cornellius-gp/gpytorch

What it solves

GPyTorch addresses the challenge of creating Gaussian process (GP) models that are scalable, flexible, and modular. It specifically overcomes the performance bottlenecks of traditional GP inference by leveraging GPU acceleration and efficient numerical linear algebra.

How it works

Unlike many GP libraries that rely on Cholesky decomposition, GPyTorch performs most inference operations using numerical linear algebra techniques, such as preconditioned conjugate gradients. It utilizes a LinearOperator interface to handle matrix multiplications with the kernel matrix and its derivatives, allowing for significantly better GPU utilization and the implementation of advanced scalable GP techniques.

Who it’s for

It is designed for researchers and developers who need to implement Gaussian process models that can scale to large datasets and integrate seamlessly with deep learning frameworks.

Highlights

• Significant GPU acceleration via MVM-based inference.
• Implementations of state-of-the-art scalable algorithms including SKI/KISS-GP, stochastic Lanczos expansions, LOVE, SKIP, and stochastic variational deep kernel learning.
• Easy integration with PyTorch and other deep learning frameworks.
• Modular design for creating flexible GP models.