@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""
check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type Description
bool

True if all required packages are installed, False otherwise.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for r in cls.get_requirements():
        try:
            # First check if the base package is installed
            dist = pkg_resources.get_distribution(r)

            # Next, check if the dependencies (including extras) are
            # installed
            deps: List[Requirement] = []

            _, extras = parse_requirement(r)
            if extras:
                extra_list = extras[1:-1].split(",")
                for extra in extra_list:
                    try:
                        requirements = dist.requires(extras=[extra])  # type: ignore[arg-type]
                    except pkg_resources.UnknownExtra as e:
                        logger.debug(f"Unknown extra: {str(e)}")
                        return False
                    deps.extend(requirements)
            else:
                deps = dist.requires()

            for ri in deps:
                try:
                    # Remove the "extra == ..." part from the requirement string
                    cleaned_req = re.sub(
                        r"; extra == \"\w+\"", "", str(ri)
                    )
                    pkg_resources.get_distribution(cleaned_req)
                except pkg_resources.DistributionNotFound as e:
                    logger.debug(
                        f"Unable to find required dependency "
                        f"'{e.req}' for requirement '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False
                except pkg_resources.VersionConflict as e:
                    logger.debug(
                        f"Package version '{e.dist}' does not match "
                        f"version '{e.req}' required by '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False

        except pkg_resources.DistributionNotFound as e:
            logger.debug(
                f"Unable to find required package '{e.req}' for "
                f"integration {cls.NAME}."
            )
            return False
        except pkg_resources.VersionConflict as e:
            logger.debug(
                f"Package version '{e.dist}' does not match version "
                f"'{e.req}' necessary for integration {cls.NAME}."
            )
            return False

    logger.debug(
        f"Integration {cls.NAME} is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True
flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type Description
List[Type[Flavor]]

A list of new stack component flavors.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []
get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None
python_version Optional[str]

The Python version to use for the requirements.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: Optional[str] = None,
) -> List[str]:
    """Method to get the requirements for the integration.

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

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS
get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

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

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

Abstract method to declare new plugin flavors.

Returns:

Type Description
List[Type[BasePluginFlavor]]

A list of new plugin flavors.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

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

PigeonIntegration

Bases: Integration

Definition of Pigeon integration for ZenML.

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

Declare the stack component flavors for the Pigeon integration.

Returns:

Type Description
List[Type[Flavor]]

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

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

    return [PigeonAnnotatorFlavor]

Modules

annotators

Initialization of the Pigeon annotators submodule.

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

Bases: BaseAnnotator

Annotator for using Pigeon in Jupyter notebooks.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

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

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: PigeonAnnotatorConfig property

Get the Pigeon annotator config.

Returns:

Type Description
PigeonAnnotatorConfig

The Pigeon annotator config.
Functions
add_dataset(**kwargs: Any) -> Any

Add a dataset (annotation file) to the Pigeon annotator.

Parameters:

Name Type Description Default
**kwargs Any

keyword arguments.

 {}

Raises:

Type Description
NotImplementedError

Pigeon annotator does not support adding datasets.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def add_dataset(self, **kwargs: Any) -> Any:
    """Add a dataset (annotation file) to the Pigeon annotator.

    Args:
        **kwargs: keyword arguments.

    Raises:
        NotImplementedError: Pigeon annotator does not support adding datasets.
    """
    raise NotImplementedError(
        "Pigeon annotator does not support adding datasets."
    )
annotate(data: List[Any], options: List[str], display_fn: Optional[Any] = None) -> List[Tuple[Any, Any]]

Annotate with the Pigeon annotator in the Jupyter notebook.

Parameters:

Name Type Description Default
data List[Any]

List of examples to annotate.

 required
options List[str]

List of labels to choose from.

 required
display_fn Optional[Any]

Optional function to display examples.

 None

Returns:

Type Description
List[Tuple[Any, Any]]

A list of tuples containing (example, label) for each annotated example.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def annotate(
    self,
    data: List[Any],
    options: List[str],
    display_fn: Optional[Any] = None,
) -> List[Tuple[Any, Any]]:
    """Annotate with the Pigeon annotator in the Jupyter notebook.

