--- title: "Awesome-AutoDL vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-keras-team-autokeras" tools: ["d-x-y-awesome-autodl", "keras-team-autokeras"] --- # Awesome-AutoDL vs autokeras *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 autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. [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. [autokeras](http://autokeras.com/) has 9.3k stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | AutoML library for deep learning | | Stars | 2,339 | 9,328 | | Forks | 319 | 1,393 | | Open issues | 2 | 161 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | Apache-2.0 | | Categories | Developer Tools, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1408d | 251d | | Open issues (now) | 2 | 161 | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/keras-team-autokeras/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: autokeras - **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, autokeras is Apache-2.0. - Tags unique to Awesome-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). ### Choose autokeras if… - License: autokeras is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to autokeras: keras, machine-learning, tensorflow. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ## 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 autokeras - When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. - If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ## Common questions ### What is the difference between Awesome-AutoDL and autokeras? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over autokeras? Choose Awesome-AutoDL over autokeras when License: Awesome-AutoDL is MIT, autokeras is Apache-2.0; Tags unique to Awesome-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 choose autokeras over Awesome-AutoDL? Choose autokeras over Awesome-AutoDL when License: autokeras is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to autokeras: keras, machine-learning, tensorflow; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### 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 autokeras? When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ### Is Awesome-AutoDL or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and autokeras open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, autokeras: Apache-2.0). ### Where can I find alternatives to Awesome-AutoDL or autokeras? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [autokeras markdown twin](/tools/keras-team-autokeras/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-keras-team-autokeras.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or autokeras? Awesome-AutoDL: Dormant. autokeras: Slowing. 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 autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [autokeras trust report](/tools/keras-team-autokeras/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/_