What is GPflow?
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 Artem Artemev, 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, 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.
What does GPflow do?
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.
GPflow uses TensorFlow for running computations, which allows fast execution on GPUs, and uses Python 3.5 or above.