GPflow: A Gaussian process library using TensorFlow

TL;DR

GPflow is a software library that leverages TensorFlow to efficiently implement Gaussian processes with variational inference, automatic differentiation, and GPU support, emphasizing reliable and concise code.

Contribution

It introduces a GPU-enabled, variational inference-based Gaussian process library with a focus on software quality and ease of use using TensorFlow.

Findings

Supports GPU acceleration for Gaussian processes

Uses variational inference as the main approximation method

Offers concise, automatically differentiated code

Abstract

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.