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Gaussian process methods in TensorFlow

Project description

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Website | Documentation (release) | Documentation (develop) | Glossary

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.

GPflow is an open source project. If you have relevant skills and are interested in contributing then please do contact us (see "The GPflow community" section below).

What does GPflow do?

GPflow implements modern Gaussian process inference for composable kernels and likelihoods. The online documentation (develop)/(master) contains more details.

GPflow 2.0 uses TensorFlow 2.1+ for running computations, which allows fast execution on GPUs, and uses Python ≥ 3.6.

Install GPflow 2

Latest release from PyPI

pip install gpflow

The current release, 2.0.0, requires TensorFlow ≥ 2.1 and TensorFlow Probability ≥ 0.9.

Latest source from GitHub

Be aware that the develop branch may change regularly, and new commits may break your code.

In a check-out of the develop branch of the GPflow GitHub repository, run

pip install -e .

Alternatively, you can install the latest GitHub develop version using pip:

pip install git+https://github.com/GPflow/GPflow.git@develop#egg=gpflow

This will automatically install all required dependencies.

Install the last GPflow version compatible with TensorFlow 1:

We have stopped development and support for GPflow based on TensorFlow 1.x.

The latest available release is v1.5.1. Documentation and tutorials will remain available.

Getting Started with GPflow 2.0

There is an "Intro to GPflow 2.0" Jupyter notebook; check it out for details. To convert your code from GPflow 1 check the GPflow 2 upgrade guide.

The GPflow Community

Getting help

Bugs, feature requests, pain points, annoying design quirks, etc: Please use GitHub issues to flag up bugs/issues/pain points, suggest new features, and discuss anything else related to the use of GPflow that in some sense involves changing the GPflow code itself. You can make use of the labels such as bug, discussion, feature, feedback, etc. We positively welcome comments or concerns about usability, and suggestions for changes at any level of design.

We aim to respond to issues promptly, but if you believe we may have forgotten about an issue, please feel free to add another comment to remind us.

"How-to-use" questions: Please use Stack Overflow (gpflow tag) to ask questions that relate to "how to use GPflow", i.e. questions of understanding rather than issues that require changing GPflow code. (If you are unsure where to ask, you are always welcome to open a GitHub issue; we may then ask you to move your question to Stack Overflow.)

Slack workspace

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.

Contributing

All constructive input is gratefully received. For more information, see the notes for contributors.

Projects using GPflow

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

Note that the new GPflow 2.0 release was published on 31 March 2020. The projects listed below have not been checked for compatibility with the 2.0 branch, and it is up to their authors whether they will upgrade to GPflow 2.0. We encourage all projects to upgrade, as GPflow 2.0 contains numerous improvements on GPflow 1.

Project Description
GPflowOpt Bayesian Optimization using GPflow.
VFF Variational Fourier Features for Gaussian Processes.
Doubly-Stochastic-DGP Deep Gaussian Processes with Doubly Stochastic Variational Inference.
BranchedGP Gaussian processes with branching kernels.
heterogp Heteroscedastic noise for sparse variational GP.
widedeepnetworks Measuring the relationship between random wide deep neural networks and GPs.
orth_decoupled_var_gps Variationally sparse GPs with orthogonally decoupled bases
kernel_learning Implementation of "Differentiable Compositional Kernel Learning for Gaussian Processes".
VBPP Implementation of "Variational Bayes for Point Processes".
DGPs_with_IWVI Deep Gaussian Processes with Importance-Weighted Variational Inference
kerndisc Library for automated kernel structure discovery in univariate data

If you would like your project listed here, let us know - or simply open a pull request that adds your project to the table above!

Compatibility

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.

Citing GPflow

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}
}

Since the publication of the GPflow paper, the software has been significantly extended with the framework for interdomain approximations and multioutput priors. We review the framework and describe the design in an arXiv paper, which can be cited by users.

@article{GPflow2020multioutput,
  author = {{van der Wilk}, Mark and Dutordoir, Vincent and John, ST and
            Artemev, Artem and Adam, Vincent and Hensman, James},
  title = {A Framework for Interdomain and Multioutput {G}aussian Processes},
  year = {2020},
  journal = {arXiv:2003.01115},
  url = {https://arxiv.org/abs/2003.01115}
}

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