    Args:
        data: List of examples to annotate.
        options: List of labels to choose from.
        display_fn: Optional function to display examples.

    Returns:
        A list of tuples containing (example, label) for each annotated example.
    """
    annotations = self._annotate(data, options, display_fn)
    return annotations
delete_dataset(**kwargs: Any) -> None

Delete a dataset (annotation file).

Takes the dataset_name argument from the kwargs.

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments containing the dataset_name to delete.

 {}

Raises:

Type Description
ValueError

Dataset name is required to delete a dataset.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def delete_dataset(self, **kwargs: Any) -> None:
    """Delete a dataset (annotation file).

    Takes the `dataset_name` argument from the kwargs.

    Args:
        **kwargs: Keyword arguments containing the `dataset_name` to delete.

    Raises:
        ValueError: Dataset name is required to delete a dataset.
    """
    dataset_name = kwargs.get("dataset_name")
    if not dataset_name:
        raise ValueError(
            "Dataset name (`dataset_name`) is required to delete a dataset."
        )
    dataset_path = os.path.join(self.config.output_dir, dataset_name)
    os.remove(dataset_path)
get_dataset(**kwargs: Any) -> List[Tuple[Any, Any]]

Get the annotated examples from a dataset (annotation file).

Takes the dataset_name argument from the kwargs.

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments containing the dataset_name to retrieve.

 {}

Returns:

Type Description
List[Tuple[Any, Any]]

A list of tuples containing (example, label) for each annotated
List[Tuple[Any, Any]]

example.

Raises:

Type Description
ValueError

Dataset name is required to retrieve a dataset.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_dataset(self, **kwargs: Any) -> List[Tuple[Any, Any]]:
    """Get the annotated examples from a dataset (annotation file).

    Takes the `dataset_name` argument from the kwargs.

    Args:
        **kwargs: Keyword arguments containing the `dataset_name` to retrieve.

    Returns:
        A list of tuples containing (example, label) for each annotated
        example.

    Raises:
        ValueError: Dataset name is required to retrieve a dataset.
    """
    dataset_name = kwargs.get("dataset_name")
    if not dataset_name:
        raise ValueError(
            "Dataset name (`dataset_name`) is required to retrieve a dataset."
        )
    dataset_path = os.path.join(self.config.output_dir, dataset_name)
    with open(dataset_path, "r") as f:
        annotations = json.load(f)
    return cast(List[Tuple[Any, Any]], annotations)
get_dataset_names() -> List[str]

List dataset names (annotation file names) in the output directory.

Returns:

Type Description
List[str]

A list of dataset names (annotation file names).
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_dataset_names(self) -> List[str]:
    """List dataset names (annotation file names) in the output directory.

    Returns:
        A list of dataset names (annotation file names).
    """
    return self.get_datasets()
get_dataset_stats(dataset_name: str) -> Tuple[int, int]

List labeled and unlabeled examples in a dataset (annotation file).

Parameters:

Name Type Description Default
dataset_name str

Name of the dataset (annotation file).

 required

Returns:

Type Description
Tuple[int, int]

A tuple containing (num_labeled_examples, num_unlabeled_examples).
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_dataset_stats(self, dataset_name: str) -> Tuple[int, int]:
    """List labeled and unlabeled examples in a dataset (annotation file).

    Args:
        dataset_name: Name of the dataset (annotation file).

    Returns:
        A tuple containing (num_labeled_examples, num_unlabeled_examples).
    """
    dataset_path = os.path.join(self.config.output_dir, dataset_name)
    num_labeled_examples = 0
    # Placeholder as logic to determine this is not implemented
    num_unlabeled_examples = 0

    try:
        with open(dataset_path, "r") as file:
            num_labeled_examples = sum(1 for _ in file)
    except FileNotFoundError:
        logger.error(f"File not found: {dataset_path}")

    return num_labeled_examples, num_unlabeled_examples
get_datasets() -> List[str]

Get a list of datasets (annotation files) in the output directory.

