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Neptune

zenml.integrations.neptune

Module containing Neptune integration.

Attributes

NEPTUNE = 'neptune' module-attribute

NEPTUNE_MODEL_EXPERIMENT_TRACKER_FLAVOR = 'neptune' 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 []

NeptuneIntegration

Bases: Integration

Definition of the neptune.ai integration with ZenML.

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

Declare the stack component flavors for the Neptune integration.

Returns:

Type Description
List[Type[Flavor]]

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

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

    return [
        NeptuneExperimentTrackerFlavor,
    ]

Modules

experiment_trackers

Initialization of Neptune experiment tracker.

Classes
NeptuneExperimentTracker(*args: Any, **kwargs: Any)

Bases: BaseExperimentTracker

Track experiments using neptune.ai.

Initialize the experiment tracker.

Parameters:

Name Type Description Default
*args Any

Variable length argument list.

 ()
**kwargs Any

Arbitrary keyword arguments.

 {}
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def __init__(self, *args: Any, **kwargs: Any) -> None:
    """Initialize the experiment tracker.

    Args:
        *args: Variable length argument list.
        **kwargs: Arbitrary keyword arguments.
    """
    super().__init__(*args, **kwargs)
    self.run_state: RunProvider = RunProvider()
Attributes
config: NeptuneExperimentTrackerConfig property

Returns the NeptuneExperimentTrackerConfig config.

Returns:

Type Description
NeptuneExperimentTrackerConfig

The configuration.
settings_class: Optional[Type[BaseSettings]] property

Settings class for the Neptune experiment tracker.

Returns:

Type Description
Optional[Type[BaseSettings]]

The settings class.
Functions
cleanup_step_run(info: StepRunInfo, step_failed: bool) -> None

Stop the Neptune run.

Parameters:

Name Type Description Default
info StepRunInfo

Info about the step that was executed.

 required
step_failed bool

Whether the step failed or not.

 required
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def cleanup_step_run(self, info: "StepRunInfo", step_failed: bool) -> None:
    """Stop the Neptune run.

    Args:
        info: Info about the step that was executed.
        step_failed: Whether the step failed or not.
    """
    self.run_state.active_run.sync()
    self.run_state.active_run.stop()
    self.run_state.reset()
get_step_run_metadata(info: StepRunInfo) -> Dict[str, MetadataType]

Get component- and step-specific metadata after a step ran.

Parameters:

Name Type Description Default
info StepRunInfo

Info about the step that was executed.

 required

Returns:

Type Description
Dict[str, MetadataType]

A dictionary of metadata.
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def get_step_run_metadata(
    self, info: "StepRunInfo"
) -> Dict[str, "MetadataType"]:
    """Get component- and step-specific metadata after a step ran.

    Args:
        info: Info about the step that was executed.

    Returns:
        A dictionary of metadata.
    """
    run_url = self.run_state.active_run.get_url()
    return {
        METADATA_EXPERIMENT_TRACKER_URL: Uri(run_url),
    }
prepare_step_run(info: StepRunInfo) -> None

Initializes a Neptune run and stores it in the run_state object.

The run object can then be accessed later from other places, such as a step.

Parameters:

Name Type Description Default
info StepRunInfo

Info about the step that was executed.

 required
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def prepare_step_run(self, info: "StepRunInfo") -> None:
    """Initializes a Neptune run and stores it in the run_state object.

    The run object can then be accessed later from other places, such as a step.

    Args:
        info: Info about the step that was executed.
    """
    settings = cast(
        NeptuneExperimentTrackerSettings, self.get_settings(info)
    )

    self.run_state.initialize(
        project=self.config.project,
        token=self.config.api_token,
        run_name=info.run_name,
        tags=list(settings.tags),
    )
Modules
neptune_experiment_tracker

Implementation of Neptune Experiment Tracker.

Classes
NeptuneExperimentTracker(*args: Any, **kwargs: Any)

Bases: BaseExperimentTracker

Track experiments using neptune.ai.

Initialize the experiment tracker.

Parameters:

Name Type Description Default
*args Any

Variable length argument list.

 ()
**kwargs Any

Arbitrary keyword arguments.

 {}
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def __init__(self, *args: Any, **kwargs: Any) -> None:
    """Initialize the experiment tracker.

    Args:
        *args: Variable length argument list.
        **kwargs: Arbitrary keyword arguments.
    """
    super().__init__(*args, **kwargs)
    self.run_state: RunProvider = RunProvider()
Attributes
config: NeptuneExperimentTrackerConfig property

Returns the NeptuneExperimentTrackerConfig config.

