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Contributing guidelines

Pull Request Checklist

Before sending your pull requests, make sure you do the following:

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).

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 how to.

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, start working through the TensorFlow codebase, navigate to the Github "issues" tab and start looking through interesting issues. If you are not sure of where to start, then start by trying one of the smaller/easier issues here i.e. issues with the "good first issue" label and then 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

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 check a file with pylint against TensorFlow's custom style definition:

pip install pylint
pylint --rcfile=tensorflow/tools/ci_build/pylintrc myfile.py

Note pylint --rcfile=tensorflow/tools/ci_build/pylintrc should run from the top level tensorflow directory.

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:devel and tensorflow/tensorflow: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:

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

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

    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/...

    For a single component e.g. softmax op:

    bazel test ${flags} tensorflow/python/kernel_tests:softmax_op_test

    For a single/parameterized test e.g. test_capture_variables in tensorflow/python/saved_model/load_test.py:

    (Requires python>=3.7)

    bazel test ${flags} //tensorflow/python/saved_model:load_test --test_filter=*LoadTest.test_capture_variables*

    Note: You can add --test_sharding_strategy=disabled to the flags to disable the sharding so that all the test outputs are in one file. However, it may slow down the tests for not running in parallel and may cause the test to timeout but it could be useful when you need to execute a single test or more in general your filtered/selected tests have a very low execution time and the sharding could create an overhead on the test execution.

  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.

Running doctest for testable docstring

There are two ways to test the code in the docstring locally:

  1. If you are only changing the docstring of a class/function/method, then you can test it by passing that file's path to tf_doctest.py. For example:

    python tf_doctest.py --file=<file_path>

    This will run it using your installed version of TensorFlow. To be sure you're running the same code that you're testing:

  2. If you are changing the code and the docstring of a class/function/method, then you will need to build TensorFlow from source. Once you are setup to build from source, you can run the tests:

    bazel run //tensorflow/tools/docs:tf_doctest

    or

    bazel run //tensorflow/tools/docs:tf_doctest -- --module=ops.array_ops

    The --module is relative to tensorflow.python.

Debug builds

When building Tensorflow, passing --config=dbg to Bazel will build with debugging information and without optimizations, allowing you to use GDB or other debuggers to debug C++ code. For example, you can build the pip package with debugging information by running:

bazel build --config=dbg //tensorflow/tools/pip_package:build_pip_package

TensorFlow kernels and TensorFlow's dependencies are still not built with debugging information with --config=dbg, as issues occur on Linux if there is too much debug info (see this GitHub issue for context). If you want to debug a kernel, you can compile specific files with -g using the --per_file_copt bazel option. For example, if you want to debug the Identity op, which are in files starting with identity_op, you can run

bazel build --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g //tensorflow/tools/pip_package:build_pip_package

Note that the --config=dbg option is not officially supported.