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doc: remove confusing information from the Readme. (#387)
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‎CONTRIBUTING.md

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# Contributing Guidelines
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Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
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documentation, we greatly value feedback and contributions from our community.
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Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
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information to effectively respond to your bug report or contribution.
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## Reporting Bugs/Feature Requests
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We welcome you to use the GitHub issue tracker to report bugs or suggest features.
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When filing an issue, please check [existing open](https://github.com/aws/sagemaker-tensorflow-training-toolkit/issues), or [recently closed](https://github.com/aws/sagemaker-tensorflow-training-toolkit/issues?utf8=%E2%9C%93&q=is%3Aissue%20is%3Aclosed%20), issues to make sure somebody else hasn't already
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reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
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* A reproducible test case or series of steps
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* The version of our code being used
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* Any modifications you've made relevant to the bug
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* Anything unusual about your environment or deployment
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## Contributing via Pull Requests
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Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
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1. You are working against the latest source on the *master* branch.
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2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.
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3. You open an issue to discuss any significant work - we would hate for your time to be wasted.
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To send us a pull request, please:
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1. Fork the repository.
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2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
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3. Ensure local tests pass.
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4. Commit to your fork using clear commit messages.
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5. Send us a pull request, answering any default questions in the pull request interface.
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6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
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GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and
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[creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
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## Finding contributions to work on
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Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels ((enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any ['help wanted'](https://github.com/aws/sagemaker-tensorflow-training-toolkit/labels/help%20wanted) issues is a great place to start.
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## Code of Conduct
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This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
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For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
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opensource-codeofconduct@amazon.com with any additional questions or comments.
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## Security issue notifications
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If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
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## Licensing
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See the [LICENSE](https://github.com/aws/sagemaker-tensorflow-training-toolkit//blob/master/LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.
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We may ask you to sign a [Contributor License Agreement (CLA)](http://en.wikipedia.org/wiki/Contributor_License_Agreement) for larger changes.

