Tensorflow

zenml.integrations.tensorflow

Initialization for TensorFlow integration.

Attributes

TENSORFLOW = 'tensorflow' module-attribute

logger = get_logger(__name__) 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 requirement in cls.get_requirements():
        parsed_requirement = Requirement(requirement)

        if not requirement_installed(parsed_requirement):
            logger.debug(
                "Requirement '%s' for integration '%s' is not installed "
                "or installed with the wrong version.",
                requirement,
                cls.NAME,
            )
            return False

        dependencies = get_dependencies(parsed_requirement)

        for dependency in dependencies:
            if not requirement_installed(dependency):
                logger.debug(
                    "Requirement '%s' for integration '%s' is not "
                    "installed or installed with the wrong version.",
                    dependency,
                    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 []

TensorflowIntegration

Bases: Integration

Definition of Tensorflow integration for ZenML.

Functions

activate() -> None classmethod

Activates the integration.

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

@classmethod
def activate(cls) -> None:
    """Activates the integration."""
    # need to import this explicitly to load the Tensorflow file IO support
    # for S3 and other file systems
    if not platform.system() == "Darwin" or not platform.machine() == "arm64":
        import tensorflow_io  # type: ignore

    from zenml.integrations.tensorflow import materializers  # noqa

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

Defines platform specific requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system.	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/tensorflow/__init__.py

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

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

    Returns:
        A list of requirements.
    """
    target_os = target_os or platform.system()
    if target_os == "Darwin" and platform.machine() == "arm64":
        requirements = [
            "tensorflow-macos>=2.12,<2.15",
        ]
    else:
        requirements = [
            "tensorflow>=2.12,<2.15",
            "tensorflow_io>=0.24.0",
        ]

    return requirements

Functions

get_logger(logger_name: str) -> logging.Logger

Main function to get logger name,.

Parameters:

Name	Type	Description	Default
logger_name	str	Name of logger to initialize.	required

Returns:

Type	Description
Logger	A logger object.

Source code in src/zenml/logger.py

def get_logger(logger_name: str) -> logging.Logger:
    """Main function to get logger name,.

    Args:
        logger_name: Name of logger to initialize.

    Returns:
        A logger object.
    """
    logger = logging.getLogger(logger_name)
    logger.setLevel(get_logging_level().value)
    logger.addHandler(get_console_handler())

    logger.propagate = False
    return logger

Modules

materializers

Initialization for the TensorFlow materializers.

Classes

Modules

keras_materializer

Implementation of the TensorFlow Keras materializer.

Classes

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

Bases: BaseMaterializer

Materializer to read/write Keras models.

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: tf_keras.Model) -> Dict[str, MetadataType]

Extract metadata from the given Model object.

Parameters:

Name	Type	Description	Default
model	Model	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/tensorflow/materializers/keras_materializer.py

def extract_metadata(
    self, model: tf_keras.Model
) -> 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 {
        "num_layers": len(model.layers),
        "num_params": count_params(model.weights),
        "num_trainable_params": count_params(model.trainable_weights),
    }

load(data_type: Type[Any]) -> tf_keras.Model

Reads and returns a Keras model after copying it to temporary path.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the data to read.	required

Returns:

Type	Description
Model	A keras.Model model.

Source code in src/zenml/integrations/tensorflow/materializers/keras_materializer.py

def load(self, data_type: Type[Any]) -> tf_keras.Model:
    """Reads and returns a Keras model after copying it to temporary path.

    Args:
        data_type: The type of the data to read.

    Returns:
        A keras.Model model.
    """
    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        # Copy from artifact store to temporary directory
        temp_model_file = os.path.join(temp_dir, self.MODEL_FILE_NAME)
        io_utils.copy_dir(self.uri, temp_dir)

        # Load the model from the temporary directory
        model = tf.keras.models.load_model(temp_model_file)

        return model

save(model: tf_keras.Model) -> None

Writes a keras model to the artifact store.

Parameters:

Name	Type	Description	Default
model	Model	A keras.Model model.	required

Source code in src/zenml/integrations/tensorflow/materializers/keras_materializer.py

def save(self, model: tf_keras.Model) -> None:
    """Writes a keras model to the artifact store.

    Args:
        model: A keras.Model model.
    """
    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        temp_model_file = os.path.join(temp_dir, self.MODEL_FILE_NAME)
        model.save(temp_model_file)
        io_utils.copy_dir(temp_dir, self.uri)

Modules

tf_dataset_materializer

Implementation of the TensorFlow dataset materializer.

Classes

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

Bases: BaseMaterializer

Materializer to read data to and from tf.data.Dataset.

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(dataset: tf.data.Dataset) -> Dict[str, MetadataType]

Extract metadata from the given Dataset object.

Parameters:

Name	Type	Description	Default
dataset	Dataset	The Dataset object to extract metadata from.	required

Returns:

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

Source code in src/zenml/integrations/tensorflow/materializers/tf_dataset_materializer.py

def extract_metadata(
    self, dataset: tf.data.Dataset
) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given `Dataset` object.

    Args:
        dataset: The `Dataset` object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    return {"length": len(dataset)}

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

Reads data into tf.data.Dataset.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the data to read.	required

Returns:

Type	Description
Any	A tf.data.Dataset object.

Source code in src/zenml/integrations/tensorflow/materializers/tf_dataset_materializer.py

def load(self, data_type: Type[Any]) -> Any:
    """Reads data into tf.data.Dataset.

    Args:
        data_type: The type of the data to read.

    Returns:
        A tf.data.Dataset object.
    """
    with self.get_temporary_directory(delete_at_exit=False) as temp_dir:
        io_utils.copy_dir(self.uri, temp_dir)
        path = os.path.join(temp_dir, DEFAULT_FILENAME)
        dataset = tf.data.Dataset.load(path)
        return dataset

save(dataset: tf.data.Dataset) -> None

Persists a tf.data.Dataset object.

Parameters:

Name	Type	Description	Default
dataset	Dataset	The dataset to persist.	required

Source code in src/zenml/integrations/tensorflow/materializers/tf_dataset_materializer.py

def save(self, dataset: tf.data.Dataset) -> None:
    """Persists a tf.data.Dataset object.

    Args:
        dataset: The dataset to persist.
    """
    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        path = os.path.join(temp_dir, DEFAULT_FILENAME)
        tf.data.Dataset.save(
            dataset, path, compression=None, shard_func=None
        )
        io_utils.copy_dir(temp_dir, self.uri)

Modules