JasperSnoek/spearmint
Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012 observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4975
- days_rel: n/a
- days_push: 3330
- n_releases_24m: 0
Adoption not part of the score
1392 stars · 332 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Spearmint is a Python package for Bayesian optimization of machine learning algorithms, iteratively tuning parameters to minimize an objective in as few experiment runs as possible. This is the original legacy repository, now maintained only for bug fixes, with development moved to the HIPS/Spearmint codebase.
Use cases
- bayesian optimization of machine learning hyperparameters
- minimize an objective function in as few runs as possible
- automatically run and tune experiments in parallel
- tune parameters of expensive black-box functions
- hyperparameter search using expected improvement or UCB acquisition functions
When to choose
- you need the original 2012 algorithms under a permissive license
- you want to swap out acquisition function and driver modules
- you must run optimization experiments in parallel on a machine or cluster
When to avoid
- you need modern Python support (this requires Python 2.7)
- you want active development or new features
- you prefer the overhauled HIPS/Spearmint codebase
- you need commercial use of the newer implementation without license restrictions
Facets
library · maturity maintenance
machine-learning benchmarking cli machine-learning developer-tools python bayesian-optimization hyperparameter-tuning python2 legacy algorithms linux macos
1 source
- readme: https://github.com/JasperSnoek/spearmint · fetched 2026-08-28 · 2eadc9d86901
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| JasperSnoek/spearmint | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="JasperSnoek/spearmint")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem