Skip to main content
  • Home
  • Development
  • Documentation
  • Donate
  • Operational login
  • Browse the archive

swh logo
SoftwareHeritage
Software
Heritage
Archive
Features
  • Search

  • Downloads

  • Save code now

  • Add forge now

  • Help

https://github.com/GPflow/GPflow
16 July 2025, 10:58:06 UTC
  • Code
  • Branches (230)
  • Releases (4)
  • Visits
    • Branches
    • Releases
    • HEAD
    • refs/heads/0.5.0
    • refs/heads/alan/hackathon_docs
    • refs/heads/alan/quickfix/multioutput_kernels
    • refs/heads/andrewl/fix_GPR_posterior_leading_dim
    • refs/heads/avullo-willcowley/working-bee-ef1
    • refs/heads/awav/check-det
    • refs/heads/awav/develop-2.0/hmc-helper
    • refs/heads/awav/develop-2.0/update-parameter-docstring
    • refs/heads/awav/different-stuff
    • refs/heads/awav/gpflow-2.0-squared_distances
    • refs/heads/awav/inference
    • refs/heads/awav/kernel-structure
    • refs/heads/awav/likelihood-variance
    • refs/heads/awav/natural-gradients
    • refs/heads/awav/release-pip-package
    • refs/heads/awav/release-testing-branch
    • refs/heads/awav/sparse-tensors-support
    • refs/heads/awav/tensor-functor
    • refs/heads/awav/tfp
    • refs/heads/awav_documentation
    • refs/heads/blockkernel
    • refs/heads/bump_version_to_2_10
    • refs/heads/cone
    • refs/heads/deep
    • refs/heads/deep_arccos_kernel
    • refs/heads/dependabot/pip/black-24.3.0
    • refs/heads/develop
    • refs/heads/develop-1.0
    • refs/heads/eps_conditionals
    • refs/heads/fast-grad
    • refs/heads/felix/pathologies_experiment
    • refs/heads/fergus/anisotropy
    • refs/heads/fergus/het_sgpr
    • refs/heads/fergus/linear
    • refs/heads/fergus/linear_noise
    • refs/heads/fergus/notebook
    • refs/heads/fergus/random_init_lin_noise
    • refs/heads/freeze_as_float32
    • refs/heads/fxsm/new_svgp
    • refs/heads/gh-pages
    • refs/heads/gplvm
    • refs/heads/gplvm-fullcov
    • refs/heads/gustavocmv/heterocedastic-gaussian-likelihood
    • refs/heads/gustavocmv/multiclass-likelihood-quadrature-test
    • refs/heads/gustavocmv/ndiagghquadrature-property-setter
    • refs/heads/gustavocmv/varying-noise-notebook-comment
    • refs/heads/hugh/broadcasting_matched_multi_sample
    • refs/heads/hughsalimbeni-broadcasting-conditional
    • refs/heads/icouckuy-derivatives
    • refs/heads/james-vincent/sgpr
    • refs/heads/jax
    • refs/heads/jesper/1956/stateless_random
    • refs/heads/jesper/dimobjs
    • refs/heads/jesper/dimobjswtf
    • refs/heads/jesper/fix_gpflux
    • refs/heads/jesper/for_hv
    • refs/heads/jesper/jn_gps
    • refs/heads/jesper/warped_halton
    • refs/heads/joelb/type-hint-config
    • refs/heads/john-bradshaw/binary-class-GP
    • refs/heads/john-bradshaw/derivative-observations
    • refs/heads/john-bradshaw/linear-features-for-kernels
    • refs/heads/john-bradshaw/linear-features-for-kernels-gpflow1.0
    • refs/heads/john/linear
    • refs/heads/khurram/scipy_xla
    • refs/heads/mark/jitter
    • refs/heads/master
    • refs/heads/master-1.0
    • refs/heads/master_profile_mnist
    • refs/heads/merging-masters
    • refs/heads/mnist
    • refs/heads/mnist_datatypes
    • refs/heads/multiclass_slicing
    • refs/heads/multioutput
    • refs/heads/nbtest
    • refs/heads/nested_models_recompilation
    • refs/heads/nicolas/sinc_kernel
    • refs/heads/parallel_tests
    • refs/heads/profiling_mods
    • refs/heads/profiling_mods_paper
    • refs/heads/pypi
    • refs/heads/requirements_fix
    • refs/heads/revert-1511-master
    • refs/heads/robustmax_epsilon_learnable
    • refs/heads/sc336/2.7.0-master-merge-again
    • refs/heads/sc336/2.7.0-merge
    • refs/heads/sc336/2.7.1-preparation
    • refs/heads/sc336/3.8_enum_bug
    • refs/heads/sc336/GroupingKey_error
    • refs/heads/sc336/categorical_kernel
    • refs/heads/sc336/dgp_components
    • refs/heads/sc336/fix_conflicts
    • refs/heads/sc336/key_rotation
    • refs/heads/sc336/mypy-sudden-failure
    • refs/heads/sc336/notebook-kernel_link
    • refs/heads/sc336/python-3-11
    • refs/heads/sc336/sphinx_version_switcher
    • refs/heads/sc336/v2.6.4
    • refs/heads/sc336/v2.6.5
    • refs/heads/sc336/version-numbers
    • refs/heads/sergio_pasc/gpflow-2.0/adapt-sgpmc-and-gpmc
    • refs/heads/sergio_pasc/gpflow-2.0/adapt-sgpr
    • refs/heads/sergio_pasc/gpflow-2.0/move-gplvm-tests
    • refs/heads/sergio_pasc/gpflow-2.0/move-multioutput-features-tests
