Package: gpytorch
GPyTorchGPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease.Internally, GPyTorch differs from many existing approaches to GP inference by performing most inference operations using numerical linear algebra techniques like preconditioned conjugate gradients.
Implementing a scalable GP method is as simple as providing a matrix multiplication routine with the kernel matrix and its derivative via ourLinearOperatorinterface,
or by composing many of our already existingLinearOperators.
This allows not only for easy implementation of popular scalable GP techniques,
but often also for significantly improved utilization of GPU computing compared to solvers based on the Cholesky decomposition.GPyTorch provides (1) significant GPU acceleration (through MVM based inference);
(2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility (SKI/KISS-GP,stochastic Lanczos expansions,LOVE,SKIP,stochastic variationaldeep kernel learning, ...);
(3) easy integration with deep learning frameworks.Examples, Tutorials, and DocumentationSee ourdocumentation, examples, tutorialson how to construct all sorts of models in GPyTorch.InstallationRequirements:Python >= 3.8PyTorch >= 1.11Install GPyTorch using pip or conda:pipinstallgpytorch
condainstallgpytorch-cgpytorch(To use packages globally but install GPyTorch as a user-only package, usepip install --userabove.)Latest (Unstable) VersionTo upgrade to the latest (unstable) version, runpipinstall--upgradegit+https://github.com/cornellius-gp/linear_operator.git
pipinstall--upgradegit+https://github.com/cornellius-gp/gpytorch.gitDevelopment versionIf you are contributing a pull request, it is best to perform a manual installation:gitclonehttps://github.com/cornellius-gp/gpytorch.gitcdgpytorch
pipinstall-e.[dev,examples,test,pyro,keops]To generate the documentation locally, you will also need to run the following command
from the linear_operator folder:pipinstall-rdocs/requirements.txtArchLinux PackageNote: Experimental AUR package. For most users, we recommend installation by conda or pip.GPyTorch is also available on theArchLinux User Repository(AUR).
You can install it with anAUR helper, likeyay, as follows:yay-Spython-gpytorchTo discuss any issues related to this AUR package refer to the comments section ofpython-gpytorch.Citing UsIf you use GPyTorch, please cite the following papers:Gardner, Jacob R., Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson. "GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration." In Advances in Neural Information Processing Systems (2018).@inproceedings{gardner2018gpytorch,
  title={GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration},
  author={Gardner, Jacob R and Pleiss, Geoff and Bindel, David and Weinberger, Kilian Q and Wilson, Andrew Gordon},
  booktitle={Advances in Neural Information Processing Systems},
  year={2018}
}ContributingSee the contributing guidelinesCONTRIBUTING.mdfor information on submitting issues and pull requests.The TeamGPyTorch is primarily maintained by:Jake Gardner(University of Pennsylvania)Geoff Pleiss(Columbia University)Kilian Weinberger(Cornell University)Andrew Gordon Wilson(New York University)Max Balandat(Meta)We would like to thank our other contributors including (but not limited to)
Eytan Bakshy,
Wesley Maddox,
Ke Alexander Wang,
Ruihan Wu,
Sait Cakmak,
David Eriksson,
Sam Daulton,
Martin Jankowiak,
Sam Stanton,
Zitong Zhou,
David Arbour,
Karthik Rajkumar,
Bram Wallace,
Jared Frank,
and many more!AcknowledgementsDevelopment of GPyTorch is supported by funding from
theBill and Melinda Gates Foundation,
theNational Science Foundation,SAP,
theSimons Foundation,
and theGatsby Charitable Trust.LicenseGPyTorch isMIT licensed.