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[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. To convert your code from GPflow 1 check the [GPflow 2 upgrade guide](https://github.com/GPflow/GPflow/blob/develop/doc/source/notebooks/gpflow_2_upgrade/gpflow2_upgrade_guide.ipynb).

- **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| 
| [kernel_learning](https://github.com/frgsimpson/kernel_learning) | Implementation of "Differentiable Compositional Kernel Learning for Gaussian Processes".|
| [VBPP](https://github.com/st--/vbpp) | Implementation of "Variational Bayes for Point Processes".|
| [DGPs_with_IWVI](https://github.com/hughsalimbeni/DGPs_with_IWVI) | Deep Gaussian Processes with Importance-Weighted Variational Inference|


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}
}
```