Spearmint

Overview

Spearmint is a Python library for Bayesian optimization, enabling efficient hyperparameter tuning of machine learning models. It uses Gaussian processes to model the objective function and selects the next parameters to evaluate via expected improvement. The codebase is designed for sequential optimization where each evaluation is expensive.

Best for

Best for
Researchers and engineers needing a reliable Bayesian optimization library for expensive black-box functions

Use cases

• Tuning hyperparameters of deep learning models
• Optimizing simulation parameters with costly evaluations
• Automating experiment design for scientific computing

Notes

Spearmint is a Python library for Bayesian optimization, enabling efficient hyperparameter tuning of machine learning models. It uses Gaussian processes to model the objective function and selects the next parameters to evaluate via expected improvement. The codebase is designed for sequential optimization where each evaluation is expensive.

1,568 stars on GitHub. Last updated 2019-12-27.

Use cases

• Tuning hyperparameters of deep learning models
• Optimizing simulation parameters with costly evaluations
• Automating experiment design for scientific computing

Pros

• Proven Bayesian optimization algorithm with solid theoretical foundation
• Well-documented codebase with 1.5k+ GitHub stars
• Handles noisy objective functions gracefully

Cons

• Limited to sequential optimization, no parallel evaluation support
• Requires manual integration into existing workflows
• Not actively maintained; last updates are several years old

Indexed from awesome-llmops and enriched against its public facts.