--- title: "Awesome-AutoDL vs keras-tuner" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-keras-team-keras-tuner" tools: ["d-x-y-awesome-autodl", "keras-team-keras-tuner"] --- # Awesome-AutoDL vs keras-tuner *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 keras-tuner if kerasTuner is a hyperparameter tuning library for Keras focused on Python and TensorFlow environments. [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. [keras-tuner](https://keras.io/keras_tuner/) has 2.9k stars, 404 forks, and 240 open issues, last pushed Dec 1, 2025. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [keras-tuner's repository](https://github.com/keras-team/keras-tuner). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [keras-tuner](/tools/keras-team-keras-tuner.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | A Hyperparameter Tuning Library for Keras | | Stars | 2,339 | 2,923 | | Forks | 319 | 404 | | Open issues | 2 | 240 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | KerasTuner is a hyperparameter tuning library for Keras focused on Python and TensorFlow environments. | | 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 | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [keras-tuner](/tools/keras-team-keras-tuner.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1408d | 245d | | Open issues (now) | 2 | 240 | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/keras-team-keras-tuner/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: keras-tuner - **Adopt for:** KerasTuner is a hyperparameter tuning library for Keras focused on Python and TensorFlow environments. ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, keras-tuner is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas. - 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 keras-tuner if… - License: keras-tuner is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to keras-tuner: hyperparameter-optimization, keras, machine-learning, tensorflow. - - Use when you are working with TensorFlow 2.0+ and Python 3.8+, specifically if your project relies heavily on these technologies. ## 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 keras-tuner - - Avoid if your current machine-learning stack does not include Python and TensorFlow 2.0+ as primary dependencies. - - Not recommended if you seek hyperparameter tuning solutions that are more generic or compatible with a wider range of ML frameworks beyond Keras. ## Common questions ### What is the difference between Awesome-AutoDL and keras-tuner? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. keras-tuner: A Hyperparameter Tuning Library for Keras. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over keras-tuner? Choose Awesome-AutoDL over keras-tuner when License: Awesome-AutoDL is MIT, keras-tuner is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas; 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 keras-tuner over Awesome-AutoDL? Choose keras-tuner over Awesome-AutoDL when License: keras-tuner is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to keras-tuner: hyperparameter-optimization, keras, machine-learning, tensorflow; - Use when you are working with TensorFlow 2.0+ and Python 3.8+, specifically if your project relies heavily on these technologies. ### 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 keras-tuner? - Avoid if your current machine-learning stack does not include Python and TensorFlow 2.0+ as primary dependencies. - Not recommended if you seek hyperparameter tuning solutions that are more generic or compatible with a wider range of ML frameworks beyond Keras. ### Is Awesome-AutoDL or keras-tuner more popular on GitHub? keras-tuner has more GitHub stars (2,923 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and keras-tuner open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, keras-tuner: Apache-2.0). ### Where can I find alternatives to Awesome-AutoDL or keras-tuner? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [keras-tuner alternatives](/tools/keras-team-keras-tuner/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [keras-tuner markdown twin](/tools/keras-team-keras-tuner/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-keras-tuner.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or keras-tuner? Awesome-AutoDL: Dormant. keras-tuner: 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 keras-tuner? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [keras-tuner trust report](/tools/keras-team-keras-tuner/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/_