An implementation of Gaussian Processes in Pytorch
Project description
GPyTorch
GPyTorch 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 all inference operations using modern 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 our LazyTensor interface, or by composing many of our already existing LazyTensors. 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 variational deep kernel learning, ...); (3) easy integration with deep learning frameworks.
Examples, Tutorials, and Documentation
See our numerous examples and tutorials on how to construct all sorts of models in GPyTorch.
Installation
Requirements:
- Python >= 3.7
- PyTorch >= 1.10
Install GPyTorch using pip or conda:
pip install gpytorch
conda install gpytorch -c gpytorch
(To use packages globally but install GPyTorch as a user-only package, use pip install --user above.)
Latest (unstable) version
To upgrade to the latest (unstable) version, run
pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
ArchLinux Package
Note: Experimental AUR package. For most users, we recommend installation by conda or pip.
GPyTorch is also available on the ArchLinux User Repository (AUR).
You can install it with an AUR helper, like yay, as follows:
yay -S python-gpytorch
To discuss any issues related to this AUR package refer to the comments section of
python-gpytorch.
Citing Us
If you use GPyTorch, please cite the following papers:
@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}
}
Development
To run the unit tests:
python -m unittest
By default, the random seeds are locked down for some of the tests. If you want to run the tests without locking down the seed, run
UNLOCK_SEED=true python -m unittest
If you plan on submitting a pull request, please make use of our pre-commit hooks to ensure that your commits adhere to the general style guidelines enforced by the repo. To do this, navigate to your local repository and run:
pip install pre-commit
pre-commit install
From then on, this will automatically run flake8, isort, black and other tools over the files you commit each time you commit to gpytorch or a fork of it.
The Team
GPyTorch 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) David Arbour, Eytan Bakshy, David Eriksson, Jared Frank, Sam Stanton, Bram Wallace, Ke Alexander Wang, Ruihan Wu.
Acknowledgements
Development of GPyTorch is supported by funding from the Bill and Melinda Gates Foundation, the National Science Foundation, SAP, the Simons Foundation, and the Gatsby Charitable Trust.
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