Before sending your pull requests, make sure you followed this list.
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.
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.
Before sending your pull request for review, make sure your changes are consistent with the guidelines and follow the TensorFlow coding style.
Include a license at the top of new files.
Bazel BUILD files also need to include a license section, e.g., BUILD example.
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
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
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.
There are two ways to run TensorFlow unit tests.
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.
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
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/...
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.
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