Contributing Guidelines
🎉 First off, thank you for considering contributing to our project! 🎉
This is an active-developnig project for implementing machine learning algorithm to hign energy phyics problem. We are eager to have experts like you to improve the code ! First-time contributors are especially welcome. These are some of the many ways to contribute:
- 🐛 Submitting bug reports and feature requests
- 📝 Writing tutorials or examples
- 🔍 Fixing typos and improving the documentation
- 💡 Writing code for everyone to use
If you get stuck at any point you can create an issue on GitHub.
Short summary
- Search the existing issues. Create one for a bug, feature, or substantial change if it is not already being discussed.
- Create a short-lived branch from an up-to-date
mainbranch. - Make a focused change and add or update tests and documentation when needed.
- Run the relevant formatters and tests.
- Push the branch and open a pull request (PR) into
main. - Address review comments, wait for the checks to pass, and merge after approval.
This is commonly called GitHub Flow. Our working group is still growing, so we do not maintain separate develop or long-lived feature branches. For an overview of other approaches, see Comparing Git workflows.
How we work together
- Be kind and assume good intent. Reviews are about improving the work, not judging the contributor.
- Keep each PR focused on one topic. Small PRs are easier to understand, test, and review.
- Efficient communication is essential. Ask around to see if anyone is already working on the same task.
- Ask early when a proposed change affects data formats, physics definitions, training framework, etc ...
- Never commit passwords, access tokens, private keys, or restricted data.
- Keep AI-generated code small. Avoid unnecessary code and repeating logics when using AI (see a guideline in ponytail).