Everyone is welcome to contribute, and we value everybody's contribution. Code is thus not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.
It also helps us if you spread the word: reference the library from blog posts on the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply star the repo to say "thank you".
There are 4 ways you can contribute to lightly:
- Fixing outstanding issues with the existing code;
- Implementing new models;
- Contributing to the examples or to the documentation;
- Submitting issues related to bugs or desired new features.
All are equally valuable to the community.
Do your best to follow these guidelines when submitting an issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.
First, please make sure the bug was not already reported (use the search bar on Github under Issues).
- Include your OS type and version, the versions of Python, PyTorch, and PyTorch Lightning.
- A code snippet that allows us to reproduce the bug in less than 30s.
- Provide the full traceback if an exception is raised.
Awesome! Please provide the following information:
- Short description of the model and link to the paper;
- Link to the implementation if it's open source;
If you are willing to contribute the model yourself, let us know so we can best guide you.
A world-class feature request addresses the following points:
- Motivation first:
- Is it related to a problem/frustration with the library? If so, please explain why. Providing a code snippet that demonstrates the problem is best.
- Is it related to something you would need for a project? We'd love to hear about it!
- Is it something you worked on and think could benefit the community? Awesome! Tell us what problem it solved for you.
- Provide a code snippet that demonstrates its future use;
- Attach any additional information (drawings, screenshots, etc.) you think may help.
Before writing code, we strongly advise you to search through the exising PRs or issues to make sure that nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback.
Follow these steps to start contributing:
-
Fork the repository by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account.
-
Clone your fork to your local disk, and add the base repository as a remote:
$ git clone [email protected]:lightly-ai/lightly.git $ cd lightly $ git remote add upstream https://github.com/lightly-ai/lightly.git
-
Create a new branch to hold your development changes:
$ git checkout -b a_descriptive_name_for_my_changes
do not work on the
master
branch. -
Set up a development environment by running the following command in a virtual environment:
$ pip install -e ".[dev]"
-
Develop the features on your branch.
As you work on the features, you should make sure that the test suite passes:
$ make test
If you're modifying documents under
docs/source
, make sure to validate that they can still be built. This check also runs in CI.$ cd docs $ make html
Once you're happy with your changes, add changed files using
git add
and make a commit withgit commit
to record your changes locally:$ git add modified_file.py $ git commit
Please write good commit messages.
It is a good idea to sync your copy of the code with the original repository regularly. This way you can quickly account for changes:
$ git fetch upstream $ git rebase upstream/develop
Push the changes to your account using:
$ git push -u origin a_descriptive_name_for_my_changes
-
Once you are satisfied, go to the webpage of your fork on GitHub. Click on 'Pull request' to send your changes to the project maintainers for review.
-
It's ok if maintainers ask you for changes. It happens to core contributors too! So everyone can see the changes in the Pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request.
lightly
follows the Google styleguide and the PyTorch styleguide by Igor Susmelj.
Check our documentation writing guide for more information.