Skip to main content

Gaussian processes in JAX.

Project description

GPJax's logo

codecov CodeFactor Netlify Status PyPI version DOI Downloads Slack Invite

Quickstart | Install guide | Documentation | Slack Community

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 support

GPJax was founded by Thomas Pinder. Today, the maintenance of GPJax is undertaken by Thomas Pinder and Daniel Dodd.

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.

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

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

Simple example

Let us import some dependencies and simulate a toy dataset $\mathcal{D}$.

import gpjax as gpx
from jax import grad, jit
import jax.numpy as jnp
import jax.random as jr
import optax as ox

key = jr.PRNGKey(123)

f = lambda x: 10 * jnp.sin(x)

n = 50
x = jr.uniform(key=key, minval=-3.0, maxval=3.0, shape=(n,1)).sort()
y = f(x) + jr.normal(key, shape=(n,1))
D = gpx.Dataset(X=x, y=y)

# Construct the prior
meanf = gpx.mean_functions.Zero()
kernel = gpx.kernels.RBF()
prior = gpx.Prior(mean_function=meanf, kernel = kernel)

# Define a likelihood
likelihood = gpx.Gaussian(num_datapoints = n)

# Construct the posterior
posterior = prior * likelihood

# Define an optimiser
optimiser = ox.adam(learning_rate=1e-2)

# Define the marginal log-likelihood
negative_mll = jit(gpx.objectives.ConjugateMLL(negative=True))

# Obtain Type 2 MLEs of the hyperparameters
opt_posterior, history =

# Infer the predictive posterior distribution
xtest = jnp.linspace(-3., 3., 100).reshape(-1, 1)
latent_dist = opt_posterior(xtest, D)
predictive_dist = opt_posterior.likelihood(latent_dist)

# Obtain the predictive mean and standard deviation
pred_mean = predictive_dist.mean()
pred_std = predictive_dist.stddev()


Stable version

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

pip install gpjax


We recommend you check your installation version:

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

Development version


This version is possibly unstable and may contain bugs.


We advise you create virtual environment before installing:

conda create -n gpjax_experimental python=3.10.0
conda activate gpjax_experimental

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

poetry run pytest

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

git clone
cd GPJax
poetry install

Citing GPJax

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

  doi = {10.21105/joss.04455},
  url = {},
  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 details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gpjax-0.6.2.tar.gz (56.2 kB view hashes)

Uploaded source

Built Distribution

gpjax-0.6.2-py3-none-any.whl (98.2 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page