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Contributing guidelines

Pull Request Checklist

Before sending your pull requests, make sure you followed this list.

How to become a contributor and submit your own code

Contributor License Agreements

We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

Please fill out either the individual or corporate Contributor License Agreement (CLA).

  • If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an individual CLA.
  • If you work for a company that wants to allow you to contribute your work, then you'll need to sign a corporate CLA.

Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.

NOTE: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.

Contributing code

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, Github has a howto.

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply ready to pull label to your change. This means we are
working on getting your pull request submitted to our internal repository. After
the change has been submitted internally, your pull request will be merged
automatically on GitHub.

If you want to contribute but you're not sure where to start, take a look at the
issues with the "contributions welcome" label.
These are issues that we believe are particularly well suited for outside
contributions, often because we probably won't get to them right now. If you
decide to start on an issue, leave a comment so that other people know that
you're working on it. If you want to help out, but not alone, use the issue
comment thread to coordinate.

Contribution guidelines and standards

Before sending your pull request for
review,
make sure your changes are consistent with the guidelines and follow the
TensorFlow coding style.

General guidelines and philosophy for contribution

  • Include unit tests when you contribute new features, as they help to a)
    prove that your code works correctly, and b) guard against future breaking
    changes to lower the maintenance cost.
  • Bug fixes also generally require unit tests, because the presence of bugs
    usually indicates insufficient test coverage.
  • Keep API compatibility in mind when you change code in core TensorFlow,
    e.g., code in
    tensorflow/core
    and
    tensorflow/python.
    TensorFlow has reached version 1 and hence cannot make
    non-backward-compatible API changes without a major release. Reviewers of
    your pull request will comment on any API compatibility issues.
  • When you contribute a new feature to TensorFlow, the maintenance burden is
    (by default) transferred to the TensorFlow team. This means that benefit of
    the contribution must be compared against the cost of maintaining the
    feature.
  • Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    tensorflow/addons to get some
    airtime before decision is made regarding whether they are to be migrated to
    the core.

License

Include a license at the top of new files.

Bazel BUILD files also need to include a license section, e.g.,
BUILD example.

C++ coding style

Changes to TensorFlow C++ code should conform to
Google C++ Style Guide.

Use clang-tidy to check your C/C++ changes. To install clang-tidy on ubuntu:16.04, do:

apt-get install -y clang-tidy

You can check a C/C++ file by doing:

clang-format <my_cc_file> --style=google > /tmp/my_cc_file.cc
diff <my_cc_file> /tmp/my_cc_file.cc

Python coding style

Changes to TensorFlow Python code should conform to
Google Python Style Guide

Use pylint to check your Python changes. To install pylint and
retrieve TensorFlow's custom style definition:

pip install pylint
wget -O /tmp/pylintrc https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/tools/ci_build/pylintrc

To check a file with pylint:

pylint --rcfile=/tmp/pylintrc myfile.py

Coding style for other languages

Running sanity check

If you have Docker installed on your system, you can perform a sanity check on
your changes by running the command:

tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

Running unit tests

There are two ways to run TensorFlow unit tests.

  1. Using tools and libraries installed directly on your system.

    Refer to the
    CPU-only developer Dockerfile
    and
    GPU developer Dockerfile
    for the required packages. Alternatively, use the said
    Docker images, e.g.,
    tensorflow/tensorflow:nightly-devel and
    tensorflow/tensorflow:nightly-devel-gpu for development to avoid
    installing the packages directly on your system (in which case remember to
    change directory from /root to /tensorflow once you get into the running
    container so bazel can find the tensorflow workspace).

    Once you have the packages installed, you can run a specific unit test in
    bazel by doing as follows:

    If the tests are to be run on GPU, add CUDA paths to LD_LIBRARY_PATH and add
    the cuda option flag

    ```bash
    export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"

    export flags="--config=opt --config=cuda -k"
    ```

    For example, to run all tests under tensorflow/python, do:

    bazel test ${flags} //tensorflow/python/...

  2. Using Docker and TensorFlow's CI scripts.

    # Install Docker first, then this will build and run cpu tests
tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...

    See
    TensorFlow Builds
    for details.