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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, Hugh Salimbeni, Ivo Couckuyt, James Hensman, Keisuke Fujii, Mark van der Wilk, Mikhail Beck, Pablo Leon-Villagra, Rasmus Bonnevie, 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.

GPflow implements modern Gaussian process inference for composable kernels and likelihoods. The online documentation (develop)/(master) contains more details. The interface follows on from GPy, and the docs have further discussion of the comparison.

GPflow uses TensorFlow for running computations, which allows fast execution on GPUs, and uses Python 3.5 or above.

GPflow latest release can be installed using pip:

pip install gpflow

A different option to install GPflow requires installation of TensorFlow first. Please see instructions on the main TensorFlow webpage. You will need at least version 1.10 (we aim to support the latest version). We find that for most users pip installation is the fastest way to get going. Then, 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.

Get started with our manual (develop) with examples and tutorials.

To compile the GPflow documentation locally:

1. Install doc dependencies

pip install sphinx sphinx_rtd_theme numpydoc nbsphinx

2. Install pandoc

pip install pandoc

If pandoc does not install via pip, or step 4 does not work, go to pandoc.org/installing.html

3. Change directory to doc

cd doc

4. Compile the documentation as html

make html

5. Check documentation locally by opening (in a browser) doc/build/html/index.html

We also provide a Docker image which can be run using

docker run -it -p 8888:8888 gpflow/gpflow

The image can be generated using our Dockerfile.

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

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.

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

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

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}
}