{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"BoTorch provides a modular, PyTorch-based framework for building Bayesian optimization algorithms with probabilistic models, acquisition functions, and optimizers.","skillfed_tags":["bayesian-optimization","gaussian-processes","pytorch-based"],"use_cases":["Compose custom Bayesian optimization algorithms from probabilistic models, acquisition functions, and optimizers for research.","Optimize expensive black-box functions using Gaussian Process models with GPU acceleration for large-scale optimization.","Integrate Bayesian optimization into deep learning pipelines using PyTorch's autodifferentiation and GPU support.","Experiment with advanced probabilistic models like multi-task GPs or deep GPs for structured optimization problems.","Build acquisition function variants using the reparameterization trick without restrictive modeling assumptions."],"what_it_does":"BoTorch is a Bayesian optimization library built on PyTorch that lets researchers and practitioners compose optimization algorithms from modular components: probabilistic models (especially Gaussian Processes via gpytorch), acquisition functions, and optimizers. It leverages PyTorch's autodifferentiation and GPU support to enable efficient Monte Carlo-based acquisition function optimization without restrictive modeling assumptions.\n\nThe library targets researchers and sophisticated practitioners doing active research on Bayesian optimization algorithms. BoTorch supports advanced probabilistic models including multi-task Gaussian Processes, deep kernel learning, and deep GPs, making it suitable for complex optimization problems that integrate with deep learning architectures.","worth_installing":"Yes, if you are a researcher or sophisticated practitioner actively developing Bayesian optimization algorithms. The library is actively maintained, has low install friction, and offers a modular, extensible interface backed by PyTorch's performance and GPU support. No if you are an end-user seeking a simple optimization tool. The beta status and requirement for Python >= 3.11 and PyTorch >= 2.0.1 reflect active development."},"id":"botorch","links":{"html":"https://skillfed.io/packages/botorch","md":"https://skillfed.io/packages/botorch.md","pypi":"https://pypi.org/project/botorch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-08","license_spdx":"MIT","license_treatment":"permissive","name":"botorch","python_support":"supports_current","summary":"Bayesian Optimization in PyTorch"},"popularity":{"monthly_downloads":827975,"position":4955,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.18.1"}