estimator.py

# Copyright 2018-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# 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
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""Placeholder docstring"""
from __future__ import absolute_import

import logging

from sagemaker.estimator import Framework
from sagemaker.fw_utils import (
    framework_name_from_image,
    framework_version_from_tag,
    empty_framework_version_warning,
    python_deprecation_warning,
    is_version_equal_or_higher,
)
from sagemaker.pytorch import defaults
from sagemaker.pytorch.model import PyTorchModel
from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT

logger = logging.getLogger("sagemaker")


class PyTorch(Framework):
    """Handle end-to-end training and deployment of custom PyTorch code."""

    __framework_name__ = "pytorch"

    LATEST_VERSION = defaults.LATEST_VERSION

    def __init__(
        self,
        entry_point,
        source_dir=None,
        hyperparameters=None,
        py_version=defaults.PYTHON_VERSION,
        framework_version=None,
        image_name=None,
        **kwargs
    ):
        """This ``Estimator`` executes an PyTorch script in a managed PyTorch
        execution environment, within a SageMaker Training Job. The managed
        PyTorch environment is an Amazon-built Docker container that executes
        functions defined in the supplied ``entry_point`` Python script.

        Training is started by calling
        :meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator.
        After training is complete, calling
        :meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted
        SageMaker endpoint and returns an
        :class:`~sagemaker.amazon.pytorch.model.PyTorchPredictor` instance that
        can be used to perform inference against the hosted model.

        Technical documentation on preparing PyTorch scripts for SageMaker
        training and using the PyTorch Estimator is available on the project
        home-page: https://github.com/aws/sagemaker-python-sdk

        Args:
            entry_point (str): Path (absolute or relative) to the Python source
                file which should be executed as the entry point to training.
                This should be compatible with either Python 2.7 or Python 3.5.
            source_dir (str): Path (absolute or relative) to a directory with
                any other training source code dependencies aside from tne entry
                point file (default: None). Structure within this directory are
                preserved when training on Amazon SageMaker.
            hyperparameters (dict): Hyperparameters that will be used for
                training (default: None). The hyperparameters are made
                accessible as a dict[str, str] to the training code on
                SageMaker. For convenience, this accepts other types for keys
                and values, but ``str()`` will be called to convert them before
                training.
            py_version (str): Python version you want to use for executing your
                model training code (default: 'py3'). One of 'py2' or 'py3'.
            framework_version (str): PyTorch version you want to use for
                executing your model training code. List of supported versions
                https://github.com/aws/sagemaker-python-sdk#pytorch-sagemaker-estimators.
                If not specified, this will default to 0.4.
            image_name (str): If specified, the estimator will use this image
                for training and hosting, instead of selecting the appropriate
                SageMaker official image based on framework_version and
                py_version. It can be an ECR url or dockerhub image and tag.

                Examples:
                    * ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
                    * ``custom-image:latest``

            **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework`
                constructor.

        .. tip::

            You can find additional parameters for initializing this class at
            :class:`~sagemaker.estimator.Framework` and
            :class:`~sagemaker.estimator.EstimatorBase`.
        """
        if framework_version is None:
            logger.warning(
                empty_framework_version_warning(defaults.PYTORCH_VERSION, self.LATEST_VERSION)
            )
        self.framework_version = framework_version or defaults.PYTORCH_VERSION

        if "enable_sagemaker_metrics" not in kwargs:
            # enable sagemaker metrics for PT v1.3 or greater:
            if is_version_equal_or_higher([1, 3], self.framework_version):
                kwargs["enable_sagemaker_metrics"] = True

        super(PyTorch, self).__init__(
            entry_point, source_dir, hyperparameters, image_name=image_name, **kwargs
        )

        if py_version == "py2":
            logger.warning(
                python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
            )

        self.py_version = py_version

    def create_model(
        self,
        model_server_workers=None,
        role=None,
        vpc_config_override=VPC_CONFIG_DEFAULT,
        entry_point=None,
        source_dir=None,
        dependencies=None,
        **kwargs
    ):
        """Create a SageMaker ``PyTorchModel`` object that can be deployed to an
        ``Endpoint``.

        Args:
            model_server_workers (int): Optional. The number of worker processes
                used by the inference server. If None, server will use one
                worker per vCPU.
            role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
                which is also used during transform jobs. If not specified, the
                role from the Estimator will be used.
            vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
                the model. Default: use subnets and security groups from this Estimator.
                * 'Subnets' (list[str]): List of subnet ids.
                * 'SecurityGroupIds' (list[str]): List of security group ids.
            entry_point (str): Path (absolute or relative) to the local Python source file which
                should be executed as the entry point to training. If not specified, the training
                entry point is used.
            source_dir (str): Path (absolute or relative) to a directory with any other serving
                source code dependencies aside from the entry point file.
                If not specified, the model source directory from training is used.
            dependencies (list[str]): A list of paths to directories (absolute or relative) with
                any additional libraries that will be exported to the container.
                If not specified, the dependencies from training are used.
            **kwargs: Additional kwargs passed to the :class:`~sagemaker.pytorch.model.PyTorchModel`
                constructor.

        Returns:
            sagemaker.pytorch.model.PyTorchModel: A SageMaker ``PyTorchModel``
            object. See :func:`~sagemaker.pytorch.model.PyTorchModel` for full details.
        """
        return PyTorchModel(
            self.model_data,
            role or self.role,
            entry_point or self.entry_point,
            source_dir=(source_dir or self._model_source_dir()),
            enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
            name=self._current_job_name,
            container_log_level=self.container_log_level,
            code_location=self.code_location,
            py_version=self.py_version,
            framework_version=self.framework_version,
            image=self.image_name,
            model_server_workers=model_server_workers,
            sagemaker_session=self.sagemaker_session,
            vpc_config=self.get_vpc_config(vpc_config_override),
            dependencies=(dependencies or self.dependencies),
        )

    @classmethod
    def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
        """Convert the job description to init params that can be handled by the
        class constructor

        Args:
            job_details: the returned job details from a describe_training_job
                API call.
            model_channel_name (str): Name of the channel where pre-trained
                model data will be downloaded.

        Returns:
            dictionary: The transformed init_params
        """
        init_params = super(PyTorch, cls)._prepare_init_params_from_job_description(
            job_details, model_channel_name
        )
        image_name = init_params.pop("image")
        framework, py_version, tag, _ = framework_name_from_image(image_name)

        if not framework:
            # If we were unable to parse the framework name from the image it is not one of our
            # officially supported images, in this case just add the image to the init params.
            init_params["image_name"] = image_name
            return init_params

        init_params["py_version"] = py_version
        init_params["framework_version"] = framework_version_from_tag(tag)

        training_job_name = init_params["base_job_name"]

        if framework != cls.__framework_name__:
            raise ValueError(
                "Training job: {} didn't use image for requested framework".format(
                    training_job_name
                )
            )

        return init_params