--- title: "HPOBench vs awesome-automl-papers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-hpobench-vs-hibayesian-awesome-automl-papers" tools: ["automl-hpobench", "hibayesian-awesome-automl-papers"] --- # HPOBench vs awesome-automl-papers *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-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. [HPOBench](https://github.com/automl/HPOBench) reports 170 GitHub stars, 36 forks, and 34 open issues, last pushed May 21, 2025. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) has 4.2k stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. Figures are from public GitHub metadata via [HPOBench's repository](https://github.com/automl/HPOBench) and [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers). | | [HPOBench](/tools/automl-hpobench.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Tagline | A collection of hyperparameter optimization benchmark problems | A curated list of automated machine learning papers and resources. | | Stars | 170 | 4,155 | | Forks | 36 | 678 | | Open issues | 34 | 2 | | Language | Python | - | | Adopt for | HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | | Persona | - | - | | Runtime | - | - | | License | HPOBench is open source under the Apache-2.0 license. | Apache-2.0 | | Categories | Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HPOBench](/tools/automl-hpobench.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Days since push | 439d | 784d | | Open issues (now) | 34 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-hpobench/trust.md) | [trust report](/tools/hibayesian-awesome-automl-papers/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-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## 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, python. - When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments. ### Choose awesome-automl-papers if… - Tags unique to awesome-automl-papers: feature-engineering, neural-architecture-search. - Also covers Evaluation & Observability. - When you need a curated list of academic materials to research or learn about AutoML technologies ## 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-papers - If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources - When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ## Common questions ### What is the difference between HPOBench and awesome-automl-papers? HPOBench: A collection of hyperparameter optimization benchmark problems. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose HPOBench over awesome-automl-papers? Choose HPOBench over awesome-automl-papers 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, 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-papers over HPOBench? Choose awesome-automl-papers over HPOBench when Tags unique to awesome-automl-papers: feature-engineering, neural-architecture-search; Also covers Evaluation & Observability; When you need a curated list of academic materials to research or learn about AutoML technologies. ### 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-papers? If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ### Is HPOBench or awesome-automl-papers more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 170). Stars measure visibility, not whether either tool fits your constraints. ### Are HPOBench and awesome-automl-papers open source? Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, awesome-automl-papers: Apache-2.0). ### Where can I find alternatives to HPOBench or awesome-automl-papers? GraphCanon lists graph-backed alternatives at [HPOBench alternatives](/tools/automl-hpobench/alternatives) and [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) ([HPOBench markdown twin](/tools/automl-hpobench/alternatives.md), [awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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-hibayesian-awesome-automl-papers.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-papers? HPOBench: Dormant. awesome-automl-papers: 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 HPOBench and awesome-automl-papers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HPOBench trust report](/tools/automl-hpobench/trust); [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/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/_