Returns:

Type Description
List[str]

A list of dataset names (annotation file names) (or empty list when no datasets are present).
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_datasets(self) -> List[str]:
    """Get a list of datasets (annotation files) in the output directory.

    Returns:
        A list of dataset names (annotation file names) (or empty list when no datasets are present).
    """
    output_dir = self.config.output_dir
    try:
        return [f for f in os.listdir(output_dir) if f.endswith(".txt")]
    except FileNotFoundError:
        return []
get_labeled_data(**kwargs: Any) -> List[Tuple[Any, Any]]

Get the labeled examples from a dataset (annotation file).

Takes the dataset_name argument from the kwargs.

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments containing the dataset_name to retrieve.

 {}

Returns:

Type Description
List[Tuple[Any, Any]]

A list of tuples containing (example, label) for each labeled
List[Tuple[Any, Any]]

example.

Raises:

Type Description
ValueError

Dataset name is required to retrieve labeled data.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_labeled_data(self, **kwargs: Any) -> List[Tuple[Any, Any]]:
    """Get the labeled examples from a dataset (annotation file).

    Takes the `dataset_name` argument from the kwargs.

    Args:
        **kwargs: Keyword arguments containing the `dataset_name` to retrieve.

    Returns:
        A list of tuples containing (example, label) for each labeled
        example.

    Raises:
        ValueError: Dataset name is required to retrieve labeled data.
    """
    if dataset_name := kwargs.get("dataset_name"):
        return self.get_dataset(dataset_name=dataset_name)
    else:
        raise ValueError(
            "Dataset name (`dataset_name`) is required to retrieve labeled data."
        )
get_unlabeled_data(**kwargs: Any) -> Any

Get the unlabeled examples from a dataset (annotation file).

Parameters:

Name Type Description Default
**kwargs Any

keyword arguments.

 {}

Raises:

Type Description
NotImplementedError

Pigeon annotator does not support retrieving unlabeled data.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_unlabeled_data(self, **kwargs: Any) -> Any:
    """Get the unlabeled examples from a dataset (annotation file).

    Args:
        **kwargs: keyword arguments.

    Raises:
        NotImplementedError: Pigeon annotator does not support retrieving unlabeled data.
    """
    raise NotImplementedError(
        "Pigeon annotator does not support retrieving unlabeled data."
    )
get_url() -> str

Get the URL of the Pigeon annotator.

Raises:

Type Description
NotImplementedError

Pigeon annotator does not have a URL.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_url(self) -> str:
    """Get the URL of the Pigeon annotator.

    Raises:
        NotImplementedError: Pigeon annotator does not have a URL.
    """
    raise NotImplementedError("Pigeon annotator does not have a URL.")
get_url_for_dataset(dataset_name: str) -> str

Get the URL of the Pigeon annotator for a specific dataset.

Parameters:

Name Type Description Default
dataset_name str

Name of the dataset (annotation file).

 required

Raises:

Type Description
NotImplementedError

Pigeon annotator does not have a URL.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_url_for_dataset(self, dataset_name: str) -> str:
    """Get the URL of the Pigeon annotator for a specific dataset.

    Args:
        dataset_name: Name of the dataset (annotation file).

    Raises:
        NotImplementedError: Pigeon annotator does not have a URL.
    """
    raise NotImplementedError("Pigeon annotator does not have a URL.")
launch(**kwargs: Any) -> None

Launch the Pigeon annotator in the Jupyter notebook.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the annotation client.

 {}

Raises:

Type Description
NotImplementedError

Pigeon annotator does not support launching with a URL.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def launch(self, **kwargs: Any) -> None:
    """Launch the Pigeon annotator in the Jupyter notebook.

    Args:
        **kwargs: Additional keyword arguments to pass to the annotation client.

    Raises:
        NotImplementedError: Pigeon annotator does not support launching with a URL.
    """
    raise NotImplementedError(
        "Pigeon annotator does not support launching with a URL."
    )
Modules
pigeon_annotator

Pigeon annotator.

