--- title: "HPOBench vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-hpobench-vs-keras-team-autokeras" tools: ["automl-hpobench", "keras-team-autokeras"] --- # HPOBench vs autokeras *GraphCanon updated Aug 4, 2026* ## Verdict Pick HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios; 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+. [HPOBench](https://github.com/automl/HPOBench) reports 170 GitHub stars, 36 forks, and 34 open issues, last pushed May 21, 2025. [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 [HPOBench's repository](https://github.com/automl/HPOBench) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [HPOBench](/tools/automl-hpobench.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | A collection of hyperparameter optimization benchmark problems | AutoML library for deep learning | | Stars | 170 | 9,328 | | Forks | 36 | 1,393 | | Open issues | 34 | 161 | | Language | Python | Python | | Adopt for | HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. | 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 | HPOBench is open source under the Apache-2.0 license. | Apache-2.0 | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HPOBench](/tools/automl-hpobench.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 439d | 251d | | Open issues (now) | 34 | 161 | | Full report | [trust report](/tools/automl-hpobench/trust.md) | [trust report](/tools/keras-team-autokeras/trust.md) | ## Shared compatibility - **Python**: [HPOBench](/tools/automl-hpobench.md) - Python runtime; [autokeras](/tools/keras-team-autokeras.md) - Python runtime ## Decision facts: HPOBench - **Pricing:** freemium - **Requirements:** The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step. - **Adopt for:** HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. - **License detail:** HPOBench is open source under the Apache-2.0 license. ## 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 HPOBench if… - Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.. - Tags unique to HPOBench: bayesian-optimization, benchmark, hyperparameter-optimization, python. - When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### Choose autokeras if… - 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 HPOBench - Avoid HPOBench if your project does not require Python or you are looking for a platform that exclusively focuses on the automation of model selection without hyperparameter optimization. - If you prefer tools with built-in support for multiple programming languages, rather than focusing solely on Python as is the case with HPOBench. ## 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 HPOBench and autokeras? HPOBench: A collection of hyperparameter optimization benchmark problems. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose HPOBench over autokeras? Choose HPOBench over autokeras when Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.; Tags unique to HPOBench: bayesian-optimization, benchmark, hyperparameter-optimization, python; When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### When should I choose autokeras over HPOBench? Choose autokeras over HPOBench when 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 HPOBench? Avoid HPOBench if your project does not require Python or you are looking for a platform that exclusively focuses on the automation of model selection without hyperparameter optimization. If you prefer tools with built-in support for multiple programming languages, rather than focusing solely on Python as is the case with HPOBench. ### 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 HPOBench or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 170). Stars measure visibility, not whether either tool fits your constraints. ### Are HPOBench and autokeras open source? Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, autokeras: Apache-2.0). ### Where can I find alternatives to HPOBench or autokeras? GraphCanon lists graph-backed alternatives at [HPOBench alternatives](/tools/automl-hpobench/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([HPOBench markdown twin](/tools/automl-hpobench/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-hpobench-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, HPOBench or autokeras? HPOBench: 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 HPOBench and autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HPOBench trust report](/tools/automl-hpobench/trust); [autokeras trust report](/tools/keras-team-autokeras/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-hpobench`](/api/graphcanon/graph?tool=automl-hpobench) - 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/_