Package: gpflux
GPfluxDocumentation|Tutorials|API reference|SlackWhat does GPflux do?GPflux is a toolbox dedicated to Deep Gaussian processes (DGP), the hierarchical extension of Gaussian processes (GP).GPflux uses the mathematical building blocks fromGPflowand marries these with the powerful layered deep learning API provided byKeras.
This combination leads to a framework that can be used for:researching new (deep) Gaussian process models, andbuilding, training, evaluating and deploying (deep) Gaussian processes in a modern way — making use of the tools developed by the deep learning community.Getting startedIn theDocumentation, we have multipleTutorialsshowing the basic functionality of the toolbox, abenchmark implementationand a comprehensiveAPI reference.Install GPfluxThis project is assuming you are usingpython3.For usersTo install the latest (stable) release of the toolbox fromPyPI, usepip:$pipinstallgpfluxFor contributorsTo install this project in editable mode, run the commands below from the root directory of theGPfluxrepository.makeinstallCheck that the installation was successful by running the tests:maketestYou can have a peek at theMakefilefor the commands.The Secondmind Labs 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 GPflux that in some sense involves changing the GPflux code itself. 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.Slack workspaceWe have a publicSecondmind Labs slack workspace. Please use thisinvite linkand join the #gpflux channel, whether you'd just like to ask short informal questions or want to be involved in the discussion and future development of GPflux.ContributingAll constructive input is very much welcome. For detailed information, see theguidelines for contributors.MaintainersGPflux was originally created atSecondmind Labsand is now actively maintained by (in alphabetical order)Vincent DutordoirandST John.We are grateful toall contributorswho have helped shape GPflux.GPflux is an open source project. If you have relevant skills and are interested in contributing then please do contact us (see"The Secondmind Labs Community" sectionabove).We are very grateful to our Secondmind Labs colleagues, maintainers ofGPflow,TriesteandBellman, for their help with creating contributing guidelines, instructions for users and open-sourcing in general.Citing GPfluxTo cite GPflux, please reference ourarXiv paperwhere we review the framework and describe the design. Sample Bibtex is given below:@article{dutordoir2021gpflux,
    author = {Dutordoir, Vincent and Salimbeni, Hugh and Hambro, Eric and McLeod, John and
        Leibfried, Felix and Artemev, Artem and van der Wilk, Mark and Deisenroth, Marc P.
        and Hensman, James and John, ST},
    title = {GPflux: A library for Deep Gaussian Processes},
    year = {2021},
    journal = {arXiv:2104.05674},
    url = {https://arxiv.org/abs/2104.05674}
}LicenseApache License 2.0