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Hyperopt supports distributed asynchronous hyperparameter optimization in Python.
About Hyperopt
Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, including real-valued, discrete, and conditional dimensions. It is used for distributed asynchronous hyperparameter optimization.
It provides Random Search, Tree of Parzen Estimators (TPE), and Adaptive TPE, with parallel execution through Apache Spark and MongoDB. Gaussian process and regression-tree based Bayesian optimization methods are not currently implemented.
Key features
- Serial and parallel optimization over awkward search spaces
- Search over real-valued, discrete, and conditional dimensions
- Tree of Parzen Estimators (TPE) algorithm
- Adaptive TPE algorithm
- Random Search algorithm
- Parallel execution via Apache Spark or MongoDB
Best for: Python users who need to tune models with mixed-type or conditional search spaces in serial or parallel runs.
Good for
- Supports both serial and parallel optimization
- Handles real-valued, discrete, and conditional dimensions
- Provides SparkTrials and MongoTrials extras for distributed runs
Watch out for
- Gaussian process and regression-tree based Bayesian optimization methods are not implemented
Hyperopt at a glance
- Website
- github.com/hyperopt/hyperopt
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Hyperopt FAQ
How do I install Hyperopt?
Install from PyPI with pip install hyperopt, or with uv using uv add hyperopt.
Which optimization algorithms are implemented?
Random Search, Tree of Parzen Estimators (TPE), and Adaptive TPE.
How can Hyperopt runs be parallelized?
All algorithms can be parallelized using Apache Spark and MongoDB, via SparkTrials and MongoTrials.
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