Returns:

Type Description
NeptuneExperimentTrackerConfig

The configuration.
settings_class: Optional[Type[BaseSettings]] property

Settings class for the Neptune experiment tracker.

Returns:

Type Description
Optional[Type[BaseSettings]]

The settings class.
Functions
cleanup_step_run(info: StepRunInfo, step_failed: bool) -> None

Stop the Neptune run.

Parameters:

Name Type Description Default
info StepRunInfo

Info about the step that was executed.

 required
step_failed bool

Whether the step failed or not.

 required
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def cleanup_step_run(self, info: "StepRunInfo", step_failed: bool) -> None:
    """Stop the Neptune run.

    Args:
        info: Info about the step that was executed.
        step_failed: Whether the step failed or not.
    """
    self.run_state.active_run.sync()
    self.run_state.active_run.stop()
    self.run_state.reset()
get_step_run_metadata(info: StepRunInfo) -> Dict[str, MetadataType]

Get component- and step-specific metadata after a step ran.

Parameters:

Name Type Description Default
info StepRunInfo

Info about the step that was executed.

 required

Returns:

Type Description
Dict[str, MetadataType]

A dictionary of metadata.
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def get_step_run_metadata(
    self, info: "StepRunInfo"
) -> Dict[str, "MetadataType"]:
    """Get component- and step-specific metadata after a step ran.

    Args:
        info: Info about the step that was executed.

    Returns:
        A dictionary of metadata.
    """
    run_url = self.run_state.active_run.get_url()
    return {
        METADATA_EXPERIMENT_TRACKER_URL: Uri(run_url),
    }
prepare_step_run(info: StepRunInfo) -> None

Initializes a Neptune run and stores it in the run_state object.

The run object can then be accessed later from other places, such as a step.

Parameters:

Name Type Description Default
info StepRunInfo

Info about the step that was executed.

 required
Source code in src/zenml/integrations/neptune/experiment_trackers/neptune_experiment_tracker.py 
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def prepare_step_run(self, info: "StepRunInfo") -> None:
    """Initializes a Neptune run and stores it in the run_state object.

    The run object can then be accessed later from other places, such as a step.

    Args:
        info: Info about the step that was executed.
    """
    settings = cast(
        NeptuneExperimentTrackerSettings, self.get_settings(info)
    )

    self.run_state.initialize(
        project=self.config.project,
        token=self.config.api_token,
        run_name=info.run_name,
        tags=list(settings.tags),
    )
run_state

Contains objects that create a Neptune run and store its state throughout the pipeline.

Classes
RunProvider()

Singleton object used to store and persist a Neptune run state across the pipeline.

Initialize RunProvider. Called with no arguments.

Source code in src/zenml/integrations/neptune/experiment_trackers/run_state.py 
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def __init__(self) -> None:
    """Initialize RunProvider. Called with no arguments."""
    self._active_run: Optional["Run"] = None
    self._project: Optional[str] = None
    self._run_name: Optional[str] = None
    self._token: Optional[str] = None
    self._tags: Optional[List[str]] = None
    self._initialized = False
Attributes
active_run: Run property

Initializes a new neptune run every time it is called.

The run is closed and the active run state is set to stopped after each step is completed.

Returns:

Type Description
Run

Neptune run object
initialized: bool property

If the run state is initialized.

Returns:

Type Description
bool

If the run state is initialized.
project: Optional[Any] property

Getter for project name.

Returns:

Type Description
Optional[Any]

Name of the project passed to the RunProvider.
run_name: Optional[Any] property

Getter for run name.

Returns:

Type Description
Optional[Any]

Name of the pipeline run.
tags: Optional[Any] property

Getter for run tags.

Returns:

Type Description
Optional[Any]

Tags associated with a Neptune run.
token: Optional[Any] property

Getter for API token.

Returns:

Type Description
Optional[Any]

Neptune API token passed to the RunProvider.
Functions
initialize(project: Optional[str] = None, token: Optional[str] = None, run_name: Optional[str] = None, tags: Optional[List[str]] = None) -> None

Initialize the run state.

Parameters:

Name Type Description Default
project Optional[str]

The neptune project.

 None
token Optional[str]

The neptune token.

 None
run_name Optional[str]

The neptune run name.

 None
tags Optional[List[str]]

Tags for the neptune run.

 None
Source code in src/zenml/integrations/neptune/experiment_trackers/run_state.py 
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def initialize(
    self,
    project: Optional[str] = None,
    token: Optional[str] = None,
    run_name: Optional[str] = None,
    tags: Optional[List[str]] = None,
) -> None:
    """Initialize the run state.

