GPyTorch (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.

GPyTorch primarily works by creating an accurate kernel approximation (typically via SKI) which admits fast matrix vector multiplies (MVMs) for iterative pre-conditioned MVM-based scalable inference and learning.

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.

This package is currently under development, and is likely to change. Some things you can do right now:

Simple GP regression (example here)
Simple GP classification (example here)
Multitask GP regression (example here)
Scalable GP regression using kernel interpolation (example here)
Scalable GP classification using kernel interpolation (example here)
Deep kernel learning (example here)
And (more!)

If you use GPyTorch, please cite the following papers:

Gardner, Jacob R., Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, and Andrew Gordon Wilson. "Product Kernel Interpolation for Scalable Gaussian Processes." In AISTATS (2018).

@inproceedings{gardner2018product,
  title={Product Kernel Interpolation for Scalable Gaussian Processes},
  author={Gardner, Jacob R and Pleiss, Geoff and Wu, Ruihan and Weinberger, Kilian Q and Wilson, Andrew Gordon},
  booktitle={AISTATS},
  year={2018}
}

Pleiss, Geoff, Jacob R. Gardner, Kilian Q. Weinberger, and Andrew Gordon Wilson. "Constant-Time Predictive Distributions for Gaussian Processes." arXiv preprint arXiv:1803.06058 (2018).

@article{pleiss2018constant,
  title={Constant-Time Predictive Distributions for Gaussian Processes},
  author={Pleiss, Geoff and Gardner, Jacob R and Weinberger, Kilian Q and Wilson, Andrew Gordon},
  journal={arXiv preprint arXiv:1803.06058},
  year={2018}
}

Installation

Global installation

The easiest way to install GPyTorch is by installing PyTorch >= 0.4.0 using the appropriate command from here, and then installing GPyTorch using pip:

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

To use packages globally but install GPyTorch as a user-only package, use pip install --user above.

Installation in a conda environment

We also provide two conda environment files, environment.yml and environment_cuda90.yml. As an example, to install GPyTorch in a conda environment with cuda support, run:

git clone git+https://github.com/cornellius-gp/gpytorch.git
conda create -f gpytorch/environment_cuda.yml
source activate gpytorch
pip install gpytorch/

Documentation

Still a work in progress. For now, please refer to the following example Jupyter notebooks.

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

Founding Team

GPyTorch is developed at Cornell University by

We would like to thank our other contributors including (but not limited to) Max Balandat, Ruihan Wu, Bram Wallace, Jared Frank.

Acknowledgements

Development of GPyTorch is supported by funding from the Bill and Melinda Gates Foundation.

orionr/gpytorch