Website | Documentation (release) | Documentation (develop) | Glossary
- What does GPflow do?
- Installation
- Getting Started with GPflow 2.0
- The GPflow Community
- Version Compatibility
- Citing GPflow
GPflow is a package for building Gaussian process models in Python. It implements modern Gaussian process inference for composable kernels and likelihoods.
GPflow builds on TensorFlow 2.4+ and TensorFlow Probability for running computations, which allows fast execution on GPUs.
The online documentation (develop)/(master) contains more details.
It was originally created by James Hensman and Alexander G. de G. Matthews. It is now actively maintained by (in alphabetical order) Artem Artemev, 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 depends on both TensorFlow (TF, version ≥ 2.4) and TensorFlow Probability (TFP, version ≥ 0.12). We support Python ≥ 3.7.
NOTE: TensorFlow Probability releases are tightly coupled to TensorFlow, e.g. TFP 0.14 requires TF>=2.6, TFP 0.13 requires TF>=2.5, and TFP 0.12 requires TF>=2.4. Unfortunately, this is not specified in TFP's dependencies. So if you already have an (older) version of TensorFlow installed, GPflow will pull in the latest TFP, which will be incompatible. If you get errors such as ImportError: This version of TensorFlow Probability requires TensorFlow version >= 2.4, you have to either upgrade TensorFlow (pip install -U tensorflow) or manually install an older version of the tensorflow_probability package.
pip install gpflowBe 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.
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.
Projects building on GPflow and demonstrating its usage are listed below. The following projects are based on the current GPflow 2.x release:
| Project | Description |
|---|---|
| Trieste | Bayesian optimization with TensorFlow, with out-of-the-box support for GPflow (2.x) models. |
| VFF | Variational Fourier Features for Gaussian Processes (GPflow 2.x version) |
| BranchedGP | Gaussian processes with branching kernels. |
| VBPP | Implementation of "Variational Bayes for Point Processes". |
| Gaussian Process Regression on Molecules | GPs to predict molecular properties by creating a custom-defined Tanimoto kernel to operate on Morgan fingerprints |
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!
The following projects build on older versions of GPflow (pre-2020); we encourage their authors to upgrade to GPflow 2.
| Project | Description |
|---|---|
| GPflowOpt | Bayesian Optimization using GPflow (stable release requires GPflow 0.5). |
| Doubly-Stochastic-DGP | Deep Gaussian Processes with Doubly Stochastic Variational Inference. |
| 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". |
| DGPs_with_IWVI | Deep Gaussian Processes with Importance-Weighted Variational Inference |
| kerndisc | Library for automated kernel structure discovery in univariate data |
| Signature covariances | kernels for (time)series as inputs |
| Structured-DGP | Adding more structure to the variational posterior of the Doubly Stochastic Deep Gaussian Process |
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.
We have stopped development and support for GPflow based on TensorFlow 1. The latest release supporting TensorFlow 1 is v1.5.1. Documentation and tutorials will remain available.
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}
}