--- title: "autokeras vs hyperband" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/keras-team-autokeras-vs-zygmuntz-hyperband" tools: ["keras-team-autokeras", "zygmuntz-hyperband"] --- # autokeras vs hyperband *GraphCanon updated Aug 4, 2026* ## Verdict Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. [autokeras](http://autokeras.com/) reports 9.3k GitHub stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. [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 [autokeras's repository](https://github.com/keras-team/autokeras) and [hyperband's repository](https://github.com/zygmuntz/hyperband). | | [autokeras](/tools/keras-team-autokeras.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Tagline | AutoML library for deep learning | Tuning hyperparams fast with Hyperband | | Stars | 9,328 | 599 | | Forks | 1,393 | 73 | | Open issues | 161 | 9 | | Language | Python | Python | | Adopt for | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autokeras](/tools/keras-team-autokeras.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 251d | 2910d | | Open issues (now) | 161 | 9 | | Owner type | Organization | User | | Full report | [trust report](/tools/keras-team-autokeras/trust.md) | [trust report](/tools/zygmuntz-hyperband/trust.md) | ## Shared compatibility - **Python**: [autokeras](/tools/keras-team-autokeras.md) - Python runtime; [hyperband](/tools/zygmuntz-hyperband.md) - Python runtime ## Decision facts: autokeras - **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. ## 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 autokeras if… - License: autokeras is Apache-2.0, hyperband is Other. - Tags unique to autokeras: autodl, automl, deep-learning, keras. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### Choose hyperband if… - License: hyperband is Other, autokeras is Apache-2.0. - Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, 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 autokeras - When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. - If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ## 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 autokeras and hyperband? autokeras: AutoML library for deep learning. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories. ### When should I choose autokeras over hyperband? Choose autokeras over hyperband when License: autokeras is Apache-2.0, hyperband is Other; Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### When should I choose hyperband over autokeras? Choose hyperband over autokeras when License: hyperband is Other, autokeras is Apache-2.0; Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, 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 autokeras? When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ### 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 autokeras or hyperband more popular on GitHub? autokeras has more GitHub stars (9,328 vs 599). Stars measure visibility, not whether either tool fits your constraints. ### Are autokeras and hyperband open source? Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, hyperband: Other). ### Where can I find alternatives to autokeras or hyperband? GraphCanon lists graph-backed alternatives at [autokeras alternatives](/tools/keras-team-autokeras/alternatives) and [hyperband alternatives](/tools/zygmuntz-hyperband/alternatives) ([autokeras markdown twin](/tools/keras-team-autokeras/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/keras-team-autokeras-vs-zygmuntz-hyperband.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autokeras or hyperband? autokeras: 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 autokeras and hyperband? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autokeras trust report](/tools/keras-team-autokeras/trust); [hyperband trust report](/tools/zygmuntz-hyperband/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=keras-team-autokeras`](/api/graphcanon/graph?tool=keras-team-autokeras) - 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/_