--- title: "awesome-AutoML vs hyperband" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/windmaple-awesome-automl-vs-zygmuntz-hyperband" tools: ["windmaple-awesome-automl", "zygmuntz-hyperband"] --- # awesome-AutoML vs hyperband *GraphCanon updated Aug 4, 2026* ## 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. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) reports 941 GitHub stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. [hyperband](http://fastml.com/tuning-hyperparams-fast-with-hyperband/) has 599 stars, 73 forks, and 9 open issues, last pushed Aug 15, 2018. Figures are from public GitHub metadata via [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML) and [hyperband's repository](https://github.com/zygmuntz/hyperband). | | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Tagline | Curating AutoML research and resources | Tuning hyperparams fast with Hyperband | | Stars | 941 | 599 | | Forks | 156 | 73 | | Open issues | 1 | 9 | | Language | - | Python | | Adopt for | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | Other | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 133d | 2910d | | Open issues (now) | 1 | 9 | | Full report | [trust report](/tools/windmaple-awesome-automl/trust.md) | [trust report](/tools/zygmuntz-hyperband/trust.md) | ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Decision facts: hyperband - **Adopt for:** Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/windmaple-awesome-automl/alternatives) and [hyperband alternatives](/tools/zygmuntz-hyperband/alternatives) ([awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/alternatives.md), [hyperband markdown twin](/tools/zygmuntz-hyperband/alternatives.md)), 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](/compare/windmaple-awesome-automl-vs-zygmuntz-hyperband.md) 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](/tools/windmaple-awesome-automl/trust); [hyperband trust report](/tools/zygmuntz-hyperband/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=windmaple-awesome-automl`](/api/graphcanon/graph?tool=windmaple-awesome-automl) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_