Update 'CONTRIBUTING.md'
Improve the contributing guideline
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CONTRIBUTING.md
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CONTRIBUTING.md
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# Contributing guidelines
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## Pull Request Checklist
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## Push Request Checklist
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Before sending your pull requests, make sure you followed this list.
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- Read [contributing guidelines](CONTRIBUTING.md).
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- Read [Code of Conduct](CODE_OF_CONDUCT.md).
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- Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
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- Check if my changes are consistent with the [guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
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- Changes are consistent with the [Coding Style](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#c-coding-style).
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- Run [Unit Tests](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#running-unit-tests).
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- Check if my changes are consistent with the [guidelines](CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
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- Changes are consistent with the [Coding Style](CONTRIBUTING.md#coding-style).
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- Run [Unit Tests](CONTRIBUTING.md#running-unit-tests).
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## How to become a contributor and submit your own code
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### Contributor License Agreements
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We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.
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CERC Libs is released under [LGPL license](LICENSE.md) so even if we'd love to accept your patches, Before we can take them, please be sure that you are the intelectual property owner of your code and that do you fully understand and respect our software license.
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Please fill out either the individual or corporate Contributor License Agreement (CLA).
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* 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](https://code.google.com/legal/individual-cla-v1.0.html).
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* If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).
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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.
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***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.
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***NOTE***: Only original source code from you can be accepted into the main repository.
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### Contributing code
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If you have improvements to TensorFlow, send us your pull requests! For those
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just getting started, Github has a
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[how to](https://help.github.com/articles/using-pull-requests/).
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If you have improvements to CERC Libs, or you want to extend the functionality, please send us your pull request as seen at [git pull request documentation](https://git-scm.com/docs/git-request-pull)
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TensorFlow team members will be assigned to review your pull requests. Once the
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pull requests are approved and pass continuous integration checks, a TensorFlow
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team member will apply `ready to pull` label to your change. This means we are
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working on getting your pull request submitted to our internal repository. After
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the change has been submitted internally, your pull request will be merged
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automatically on GitHub.
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If you want to contribute, start working through the TensorFlow codebase,
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navigate to the
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[Github "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
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looking through interesting issues. If you are not sure of where to start, then
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start by trying one of the smaller/easier issues here i.e.
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[issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue)
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and then take a look at the
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[issues with the "contributions welcome" label](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome).
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These are issues that we believe are particularly well suited for outside
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contributions, often because we probably won't get to them right now. If you
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decide to start on an issue, leave a comment so that other people know that
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you're working on it. If you want to help out, but not alone, use the issue
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comment thread to coordinate.
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Once the pull requests are approved and pass continuous integration checks, a team member will apply `ready to pull` label to your change and your pull request will be merged on CERC Libs and become an integral part of Insel4D platform.
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If you prefer to contribute, instead to add a new functionality you can also take a look into our ticket system and try to fix any of the listed issues.
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### Contribution guidelines and standards
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Before sending your pull request for
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[review](https://github.com/tensorflow/tensorflow/pulls),
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Before sending your pull request for review,
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make sure your changes are consistent with the guidelines and follow the
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TensorFlow coding style.
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CERC Libs coding style.
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#### General guidelines and philosophy for contribution
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@ -67,143 +40,48 @@ TensorFlow coding style.
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changes to lower the maintenance cost.
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* Bug fixes also generally require unit tests, because the presence of bugs
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usually indicates insufficient test coverage.
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* Keep API compatibility in mind when you change code in core TensorFlow,
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e.g., code in
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[tensorflow/core](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core)
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and
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[tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python).
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TensorFlow has passed version 1.0 and hence cannot make
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non-backward-compatible API changes without a major release. Reviewers of
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your pull request will comment on any API compatibility issues.
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* When you contribute a new feature to TensorFlow, the maintenance burden is
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(by default) transferred to the TensorFlow team. This means that the benefit
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of the contribution must be compared against the cost of maintaining the
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feature.
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* Full new features (e.g., a new op implementing a cutting-edge algorithm)
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typically will live in
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[tensorflow/addons](https://github.com/tensorflow/addons) to get some
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airtime before a decision is made regarding whether they are to be migrated
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to the core.
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* As every PR requires several CPU/GPU hours of CI testing, we discourage
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submitting PRs to fix one typo, one warning,etc. We recommend fixing the
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same issue at the file level at least (e.g.: fix all typos in a file, fix
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all compiler warning in a file, etc.)
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* Keep backward compatibility in mind when you change code in CERC Libs and
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if you need to broke the backward compatibility please ensure that you:.
