config.py

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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
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#     http://aws.amazon.com/apache2.0/
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"""Configuration for the SageMaker Training Compiler."""
from __future__ import absolute_import
import logging

from sagemaker.training_compiler.config import TrainingCompilerConfig as BaseConfig

logger = logging.getLogger(__name__)


class TrainingCompilerConfig(BaseConfig):
    """The SageMaker Training Compiler configuration class."""

    SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "g4dn", "p4"]

    def __init__(
        self,
        enabled=True,
        debug=False,
    ):
        """This class initializes a ``TrainingCompilerConfig`` instance.

        `Amazon SageMaker Training Compiler
        <https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_
        is a feature of SageMaker Training
        and speeds up training jobs by optimizing model execution graphs.

        You can compile Hugging Face models
        by passing the object of this configuration class to the ``compiler_config``
        parameter of the :class:`~sagemaker.huggingface.HuggingFace`
        estimator.

        Args:
            enabled (bool): Optional. Switch to enable SageMaker Training Compiler.
                The default is ``True``.
            debug (bool): Optional. Whether to dump detailed logs for debugging.
                This comes with a potential performance slowdown.
                The default is ``False``.

        **Example**: The following code shows the basic usage of the
        :class:`sagemaker.huggingface.TrainingCompilerConfig()` class
        to run a HuggingFace training job with the compiler.

        .. code-block:: python

            from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig

            huggingface_estimator=HuggingFace(
                ...
                compiler_config=TrainingCompilerConfig()
            )

        .. seealso::

            For more information about how to enable SageMaker Training Compiler
            for various training settings such as using TensorFlow-based models,
            PyTorch-based models, and distributed training,
            see `Enable SageMaker Training Compiler
            <https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler-enable.html>`_
            in the `Amazon SageMaker Training Compiler developer guide
            <https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_.

        """

        super(TrainingCompilerConfig, self).__init__(enabled=enabled, debug=debug)

    @classmethod
    def validate(
        cls,
        estimator,
    ):
        """Checks if SageMaker Training Compiler is configured correctly.

        Args:
            estimator (str): A estimator object
                If SageMaker Training Compiler is enabled, it will validate whether
                the estimator is configured to be compatible with Training Compiler.

        Raises:
            ValueError: Raised if the requested configuration is not compatible
                        with SageMaker Training Compiler.
        """

        super(TrainingCompilerConfig, cls).validate(estimator)

        if estimator.image_uri:
            error_helper_string = (
                "Overriding the image URI is currently not supported "
                "for SageMaker Training Compiler."
                "Specify the following parameters to run the Hugging Face training job "
                "with SageMaker Training Compiler enabled: "
                "transformer_version, tensorflow_version or pytorch_version, and compiler_config."
            )
            raise ValueError(error_helper_string)