GraphCanon updated Oct 2, 2026 · GitHub synced Oct 2, 2026
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Decision brief
Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
Good fit when
- When you need to optimize machine learning model parameters on a distributed system asynchronously.
- For tasks requiring the use of specific optimizers such as Tree of Parzen Estimators (TPE) or Annealing within Python environments.
Avoid when
- If your project does not support asynchronous execution, opting for synchronous tools might be more suitable.
- Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (3d since push)
- As of Oct 2, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 2, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install hyperoptSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A Python library for distributed asynchronous hyperparameter optimization providing optimizers like TPE and Annealing to find the best parameters for machine learning algorithms.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Oct 2, 2026
- Languages
- python
Source: github.language+pyproject.toml · Oct 2, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Getting started Install hyperopt from PyPI ```bash pip install hyperopt
For agents
This page has a .md twin and JSON over the API.