    • refs/heads/sergio_pasc/gpflow-2.0/move-quadrature-tests
    • refs/heads/sergio_pasc/gpflow-2.0/move-tests-methods
    • refs/heads/sergio_pasc/gpflow-2.0/ordinal_regression
    • refs/heads/sergio_pasc/gpflow-2.0/parameter_transform
    • refs/heads/sergio_pasc/gpflow-2.0/remove_training_loop
    • refs/heads/spascual/add-mailmap-file
    • refs/heads/st---metaautoflow
    • refs/heads/st/clean_up_broadcasting_conditionals
    • refs/heads/st/ericpena/natgrad-change
    • refs/heads/st/fix_active_dims_2
    • refs/heads/st/fix_config_module_docstring
    • refs/heads/st/fix_test_method_equivalence
    • refs/heads/st/fix_typo
    • refs/heads/st/fxsm
    • refs/heads/st/fxsm_closure
    • refs/heads/st/inv_probit_jitter_arg
    • refs/heads/st/new_svgp
    • refs/heads/st/posterior
    • refs/heads/st/posterior_with_linear_operators
    • refs/heads/st/quickfix/dispatch_docs
    • refs/heads/st/quickfix/posterior
    • refs/heads/st/quickfix_num_latent
    • refs/heads/st/rename_slow_multioutput
    • refs/heads/st/reorder_covariances
    • refs/heads/st/rescue_754
    • refs/heads/st/snowflake_kernel
    • refs/heads/st/test_cleanup
    • refs/heads/st/test_for_shape_check
    • refs/heads/st/test_kernel
    • refs/heads/st/triangular
    • refs/heads/st_FITCvsVFE_2.0
    • refs/heads/stef/fxsm
    • refs/heads/tf2.0-compatible
    • refs/heads/transform_on_sided
    • refs/heads/uri/bump_version_to_2_9_1
    • refs/heads/uri/bump_version_to_2_9_2
    • refs/heads/uri/clarify_training_loss_ref_in_tutorial
    • refs/heads/uri/dont_use_enums_for_default_values
    • refs/heads/uri/fix_benchmarks
    • refs/heads/uri/heteroskedastic_linear_mean
    • refs/heads/uri/investigate_scipy_memory_leak
    • refs/heads/uri/investigate_tf214_memory_leak
    • refs/heads/uri/move_prod_to_2_14
    • refs/heads/uri/numpy_2
    • refs/heads/uri/pickling_scipy_optimizer
    • refs/heads/uri/prod_test_environment
    • refs/heads/uri/quickfix/dont_round_small_values_in_summary
    • refs/heads/uri/quickfix/mypy_fixes
    • refs/heads/uri/release_2_8_1
    • refs/heads/uri/release_2_9_0
    • refs/heads/uri/support_tf_2_12
    • refs/heads/uri/test
    • refs/heads/uri/test_sgpr_changepoint_issue
    • refs/heads/uri/test_tf_2_16
    • refs/heads/uri/track_loss_history
    • refs/heads/uri/update_max_tf_version
    • refs/heads/uri/update_prod_tf_version
    • refs/heads/v1.5.1-docs
    • refs/heads/va/additive_models
    • refs/heads/va/gpf/conditional_kernel
    • refs/heads/va/gpf/seeger_lik
    • refs/heads/va/h
    • refs/heads/vdutor/multiple-output-gps
    • refs/heads/vincent/add-shared-mixed-mok
    • refs/heads/vincent/cholesky-to-kl
    • refs/heads/vincent/hotfix/preslicing-lmc
    • refs/heads/vincent/hotfix/save-keras-model
    • refs/heads/vincent/introspect-conditional
    • refs/heads/vincent/more-predict-functions
    • refs/heads/vincent/nbviewer
    • refs/heads/vincent/quickfix/dynamic-shapes-quadrature
    • refs/heads/vincent/quickfix/typo
    • refs/heads/vincent/shared-mixed-mok
    • refs/heads/vincent/st/heteroscedastic
    • refs/heads/vincent/st/multi-output-likelihoods
    • refs/heads/vincent/update-readme
    • refs/heads/whitening
    • refs/tags/0.2.1
    • refs/tags/0.3.1
    • refs/tags/0.3.2
    • refs/tags/0.3.3
    • refs/tags/0.3.4
    • refs/tags/0.3.5
    • refs/tags/0.4.0
    • refs/tags/0.5.0
    • refs/tags/1.0.0
    • refs/tags/1.1.0
    • refs/tags/1.1.1
    • refs/tags/2.0.0-rc1
    • refs/tags/v1.4.1
    • refs/tags/v1.5.0
    • refs/tags/v1.5.1
    • refs/tags/v2.0.0
    • refs/tags/v2.0.1
    • refs/tags/v2.0.2
    • refs/tags/v2.0.3
    • refs/tags/v2.0.4
    • refs/tags/v2.0.5
    • refs/tags/v2.1.0
    • refs/tags/v2.1.1
    • refs/tags/v2.1.2
    • refs/tags/v2.1.3
    • refs/tags/v2.1.4
    • refs/tags/v2.1.5
    • refs/tags/v2.10.0
    • refs/tags/v2.2.0
    • refs/tags/v2.2.1
    • refs/tags/v2.3.0
    • refs/tags/v2.3.1
    • refs/tags/v2.4.0
    • refs/tags/v2.5.0
    • refs/tags/v2.5.1
    • refs/tags/v2.5.2
    • refs/tags/v2.6.0
    • refs/tags/v2.6.1
    • refs/tags/v2.6.2
    • refs/tags/v2.6.3
    • refs/tags/v2.6.4
    • refs/tags/v2.6.5
    • refs/tags/v2.7.0
    • refs/tags/v2.7.1
    • refs/tags/v2.8.0
    • refs/tags/v2.8.1
    • refs/tags/v2.9.0
    • refs/tags/v2.9.1
    • refs/tags/v2.9.2
    • v1.3.0
    • v1.2.0
    • pre-dimension-rolling
    • 0.3.9
  • e4db900
  • /
  • README.md
Raw File Download
Take a new snapshot of a software origin

