automl/SMAC3

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1253 stars · 244 forks observed · 2026-10-08

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SMAC3 is a Python library for Bayesian Optimization used to tune hyperparameters of machine learning algorithms and configure arbitrary algorithms. It combines Bayesian optimization with an aggressive racing mechanism and supports multi-fidelity, multi-objective, and multi-threaded optimization via an ask-and-tell interface.

Use cases

• tune hyperparameters of a machine learning model
• find the best configuration for my algorithm
• bayesian optimization with expensive objective functions
• multi-fidelity hyperparameter search
• multi-objective hyperparameter optimization
• automated machine learning hyperparameter tuning

When to choose

• you need robust, actively maintained Bayesian optimization for hyperparameter or algorithm configuration
• you need multi-fidelity, multi-objective, or parallel (multi-threaded) optimization natively
• you want an ask-and-tell interface or resumable optimization runs
• you work in the AutoML ecosystem (Optuna, DeepCAVE integrations)

When to avoid

• you need a command-line interface or runtime optimization, which were removed in v2.0
• you need distributed optimization across clusters - consider HyperSweeper or other tools instead
• you need simple random or grid search rather than model-based optimization

Facets

library · maturity active

machine-learning benchmarking developer-tools machine-learning artificial-intelligence data-science python windows cross-platform bayesian-optimization hyperparameter-optimization hyperparameter-tuning automl algorithm-configuration random-forest gaussian-process multi-fidelity multi-objective ask-and-tell algorithms linux macos

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markdown · JSON · MCP: product_card(name="automl/SMAC3")