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CONTRIBUTING.md
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# Contributing guidelines # Contributing guidelines
## Pull Request Checklist ## Push Request Checklist
Before sending your pull requests, make sure you followed this list. Before sending your pull requests, make sure you followed this list.
- Read [contributing guidelines](CONTRIBUTING.md). - Read [contributing guidelines](CONTRIBUTING.md).
- Read [Code of Conduct](CODE_OF_CONDUCT.md). - Read [Code of Conduct](CODE_OF_CONDUCT.md).
- Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/). - Check if my changes are consistent with the [guidelines](CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
- Check if my changes are consistent with the [guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution). - Changes are consistent with the [Coding Style](CONTRIBUTING.md#coding-style).
- Changes are consistent with the [Coding Style](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#c-coding-style). - Run [Unit Tests](CONTRIBUTING.md#running-unit-tests).
- Run [Unit Tests](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#running-unit-tests).
## How to become a contributor and submit your own code ## How to become a contributor and submit your own code
### Contributor License Agreements ### Contributor License Agreements
We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles. 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.
Please fill out either the individual or corporate Contributor License Agreement (CLA). ***NOTE***: Only original source code from you can be accepted into the main repository.
* 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).
* 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).
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 ### Contributing code
If you have improvements to TensorFlow, send us your pull requests! For those 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)
just getting started, Github has a
[how to](https://help.github.com/articles/using-pull-requests/).
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, 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.
navigate to the
[Github "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start 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.
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](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue)
and then take a look at the
[issues with the "contributions welcome" label](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome).
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 ### Contribution guidelines and standards
Before sending your pull request for Before sending your pull request for review,
[review](https://github.com/tensorflow/tensorflow/pulls),
make sure your changes are consistent with the guidelines and follow the make sure your changes are consistent with the guidelines and follow the
TensorFlow coding style. CERC Libs coding style.
#### General guidelines and philosophy for contribution #### General guidelines and philosophy for contribution
@ -67,143 +40,48 @@ TensorFlow coding style.
changes to lower the maintenance cost. changes to lower the maintenance cost.
* Bug fixes also generally require unit tests, because the presence of bugs * Bug fixes also generally require unit tests, because the presence of bugs
usually indicates insufficient test coverage. usually indicates insufficient test coverage.
* Keep API compatibility in mind when you change code in core TensorFlow, * Keep backward compatibility in mind when you change code in CERC Libs and
e.g., code in if you need to broke the backward compatibility please ensure that you:.
[tensorflow/core](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core) ..* Clearly indicate which features are affected by your changes.
and ..* Technical reasons for the changes.
[tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python).
TensorFlow has passed version 1.0 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 the 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](https://github.com/tensorflow/addons) to get some
airtime before a decision is made regarding whether they are to be migrated
to the core.
* As every PR requires several CPU/GPU hours of CI testing, we discourage
submitting PRs to fix one typo, one warning,etc. We recommend fixing the
same issue at the file level at least (e.g.: fix all typos in a file, fix
all compiler warning in a file, etc.)
* Tests should follow the * Tests should follow the
[testing best practices](https://www.tensorflow.org/community/contribute/tests) [testing best practices](CONTRIBUTING.md#testing_best_practices)
guide. guide.
* [Document your contribution](CONTRIBUTING.md#documentation)
#### License #### License
Include a license at the top of new files. At the top of any new file a small header with author contact information and the license information should be included.
* [C/C++ license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op.cc#L1)
* [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
* [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)
* [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)
* [Bash license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/ci_build/ci_sanity.sh#L2)
* [HTML license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/tf-backend.html#L2)
* [JavaScript/TypeScript license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/backend.ts#L1)
Bazel BUILD files also need to include a license section, e.g.,
[BUILD example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/BUILD#L61).
#### C++ coding style
Changes to TensorFlow C++ code should conform to
[Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
Use `clang-tidy` to check your C/C++ changes. To install `clang-tidy` on ubuntu:16.04, do:
```bash
apt-get install -y clang-tidy
```
You can check a C/C++ file by doing:
```bash
clang-format <my_cc_file> --style=google > /tmp/my_cc_file.cc
diff <my_cc_file> /tmp/my_cc_file.cc
```
"""
Name module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author name mail@concordia.ca
"""
#### Python coding style #### Python coding style
Changes to TensorFlow Python code should conform to Changes to CERC Libs python code should conform to our coding style
[Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md) [Cerc Python Style Guide](PYGUIDE.md)
Use `pylint` to check your Python changes. To install `pylint` and check a file Use `pylint` to check your Python changes. To install `pylint` and check a file
with `pylint` against TensorFlow's custom style definition: with `pylint` against Cerc custom style definition:
```bash ```bash
pip install pylint pip install pylint
pylint --rcfile=tensorflow/tools/ci_build/pylintrc myfile.py pylint --rcfile=pylintrc myfile.py
``` ```
Note `pylint --rcfile=tensorflow/tools/ci_build/pylintrc` should run from the
top level tensorflow directory.
#### Coding style for other languages <a name="testing_best_practices"></a>#### Testing best practices
* [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html) 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.
* [Google JavaScript Style Guide](https://google.github.io/styleguide/jsguide.html)
* [Google Shell Style Guide](https://google.github.io/styleguide/shell.xml)
* [Google Objective-C Style Guide](https://google.github.io/styleguide/objcguide.html)
#### Running sanity check * Test should be self contained, that implies that test preparation and cleaning up is performed at the before and after the test execution.
* We encorage you to create if possible functional tests that cover the complete workflow of the implemented functionality
* Maximize your code coverage by ensure that you are testing as much of your code as possible.
If you have Docker installed on your system, you can perform a sanity check on <a name="documentation"></a>#### Documentation
your changes by running the command:
```bash 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.
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](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile)
and
[GPU developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile)
for the required packages. Alternatively, use the said
[Docker images](https://hub.docker.com/r/tensorflow/tensorflow/tags/), 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:
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:
```bash
bazel test ${flags} //tensorflow/python/...
```
2. Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.
```bash
# 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](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build)
for details.