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 Rasmus Bonnevie, Alexis Boukouvalas, Ivo Couckuyt, Keisuke Fujii, Zoubin Ghahramani, David J. Harris, James Hensman, Pablo Leon-Villagra, Daniel Marthaler, Alexander G. de G. Matthews, Tom Nickson, Valentine Svensson and Mark van der Wilk. 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.
GPflow implements modern Gaussian process inference for composable kernels and likelihoods. The online user manual contains more details. The interface follows on from GPy, for more discussion of the comparison see this page.
Please see instructions on the main TensorFlow webpage. You will need version 1.0. We find that for most users pip installation is the fastest way to get going.
GPflow includes some tensorflow extensions that are compiled when you run setup.py. For those interested in modifying the source of GPflow, we recommend
python setup.py develop
but installation should work well too:
python setup.py install
You can run the tests with python setup.py test.
Version history is documented here.
We also provide a Docker image which can be run using
docker run -it -p 8888:8888 gpflow/gpflow
Code to generate the image can be found here
Please use gihub issues to start discussion on the use of GPflow. Tagging enquiries discussion helps us distinguish them from bugs.
All constuctive input is gratefully received. For more information, see the notes for contributors.
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}
}