--- title: "Awesome-AutoDL vs optuna" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-optuna-optuna" tools: ["d-x-y-awesome-autodl", "optuna-optuna"] --- # Awesome-AutoDL vs optuna *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 optuna if optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. [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. [optuna](https://optuna.org) has 15k stars, 1.4k forks, and 16 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 [optuna's repository](https://github.com/optuna/optuna). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [optuna](/tools/optuna-optuna.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | A hyperparameter optimization framework | | Stars | 2,339 | 14,603 | | Forks | 319 | 1,361 | | Open issues | 2 | 16 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | MIT | | 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) | [optuna](/tools/optuna-optuna.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1408d | 1d | | Open issues (now) | 2 | 16 | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/optuna-optuna/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: optuna - **Adopt for:** Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. ## Choose when ### Choose Awesome-AutoDL if… - 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 optuna if… - Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel. - When you need to streamline the hyperparameter tuning process for machine learning models built in Python. - More GitHub stars (15k vs 2.3k) - visibility, not fit. ## 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 optuna - If your project is not compatible with Python, as Optuna does not support other languages directly out of box. - Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs. ## Common questions ### What is the difference between Awesome-AutoDL and optuna? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. optuna: A hyperparameter optimization framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over optuna? Choose Awesome-AutoDL over optuna when 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 optuna over Awesome-AutoDL? Choose optuna over Awesome-AutoDL when Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel; When you need to streamline the hyperparameter tuning process for machine learning models built in Python; More GitHub stars (15k vs 2.3k) - visibility, not fit. ### 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 optuna? If your project is not compatible with Python, as Optuna does not support other languages directly out of box. Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs. ### Is Awesome-AutoDL or optuna more popular on GitHub? optuna has more GitHub stars (14,603 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and optuna open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, optuna: MIT). ### Where can I find alternatives to Awesome-AutoDL or optuna? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [optuna alternatives](/tools/optuna-optuna/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [optuna markdown twin](/tools/optuna-optuna/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-optuna-optuna.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or optuna? Awesome-AutoDL: Dormant. optuna: 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 optuna? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [optuna trust report](/tools/optuna-optuna/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/_