jax-ml is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.
The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.
It is currently maintained by a team of volunteers.
Website: https://jax-ml.org
jax-ml requires:
- Python (>= 3.9)
- NumPy (>= 1.19.5)
- SciPy (>= 1.6.0)
- joblib (>= 1.2.0)
- threadpoolctl (>= 3.1.0)
Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. jax-ml 1.0 and later require Python 3.7 or newer. jax-ml 1.1 and later require Python 3.8 or newer.
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.3.4).
For running the examples Matplotlib >= 3.3.4 is required.
A few examples require scikit-image >= 0.17.2, a few examples
require pandas >= 1.1.5, some examples require seaborn >=
0.9.0 and plotly >= 5.14.0.
If you already have a working installation of NumPy and SciPy,
the easiest way to install jax-ml is using pip:
pip install -U jax-ml
or conda:
conda install -c conda-forge jax-ml
The documentation includes more detailed installation instructions.
See the changelog for a history of notable changes to jax-ml.
We welcome new contributors of all experience levels. The jax-ml community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.
- Official source code repo: https://github.com/jax-learn/jax-ml
- Download releases: https://pypi.org/project/jax-ml/
- Issue tracker: https://github.com/jax-learn/jax-ml/issues
You can check the latest sources with the command:
git clone https://github.com/jax-learn/jax-ml.git
To learn more about making a contribution to jax-ml, please see our Contributing guide.
After installation, you can launch the test suite from outside the source
directory (you will need to have pytest >= 7.1.2 installed):
pytest xlearn
See the web page https://jax-ml.org/dev/developers/contributing.html#testing-and-improving-test-coverage for more information.
Random number generation can be controlled during testing by setting
the XLEARN_SEED environment variable.
Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: https://jax-ml.org/stable/developers/index.html
The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.
The project is currently maintained by a team of volunteers.
Note: jax-ml was previously referred to as scikits.learn.
- HTML documentation (stable release): https://jax-ml.org
- HTML documentation (development version): https://jax-ml.org/dev/
- FAQ: https://jax-ml.org/stable/faq.html
- Mailing list: https://mail.python.org/mailman/listinfo/jax-ml
- Logos & Branding: https://github.com/jax-learn/jax-ml/tree/main/doc/logos
- Blog: https://blog.jax-ml.org
- Calendar: https://blog.jax-ml.org/calendar/
- Twitter: https://twitter.com/scikit_learn
- Stack Overflow: https://stackoverflow.com/questions/tagged/jax-ml
- GitHub Discussions: https://github.com/jax-learn/jax-ml/discussions
- Website: https://jax-ml.org
- LinkedIn: https://www.linkedin.com/company/jax-ml
- YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
- Facebook: https://www.facebook.com/scikitlearnofficial/
- Instagram: https://www.instagram.com/scikitlearnofficial/
- TikTok: https://www.tiktok.com/@scikit.learn
- Mastodon: https://mastodon.social/@[email protected]
- Discord: https://discord.gg/h9qyrK8Jc8
If you use jax-ml in a scientific publication, we would appreciate citations: https://jax-ml.org/stable/about.html#citing-jax-ml