Open Source · Observability
hyperunity
A toolset for black-box hyperparameter optimisation.
Our verdict
We have not reviewed hyperunity yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About hyperunity
Hypertunity is a Python toolset for black-box hyperparameter optimisation. It provides a framework to automatically tune machine learning model hyperparameters without requiring knowledge of the inner workings of the optimisation algorithm.
Best for
Developers seeking a lightweight, extensible hyperparameter optimisation library for Python experiments.
Use cases
- Automating hyperparameter search for ML models
- Integrating custom optimisation algorithms via a plugin architecture
- Running distributed hyperparameter tuning experiments
Notes
136 stars on GitHub. Last updated 2020-01-26. Licensed Apache-2.0.
Pros
- Supports black-box optimisation without model internals
- Plugin system allows custom optimisation strategies
- Simple Python API for integration into existing workflows
Cons
- Limited community adoption with only 136 GitHub stars
- No documentation on advanced features or real-world benchmarks
- May lack built-in visualisation or monitoring tools for results
Key facts
What we have checked
- GitHub stars
- 137
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How hyperunity is growing
136 to 137
Sources
Where this comes from
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
No changes recorded yet. Releases, price changes and deprecations appear here, with their dates, once we track them for this record.
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