--- title: "HPOBench vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-hpobench-vs-windmaple-awesome-automl" tools: ["automl-hpobench", "windmaple-awesome-automl"] --- # HPOBench vs awesome-AutoML *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 awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [HPOBench](https://github.com/automl/HPOBench) reports 170 GitHub stars, 36 forks, and 34 open issues, last pushed May 21, 2025. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [HPOBench's repository](https://github.com/automl/HPOBench) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [HPOBench](/tools/automl-hpobench.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | A collection of hyperparameter optimization benchmark problems | Curating AutoML research and resources | | Stars | 170 | 941 | | Forks | 36 | 156 | | Open issues | 34 | 1 | | Language | Python | - | | Adopt for | HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | HPOBench is open source under the Apache-2.0 license. | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HPOBench](/tools/automl-hpobench.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 439d | 133d | | Open issues (now) | 34 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-hpobench/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## 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: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose HPOBench if… - License: HPOBench is Apache-2.0, awesome-AutoML is GPL-3.0. - 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, python. - When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, HPOBench is Apache-2.0. - Tags unique to awesome-AutoML: meta-learning, neural-architecture-search. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## 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 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. ## Common questions ### What is the difference between HPOBench and awesome-AutoML? HPOBench: A collection of hyperparameter optimization benchmark problems. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose HPOBench over awesome-AutoML? Choose HPOBench over awesome-AutoML when License: HPOBench is Apache-2.0, awesome-AutoML is GPL-3.0; 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, python; When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### When should I choose awesome-AutoML over HPOBench? Choose awesome-AutoML over HPOBench when License: awesome-AutoML is GPL-3.0, HPOBench is Apache-2.0; Tags unique to awesome-AutoML: meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### 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 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. ### Is HPOBench or awesome-AutoML more popular on GitHub? awesome-AutoML has more GitHub stars (941 vs 170). Stars measure visibility, not whether either tool fits your constraints. ### Are HPOBench and awesome-AutoML open source? Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to HPOBench or awesome-AutoML? GraphCanon lists graph-backed alternatives at [HPOBench alternatives](/tools/automl-hpobench/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([HPOBench markdown twin](/tools/automl-hpobench/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HPOBench or awesome-AutoML? HPOBench: Dormant. awesome-AutoML: 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 awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HPOBench trust report](/tools/automl-hpobench/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/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/_