Credit for the implementation of this code to @agermanidis in the Pigeon package and library. This code has been slightly modified to fit the ZenML framework. We use the modified code directly here because the original package (and code) is no longer compatible with more recent versions of ipywidgets.

https://github.com/agermanidis/pigeon

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

Bases: BaseAnnotator

Annotator for using Pigeon in Jupyter notebooks.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

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

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: PigeonAnnotatorConfig property

Get the Pigeon annotator config.

Returns:

Type Description
PigeonAnnotatorConfig

The Pigeon annotator config.
Functions
add_dataset(**kwargs: Any) -> Any

Add a dataset (annotation file) to the Pigeon annotator.

Parameters:

Name Type Description Default
**kwargs Any

keyword arguments.

 {}

Raises:

Type Description
NotImplementedError

Pigeon annotator does not support adding datasets.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def add_dataset(self, **kwargs: Any) -> Any:
    """Add a dataset (annotation file) to the Pigeon annotator.

    Args:
        **kwargs: keyword arguments.

    Raises:
        NotImplementedError: Pigeon annotator does not support adding datasets.
    """
    raise NotImplementedError(
        "Pigeon annotator does not support adding datasets."
    )
annotate(data: List[Any], options: List[str], display_fn: Optional[Any] = None) -> List[Tuple[Any, Any]]

Annotate with the Pigeon annotator in the Jupyter notebook.

Parameters:

Name Type Description Default
data List[Any]

List of examples to annotate.

 required
options List[str]

List of labels to choose from.

 required
display_fn Optional[Any]

Optional function to display examples.

 None

Returns:

Type Description
List[Tuple[Any, Any]]

A list of tuples containing (example, label) for each annotated example.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def annotate(
    self,
    data: List[Any],
    options: List[str],
    display_fn: Optional[Any] = None,
) -> List[Tuple[Any, Any]]:
    """Annotate with the Pigeon annotator in the Jupyter notebook.

    Args:
        data: List of examples to annotate.
        options: List of labels to choose from.
        display_fn: Optional function to display examples.

    Returns:
        A list of tuples containing (example, label) for each annotated example.
    """
    annotations = self._annotate(data, options, display_fn)
    return annotations
delete_dataset(**kwargs: Any) -> None

Delete a dataset (annotation file).

Takes the dataset_name argument from the kwargs.

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments containing the dataset_name to delete.

 {}

Raises:

Type Description
ValueError

Dataset name is required to delete a dataset.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def delete_dataset(self, **kwargs: Any) -> None:
    """Delete a dataset (annotation file).

    Takes the `dataset_name` argument from the kwargs.

    Args:
        **kwargs: Keyword arguments containing the `dataset_name` to delete.

    Raises:
        ValueError: Dataset name is required to delete a dataset.
    """
    dataset_name = kwargs.get("dataset_name")
    if not dataset_name:
        raise ValueError(
            "Dataset name (`dataset_name`) is required to delete a dataset."
        )
    dataset_path = os.path.join(self.config.output_dir, dataset_name)
    os.remove(dataset_path)
get_dataset(**kwargs: Any) -> List[Tuple[Any, Any]]

Get the annotated examples from a dataset (annotation file).

Takes the dataset_name argument from the kwargs.

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments containing the dataset_name to retrieve.

 {}

Returns:

Type Description
List[Tuple[Any, Any]]

A list of tuples containing (example, label) for each annotated
List[Tuple[Any, Any]]

example.

Raises:

Type Description
ValueError

Dataset name is required to retrieve a dataset.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_dataset(self, **kwargs: Any) -> List[Tuple[Any, Any]]:
    """Get the annotated examples from a dataset (annotation file).

    Takes the `dataset_name` argument from the kwargs.

    Args:
        **kwargs: Keyword arguments containing the `dataset_name` to retrieve.

    Returns:
        A list of tuples containing (example, label) for each annotated
        example.

