Skip to content

Latest commit

 

History

History
54 lines (33 loc) · 2.04 KB

CONTRIBUTING.md

File metadata and controls

54 lines (33 loc) · 2.04 KB
Raw

Contributing to Larq

👍 🎉 First off, thanks for taking the time to contribute! 👍 🎉

Working on your first Pull Request? You can learn how from this free series How to Contribute to an Open Source Project on GitHub.

Ask a question or raise an issue

If you have questions about Larq or just want to say Hi you can chat with us on Spectrum.

If something is not working as expected, if you run into problems with Larq or if you have ideas for missing features, please open a new issue.

Project setup

To send a Pull Request it is required to fork Larq on GitHub. After that clone it to a desired directory:

git clone https://github.com/my-username/larq.git

Install all required dependencies for local development by running:

cd larq # go into the directory you just cloned
pip install -e ".[tensorflow]" # Installs Tensorflow for CPU
# pip install -e ".[tensorflow_gpu]" # Installs Tensorflow for GPU
pip install -e ".[test]" # Installs all development dependencies

Run Unit tests

Inside the project directory run:

pytest . -n auto

A new version of the documentation will be automatically published once merged into the main branch.

Code style

We use black to format all of our code. We recommend installing it as a plugin for your favorite code editor.

Publish release

  1. Increment the version number in setup.py, and make a PR with that change.

  2. Wait until your PR is reviewed and merged.

  3. Go to the GitHub releases, edit the release notes of the draft release, change the tag to the desired version (e.g. v0.7.0) and hit "Publish release".

  4. A GitHub action will automatically publish a release to PyPI based on the tag.