MOGPJax

MOGPJax aims to provide a low-level interface to multi-output Gaussian process (GP) models in Jax, structured to give researchers maximum flexibility in extending the code to suit their own needs.

Currently the library is under major development.

Installation

Stable version

The latest stable version of MOGPJax can be installed via pip:

pip install mogpjax

Note

We recommend you check your installation version:

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

Development version

Warning

This version is possibly unstable and may contain bugs.

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

git clone https://github.com/JaxGaussianProcesses/MOGPJax.git
cd mogpjax
python -m setup develop

Note

We advise you create virtual environment before installing:

conda create -n mogpjax_ex python=3.10.0
conda activate mogpjax_ex

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

python -m pytest tests/