GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware.
Cite
@article{arxiv.1610.08733,
title = {GPflow: A Gaussian process library using TensorFlow},
author = {Alexander G. de G. Matthews and Mark van der Wilk and Tom Nickson and Keisuke Fujii and Alexis Boukouvalas and Pablo León-Villagrá and Zoubin Ghahramani and James Hensman},
journal= {arXiv preprint arXiv:1610.08733},
year = {2016}
}