Pytorch

zenml.integrations.pytorch

Initialization of the PyTorch integration.

Attributes

PYTORCH = 'pytorch' module-attribute

Classes

Integration

Base class for integration in ZenML.

Functions

activate() -> None classmethod

Abstract method to activate the integration.

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

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

check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

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

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

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

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for 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

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

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

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

Method to get the requirements for the integration.

Parameters:

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

Returns:

Type	Description
List[str]	A list of requirements.

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

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

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

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

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

Method to get the uninstall requirements for the integration.

Parameters:

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

Returns:

Type	Description
List[str]	A list of requirements.

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

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

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

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

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

Abstract method to declare new plugin flavors.

Returns:

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

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

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

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

PytorchIntegration

Bases: Integration

Definition of PyTorch integration for ZenML.

Functions

activate() -> None classmethod

Activates the integration.

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

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

Modules

materializers

Initialization of the PyTorch Materializer.

Classes

Modules

base_pytorch_materializer

Implementation of the PyTorch DataLoader materializer.

Classes

BasePyTorchMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: BaseMaterializer

Base class for PyTorch materializers.

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store

Functions

load(data_type: Type[Any]) -> Any

Uses torch.load to load a PyTorch object.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the object to load.	required

Returns:

Type	Description
Any	The loaded PyTorch object.

Source code in src/zenml/integrations/pytorch/materializers/base_pytorch_materializer.py

def load(self, data_type: Type[Any]) -> Any:
    """Uses `torch.load` to load a PyTorch object.

    Args:
        data_type: The type of the object to load.

    Returns:
        The loaded PyTorch object.
    """
    with fileio.open(os.path.join(self.uri, self.FILENAME), "rb") as f:
        # NOTE (security): The `torch.load` function uses `pickle` as
        # the default unpickler, which is NOT secure. This materializer
        # is intended for use with trusted data sources.
        return torch.load(f, weights_only=False)  # nosec

save(obj: Any) -> None

Uses torch.save to save a PyTorch object.

Parameters:

Name	Type	Description	Default
obj	Any	The PyTorch object to save.	required

Source code in src/zenml/integrations/pytorch/materializers/base_pytorch_materializer.py

def save(self, obj: Any) -> None:
    """Uses `torch.save` to save a PyTorch object.

    Args:
        obj: The PyTorch object to save.
    """
    with fileio.open(os.path.join(self.uri, self.FILENAME), "wb") as f:
        # NOTE (security): The `torch.save` function uses `cloudpickle` as
        # the default unpickler, which is NOT secure. This materializer
        # is intended for use with trusted data sources.
        torch.save(obj, f, pickle_module=cloudpickle)  # nosec

Modules

pytorch_dataloader_materializer

Implementation of the PyTorch DataLoader materializer.

Classes

PyTorchDataLoaderMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: BasePyTorchMaterializer

Materializer to read/write PyTorch dataloaders and datasets.

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store

Functions

extract_metadata(dataloader: Any) -> Dict[str, MetadataType]

Extract metadata from the given dataloader or dataset.

Parameters:

Name	Type	Description	Default
dataloader	Any	The dataloader or dataset to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/integrations/pytorch/materializers/pytorch_dataloader_materializer.py

def extract_metadata(self, dataloader: Any) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given dataloader or dataset.

    Args:
        dataloader: The dataloader or dataset to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    metadata: Dict[str, "MetadataType"] = {}
    if isinstance(dataloader, DataLoader):
        if hasattr(dataloader.dataset, "__len__"):
            metadata["num_samples"] = len(dataloader.dataset)
        if dataloader.batch_size:
            metadata["batch_size"] = dataloader.batch_size
        metadata["num_batches"] = len(dataloader)
    elif isinstance(dataloader, Dataset):
        if hasattr(dataloader, "__len__"):
            metadata["num_samples"] = len(dataloader)
    return metadata

pytorch_module_materializer

Implementation of the PyTorch Module materializer.

Classes

PyTorchModuleMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: BasePyTorchMaterializer

Materializer to read/write Pytorch models.

Inspired by the guide: https://pytorch.org/tutorials/beginner/saving_loading_models.html

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store

Functions

extract_metadata(model: Module) -> Dict[str, MetadataType]

Extract metadata from the given Model object.

Parameters:

Name	Type	Description	Default
model	Module	The Model object to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/integrations/pytorch/materializers/pytorch_module_materializer.py

def extract_metadata(self, model: Module) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given `Model` object.

    Args:
        model: The `Model` object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    return {**count_module_params(model)}

save(model: Module) -> None

Writes a PyTorch model, as a model and a checkpoint.

Parameters:

Name	Type	Description	Default
model	Module	A torch.nn.Module or a dict to pass into model.save	required

Source code in src/zenml/integrations/pytorch/materializers/pytorch_module_materializer.py

def save(self, model: Module) -> None:
    """Writes a PyTorch model, as a model and a checkpoint.

    Args:
        model: A torch.nn.Module or a dict to pass into model.save
    """
    # Save entire model to artifact directory, This is the default behavior
    # for loading model in development phase (training, evaluation)
    super().save(model)

    # Also save model checkpoint to artifact directory,
    # This is the default behavior for loading model in production phase (inference)
    if isinstance(model, Module):
        with fileio.open(
            os.path.join(self.uri, CHECKPOINT_FILENAME), "wb"
        ) as f:
            # NOTE (security): The `torch.save` function uses `cloudpickle` as
            # the default unpickler, which is NOT secure. This materializer
            # is intended for use with trusted data sources.
            torch.save(model.state_dict(), f, pickle_module=cloudpickle)  # nosec

Functions Modules

utils

PyTorch utils.

Functions

count_module_params(module: torch.nn.Module) -> Dict[str, int]

Get the total and trainable parameters of a module.

Parameters:

Name	Type	Description	Default
module	Module	The module to get the parameters of.	required

Returns:

Type	Description
Dict[str, int]	A dictionary with the total and trainable parameters.

Source code in src/zenml/integrations/pytorch/utils.py

def count_module_params(module: torch.nn.Module) -> Dict[str, int]:
    """Get the total and trainable parameters of a module.

    Args:
        module: The module to get the parameters of.

    Returns:
        A dictionary with the total and trainable parameters.
    """
    total_params = sum([param.numel() for param in module.parameters()])
    trainable_params = sum(
        [param.numel() for param in module.parameters() if param.requires_grad]
    )
    return {
        "num_params": total_params,
        "num_trainable_params": trainable_params,
    }