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GPJax aims to provide a low-level interface to Gaussian process (GP) models in Jax, structured to give researchers maximum flexibility in extending the code to suit their own needs. The idea is that the code should be as close as possible to the maths we write on paper when working with GP models.

Package organisation

Contributions

We would be delighted to receive contributions from interested individuals and groups. To learn how you can get involved, please read our guide for contributing. If you have any questions, we encourage you to open an issue. For broader conversations, such as best GP fitting practices or questions about the mathematics of GPs, we invite you to open a discussion.

Another way you can contribute to GPJax is through issue triaging. This can include reproducing bug reports, asking for vital information such as version numbers and reproduction instructions, or identifying stale issues. If you would like to begin triaging issues, an easy way to get started is to subscribe to GPJax on CodeTriage.

As a contributor to GPJax, you are expected to abide by our code of conduct. If you feel that you have either experienced or witnessed behaviour that violates this standard, then we ask that you report any such behaviours through this form or reach out to one of the project's gardeners.

Feel free to join our Slack Channel, where we can discuss the development of GPJax and broader support for Gaussian process modelling.

We appreciate all the contributors to GPJax who have helped to shape GPJax into the package it is today.

Supported methods and interfaces

Notebook examples

Guides for customisation

Conversion between .ipynb and .py

Above examples are stored in examples directory in the double percent (py:percent) format. Checkout jupytext using-cli for more info.

  • To convert example.py to example.ipynb, run:
jupytext --to notebook example.py
  • To convert example.ipynb to example.py, run:
jupytext --to py:percent example.ipynb

Installation

Stable version

The latest stable version of GPJax can be installed from PyPI:

pip install gpjax

or from conda-forge:

# with Pixi
pixi add gpjax
# or with conda
conda install --channel conda-forge gpjax

Note

We recommend you check your installation version:

python -c 'import gpjax; print(gpjax.__version__)'

Development version

Warning

This version is possibly unstable and may contain bugs.

Note

We advise you create virtual environment before installing:

conda create -n gpjax_experimental python=3.11.0
conda activate gpjax_experimental

Clone a copy of the repository to your local machine and run the setup configuration in development mode.

git clone https://github.com/JaxGaussianProcesses/GPJax.git
cd GPJax
uv venv
uv sync --extra dev

We recommend you check your installation passes the supplied unit tests:

uv run poe all-tests

Citing GPJax

If you use GPJax in your research, please cite our JOSS paper.

@article{Pinder2022,
  doi = {10.21105/joss.04455},
  url = {https://doi.org/10.21105/joss.04455},
  year = {2022},
  publisher = {The Open Journal},
  volume = {7},
  number = {75},
  pages = {4455},
  author = {Thomas Pinder and Daniel Dodd},
  title = {GPJax: A Gaussian Process Framework in JAX},
  journal = {Journal of Open Source Software}
}

Project links

Data verified by PyPI on Oct 11, 2025
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Key dates

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  • Released:
Newer release available (0.18.0)

1 maintainer

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Avatar for thomaspinder from gravatar.com thomaspinder

Credits

Author: Thomas Pinder

GitHub Statistics

Data verified by PyPI on Oct 11, 2025
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.11

Provides Extra

dev docs

Tags

gaussian-processes jax machine-learning bayesian

Classifiers

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