    Args:
        project: The neptune project.
        token: The neptune token.
        run_name: The neptune run name.
        tags: Tags for the neptune run.
    """
    self._project = project
    self._token = token
    self._run_name = run_name
    self._tags = tags
    self._initialized = True
reset() -> None

Reset the run state.

Source code in src/zenml/integrations/neptune/experiment_trackers/run_state.py 
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def reset(self) -> None:
    """Reset the run state."""
    self._active_run = None
    self._project = None
    self._run_name = None
    self._token = None
    self._tags = None
    self._initialized = False
Functions
get_neptune_run() -> Run

Helper function to fetch an existing Neptune run or create a new one.

Returns:

Type Description
Run

Neptune run object

Raises:

Type Description
RuntimeError

When unable to fetch the active neptune run.
Source code in src/zenml/integrations/neptune/experiment_trackers/run_state.py 
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def get_neptune_run() -> "Run":
    """Helper function to fetch an existing Neptune run or create a new one.

    Returns:
        Neptune run object

    Raises:
        RuntimeError: When unable to fetch the active neptune run.
    """
    from zenml.integrations.neptune.experiment_trackers import (
        NeptuneExperimentTracker,
    )

    experiment_tracker = Client().active_stack.experiment_tracker

    if not experiment_tracker:
        raise RuntimeError(
            "Unable to get neptune run: Missing experiment tracker in the "
            "active stack."
        )

    if not isinstance(experiment_tracker, NeptuneExperimentTracker):
        raise RuntimeError(
            "Unable to get neptune run: Experiment tracker in the active "
            f"stack ({experiment_tracker.flavor}) is not a neptune experiment "
            "tracker."
        )

    run_state = experiment_tracker.run_state
    if not run_state.initialized:
        raise RuntimeError(
            "Unable to get neptune run: The experiment tracker has not been "
            "initialized. To solve this, make sure you use the experiment "
            "tracker in your step. See "
            "https://docs.zenml.io/stack-components/experiment-trackers/neptune#how-do-you-use-it "
            "for more information."
        )

    return experiment_tracker.run_state.active_run

flavors

Neptune integration flavors.

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

Bases: BaseExperimentTrackerConfig

Config for the Neptune experiment tracker.

If attributes are left as None, the neptune.init_run() method will try to find the relevant values in the environment

Attributes:

Name Type Description
project Optional[str]

name of the Neptune project you want to log the metadata to
api_token Optional[str]

your Neptune API token
Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

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

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

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

        if value is None:
            continue

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

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

    super().__init__(**kwargs)
NeptuneExperimentTrackerFlavor

Bases: BaseExperimentTrackerFlavor

Class for the NeptuneExperimentTrackerFlavor.

Attributes
config_class: Type[NeptuneExperimentTrackerConfig] property

Returns NeptuneExperimentTrackerConfig config class.

Returns:

Type Description
Type[NeptuneExperimentTrackerConfig]

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[NeptuneExperimentTracker] property

Implementation class for this flavor.

Returns:

Type Description
Type[NeptuneExperimentTracker]

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.
NeptuneExperimentTrackerSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Settings for the Neptune experiment tracker.

Attributes:

Name Type Description
tags Set[str]

Tags for the Neptune run.
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)
Modules
neptune_experiment_tracker_flavor

Neptune experiment tracker flavor.

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

Bases: BaseExperimentTrackerConfig

Config for the Neptune experiment tracker.

If attributes are left as None, the neptune.init_run() method will try to find the relevant values in the environment

Attributes:

Name Type Description
project Optional[str]

name of the Neptune project you want to log the metadata to
api_token Optional[str]

your Neptune API token
Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

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

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

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

        if value is None:
            continue

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

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

    super().__init__(**kwargs)
NeptuneExperimentTrackerFlavor

Bases: BaseExperimentTrackerFlavor

Class for the NeptuneExperimentTrackerFlavor.

Attributes
config_class: Type[NeptuneExperimentTrackerConfig] property

Returns NeptuneExperimentTrackerConfig config class.

Returns:

Type Description
Type[NeptuneExperimentTrackerConfig]

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[NeptuneExperimentTracker] property

Implementation class for this flavor.

Returns:

Type Description
Type[NeptuneExperimentTracker]

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.
NeptuneExperimentTrackerSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Settings for the Neptune experiment tracker.

Attributes:

Name Type Description
tags Set[str]

Tags for the Neptune run.
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)
Functions

neptune_constants

Some constants for reading environment variables.