    Raises:
        ValueError: Dataset name is required to retrieve a dataset.
    """
    dataset_name = kwargs.get("dataset_name")
    if not dataset_name:
        raise ValueError(
            "Dataset name (`dataset_name`) is required to retrieve a dataset."
        )
    dataset_path = os.path.join(self.config.output_dir, dataset_name)
    with open(dataset_path, "r") as f:
        annotations = json.load(f)
    return cast(List[Tuple[Any, Any]], annotations)
get_dataset_names() -> List[str]

List dataset names (annotation file names) in the output directory.

Returns:

Type Description
List[str]

A list of dataset names (annotation file names).
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_dataset_names(self) -> List[str]:
    """List dataset names (annotation file names) in the output directory.

    Returns:
        A list of dataset names (annotation file names).
    """
    return self.get_datasets()
get_dataset_stats(dataset_name: str) -> Tuple[int, int]

List labeled and unlabeled examples in a dataset (annotation file).

Parameters:

Name Type Description Default
dataset_name str

Name of the dataset (annotation file).

 required

Returns:

Type Description
Tuple[int, int]

A tuple containing (num_labeled_examples, num_unlabeled_examples).
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_dataset_stats(self, dataset_name: str) -> Tuple[int, int]:
    """List labeled and unlabeled examples in a dataset (annotation file).

    Args:
        dataset_name: Name of the dataset (annotation file).

    Returns:
        A tuple containing (num_labeled_examples, num_unlabeled_examples).
    """
    dataset_path = os.path.join(self.config.output_dir, dataset_name)
    num_labeled_examples = 0
    # Placeholder as logic to determine this is not implemented
    num_unlabeled_examples = 0

    try:
        with open(dataset_path, "r") as file:
            num_labeled_examples = sum(1 for _ in file)
    except FileNotFoundError:
        logger.error(f"File not found: {dataset_path}")

    return num_labeled_examples, num_unlabeled_examples
get_datasets() -> List[str]

Get a list of datasets (annotation files) in the output directory.

Returns:

Type Description
List[str]

A list of dataset names (annotation file names) (or empty list when no datasets are present).
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_datasets(self) -> List[str]:
    """Get a list of datasets (annotation files) in the output directory.

    Returns:
        A list of dataset names (annotation file names) (or empty list when no datasets are present).
    """
    output_dir = self.config.output_dir
    try:
        return [f for f in os.listdir(output_dir) if f.endswith(".txt")]
    except FileNotFoundError:
        return []
get_labeled_data(**kwargs: Any) -> List[Tuple[Any, Any]]

Get the labeled examples from a dataset (annotation file).

Takes the dataset_name argument from the kwargs.

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments containing the dataset_name to retrieve.

 {}

Returns:

Type Description
List[Tuple[Any, Any]]

A list of tuples containing (example, label) for each labeled
List[Tuple[Any, Any]]

example.

Raises:

Type Description
ValueError

Dataset name is required to retrieve labeled data.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_labeled_data(self, **kwargs: Any) -> List[Tuple[Any, Any]]:
    """Get the labeled examples from a dataset (annotation file).

    Takes the `dataset_name` argument from the kwargs.

    Args:
        **kwargs: Keyword arguments containing the `dataset_name` to retrieve.

    Returns:
        A list of tuples containing (example, label) for each labeled
        example.

    Raises:
        ValueError: Dataset name is required to retrieve labeled data.
    """
    if dataset_name := kwargs.get("dataset_name"):
        return self.get_dataset(dataset_name=dataset_name)
    else:
        raise ValueError(
            "Dataset name (`dataset_name`) is required to retrieve labeled data."
        )
get_unlabeled_data(**kwargs: Any) -> Any

Get the unlabeled examples from a dataset (annotation file).

Parameters:

Name Type Description Default
**kwargs Any

keyword arguments.

 {}

Raises:

Type Description
NotImplementedError

Pigeon annotator does not support retrieving unlabeled data.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_unlabeled_data(self, **kwargs: Any) -> Any:
    """Get the unlabeled examples from a dataset (annotation file).

