Metadata-Version: 2.1
Name: sagemaker_tensorflow_training
Version: 20.3.0
Summary: Open source library for creating TensorFlow containers to run on Amazon SageMaker.
Home-page: https://github.com/aws/sagemaker-tensorflow-containers
Author: Amazon Web Services
License: Apache License 2.0
Description: =====================================
        SageMaker TensorFlow Training Toolkit
        =====================================
        
        The SageMaker TensorFlow Training Toolkit is an open source library for making the
        TensorFlow framework run on `Amazon SageMaker <https://aws.amazon.com/documentation/sagemaker/>`__.
        
        This repository also contains Dockerfiles which install this library, TensorFlow, and dependencies
        for building SageMaker TensorFlow images.
        
        For information on running TensorFlow jobs on SageMaker:
        
        - `SageMaker Python SDK documentation <https://sagemaker.readthedocs.io/en/stable/using_tf.html>`__
        - `SageMaker Notebook Examples <https://github.com/awslabs/amazon-sagemaker-examples>`__
        
        Table of Contents
        -----------------
        
        #. `Getting Started <#getting-started>`__
        #. `Building your Image <#building-your-image>`__
        #. `Running the tests <#running-the-tests>`__
        
        Getting Started
        ---------------
        
        Prerequisites
        ~~~~~~~~~~~~~
        
        Make sure you have installed all of the following prerequisites on your
        development machine:
        
        - `Docker <https://www.docker.com/>`__
        
        For Testing on GPU
        ^^^^^^^^^^^^^^^^^^
        
        -  `Nvidia-Docker <https://github.com/NVIDIA/nvidia-docker>`__
        
        Recommended
        ^^^^^^^^^^^
        
        -  A Python environment management tool. (e.g.
           `PyEnv <https://github.com/pyenv/pyenv>`__,
           `VirtualEnv <https://virtualenv.pypa.io/en/stable/>`__)
        
        Building your Image
        -------------------
        
        `Amazon SageMaker <https://aws.amazon.com/documentation/sagemaker/>`__
        utilizes Docker containers to run all training jobs & inference endpoints.
        
        The Docker images are built from the Dockerfiles specified in
        `docker/ <https://github.com/aws/sagemaker-tensorflow-containers/tree/master/docker>`__.
        
        The Dockerfiles are grouped based on TensorFlow version and separated
        based on Python version and processor type.
        
        The Dockerfiles for TensorFlow 2.0+ are available in the
        `tf-2 <https://github.com/aws/sagemaker-tensorflow-container/tree/tf-2>`__ branch.
        
        To build the images, first copy the files under
        `docker/build_artifacts/ <https://github.com/aws/sagemaker-tensorflow-container/tree/tf-2/docker/build_artifacts>`__
        to the folder container the Dockerfile you wish to build.
        
        ::
        
            # Example for building a TF 2.1 image with Python 3
            cp docker/build_artifacts/* docker/2.1.0/py3/.
        
        After that, go to the directory containing the Dockerfile you wish to build,
        and run ``docker build`` to build the image.
        
        ::
        
            # Example for building a TF 2.1 image for CPU with Python 3
            cd docker/2.1.0/py3
            docker build -t tensorflow-training:2.1.0-cpu-py3 -f Dockerfile.cpu .
        
        Don't forget the period at the end of the ``docker build`` command!
        
        Running the tests
        -----------------
        
        Running the tests requires installation of the SageMaker TensorFlow Training Toolkit code and its test
        dependencies.
        
        ::
        
            git clone https://github.com/aws/sagemaker-tensorflow-container.git
            cd sagemaker-tensorflow-container
            pip install -e .[test]
        
        Tests are defined in
        `test/ <https://github.com/aws/sagemaker-tensorflow-container/tree/master/test>`__
        and include unit, integration and functional tests.
        
        Unit Tests
        ~~~~~~~~~~
        
        If you want to run unit tests, then use:
        
        ::
        
            # All test instructions should be run from the top level directory
            pytest test/unit
        
        Integration Tests
        ~~~~~~~~~~~~~~~~~
        
        Running integration tests require `Docker <https://www.docker.com/>`__ and `AWS
        credentials <https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html>`__,
        as the integration tests make calls to a couple AWS services. The integration and functional
        tests require configurations specified within their respective
        `conftest.py <https://github.com/aws/sagemaker-tensorflow-containers/blob/master/test/integration/conftest.py>`__.Make sure to update the account-id and region at a minimum.
        
        Integration tests on GPU require `Nvidia-Docker <https://github.com/NVIDIA/nvidia-docker>`__.
        
        Before running integration tests:
        
        #. Build your Docker image.
        #. Pass in the correct pytest arguments to run tests against your Docker image.
        
        If you want to run local integration tests, then use:
        
        ::
        
            # Required arguments for integration tests are found in test/integ/conftest.py
            pytest test/integration --docker-base-name <your_docker_image> \
                                    --tag <your_docker_image_tag> \
                                    --framework-version <tensorflow_version> \
                                    --processor <cpu_or_gpu>
        
        ::
        
            # Example
            pytest test/integration --docker-base-name preprod-tensorflow \
                                    --tag 1.0 \
                                    --framework-version 1.4.1 \
                                    --processor cpu
        
        Functional Tests
        ~~~~~~~~~~~~~~~~
        
        Functional tests are removed from the current branch, please see them in older branch `r1.0 <https://github.com/aws/sagemaker-tensorflow-container/tree/r1.0#functional-tests>`__.
        
        Contributing
        ------------
        
        Please read
        `CONTRIBUTING.md <https://github.com/aws/sagemaker-tensorflow-containers/blob/master/CONTRIBUTING.md>`__
        for details on our code of conduct, and the process for submitting pull
        requests to us.
        
        License
        -------
        
        SageMaker TensorFlow Containers is licensed under the Apache 2.0 License. It is copyright 2018
        Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at:
        http://aws.amazon.com/apache2.0/
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Provides-Extra: test
Provides-Extra: benchmark