If the archived software origin currently browsed is not synchronized with its upstream version (for instance when new commits have been issued), you can explicitly request Software Heritage to take a new snapshot of it.

Use the form below to proceed. Once a request has been submitted and accepted, it will be processed as soon as possible. You can then check its processing state by visiting this dedicated page.
swh spinner

Processing "take a new snapshot" request ...

Permalinks

To reference or cite the objects present in the Software Heritage archive, permalinks based on SoftWare Hash IDentifiers (SWHIDs) must be used.
Select below a type of object currently browsed in order to display its associated SWHID and permalink.

  • content
  • directory
  • revision
  • snapshot
origin badgecontent badge Iframe embedding
swh:1:cnt:e8824f4e5375f6fa09b9144b484010f18efd6996
origin badgedirectory badge Iframe embedding
swh:1:dir:e4db900e8d3899156da8f075a26bd79af452c2d3
origin badgerevision badge
swh:1:rev:b8a05fb755d8b420d55d1b20dcc9559cf83dc152
origin badgesnapshot badge
swh:1:snp:239fa20181c27ca73831654c9488af916c079076
Citations

This interface enables to generate software citations, provided that the root directory of browsed objects contains a citation.cff or codemeta.json file.
Select below a type of object currently browsed in order to generate citations for them.

  • content
  • directory
  • revision
  • snapshot
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Tip revision: b8a05fb755d8b420d55d1b20dcc9559cf83dc152 authored by ST John on 04 January 2020, 00:18:23 UTC
Merge branch 'develop' into st/posterior
Tip revision: b8a05fb
README.md
<div style="text-align:center">
<img width="500" height="200" src="./doc/source/_static/gpflow_logo.svg">
</div>

[![CircleCI](https://circleci.com/gh/GPflow/GPflow/tree/develop.svg?style=svg)](https://circleci.com/gh/GPflow/GPflow/tree/develop)
[![Coverage Status](http://codecov.io/github/GPflow/GPflow/coverage.svg?branch=master)](http://codecov.io/github/GPflow/GPflow?branch=master)
[![Documentation Status](https://readthedocs.org/projects/gpflow/badge/?version=master)](http://gpflow.readthedocs.io/en/master/?badge=master)

