zaouk / gpflow

Gaussian processes in TensorFlow

Website | Documentation (develop/v2.0) | Documentation (v1.5) | Glossary

GPflow is a package for building Gaussian process models in python, using TensorFlow. It was originally created and is now managed by James Hensman and Alexander G. de G. Matthews. The full list of contributors (in alphabetical order) is Alexander G. de G. Matthews, Alexis Boukouvalas, Artem Artemev, Daniel Marthaler, David J . Harris, Eric Hambro, Hugh Salimbeni, Ivo Couckuyt, James Hensman, Keisuke Fujii, Mark van der Wilk, Mikhail Beck, Pablo Leon -Villagra, Rasmus Bonnevie, Sergio Pascual-Diaz, ST John, Tom Nickson, Valentine Svensson, Vincent Dutordoir, Zoubin Ghahramani. GPflow is an open source project so if you feel you have some relevant skills and are interested in contributing then please do contact us.

What does GPflow do?

GPflow implements modern Gaussian process inference for composable kernels and likelihoods. The online documentation (develop)/(master) contains more details.

GPflow 2.0 uses TensorFlow 2.0 for running computations, which allows fast execution on GPUs, and uses Python ≥ 3.6.

Install GPflow

• From source

  With the release of TensorFlow 2.0 and Tensorflow Probability 0.8, you should only need to run

  pip install -e .

  in a check-out of the develop branch of the GPflow github repository.

• Using pip

  pip install gpflow

Getting Started with GPflow 2.0

There is an "Intro to GPflow 2.0" Jupyter notebook. Check it out for details.

• GPflow 1.0

  We have stopped development and support for GPflow based on TensorFlow 1.0. We now accept only bug fixes to GPflow 1.0 in the develop-1.0 branch. The latest available release is v1.5.1. Documentation and tutorials will remain available.

Getting help

Please use GitHub issues to start discussion on the use of GPflow. Tagging enquiries discussion helps us distinguish them from bugs.

Contributing

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

Compatibility

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.

Project	Description
GPflowOpt	Bayesian Optimization using GPflow.
VFF	Variational Fourier Features for Gaussian Processes.
Doubly-Stochastic-DGP	Deep Gaussian Processes with Doubly Stochastic Variational Inference.
BranchedGP	Gaussian processes with branching kernels.
heterogp	Heteroscedastic noise for sparse variational GP.
widedeepnetworks	Measuring the relationship between random wide deep neural networks and GPs.
orth_decoupled_var_gps	Variationally sparse GPs with orthogonally decoupled bases
kernel_learning	Implementation of "Differentiable Compositional Kernel Learning for Gaussian Processes".
VBPP	Implementation of "Variational Bayes for Point Processes".
DGPs_with_IWVI	Deep Gaussian Processes with Importance-Weighted Variational Inference

Let us know if you would like your project listed here.

Citing GPflow

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

@ARTICLE{GPflow2017,
   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     = {http://jmlr.org/papers/v18/16-537.html}
}