Basic Usage

This folder contains notebooks for basic usage of the package, e.g. things like dealing with hyperparameters, parameter constraints and priors, and saving and loading models.

Before checking these out, you may want to check out our simple GP regression tutorial that details the anatomy of a GPyTorch model.

• Check out our Tutorial on Hyperparameters for information on things like raw versus actual parameters, constraints, priors and more.

• The Saving and Loading Models notebook details how to save and load GPyTorch models on disk.

• The Kernels with Additive or Product Structure notebook describes how to compose kernels additively or multiplicatively, whether for expressivity, sample efficiency, or scalability.

• The Implementing a Custom Kernel notebook details how to write your own custom kernel in GPyTorch.

• The Tutorial on Metrics describes various metrics provided by GPyTorch for assessing the generalization of GP models.