--- title: "HPOBench vs Awesome-AutoDL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-hpobench-vs-d-x-y-awesome-autodl" tools: ["automl-hpobench", "d-x-y-awesome-autodl"] --- # HPOBench vs Awesome-AutoDL *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-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. [HPOBench](https://github.com/automl/HPOBench) reports 170 GitHub stars, 36 forks, and 34 open issues, last pushed May 21, 2025. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) has 2.3k stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. Figures are from public GitHub metadata via [HPOBench's repository](https://github.com/automl/HPOBench) and [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL). | | [HPOBench](/tools/automl-hpobench.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Tagline | A collection of hyperparameter optimization benchmark problems | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | | Stars | 170 | 2,339 | | Forks | 36 | 319 | | Open issues | 34 | 2 | | Language | Python | Python | | Adopt for | HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios. | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | | Persona | - | - | | Runtime | - | - | | License | HPOBench is open source under the Apache-2.0 license. | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HPOBench](/tools/automl-hpobench.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Days since push | 439d | 1408d | | Open issues (now) | 34 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-hpobench/trust.md) | [trust report](/tools/d-x-y-awesome-autodl/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-AutoDL - **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. - **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. ## Choose when ### Choose HPOBench if… - License: HPOBench is Apache-2.0, Awesome-AutoDL is MIT. - 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 Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, HPOBench is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization. - Also covers Developer Tools. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ## 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-AutoDL - Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. - Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ## Common questions ### What is the difference between HPOBench and Awesome-AutoDL? HPOBench: A collection of hyperparameter optimization benchmark problems. Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. See the comparison table for live GitHub stats and shared categories. ### When should I choose HPOBench over Awesome-AutoDL? Choose HPOBench over Awesome-AutoDL when License: HPOBench is Apache-2.0, Awesome-AutoDL is MIT; 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 Awesome-AutoDL over HPOBench? Choose Awesome-AutoDL over HPOBench when License: Awesome-AutoDL is MIT, HPOBench is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization; Also covers Developer Tools; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ### 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-AutoDL? Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ### Is HPOBench or Awesome-AutoDL more popular on GitHub? Awesome-AutoDL has more GitHub stars (2,339 vs 170). Stars measure visibility, not whether either tool fits your constraints. ### Are HPOBench and Awesome-AutoDL open source? Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, Awesome-AutoDL: MIT). ### Where can I find alternatives to HPOBench or Awesome-AutoDL? GraphCanon lists graph-backed alternatives at [HPOBench alternatives](/tools/automl-hpobench/alternatives) and [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) ([HPOBench markdown twin](/tools/automl-hpobench/alternatives.md), [Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/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-d-x-y-awesome-autodl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HPOBench or Awesome-AutoDL? HPOBench: Dormant. Awesome-AutoDL: 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-AutoDL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HPOBench trust report](/tools/automl-hpobench/trust); [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/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/_