--- title: "Awesome-AutoDL vs hyperopt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-hyperopt-hyperopt" tools: ["d-x-y-awesome-autodl", "hyperopt-hyperopt"] --- # Awesome-AutoDL vs hyperopt *GraphCanon updated Aug 4, 2026* ## Verdict Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) reports 2.3k GitHub stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. [hyperopt](http://hyperopt.github.io/hyperopt) has 7.6k stars, 1.1k forks, and 9 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [hyperopt's repository](https://github.com/hyperopt/hyperopt). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | Distributed Asynchronous Hyperparameter Optimization in Python | | Stars | 2,339 | 7,598 | | Forks | 319 | 1,075 | | Open issues | 2 | 9 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | Other | | Categories | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1408d | 0d | | Open issues (now) | 2 | 9 | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/hyperopt-hyperopt/trust.md) | ## 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. ## Decision facts: hyperopt - **Adopt for:** Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, hyperopt is Other. - Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning. - 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). ### Choose hyperopt if… - License: hyperopt is Other, Awesome-AutoDL is MIT. - Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization. - When you need to optimize machine learning model parameters on a distributed system asynchronously. ## 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. ## When NOT to use hyperopt - If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. - Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options. ## Common questions ### What is the difference between Awesome-AutoDL and hyperopt? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over hyperopt? Choose Awesome-AutoDL over hyperopt when License: Awesome-AutoDL is MIT, hyperopt is Other; Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; 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 choose hyperopt over Awesome-AutoDL? Choose hyperopt over Awesome-AutoDL when License: hyperopt is Other, Awesome-AutoDL is MIT; Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization; When you need to optimize machine learning model parameters on a distributed system asynchronously. ### 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. ### When should I avoid hyperopt? If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options. ### Is Awesome-AutoDL or hyperopt more popular on GitHub? hyperopt has more GitHub stars (7,598 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and hyperopt open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, hyperopt: Other). ### Where can I find alternatives to Awesome-AutoDL or hyperopt? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [hyperopt alternatives](/tools/hyperopt-hyperopt/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [hyperopt markdown twin](/tools/hyperopt-hyperopt/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/d-x-y-awesome-autodl-vs-hyperopt-hyperopt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or hyperopt? Awesome-AutoDL: Dormant. hyperopt: Very active. 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 Awesome-AutoDL and hyperopt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [hyperopt trust report](/tools/hyperopt-hyperopt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=d-x-y-awesome-autodl`](/api/graphcanon/graph?tool=d-x-y-awesome-autodl) - 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/_