BoTorch is a library for Bayesian Optimization built on PyTorch.
BoTorch is currently in beta and under active development!
Why BoTorch ?
BoTorch
- Provides a modular and easily extensible interface for composing Bayesian optimization primitives, including probabilistic models, acquisition functions, and optimizers.
- Harnesses the power of PyTorch, including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, and a dynamic computation graph.
- Supports Monte Carlo-based acquisition functions via the reparameterization trick, which makes it straightforward to implement new ideas without having to impose restrictive assumptions about the underlying model.
- Enables seamless integration with deep and/or convolutional architectures in PyTorch.
- Has first-class support for state-of-the art probabilistic models in GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference.
Target Audience
The primary audience for hands-on use of BoTorch are researchers and sophisticated practitioners in Bayesian Optimization and AI. We recommend using BoTorch as a low-level API for implementing new algorithms for Ax. Ax has been designed to be an easy-to-use platform for end-users, which at the same time is flexible enough for Bayesian Optimization researchers to plug into for handling of feature transformations, (meta-)data management, storage, etc. We recommend that end-users who are not actively doing research on Bayesian Optimization simply use Ax.
Installation
Installation Requirements
- Python >= 3.11
- PyTorch >= 2.0.1
- gpytorch >= 1.14
- linear_operator >= 0.6
- pyro-ppl >= 1.8.4
- scipy
- multiple-dispatch
Option 1: Installing the latest release
The latest release of BoTorch is easily installed via pip:
pip install botorch
Note: Make sure the pip being used is actually the one from the newly created
Conda environment. If you're using a Unix-based OS, you can use which pip to check.
BoTorch stopped publishing
an official Anaconda package to the pytorch channel after the 0.12 release. However,
users can still use the package published to the conda-forge channel and install botorch via
conda install botorch -c gpytorch -c conda-forge
Option 2: Installing from latest main branch
If you would like to try our bleeding edge features (and don't mind potentially
running into the occasional bug here or there), you can install the latest
development version directly from GitHub. You may also want to install the
current gpytorch and linear_operator development versions:
pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git
pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
pip install --upgrade git+https://github.com/meta-pytorch/botorch.git
Option 3: Editable/dev install
If you want to contribute to BoTorch, you will want to install editably so that you can change files and have the changes reflected in your local install.
If you want to install the current gpytorch and linear_operator development versions, as in Option 2, do that
before proceeding.
Option 3a: Bare-bones editable install
git clone https://github.com/meta-pytorch/botorch.git
cd botorch
pip install -e .
Option 3b: Editable install with development and tutorials dependencies
git clone https://github.com/meta-pytorch/botorch.git
cd botorch
pip install -e ".[dev, tutorials]"
dev: Specifies tools necessary for development (testing, linting, docs building; see Contributing below).tutorials: Also installs all packages necessary for running the tutorial notebooks.- You can also install either the dev or tutorials dependencies without installing both, e.g. by changing the last command to
pip install -e ".[dev]".
Getting Started
Here's a quick run down of the main components of a Bayesian optimization loop. For more details see our Documentation and the Tutorials.
- Fit a Gaussian Process model to data
import torch
from botorch.models import SingleTaskGP
from botorch.models.transforms import Normalize
from botorch.fit import fit_gpytorch_mll
from gpytorch.mlls import ExactMarginalLogLikelihood
# Double precision is highly recommended for GPs.
# See https://github.com/meta-pytorch/botorch/discussions/1444
train_X = torch.rand(10, 2, dtype=torch.double) * 2
Y = 1 - (train_X - 0.5).norm(dim=-1, keepdim=True) # explicit output dimension
Y += 0.1 * torch.rand_like(Y)
gp = SingleTaskGP(
train_X=train_X,
train_Y=Y,
input_transform=Normalize(d=2),
)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
fit_gpytorch_mll(mll)
- Construct an acquisition function
from botorch.acquisition import LogExpectedImprovement
logEI = LogExpectedImprovement(model=gp, best_f=Y.max())
- Optimize the acquisition function
from botorch.optim import optimize_acqf
bounds = torch.stack([torch.zeros(2), torch.ones(2)]).to(torch.double)
candidate, acq_value = optimize_acqf(
logEI, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
)
Citing BoTorch
If you use BoTorch, please cite the following paper:
@inproceedings{balandat2020botorch,
title={{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}},
author={Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan},
booktitle = {Advances in Neural Information Processing Systems 33},
year={2020},
url = {http://arxiv.org/abs/1910.06403}
}
See here for an incomplete selection of peer-reviewed papers that build off of BoTorch.
Contributing
See the CONTRIBUTING file for how to help out.
License
BoTorch is MIT licensed, as found in the LICENSE file.
4 maintainers
Release files for botorch 0.18.1
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Total release size: 11.1 MB
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