Package: gpflow
Website|Documentation (release)|Documentation (develop)|GlossaryTable of ContentsWhat does GPflow do?InstallationGetting Started with GPflow 2.0The GPflow CommunityGetting helpSlack workspaceContributingProjects using GPflowVersion CompatibilityTensorFlow 1.x and GPflow 1.xCiting GPflowWhat does GPflow do?GPflow is a package for building Gaussian process models in Python.
It implements modern Gaussian process inference for composable kernels and likelihoods.GPflow builds onTensorFlow 2.4+andTensorFlow Probabilityfor running computations, which allows fast execution on GPUs.Theonline documentation (latest release)/(develop)contains more details.MaintainersIt was originally created byJames HensmanandAlexander G. de G. Matthews.
It is now actively maintained by (in alphabetical order)Artem Artemev,Mark van der Wilk,ST John,
andVincent Dutordoir.
GPflow would not be the same without the community.We are grateful toall contributorswho 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" sectionbelow).InstallationRequirementsGPflow 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 isnotspecified 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 asImportError: 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 thetensorflow_probabilitypackage.Latest (stable) release from PyPIpipinstallgpflowLatest (bleeding-edge) source from GitHubBe aware that thedevelopbranch may change regularly, and new commits may break your code.In a check-out of thedevelopbranch of theGPflow GitHub repository, runpipinstall-e.Alternatively, you can install the latest GitHubdevelopversion usingpip:pipinstallgit+https://github.com/GPflow/GPflow.git@develop#egg=gpflowThis will automatically install all required dependencies.Getting Started with GPflow 2.0There is an"Intro to GPflow 2.0"Jupyter notebook; check it out for details.
To convert your code from GPflow 1 check theGPflow 2 upgrade guide.The GPflow CommunityGetting helpBugs, feature requests, pain points, annoying design quirks, etc:Please useGitHub issuesto 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 thelabelssuch asbug,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 useStack 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 workspaceWe have a publicGPflow slack workspace. Please use thisinvite linkif you'd like to join, whether to ask short informal questions or to be involved in the discussion and future development of GPflow.ContributingAll constructive input is gratefully received. For more information, see thenotes for contributors.Projects using GPflowProjects building on GPflow and demonstrating its usage are listed below. The following projects are based on the current GPflow 2.x release:ProjectDescriptionTriesteBayesian optimization with TensorFlow, with out-of-the-box support for GPflow (2.x) models.VFFVariational Fourier Features for Gaussian Processes (GPflow 2.x version)BranchedGPGaussian processes with branching kernels.VBPPImplementation of "Variational Bayes for Point Processes".Gaussian Process Regression on MoleculesGPs to predict molecular properties by creating a custom-defined Tanimoto kernel to operate on Morgan fingerprintsIf you would like your project listed here, let us know - or simplyopen a pull requestthat 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.ProjectDescriptionGPflowOptBayesian Optimization using GPflow (stable release requires GPflow 0.5).Doubly-Stochastic-DGPDeep Gaussian Processes with Doubly Stochastic Variational Inference.widedeepnetworksMeasuring the relationship between random wide deep neural networks and GPs.orth_decoupled_var_gpsVariationally sparse GPs with orthogonally decoupled baseskernel_learningImplementation of "Differentiable Compositional Kernel Learning for Gaussian Processes".DGPs_with_IWVIDeep Gaussian Processes with Importance-Weighted Variational InferencekerndiscLibrary for automated kernel structure discovery in univariate dataSignature covarianceskernels for (time)series asinputsStructured-DGPAdding more structure to the variational posterior of the Doubly Stochastic Deep Gaussian ProcessVersion CompatibilityGPflow 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 GPflowmodels, 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.TensorFlow 1.x and GPflow 1.xWe have stopped development and support for GPflow based on TensorFlow 1.
The latest release supporting TensorFlow 1 isv1.5.1.Documentationandtutorialswill remain available.Citing GPflowTo cite GPflow, please reference theJMLR 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 anarXiv 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}
}