--- title: "HpBandSter vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-hpbandster-vs-keras-team-autokeras" tools: ["automl-hpbandster", "keras-team-autokeras"] --- # HpBandSter vs autokeras *GraphCanon updated Aug 4, 2026* ## Verdict Pick HpBandSter if hpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities; 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+. [HpBandSter](https://github.com/automl/HpBandSter) reports 632 GitHub stars, 107 forks, and 66 open issues, last pushed Oct 16, 2022. [autokeras](http://autokeras.com/) has 9.3k stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. Figures are from public GitHub metadata via [HpBandSter's repository](https://github.com/automl/HpBandSter) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [HpBandSter](/tools/automl-hpbandster.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | a distributed Hyperband implementation on Steroids | AutoML library for deep learning | | Stars | 632 | 9,328 | | Forks | 107 | 1,393 | | Open issues | 66 | 161 | | Language | Python | Python | | Adopt for | HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities. | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions. | Apache-2.0 | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HpBandSter](/tools/automl-hpbandster.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1387d | 251d | | Open issues (now) | 66 | 161 | | Full report | [trust report](/tools/automl-hpbandster/trust.md) | [trust report](/tools/keras-team-autokeras/trust.md) | ## Shared compatibility - **Python**: [HpBandSter](/tools/automl-hpbandster.md) - Python runtime; [autokeras](/tools/keras-team-autokeras.md) - Python runtime ## Decision facts: HpBandSter - **Pricing:** freemium - HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs. - **Requirements:** Min 4 GB RAM; Requires Python environment. No Docker required. - **Adopt for:** HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities. - **License detail:** BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions. ## 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+. ## Choose when ### Choose HpBandSter if… - License: HpBandSter is BSD-3-Clause, autokeras is Apache-2.0. - Pricing: HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs.. - Requirements: Min 4 GB RAM; Requires Python environment. No Docker required.. - Tags unique to HpBandSter: automated-machine-learning, bayesian-optimization, hyperparameter-optimization. - HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments. ### Choose autokeras if… - License: autokeras is Apache-2.0, HpBandSter is BSD-3-Clause. - Tags unique to autokeras: autodl, deep-learning, keras, machine-learning. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ## When NOT to use HpBandSter - If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings. - Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology. ## 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. ## Common questions ### What is the difference between HpBandSter and autokeras? HpBandSter: a distributed Hyperband implementation on Steroids. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose HpBandSter over autokeras? Choose HpBandSter over autokeras when License: HpBandSter is BSD-3-Clause, autokeras is Apache-2.0; Pricing: HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs.; Requirements: Min 4 GB RAM; Requires Python environment. No Docker required.; Tags unique to HpBandSter: automated-machine-learning, bayesian-optimization, hyperparameter-optimization; HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments. ### When should I choose autokeras over HpBandSter? Choose autokeras over HpBandSter when License: autokeras is Apache-2.0, HpBandSter is BSD-3-Clause; Tags unique to autokeras: autodl, deep-learning, keras, machine-learning; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### When should I avoid HpBandSter? If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings. Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology. ### 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. ### Is HpBandSter or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 632). Stars measure visibility, not whether either tool fits your constraints. ### Are HpBandSter and autokeras open source? Yes - both are open-source projects on GitHub (HpBandSter: BSD-3-Clause, autokeras: Apache-2.0). ### Where can I find alternatives to HpBandSter or autokeras? GraphCanon lists graph-backed alternatives at [HpBandSter alternatives](/tools/automl-hpbandster/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([HpBandSter markdown twin](/tools/automl-hpbandster/alternatives.md), [autokeras markdown twin](/tools/keras-team-autokeras/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/automl-hpbandster-vs-keras-team-autokeras.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HpBandSter or autokeras? HpBandSter: Dormant. autokeras: Slowing. 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 HpBandSter and autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HpBandSter trust report](/tools/automl-hpbandster/trust); [autokeras trust report](/tools/keras-team-autokeras/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-hpbandster`](/api/graphcanon/graph?tool=automl-hpbandster) - 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/_