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..* Clearly indicate which features are affected by your changes.
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..* Technical reasons for the changes.
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* Tests should follow the
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[testing best practices](https://www.tensorflow.org/community/contribute/tests)
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[testing best practices](CONTRIBUTING.md#testing_best_practices)
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guide.
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* [Document your contribution](CONTRIBUTING.md#documentation)
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#### License
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Include a license at the top of new files.
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At the top of any new file a small header with author contact information and the license information should be included.
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* [C/C++ license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op.cc#L1)
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* [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
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* [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)
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* [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)
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* [Bash license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/ci_build/ci_sanity.sh#L2)
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* [HTML license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/tf-backend.html#L2)
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* [JavaScript/TypeScript license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/backend.ts#L1)
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"""
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Name module
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2020 Project Author name mail@concordia.ca
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"""
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Bazel BUILD files also need to include a license section, e.g.,
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[BUILD example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/BUILD#L61).
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#### C++ coding style
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Changes to TensorFlow C++ code should conform to
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[Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
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Use `clang-tidy` to check your C/C++ changes. To install `clang-tidy` on ubuntu:16.04, do:
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```bash
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apt-get install -y clang-tidy
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```
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You can check a C/C++ file by doing:
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```bash
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clang-format <my_cc_file> --style=google > /tmp/my_cc_file.cc
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diff <my_cc_file> /tmp/my_cc_file.cc
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```
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#### Python coding style
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Changes to TensorFlow Python code should conform to
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[Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)
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Changes to CERC Libs python code should conform to our coding style
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[Cerc Python Style Guide](PYGUIDE.md)
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Use `pylint` to check your Python changes. To install `pylint` and check a file
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with `pylint` against TensorFlow's custom style definition:
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with `pylint` against Cerc custom style definition:
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```bash
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pip install pylint
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pylint --rcfile=tensorflow/tools/ci_build/pylintrc myfile.py
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pylint --rcfile=pylintrc myfile.py
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```
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Note `pylint --rcfile=tensorflow/tools/ci_build/pylintrc` should run from the
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top level tensorflow directory.
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#### Coding style for other languages
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<a name="testing_best_practices"></a>#### Testing best practices
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* [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html)
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* [Google JavaScript Style Guide](https://google.github.io/styleguide/jsguide.html)
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* [Google Shell Style Guide](https://google.github.io/styleguide/shell.xml)
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* [Google Objective-C Style Guide](https://google.github.io/styleguide/objcguide.html)
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Prior to any pull request is expected that the code is both, manualy and automatically tested, to ensure at least some quality minimum, there are a few practices for unittest that we believe are important, so we encorage you to follow it.
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#### Running sanity check
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* Test should be self contained, that implies that test preparation and cleaning up is performed at the before and after the test execution.
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* We encorage you to create if possible functional tests that cover the complete workflow of the implemented functionality
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* Maximize your code coverage by ensure that you are testing as much of your code as possible.
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If you have Docker installed on your system, you can perform a sanity check on
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your changes by running the command:
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<a name="documentation"></a>#### Documentation
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```bash
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tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
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```
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This will catch most license, Python coding style and BUILD file issues that
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may exist in your changes.
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#### Running unit tests
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There are two ways to run TensorFlow unit tests.
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1. Using tools and libraries installed directly on your system.
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Refer to the
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[CPU-only developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile)
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and
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[GPU developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile)
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for the required packages. Alternatively, use the said
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[Docker images](https://hub.docker.com/r/tensorflow/tensorflow/tags/), e.g.,
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`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` for
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development to avoid installing the packages directly on your system (in
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which case remember to change directory from `/root` to `/tensorflow` once
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you get into the running container so `bazel` can find the `tensorflow`
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workspace).
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Once you have the packages installed, you can run a specific unit test in
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bazel by doing as follows:
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If the tests are to be run on GPU, add CUDA paths to LD_LIBRARY_PATH and add
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the `cuda` option flag
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```bash
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export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
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export flags="--config=opt --config=cuda -k"
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```
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For example, to run all tests under tensorflow/python, do:
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```bash
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bazel test ${flags} //tensorflow/python/...
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```
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2. Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.
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```bash
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# Install Docker first, then this will build and run cpu tests
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tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
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```
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See
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[TensorFlow Builds](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build)
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for details.
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In case of new functionality a general overview, configuration, instalation and usage manuals need to be provided by the developer, this will not only provide a nice starting point for all the future users but help you to detect any inconsistences in your design.
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