Our verdict
We have not reviewed HpBandSter 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 HpBandSter
HpBandSter is a distributed implementation of the Hyperband algorithm for hyperparameter optimization. It uses bandit-based early stopping to efficiently allocate resources to promising configurations, scaling across multiple workers.
Best for
Researchers and engineers running distributed hyperparameter optimization with limited compute budgets
Use cases
- Tuning hyperparameters for deep learning models
- Running distributed hyperparameter search on a cluster
- Accelerating model selection with early stopping
Notes
630 stars on GitHub. Last updated 2022-10-16. Licensed BSD-3-Clause.
Pros
- Efficient resource allocation via Hyperband's adaptive early stopping
- Distributed execution for scaling to large search spaces
- Open source with a straightforward Python interface
Cons
- Limited to Hyperband strategy, not a general-purpose tuner
- Requires manual setup of distributed workers and shared storage
- Less actively maintained compared to newer alternatives
Key facts
What we have checked
- Latest version
- 1.0 (released 30 July 2019)
- GitHub stars
- 633
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 HpBandSter is growing
630 to 633
Sources
Where this comes from
- GitHubOfficial
- 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
Released 1.0
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