GPyTorch (Beta Release)

News!

The Beta release is currently out! Note that it requires PyTorch >= 1.1
If you need to install the alpha release (we recommend you use the latest version though!), check out the alpha release.

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 and Tutorials

See our numerous examples and tutorials on how to construct all sorts of models in GPyTorch. These example notebooks and a walk through of GPyTorch are also available at our ReadTheDocs page here.

Installation

Requirements:

Python >= 3.6
PyTorch >= 1.1

N.B. GPyTorch will not run on PyTorch 0.4.1 or earlier versions.

First make sure that you have PyTorch (>= 1.1) installed using the appropriate command from here.

Then 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 get the latest (unstable) version, run

pip install git+https://github.com/cornellius-gp/gpytorch.git

Citing Us

If 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}
}

Documentation

For tutorials and examples, check out the examples folder.
For in-depth documentation, check out our read the docs.

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

Please lint the code with flake8.

pip install flake8  # if not already installed
flake8

The Team

GPyTorch is primarily maintained by:

Jake Gardner (Uber AI Labs)
Geoff Pleiss (Cornell University)
Kilian Weinberger (Cornell University)
Andrew Gordon Wilson (Cornell University)
Max Balandat (Facebook)

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, and SAP.

mc-robinson/gpytorch