[Website](https://gpflow.org) |
[Documentation (develop/v2.0)](https://gpflow.readthedocs.io/en/develop/) |
[Documentation (v1.5)](https://gpflow.readthedocs.io/en/v1.5.1-docs/) |
[Glossary](GLOSSARY.md)

GPflow is a package for building Gaussian process models in python, using [TensorFlow](http://www.tensorflow.org). It was originally created and is now managed by [James Hensman](http://jameshensman.github.io/) and [Alexander G. de G. Matthews](http://mlg.eng.cam.ac.uk/?portfolio=alex-matthews).
The full list of [contributors](http://github.com/GPflow/GPflow/graphs/contributors) (in alphabetical order) is
 Alexander G. de G. Matthews, Alexis Boukouvalas, [Artem Artemev](http://github.com/awav/), Daniel Marthaler, David J
 . Harris, Eric Hambro, Hugh Salimbeni, Ivo Couckuyt, James Hensman, Keisuke Fujii, Mark van der Wilk, Mikhail Beck, Pablo Leon
 -Villagra, Rasmus Bonnevie, Sergio Pascual-Diaz, ST John, Tom Nickson, Valentine Svensson, Vincent Dutordoir, Zoubin
  Ghahramani. GPflow is an open source project so if you feel you have some relevant skills and are interested in contributing then please do contact us.


## What does GPflow do?

GPflow implements modern Gaussian process inference for composable kernels and likelihoods. The [online documentation (develop)](http://gpflow.readthedocs.io/en/develop/)/[(master)](http://gpflow.readthedocs.io/en/master/) contains more details.

GPflow 2.0 uses [TensorFlow 2.0](http://www.tensorflow.org) for running computations, which allows fast execution on GPUs, and uses Python ≥ 3.6.


## Install GPflow

- From source

  With the release of _TensorFlow 2.0_ and _Tensorflow Probability_ 0.8, you should
  only need to run

  ```bash
  pip install -e .
  ```

  in a check-out of the `develop` branch of the GPflow github repository.

- Using `pip`

  ```bash
  pip install gpflow
  ```


## Getting Started with GPflow 2.0

There is an ["Intro to GPflow 2.0"](https://github.com/GPflow/GPflow/blob/develop/doc/source/notebooks/intro_to_gpflow2.ipynb) Jupyter notebook. Check it out for details.

- **GPflow 1.0**

  *We have stopped development and support for GPflow based on TensorFlow 1.0. We now accept only bug fixes to GPflow 1.0 in the **develop-1.0** branch. The latest available release is [v1.5.1](https://github.com/GPflow/GPflow/releases/tag/v1.5.1). [Documentation](https://gpflow.readthedocs.io/en/v1.5.1-docs/) and [tutorials](https://nbviewer.jupyter.org/github/GPflow/GPflow/blob/develop/doc/source/notebooks/intro.ipynb) will remain available.*


## Getting help

Please use GitHub issues to start discussion on the use of GPflow. Tagging enquiries `discussion` helps us distinguish them from bugs.

## Contributing

All constructive input is gratefully received. For more information, see the [notes for contributors](contributing.md).

## 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.

## Projects using GPflow

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

| Project | Description |
| --- | --- |
| [GPflowOpt](https://github.com/GPflow/GPflowOpt)       | Bayesian Optimization using GPflow. |
| [VFF](https://github.com/jameshensman/VFF)       | Variational Fourier Features for Gaussian Processes. |
| [Doubly-Stochastic-DGP](https://github.com/ICL-SML/Doubly-Stochastic-DGP)| Deep Gaussian Processes with Doubly Stochastic Variational Inference.|
| [BranchedGP](https://github.com/ManchesterBioinference/BranchedGP) | Gaussian processes with branching kernels.|
| [heterogp](https://github.com/Joshuaalbert/heterogp) | Heteroscedastic noise for sparse variational GP. |
| [widedeepnetworks](https://github.com/widedeepnetworks/widedeepnetworks) | Measuring the relationship between random wide deep neural networks and GPs.| 
| [orth_decoupled_var_gps](https://github.com/hughsalimbeni/orth_decoupled_var_gps) | Variationally sparse GPs with orthogonally decoupled bases| 


Let us know if you would like your project listed here.

## Citing GPflow

To cite GPflow, please reference the [JMLR paper](http://www.jmlr.org/papers/volume18/16-537/16-537.pdf). 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}
}
```

back to top

Software Heritage — Copyright (C) 2015–2025, The Software Heritage developers. License: GNU AGPLv3+.
The source code of Software Heritage itself is available on our development forge.
The source code files archived by Software Heritage are available under their own copyright and licenses.
Terms of use: Archive access, API— Contact— JavaScript license information— Web API