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).
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.
We have experienced issues with pip's pre-2020 dependency resolver; if you encounter issues with incompatible third-party package versions when installing GPflow using the pip commands below, try adding the --use-feature=2020-resolver argument.
pip install gpflowThe current release series, 2.x, requires TensorFlow ≥ 2.1 and TensorFlow Probability ≥ 0.10.1.
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=gpflowThis will automatically install all required dependencies.
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.
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.
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.)
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.
All constructive input is gratefully received. For more information, see the notes for contributors.
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 (stable release requires GPflow 0.5). |
| Trieste | Bayesian optimization with TensorFlow, with out-of-the-box support for GPflow (2.x) models. |
| 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 |
| Gaussian Process Regression on Molecules | GPs to predict molecular properties by creating a custom-defined Tanimoto kernel to operate on Morgan fingerprints |
| Structured-DGP | Adding more structure to the variational posterior of the Doubly Stochastic Deep Gaussian Process |
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!
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.
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}
}