‎README.rst

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===============================
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SageMaker TensorFlow Containers
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===============================
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=====================================
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SageMaker TensorFlow Training Toolkit
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=====================================
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SageMaker TensorFlow Containers is an open source library for making the
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TensorFlow framework run on `Amazon SageMaker <https://aws.amazon.com/documentation/sagemaker/>`__.
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SageMaker TensorFlow Training Toolkit is an open-source library for using TensorFlow to train models on Amazon SageMaker.
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This repository also contains Dockerfiles which install this library, TensorFlow, and dependencies
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for building SageMaker TensorFlow images.
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For inference, see `SageMaker TensorFlow Inference Toolkit <https://github.com/aws/sagemaker-tensorflow-serving-container>`__.
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For information on running TensorFlow jobs on SageMaker: `Python
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SDK <https://github.com/aws/sagemaker-python-sdk>`__.
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For the Dockerfiles used for building SageMaker TensorFlow Containers, see `AWS Deep Learning Containers <https://github.com/aws/deep-learning-containers>`__.
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For information on running TensorFlow jobs on Amazon SageMaker, please refer to the `SageMaker Python SDK documentation <https://github.com/aws/sagemaker-python-sdk>`__.
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For notebook examples: `SageMaker Notebook
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Examples <https://github.com/awslabs/amazon-sagemaker-examples>`__.
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Table of Contents
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-----------------
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#. `Getting Started <#getting-started>`__
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#. `Building your Image <#building-your-image>`__
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#. `Running the tests <#running-the-tests>`__
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Getting Started
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---------------
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Prerequisites
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~~~~~~~~~~~~~
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Make sure you have installed all of the following prerequisites on your
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development machine:
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- `Docker <https://www.docker.com/>`__
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For Testing on GPU
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^^^^^^^^^^^^^^^^^^
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- `Nvidia-Docker <https://github.com/NVIDIA/nvidia-docker>`__
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Recommended
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^^^^^^^^^^^
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- A Python environment management tool. (e.g.
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`PyEnv <https://github.com/pyenv/pyenv>`__,
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`VirtualEnv <https://virtualenv.pypa.io/en/stable/>`__)
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Building your Image
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-------------------
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`Amazon SageMaker <https://aws.amazon.com/documentation/sagemaker/>`__
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utilizes Docker containers to run all training jobs & inference endpoints.
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The Docker images are built from the Dockerfiles specified in
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`Docker/ <https://github.com/aws/sagemaker-tensorflow-containers/tree/master/docker>`__.
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The Docker files are grouped based on TensorFlow version and separated
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based on Python version and processor type.
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The Docker files for TensorFlow 2.0 are available in the
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`tf-2 <https://github.com/aws/sagemaker-tensorflow-container/tree/tf-2>`__ branch, in
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`docker/2.0.0/ <https://github.com/aws/sagemaker-tensorflow-container/tree/tf-2/docker/2.0.0>`__.
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The Docker images, used to run training & inference jobs, are built from
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both corresponding "base" and "final" Dockerfiles.
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Base Images
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~~~~~~~~~~~
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The "base" Dockerfile encompass the installation of the framework and all of the dependencies
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needed. It is needed before building image for TensorFlow 1.8.0 and before.
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Building a base image is not required for images for TensorFlow 1.9.0 and onwards.
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Tagging scheme is based on <tensorflow_version>-<processor>-<python_version>. (e.g. 1.4
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.1-cpu-py2)
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All "final" Dockerfiles build images using base images that use the tagging scheme
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above.
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Before building these images, you need to have a pip-installable binary of this repository saved locally. To create the SageMaker Tensorflow Container Python package:
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::
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# Create the binary
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git clone https://github.com/aws/sagemaker-tensorflow-container.git
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cd sagemaker-tensorflow-container
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python setup.py sdist
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cp dist/sagemaker_tensorflow_training*.tar.gz docker/<tensorflow_version>/sagemaker_tensorflow_training.tar.gz
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Once you have copied the tensorflow_training.tar.gz to the desired location [same directory as the Dockerfile], you can then build the image.
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If you want to build your "base" Docker image, then use:
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::
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# All build instructions assume you're building from the same directory as the Dockerfile.
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# CPU
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docker build -t tensorflow-base:<tensorflow_version>-cpu-<python_version> -f Dockerfile.cpu .
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# GPU
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docker build -t tensorflow-base:<tensorflow_version>-gpu-<python_version> -f Dockerfile.gpu .
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::
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# Example
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# CPU
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docker build -t tensorflow-base:1.4.1-cpu-py2 -f Dockerfile.cpu .
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# GPU
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docker build -t tensorflow-base:1.4.1-gpu-py2 -f Dockerfile.gpu .
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Final Images
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~~~~~~~~~~~~
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The "final" Dockerfiles encompass the installation of the SageMaker specific support code.
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For images of TensorFlow 1.8.0 and before, all "final" Dockerfiles use `base images for building <https://github
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.com/aws/sagemaker-tensorflow-containers/blob/master/docker/1.4.1/final/py2/Dockerfile.cpu#L2>`__.
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These "base" images are specified with the naming convention of
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tensorflow-base:<tensorflow_version>-<processor>-<python_version>.
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Before building "final" images:
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Build your "base" image. Make sure it is named and tagged in accordance with your "final"
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Dockerfile. Skip this step if you want to build image of Tensorflow Version 1.9.0 and above.
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If you want to build "final" Docker images, for versions 1.6 and above, you will first need to download the appropriate tensorflow pip wheel, then pass in its location as a build argument. These can be obtained from pypi. For example, the files for 1.6.0 are here:
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https://pypi.org/project/tensorflow/1.6.0/#files
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https://pypi.org/project/tensorflow-gpu/1.6.0/#files
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Note that you need to use the tensorflow-gpu wheel when building the GPU image.
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Then run:
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::
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# All build instructions assumes you're building from the same directory as the Dockerfile.
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# CPU
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docker build -t <image_name>:<tag> --build-arg py_version=<py_version> --build-arg framework_installable=<path to tensorflow binary> -f Dockerfile.cpu .
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# GPU
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docker build -t <image_name>:<tag> --build-arg py_version=<py_version> --build-arg framework_installable=<path to tensorflow binary> -f Dockerfile.gpu .
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::
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# Example
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docker build -t preprod-tensorflow:1.6.0-cpu-py2 --build-arg py_version=2
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--build-arg framework_installable=tensorflow-1.6.0-cp27-cp27mu-manylinux1_x86_64.whl -f Dockerfile.cpu .
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The dockerfiles for 1.4 and 1.5 build from source instead, so when building those, you don't need to download the wheel beforehand:
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::
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# All build instructions assumes you're building from the same directory as the Dockerfile.
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# CPU
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docker build -t <image_name>:<tag> -f Dockerfile.cpu .
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# GPU
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docker build -t <image_name>:<tag> -f Dockerfile.gpu .
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::
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# Example
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# CPU
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docker build -t preprod-tensorflow:1.4.1-cpu-py2 -f Dockerfile.cpu .
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# GPU
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docker build -t preprod-tensorflow:1.4.1-gpu-py2 -f Dockerfile.gpu .
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Running the tests
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-----------------
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Running the tests requires installation of the SageMaker TensorFlow Container code and its test
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dependencies.
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::
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git clone https://github.com/aws/sagemaker-tensorflow-containers.git
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cd sagemaker-tensorflow-containers
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pip install -e .[test]
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Tests are defined in
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`test/ <https://github.com/aws/sagemaker-tensorflow-containers/tree/master/test>`__
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and include unit, integration and functional tests.
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Unit Tests
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~~~~~~~~~~
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If you want to run unit tests, then use:
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::
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# All test instructions should be run from the top level directory
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pytest test/unit
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Integration Tests
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~~~~~~~~~~~~~~~~~
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Running integration tests require `Docker <https://www.docker.com/>`__ and `AWS
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credentials <https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html>`__,
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as the integration tests make calls to a couple AWS services. The integration and functional
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tests require configurations specified within their respective
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`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.
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Integration tests on GPU require `Nvidia-Docker <https://github.com/NVIDIA/nvidia-docker>`__.
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Before running integration tests:
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#. Build your Docker image.
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#. Pass in the correct pytest arguments to run tests against your Docker image.
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If you want to run local integration tests, then use:
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::
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# Required arguments for integration tests are found in test/integ/conftest.py
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pytest test/integration --docker-base-name <your_docker_image> \
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--tag <your_docker_image_tag> \
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--framework-version <tensorflow_version> \
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--processor <cpu_or_gpu>
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::
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# Example
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pytest test/integration --docker-base-name preprod-tensorflow \
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--tag 1.0 \
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--framework-version 1.4.1 \
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--processor cpu
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Functional Tests
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~~~~~~~~~~~~~~~~
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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>`__.
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Contributing
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------------
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Please read
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`CONTRIBUTING.md <https://github.com/aws/sagemaker-tensorflow-containers/blob/master/CONTRIBUTING.md>`__
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`CONTRIBUTING.md <https://github.com/aws/sagemaker-tensorflow-training-toolkit/blob/master/CONTRIBUTING.md>`__
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for details on our code of conduct, and the process for submitting pull
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requests to us.
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License
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-------
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SageMaker TensorFlow Containers is licensed under the Apache 2.0 License. It is copyright 2018
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SageMaker TensorFlow Training Toolkit is licensed under the Apache 2.0 License. It is copyright 2018
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Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at:
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http://aws.amazon.com/apache2.0/

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