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.