--- title: "auto-sklearn vs HPOBench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-sklearn-vs-automl-hpobench" tools: ["automl-auto-sklearn", "automl-hpobench"] --- # auto-sklearn vs HPOBench *GraphCanon updated Aug 4, 2026* ## Verdict Pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows; pick HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. [auto-sklearn](https://automl.github.io/auto-sklearn) reports 8.1k GitHub stars, 1.3k forks, and 209 open issues, last pushed Jun 29, 2026. [HPOBench](https://github.com/automl/HPOBench) has 170 stars, 36 forks, and 34 open issues, last pushed May 21, 2025. Figures are from public GitHub metadata via [auto-sklearn's repository](https://github.com/automl/auto-sklearn) and [HPOBench's repository](https://github.com/automl/HPOBench). | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [HPOBench](/tools/automl-hpobench.md) | | --- | --- | --- | | Tagline | Automated Machine Learning with scikit-learn | A collection of hyperparameter optimization benchmark problems | | Stars | 8,127 | 170 | | Forks | 1,327 | 36 | | Open issues | 209 | 34 | | Language | Python | Python | | Adopt for | auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. | HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | HPOBench is open source under the Apache-2.0 license. | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [HPOBench](/tools/automl-hpobench.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 35d | 439d | | Open issues (now) | 209 | 34 | | Full report | [trust report](/tools/automl-auto-sklearn/trust.md) | [trust report](/tools/automl-hpobench/trust.md) | ## Shared compatibility - **Python**: [auto-sklearn](/tools/automl-auto-sklearn.md) - Python runtime; [HPOBench](/tools/automl-hpobench.md) - Python runtime ## Decision facts: auto-sklearn - **Adopt for:** auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. ## 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. ## Choose when ### Choose auto-sklearn if… - License: auto-sklearn is BSD-3-Clause, HPOBench is Apache-2.0. - Tags unique to auto-sklearn: automated-machine-learning, hyperparameter-search, hyperparameter-tuning, meta-learning. - auto-sklearn ships Docker support for self-hosted deployment. - When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically. ### Choose HPOBench if… - License: HPOBench is Apache-2.0, auto-sklearn is BSD-3-Clause. - Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.. - Tags unique to HPOBench: benchmark, python. - When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ## When NOT to use auto-sklearn - If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. - In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library. ## 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. ## Common questions ### What is the difference between auto-sklearn and HPOBench? auto-sklearn: Automated Machine Learning with scikit-learn. HPOBench: A collection of hyperparameter optimization benchmark problems. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-sklearn over HPOBench? Choose auto-sklearn over HPOBench when License: auto-sklearn is BSD-3-Clause, HPOBench is Apache-2.0; Tags unique to auto-sklearn: automated-machine-learning, hyperparameter-search, hyperparameter-tuning, meta-learning; auto-sklearn ships Docker support for self-hosted deployment; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically. ### When should I choose HPOBench over auto-sklearn? Choose HPOBench over auto-sklearn when License: HPOBench is Apache-2.0, auto-sklearn is BSD-3-Clause; Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.; Tags unique to HPOBench: benchmark, python; When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### When should I avoid auto-sklearn? If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library. ### 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. ### Is auto-sklearn or HPOBench more popular on GitHub? auto-sklearn has more GitHub stars (8,127 vs 170). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-sklearn and HPOBench open source? Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, HPOBench: Apache-2.0). ### Where can I find alternatives to auto-sklearn or HPOBench? GraphCanon lists graph-backed alternatives at [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) and [HPOBench alternatives](/tools/automl-hpobench/alternatives) ([auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md), [HPOBench markdown twin](/tools/automl-hpobench/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-auto-sklearn-vs-automl-hpobench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, auto-sklearn or HPOBench? auto-sklearn: Steady. HPOBench: 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 auto-sklearn and HPOBench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-sklearn trust report](/tools/automl-auto-sklearn/trust); [HPOBench trust report](/tools/automl-hpobench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-sklearn`](/api/graphcanon/graph?tool=automl-auto-sklearn) - 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/_