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Contributing

Interested in contributing to TensorFlow Addons? We appreciate all kinds of help and are working to make this guide as comprehensive as possible. Please let us know if you think of something we could do to help lower the barrier to contributing.

Pull Requests

We gladly welcome pull requests.

Before making any changes, we recommend opening an issue (if it doesn't already exist) and discussing your proposed changes. This will let us give you advice on the proposed changes. If the changes are minor, then feel free to make them without discussion.

Want to contribute but not sure of what? Here are a few suggestions:

  1. Add a new tutorial. Located in docs/tutorials/, these are a great way to familiarize yourself and others with TF-Addons. See the guidelines for more information on how to add examples.
  2. Solve an existing issue. These range from low-level software bugs to higher-level design problems. Check out the label help wanted. If you're a new contributor, the label good first issue can be a good place to start.

All submissions, including submissions by project members, require review.

Requirements for New Contributions to the Repository

The tensorflow/addons repository contains additional functionality fitting the following criteria:

  • The functionality is not otherwise available in TensorFlow
  • Addons have to be compatible with TensorFlow 2.x.
  • The addon conforms to the code and documentation standards
  • The addon is impactful to the community (e.g. an implementation used in widely cited paper)
  • Lastly, the functionality conforms to the contribution guidelines of its subpackage.

Note: New contributions often require team-members to read a research paper and understand how it fits into the TensorFlow community. This process can take longer than typical commit reviews so please bare with us

Development Tips

Try these useful commands below:

  • Format code automatically: bash tools/run_docker.sh -c 'make code-format'
  • Run sanity check: bash tools/run_docker.sh -c 'make sanity-check'
  • Run CPU unit tests: bash tools/run_docker.sh -c 'make unit-test'
  • Run GPU unit tests: bash tools/run_docker.sh -d gpu -c 'make gpu-unit-test'
  • All of the above: bash tools/run_docker.sh -d gpu -c 'make'

Coding style

Addons provides make code-format command to format your changes automatically, don't forget to use it before pushing your code.

Please see our Style Guide for more details.

Code Testing

CI Testing

Nightly CI tests are ran and results can be found on the central README. To subscribe for alerts please join the addons-testing mailing list.

Locally Testing

CPU Testing Script

bash tools/run_docker.sh -c 'make unit-test'

GPU Testing Script

bash tools/run_docker.sh -d gpu -c 'make gpu-unit-test'

Run Manually

It is recommend that tests are ran within docker images, but should still work on host.

CPU Docker: docker run --rm -it -v ${PWD}:/addons -w /addons gcr.io/tensorflow-testing/nosla-ubuntu16.04-manylinux2010 /bin/bash

GPU Docker: docker run --runtime=nvidia --rm -it -v ${PWD}:/addons -w /addons gcr.io/tensorflow-testing/nosla-cuda10.1-cudnn7-ubuntu16.04-manylinux2010 /bin/bash

Configure:

# Temporary until we remove py2 support
ln -sf /usr/bin/python3.6 /usr/bin/python && rm /usr/bin/python2 

./configure.sh  # Links project with TensorFlow dependency

Run selected tests:

bazel test -c opt -k \
--test_timeout 300,450,1200,3600 \
--test_output=all \
--run_under=$(readlink -f tools/ci_testing/parallel_gpu_execute.sh) \
//tensorflow_addons/<test_selection>

<test_selection> can be ... for all tests or <package>:<py_test_name> for individual tests. <package> can be any package name like metrics for example. <py_test_name> can be any test name given by the BUILD file or * for all tests of the given package.

Code Reviews

All submissions, including submissions by project members, require review. We use Github pull requests for this purpose.

Contributor License Agreement

Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project. Head over to https://cla.developers.google.com/ to see your current agreements on file or to sign a new one.

You generally only need to submit a CLA once, so if you've already submitted one (even if it was for a different project), you probably don't need to do it again.