Exact GPs with Scalable (GPU) Inference

In GPyTorch, Exact GP inference is still our preferred approach to large regression datasets. By coupling GPU acceleration with BlackBox Matrix-Matrix Inference and LancZos Variance Estimates (LOVE), GPyTorch can perform inference on datasets with over 1,000,000 data points while making very few approximations.

How GPyTorch Scales Exact GPs

GPyTorch relies on two key techniques to scale exact GPs to millions of data points using GPU acceleration.

Exact GPs with GPU Acceleration

Here are examples of Exact GPs using GPU acceleration.

Scalable Kernel Approximations

While exact computations are our preferred approach, GPyTorch offer approximate kernels to reduce the asymptotic complexity of inference.

Structure-Exploiting Kernels

If your data lies on a Euclidean grid, and your GP uses a stationary kernel, the computations can be sped up dramatically. See the Grid Regression example for more info.