    Args:
        **kwargs: keyword arguments.

    Raises:
        NotImplementedError: Pigeon annotator does not support retrieving unlabeled data.
    """
    raise NotImplementedError(
        "Pigeon annotator does not support retrieving unlabeled data."
    )
get_url() -> str

Get the URL of the Pigeon annotator.

Raises:

Type Description
NotImplementedError

Pigeon annotator does not have a URL.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_url(self) -> str:
    """Get the URL of the Pigeon annotator.

    Raises:
        NotImplementedError: Pigeon annotator does not have a URL.
    """
    raise NotImplementedError("Pigeon annotator does not have a URL.")
get_url_for_dataset(dataset_name: str) -> str

Get the URL of the Pigeon annotator for a specific dataset.

Parameters:

Name Type Description Default
dataset_name str

Name of the dataset (annotation file).

 required

Raises:

Type Description
NotImplementedError

Pigeon annotator does not have a URL.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def get_url_for_dataset(self, dataset_name: str) -> str:
    """Get the URL of the Pigeon annotator for a specific dataset.

    Args:
        dataset_name: Name of the dataset (annotation file).

    Raises:
        NotImplementedError: Pigeon annotator does not have a URL.
    """
    raise NotImplementedError("Pigeon annotator does not have a URL.")
launch(**kwargs: Any) -> None

Launch the Pigeon annotator in the Jupyter notebook.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments to pass to the annotation client.

 {}

Raises:

Type Description
NotImplementedError

Pigeon annotator does not support launching with a URL.
Source code in src/zenml/integrations/pigeon/annotators/pigeon_annotator.py 
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def launch(self, **kwargs: Any) -> None:
    """Launch the Pigeon annotator in the Jupyter notebook.

    Args:
        **kwargs: Additional keyword arguments to pass to the annotation client.

    Raises:
        NotImplementedError: Pigeon annotator does not support launching with a URL.
    """
    raise NotImplementedError(
        "Pigeon annotator does not support launching with a URL."
    )
Functions

flavors

Pigeon integration flavors.

Classes
PigeonAnnotatorConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseAnnotatorConfig, PigeonAnnotatorSettings, AuthenticationConfigMixin

Config for the Pigeon annotator.

Attributes:

Name Type Description
output_dir str

The directory to store the annotations.
notebook_only bool

Whether the annotator only works within a notebook.
Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

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

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

        if value is None:
            continue

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

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

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

    super().__init__(**kwargs)
PigeonAnnotatorFlavor

Bases: BaseAnnotatorFlavor

Pigeon annotator flavor.

Attributes
config_class: Type[PigeonAnnotatorConfig] property

Returns PigeonAnnotatorConfig config class.

Returns:

Type Description
Type[PigeonAnnotatorConfig]

The config class.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[PigeonAnnotator] property

Implementation class for this flavor.

Returns:

Type Description
Type[PigeonAnnotator]

The implementation class.
logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
Modules
pigeon_annotator_flavor

Pigeon annotator flavor.

Classes
PigeonAnnotatorConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseAnnotatorConfig, PigeonAnnotatorSettings, AuthenticationConfigMixin

Config for the Pigeon annotator.

Attributes:

Name Type Description
output_dir str

The directory to store the annotations.
notebook_only bool

Whether the annotator only works within a notebook.
Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

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

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

        if value is None:
            continue

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

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

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

    super().__init__(**kwargs)
PigeonAnnotatorFlavor

Bases: BaseAnnotatorFlavor

Pigeon annotator flavor.

Attributes
config_class: Type[PigeonAnnotatorConfig] property

Returns PigeonAnnotatorConfig config class.

Returns:

Type Description
Type[PigeonAnnotatorConfig]

The config class.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[PigeonAnnotator] property

Implementation class for this flavor.

Returns:

Type Description
Type[PigeonAnnotator]

The implementation class.
logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
PigeonAnnotatorSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Settings for the Pigeon annotator.

Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

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

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

        if value is None:
            continue

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

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

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

    super().__init__(**kwargs)