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Getting Started

This section shows you how to get your feet wet with BoTorch.

Before jumping the gun, we recommend you start with the high-level Overview to learn about the basic concepts in BoTorch.

Installing BoTorch

Installation Requirements:

• Python >= 3.7
• PyTorch >= 1.10
• gpytorch >= 1.8.1
• scipy
• multiple-dispatch
• pyro-ppl >= 1.8.0

BoTorch is easily installed via Anaconda (recommended) or pip:

conda

pip

conda install botorch -c pytorch -c gpytorch -c conda-forge

pip install botorch

For more detailed installation instructions, please see the Project Readme on GitHub.

Basic Components

Here's a quick run down of the main components of a Bayesian Optimization loop.

1. Fit a Gaussian Process model to data

   import torch
   from botorch.models import SingleTaskGP
   from botorch.fit import fit_gpytorch_model
   from gpytorch.mlls import ExactMarginalLogLikelihood
   
   train_X = torch.rand(10, 2)
   Y = 1 - (train_X - 0.5).norm(dim=-1, keepdim=True)  # explicit output dimension
   Y += 0.1 * torch.rand_like(Y)
   train_Y = (Y - Y.mean()) / Y.std()
   
   gp = SingleTaskGP(train_X, train_Y)
   mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
   fit_gpytorch_model(mll);
   

2. Construct an acquisition function

   from botorch.acquisition import UpperConfidenceBound
   
   UCB = UpperConfidenceBound(gp, beta=0.1)
   

3. Optimize the acquisition function

   from botorch.optim import optimize_acqf
   
   bounds = torch.stack([torch.zeros(2), torch.ones(2)])
   candidate, acq_value = optimize_acqf(
       UCB, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
   )
   

Tutorials

Our Jupyter notebook tutorials help you get off the ground with BoTorch. View and download them here.

API Reference

For an in-depth reference of the various BoTorch internals, see our API Reference.

Contributing

You'd like to contribute to BoTorch? Great! Please see here for how to help out.