HIPS/Spearmint

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1572 stars · 326 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 that automatically tunes experiment parameters to minimize an objective in as few runs as possible. It implements algorithms from NeurIPS/ICML publications including multi-task and constrained Bayesian optimization, using MongoDB to track experiment state.

Use cases

• optimize hyperparameters of a machine learning model
• minimize an expensive black-box objective function
• run experiments that iteratively adjust parameters automatically
• perform multi-task Bayesian optimization
• optimize functions with unknown constraints
• tune noisy objective functions with Gaussian processes

When to choose

• you need Bayesian optimization for academic or non-commercial research
• you want a well-cited, publication-backed optimizer with multi-task and constrained variants
• you are comfortable running MongoDB as the experiment backend

When to avoid

• your use is commercial - the license restricts to academic and non-commercial research
• you need actively maintained software or Python 3 support
• you want a lightweight optimizer without a MongoDB dependency

Facets

library · maturity maintenance

machine-learning benchmarking data-science machine-learning artificial-intelligence python bayesian-optimization hyperparameter-optimization gaussian-processes experiment-runner mongodb non-commercial-license algorithms linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="HIPS/Spearmint")