Comparison
awesome-AutoML vs hyperband
Verdict
Pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Markdown twin · awesome-AutoML alternatives · hyperband alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | awesome-AutoML | hyperband |
|---|---|---|
| Maintenance | Slowing (133d since push) As of 3w · github_public_v1 | Dormant (2910d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-AutoML
- Curating AutoML research and resources
- hyperband
- Tuning hyperparams fast with Hyperband
Stars
- awesome-AutoML
- 941
- hyperband
- 599
Forks
- awesome-AutoML
- 156
- hyperband
- 73
Open issues
- awesome-AutoML
- 1
- hyperband
- 9
Language
- awesome-AutoML
- -
- hyperband
- Python
Adopt for
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
- hyperband
- Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Persona
- awesome-AutoML
- -
- hyperband
- -
Runtime
- awesome-AutoML
- -
- hyperband
- -
License
- awesome-AutoML
- GPL-3.0
- hyperband
- Other
Last pushed
- awesome-AutoML
- Mar 24, 2026
- hyperband
- Aug 15, 2018
Categories
- awesome-AutoML
- Model Training
- hyperband
- Model Training
Trust and health
Maintenance
- awesome-AutoML
- Slowing (36%)
- hyperband
- Dormant (18%)
Days since push
- awesome-AutoML
- 133d
- hyperband
- 2910d
Open issues (now)
- awesome-AutoML
- 1
- hyperband
- 9
Full report
- awesome-AutoML
- Trust report
- hyperband
- Trust report
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, hyperband is Other.
- Tags unique to awesome-AutoML: automl, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Choose hyperband if…
- License: hyperband is Other, awesome-AutoML is GPL-3.0.
- Tags unique to hyperband: classification, gradient-boosting, machine-learning, regression.
- Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
When NOT to use hyperband
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules.
- Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zygmuntz/hyperband) · observed Aug 4, 2026
- GitHub forks (zygmuntz/hyperband) · observed Aug 4, 2026
- Last push (zygmuntz/hyperband) · observed Aug 15, 2018
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-AutoML 941 · hyperband 599 (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-AutoML and hyperband?
- awesome-AutoML: Curating AutoML research and resources. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-AutoML over hyperband?
- Choose awesome-AutoML over hyperband when License: awesome-AutoML is GPL-3.0, hyperband is Other; Tags unique to awesome-AutoML: automl, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I choose hyperband over awesome-AutoML?
- Choose hyperband over awesome-AutoML when License: hyperband is Other, awesome-AutoML is GPL-3.0; Tags unique to hyperband: classification, gradient-boosting, machine-learning, regression; Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- When should I avoid hyperband?
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
- Is awesome-AutoML or hyperband more popular on GitHub?
- awesome-AutoML has more GitHub stars (941 vs 599). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-AutoML and hyperband open source?
- Yes - both are open-source projects on GitHub (awesome-AutoML: GPL-3.0, hyperband: Other).
- Where can I find alternatives to awesome-AutoML or hyperband?
- GraphCanon lists graph-backed alternatives at awesome-AutoML alternatives and hyperband alternatives (awesome-AutoML markdown twin, hyperband markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, awesome-AutoML or hyperband?
- awesome-AutoML: Slowing. hyperband: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for awesome-AutoML and hyperband?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-AutoML trust report; hyperband trust report.