Hyperopt

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

7592 stars · 1075 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Hyperopt is a Python library for distributed asynchronous hyperparameter optimization over search spaces with real-valued, discrete, and conditional dimensions, using algorithms like TPE. It supports parallel trials via MongoDB or Spark and includes hyperopt-sklearn for tuning scikit-learn estimators.

Use cases

• tune hyperparameters of a machine learning model
• optimize sklearn estimator parameters automatically
• run distributed hyperparameter search with Spark or MongoDB
• minimize an objective function over a conditional search space
• bayesian optimization of black-box functions
• find best model parameters via TPE

When to choose

• you need flexible search spaces with conditional and discrete dimensions
• you want to parallelize hyperparameter search across Spark or MongoDB workers
• you prefer a lightweight, mature Python optimization library with TPE

When to avoid

• you need cutting-edge optimizers like Optuna's samplers or advanced pruning
• you want first-class integration with modern deep learning frameworks
• you need active development and frequent releases

Facets

library · maturity maintenance

machine-learning benchmarking data-science machine-learning data-science developer-tools python cross-platform cloud hyperparameter-optimization bayesian-optimization tpe distributed-optimization model-selection scikit-learn docker

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For agents

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