Build Gaussian process
models in python

What is GPflow?

GPflow is a package for building Gaussian process models in python, using TensorFlow. It was originally created by James Hensman and Alexander G. de G. Matthews.
It is now actively maintained by (in alphabetical order) Alexis Boukouvalas, Artem Artemev, Eric Hambro, James Hensman, Joel Berkeley, Mark van der Wilk, ST John, and Vincent Dutordoir. GPflow would not be the same without the community. We are grateful to all contributors who have helped shape GPflow.

Join the community

GPflow is an open source project. If you have relevant skills and are interested in contributing then please do contact us!

We have a public GPflow slack workspace. Please use this invite link if you'd like to join, whether to ask short informal questions or to be involved in the discussion and future development of GPflow.

What does GPflow do?

GPflow implements modern Gaussian process inference for composable kernels and likelihoods. The online user manual contains more details.

GPflow uses TensorFlow 2.1 for running computations, which allows fast execution on GPUs, and supports Python 3.6 or above.


Latest release from PyPI

pip install gpflow

The latest release requires TensorFlow ≥ 2.1 and TensorFlow Probability ≥ 0.9 (as well as Python ≥ 3.6).

Latest development release from GitHub

Be aware that the develop branch may change regularly, and new commits may break your code.

In a check-out of the develop branch of the GPflow GitHub repository, run

pip install -e .

Alternatively, you can install the latest GitHub develop version using pip:

pip install git+

This will automatically install all required dependencies.

We document the version history on GitHub.

More info

Getting help

Bugs, feature requests, pain points, annoying design quirks, etc: Please use GitHub issues to flag up bugs/issues/pain points, suggest new features, and discuss anything else related to the use of GPflow that in some sense involves changing the GPflow code itself. You can make use of the labels such as bug, discussion, feature, feedback, etc. We positively welcome comments or concerns about usability, and suggestions for changes at any level of design.

We aim to respond to issues promptly, but if you believe we may have forgotten about an issue, please feel free to add another comment to remind us.

"How-to-use" questions: Please use Stack Overflow (gpflow tag) to ask questions that relate to "how to use GPflow", i.e. questions of understanding rather than issues that require changing GPflow code. (If you are unsure where to ask, you are always welcome to open a GitHub issue; we may then ask you to move your question to Stack Overflow.)


All constructive input is gratefully received. For more information, see the notes for contributors.


GPflow heavily depends on TensorFlow and as far as TensorFlow supports forward compatibility, GPflow should as well. The version of GPflow can give you a hint about backward compatibility. If the major version has changed then you need to check the release notes to find out how the API has been changed.

Unfortunately, there is no such thing as backward compatibility for GPflow models, which means that a model implementation can change without changing interfaces. In other words, the TensorFlow graph can be different for the same models from different versions of GPflow.

Projects using GPflow

A few projects building on GPflow and demonstrating its usage are listed below.


Bayesian Optimization using GPflow.

See project


Variational Fourier Features for Gaussian Processes.

See project


Deep Gaussian Processes with Doubly Stochastic Variational Inference.

See project


Gaussian processes with branching kernels.

See project


Tools for monitoring and checkpoining optimisation.

See project


Measuring the relationship between random wide deep neural networks and GPs.

See project

Citing GPflow

To cite GPflow, please reference the JMLR paper. Sample Bibtex is given below:

 author = {Matthews, Alexander G. de G. and {van der Wilk}, Mark and Nickson, Tom and Fujii, Keisuke. and {Boukouvalas}, Alexis and {Le{\'o}n-Villagr{\'a}}, Pablo and Ghahramani, Zoubin and Hensman, James},
 title = "{ {GP}flow: A {G}aussian process library using {T}ensor{F}low}",
 journal = {Journal of Machine Learning Research},
 year = {2017},
 month = {apr},
 volume = {18},
 number = {40},
 pages = {1-6},
 url = {}

Since the publication of the GPflow paper, the software has been significantly extended with the framework for interdomain approximations and multioutput priors. We review the framework and describe the design in an arXiv paper, which can be cited by users.

 author = {{van der Wilk}, Mark and Dutordoir, Vincent and John, ST and
 Artemev, Artem and Adam, Vincent and Hensman, James},
 title = {A Framework for Interdomain and Multioutput {G}aussian Processes},
 year = {2020},
 journal = {arXiv:2003.01115},
 url = {}

Download GPflow

Get it here