estimator.py

# Copyright 2017-2020 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,
    parameter_v2_rename_warning,
    is_version_equal_or_higher,
    warn_if_parameter_server_with_multi_gpu,
)
from sagemaker.mxnet import defaults
from sagemaker.mxnet.model import MXNetModel
from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT

logger = logging.getLogger("sagemaker")


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

    __framework_name__ = "mxnet"
    _LOWEST_SCRIPT_MODE_VERSION = ["1", "3"]

    LATEST_VERSION = defaults.LATEST_VERSION

    def __init__(
        self,
        entry_point,
        source_dir=None,
        hyperparameters=None,
        py_version="py2",
        framework_version=None,
        image_name=None,
        distributions=None,
        **kwargs
    ):
        """This ``Estimator`` executes an MXNet script in a managed MXNet
        execution environment, within a SageMaker Training Job. The managed
        MXNet 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.mxnet.model.MXNetPredictor` instance that can
        be used to perform inference against the hosted model.

        Technical documentation on preparing MXNet scripts for SageMaker
        training and using the MXNet 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, relative or an S3 URI) to a directory
                with any other training source code dependencies aside from the entry
                point file (default: None). If ``source_dir`` is an S3 URI, it must
                point to a tar.gz file. 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: 'py2'). One of 'py2' or 'py3'.
            framework_version (str): MXNet version you want to use for executing
                your model training code. List of supported versions
                https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators.
                If not specified, this will default to 1.2.1.
            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``

            distributions (dict): A dictionary with information on how to run distributed
                training (default: None). To have parameter servers launched for training,
                set this value to be ``{'parameter_server': {'enabled': True}}``.
            **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.MXNET_VERSION, self.LATEST_VERSION)
            )
        self.framework_version = framework_version or defaults.MXNET_VERSION

        if "enable_sagemaker_metrics" not in kwargs:
            # enable sagemaker metrics for MXNet v1.6 or greater:
            if is_version_equal_or_higher([1, 6], self.framework_version):
                kwargs["enable_sagemaker_metrics"] = True

        super(MXNet, 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)
            )

        if distributions is not None:
            logger.warning(parameter_v2_rename_warning("distributions", "distribution"))
            train_instance_type = kwargs.get("train_instance_type")
            warn_if_parameter_server_with_multi_gpu(
                training_instance_type=train_instance_type, distributions=distributions
            )

        self.py_version = py_version
        self._configure_distribution(distributions)

    def _configure_distribution(self, distributions):
        """
        Args:
            distributions:
        """
        if distributions is None:
            return

        if self.framework_version.split(".") < self._LOWEST_SCRIPT_MODE_VERSION:
            raise ValueError(
                "The distributions option is valid for only versions {} and higher".format(
                    ".".join(self._LOWEST_SCRIPT_MODE_VERSION)
                )
            )

        if "parameter_server" in distributions:
            enabled = distributions["parameter_server"].get("enabled", False)
            self._hyperparameters[self.LAUNCH_PS_ENV_NAME] = enabled

    def create_model(
        self,
        model_server_workers=None,
        role=None,
        vpc_config_override=VPC_CONFIG_DEFAULT,
        entry_point=None,
        source_dir=None,
        dependencies=None,
        image_name=None,
        **kwargs
    ):
        """Create a SageMaker ``MXNetModel`` 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.
            image_name (str): If specified, the estimator will use this image for 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.mxnet.model.MXNetModel`
                constructor.

        Returns:
            sagemaker.mxnet.model.MXNetModel: A SageMaker ``MXNetModel`` object.
            See :func:`~sagemaker.mxnet.model.MXNetModel` for full details.
        """
        if "image" not in kwargs:
            kwargs["image"] = image_name or self.image_name

        if "name" not in kwargs:
            kwargs["name"] = self._current_job_name

        return MXNetModel(
            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,
            container_log_level=self.container_log_level,
            code_location=self.code_location,
            py_version=self.py_version,
            framework_version=self.framework_version,
            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),
            **kwargs
        )

    @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(MXNet, 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

        # We switched image tagging scheme from regular image version (e.g. '1.0') to more
        # expressive containing framework version, device type and python version
        # (e.g. '0.12-gpu-py2'). For backward compatibility map deprecated image tag '1.0' to a
        # '0.12' framework version otherwise extract framework version from the tag itself.
        init_params["framework_version"] = (
            "0.12" if tag == "1.0" else 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