--- title: "autogluon vs Awesome-AutoDL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autogluon-autogluon-vs-d-x-y-awesome-autodl" tools: ["autogluon-autogluon", "d-x-y-awesome-autodl"] --- # autogluon vs Awesome-AutoDL *GraphCanon updated Aug 4, 2026* ## Verdict Pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. [autogluon](https://auto.gluon.ai/) reports 11k GitHub stars, 1.2k forks, and 388 open issues, last pushed Aug 3, 2026. [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 [autogluon's repository](https://github.com/autogluon/autogluon) and [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL). | | [autogluon](/tools/autogluon-autogluon.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Tagline | Fast and Accurate ML in 3 Lines of Code | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | | Stars | 10,576 | 2,339 | | Forks | 1,171 | 319 | | Open issues | 388 | 2 | | Language | Python | Python | | Adopt for | AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP. | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | | Categories | Developer Tools, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autogluon](/tools/autogluon-autogluon.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1408d | | Open issues (now) | 388 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/autogluon-autogluon/trust.md) | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | ## Decision facts: autogluon - **Adopt for:** AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP. - **License detail:** Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. ## 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 autogluon if… - License: autogluon is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, ensemble-learning. - When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis. ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, autogluon is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas. - 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 autogluon - If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. - For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this. ## 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 autogluon and Awesome-AutoDL? autogluon: Fast and Accurate ML in 3 Lines of Code. 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 autogluon over Awesome-AutoDL? Choose autogluon over Awesome-AutoDL when License: autogluon is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, ensemble-learning; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis. ### When should I choose Awesome-AutoDL over autogluon? Choose Awesome-AutoDL over autogluon when License: Awesome-AutoDL is MIT, autogluon is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas; 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 autogluon? If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this. ### 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 autogluon or Awesome-AutoDL more popular on GitHub? autogluon has more GitHub stars (10,576 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are autogluon and Awesome-AutoDL open source? Yes - both are open-source projects on GitHub (autogluon: Apache-2.0, Awesome-AutoDL: MIT). ### Where can I find alternatives to autogluon or Awesome-AutoDL? GraphCanon lists graph-backed alternatives at [autogluon alternatives](/tools/autogluon-autogluon/alternatives) and [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) ([autogluon markdown twin](/tools/autogluon-autogluon/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/autogluon-autogluon-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, autogluon or Awesome-AutoDL? autogluon: Very active. 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 autogluon and Awesome-AutoDL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autogluon trust report](/tools/autogluon-autogluon/trust); [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autogluon-autogluon`](/api/graphcanon/graph?tool=autogluon-autogluon) - 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/_