CONTRIBUTING.md

How to Contribute 💜

Since you landed on this part of the repository, we want to first of all say thank you! 💜 Contributions from the community are essential to improving Pruna, we appreciate your effort in making the repository better for everyone!

Please make sure to review and adhere to the Pruna Code of Conduct before contributing to Pruna. Any violations will be handled accordingly and result in a ban from the Pruna community and associated platforms. Contributions that do not adhere to the code of conduct will be ignored.

There are various ways you can contribute:

• Have a question? Discuss with us on Discord or check out the FAQ
• Have an idea for a new tutorial? Open an issue with a feature request or chat with us on Discord
• Found a bug? Open an issue with a bug report
• Want a new feature? Open an issue with a feature request
• Have a new algorithm to add? Check out the guide on adding algorithms
• Have a new metric to add? Check out the guide on adding metrics
• Have a new dataset to add? Check out the guide on adding datasets

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Setup

If you want to contribute to Pruna with a Pull Request, you can do so by following these steps. If it is your very first time contributing to an open source project, we recommend to start with this guide for some generally helpful tips.

1. Clone the repository

First, fork the repository by navigating to the original pruna repository on GitHub and clicking the Fork button at the top-right. This creates a copy of the repository in your own GitHub account. Then, clone the forked repository from your account to your local machine and change into its directory:

git clone https://github.com/your_username/pruna.git
cd pruna

To keep your fork up to date with the original repository, add the upstream remote:

git remote add upstream https://github.com/PrunaAI/pruna.git

Always work on a new branch rather than the main branch. You can create a new branch for your feature or fix:

git switch -c feat/new-feature

2. Installation

You have two options for installing dependencies:

Option A: Using uv with its own virtual environment (recommended)

uv sync --extra dev

This creates a virtual environment in .venv/ and installs all dependencies there, including pruna itself in editable mode. Important: This does NOT install into your current conda environment! You’ll need to use uv run for all commands.

Option B: Installing into your current environment (conda/pip)

If you want to install directly into your current conda environment or use pip:

pip install -e .
pip install -e '.[dev]'

You can then also install the pre-commit hooks with:

pre-commit install

Note: The pre-commit hooks include TruffleHog for secret detection. TruffleHog must be installed separately as a binary tool. See the TruffleHog installation instructions for your platform.

3. Develop your contribution

You are now ready to work on your contribution. Check out a branch on your forked repository and start coding! When committing your changes, we recommend following the Conventional Commit Guidelines.

git switch -c feat/new-feature
git add .
git commit -m "feat: new amazing feature setup"
git push origin feat/new-feature

Make sure to develop your contribution in a way that is well documented, concise, and easy to maintain. We will do our best to have your contribution integrated and maintained into Pruna but reserve the right to reject contributions that we do not feel are in the best interest of the project.

4. Type checking

We use Ty for static type checking. Run:

ty check src/pruna

5. Run the tests

We have a comprehensive test suite that is designed to catch potential issues before they are merged into Pruna. When you make a contribution, it is highly recommended to not only run the existing tests but also to add new tests that cover your contribution.

You can run the tests depending on which installation option you chose:

If you used Option A (uv):

uv run pytest

For specific test markers:

uv run pytest -m "cpu and not slow"

If you used Option B (pip/conda):

pytest

For specific test markers:

pytest -m "cpu and not slow"

Note: uv run automatically uses uv's virtual environment in .venv/, not your conda environment.

6. Create a Pull Request

Once you have made your changes and tested them, you can create a Pull Request. We will then review your Pull Request and get back to you as soon as possible. If there are any questions along the way, please do not hesitate to reach out on Discord.