BoTorch Tutorials

The tutorials here will help you understand and use BoTorch inyour own work. They assume that you are familiar with bothBayesian optimization (BO) and PyTorch.

If you are new to BO, we recommend you start with theAx docs and thefollowingtutorial paper.

If you are new to PyTorch, the easiest way to get started iswith theWhat is PyTorch?tutorial.

The BoTorch tutorials are grouped into the following four areas.

Using BoTorch with Ax

These tutorials give you an overview of how to leverageAx, a platform for sequentialexperimentation, in order to simplify the management of your BOloop. Doing so can help you focus on the main aspects of BO(models, acquisition functions, optimization of acquisitionfunctions), rather than tedious loop control. See ourDocumentationfor additional information.

Full Optimization Loops

In some situations (e.g. when working in a non-standard setting,or if you want to understand and control various details of theBO loop), then you may also consider working purely in BoTorch.The tutorials in this section illustrate this approach.

Bite-Sized Tutorials

Rather than guiding you through full end-to-end BO loops, thetutorials in this section focus on specific tasks that you willencounter in customizing your BO algorithms. For instance, youmay want towrite a custom acquisition functionand thenuse a custom zero-th order optimizerto optimize it.

Advanced Usage

Tutorials in this section showcase more advanced ways of usingBoTorch. For instance,this tutorialshows how to perform BO if your objective function is an image,by optimizing in the latent space of a variational auto-encoder(VAE).