meta-pytorch/botorch
Bayesian optimization in PyTorch observed · 2026-08-28
Health v2 · maintenance only
91/100
- Activity 99
- Release rhythm 75
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 72
- age_days: 2956
- days_rel: 86
- days_push: 7
- n_releases_24m: 12
Adoption not part of the score
3589 stars · 497 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
BoTorch is a Python library for Bayesian Optimization research built on top of PyTorch, part of the PyTorch ecosystem. It provides modular, extensible primitives for probabilistic models (via GPyTorch), Monte Carlo acquisition functions, and optimizers for sequential black-box optimization.
Use cases
- optimize hyperparameters of machine learning models with Bayesian optimization
- implement custom acquisition functions for black-box optimization research
- fit Gaussian process surrogate models on GPU with PyTorch autograd
- run sequential optimization of costly-to-evaluate experiments
- build multi-task or multi-fidelity Bayesian optimization algorithms
- integrate Bayesian optimization into A/B testing or scientific experiments
When to choose
- you are a researcher or sophisticated practitioner developing new Bayesian optimization algorithms
- you need low-level, modular control over models, acquisition functions, and optimizers
- you want PyTorch-native GPU support and autodifferentiation in your optimization stack
- you need Monte Carlo-based or batch acquisition functions without restrictive analytic assumptions
When to avoid
- you just want a ready-made hyperparameter tuning or experiment platform - use Ax instead
- you are not familiar with Bayesian optimization or PyTorch
- you need a simple, high-level tuning library with minimal setup
- your problem is not suited to expensive black-box function optimization
Facets
library · maturity active
machine-learning math sdk machine-learning data-science artificial-intelligence python cross-platform bayesian-optimization pytorch gaussian-processes hyperparameter-optimization black-box-optimization acquisition-functions gpytorch research algorithms gpu
10 sources
- readme: https://github.com/meta-pytorch/botorch · fetched 2026-08-28 · 4a24c8a31f6d
- homepage: https://botorch.org/ · fetched 2026-08-29 · dd21f951292d
- site_page: https://botorch.org/docs/introduction · fetched 2026-08-29 · ecb9519b368e
- site_page: https://botorch.org/docs/tutorials · fetched 2026-08-29 · ca10f6bb1017
- site_page: https://botorch.org/docs/notebooks_community · fetched 2026-08-29 · c7101aef7e13
- site_page: https://botorch.org/docs/papers · fetched 2026-08-29 · 1c2c67fcded1
- site_page: https://botorch.org/docs/next/introduction · fetched 2026-08-29 · a6fda6979a52
- site_page: https://botorch.org/docs/v0.17.2/introduction · fetched 2026-08-29 · 3f55599cc7c0
- site_page: https://botorch.org/docs/v0.17.1/introduction · fetched 2026-08-29 · 3a704e857046
- site_page: https://botorch.org/docs/v0.17.0/introduction · fetched 2026-08-29 · 2cc0819cda98
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| meta-pytorch/botorch | main | 91 |
For agents
markdown · JSON · MCP: product_card(name="meta-pytorch/botorch")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem