Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Report bugs at https://github.com/automl/CARP-S/issues.
If you are reporting a bug, please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.
Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.
CARP-S could always use more documentation, whether as part of the official CARP-S docs, in docstrings, or even on the web in blog posts, articles, and such.
The best way to send feedback is to file an issue at https://github.com/automl/CARP-S/issues.
If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that contributions are welcome :)
Ready to contribute? Here's how to set up CARP-S
for local development.
Fork the CARP-S
repo on GitHub and then clone your fork locally:
git clone [email protected]:YOUR_NAME_HERE/CARP-S.git
cd CARP-S
Install your local copy into a virtualenv.
We'll also install pre-commit
which runs some code quality checks.
python -m venv .venv
pip install -e ".[dev]"
pre-commit install
Create a branch for local development:
git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally!
When you're done making changes, check that your changes pass ruff, including testing other Python versions:
python setup.py test or pytest
Commit your changes and push your branch to GitHub:
git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-bugfix-or-feature
Submit a pull request through the GitHub website!
You can try to install all dependencies into one big environment, but probably there are package clashes.
Therefore, you can build one virtual environment for each optimizer-benchmark combination.
Either run scripts/build_envs.sh
to build all existing combinations or copy the combination and run as needed. It will create an environment with name automlsuite_${OPTIMIZER_CONTAINER_ID}_${BENCHMARK_ID}
.
Before you submit a pull request, check that it meets these guidelines:
- The pull request should include tests.
- If the pull request adds functionality, the docs should be updated.
Put your new functionality into a function with a docstring, and add the feature to the list in
README.md
. - The pull request should work for
Python 3.9
and make sure that the tests pass for all supported Python versions.
To run a subset of tests:
pytest tests/some_file.py # Run tests only in a certain file
pytest -k "test_mytest" # Find tests with a name matching "test_mytest"
A reminder for the maintainers on how to deploy.
Make sure all your changes are committed (including an entry in CHANGELOG.md
).
Update the version in pyproject.toml
, then run:
git tag "x.y.z" # Replace with your version
git push
git push --tags