Open Source · Observability
Dragonfly
An open source python library for scalable Bayesian optimisation.
Our verdict
We have not reviewed Dragonfly 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 Dragonfly
Dragonfly is an open source Python library for scalable Bayesian optimisation. It uses probabilistic models to optimize expensive black-box functions efficiently. The library supports multiple acquisition functions and parallel evaluations.
Best for
Developers needing scalable Bayesian optimisation for expensive black-box function evaluations, especially in observability and ML tuning
Use cases
- Hyperparameter tuning for machine learning models
- Optimizing simulation parameters in scientific computing
- Tuning observability system configurations
Notes
893 stars on GitHub. Last updated 2023-06-19. Licensed MIT.
Pros
- Scalable Bayesian optimisation with support for parallel evaluations
- Open source with active community contributions
- Integrates well with existing Python workflows
Cons
- Limited to Bayesian optimisation methods, not a general optimization library
- Requires careful selection of acquisition functions for best results
- Smaller community compared to alternatives like Optuna or Hyperopt
Key facts
What we have checked
- GitHub stars
- 894
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 Dragonfly is growing
893 to 894
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.
Pairs with
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