--- title: "Awesome-AutoDL vs Hypernets" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-datacanvasio-hypernets" tools: ["d-x-y-awesome-autodl", "datacanvasio-hypernets"] --- # Awesome-AutoDL vs Hypernets *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 Hypernets if hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains. [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. [Hypernets](https://hypernets.readthedocs.io/) has 265 stars, 39 forks, and 0 open issues, last pushed Apr 20, 2026. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [Hypernets's repository](https://github.com/DataCanvasIO/Hypernets). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [Hypernets](/tools/datacanvasio-hypernets.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | A General Automated Machine Learning framework for building domain-specific AutoML toolkits. | | Stars | 2,339 | 265 | | Forks | 319 | 39 | | Open issues | 2 | 0 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | Licensed under the Apache-2.0 license, allowing free use and distribution as long as copyright and licensing notices are preserved. | | 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) | [Hypernets](/tools/datacanvasio-hypernets.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1408d | 106d | | Open issues (now) | 2 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/datacanvasio-hypernets/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: Hypernets - **Adopt for:** Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains. - **License detail:** Licensed under the Apache-2.0 license, allowing free use and distribution as long as copyright and licensing notices are preserved. ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, Hypernets is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ### Choose Hypernets if… - License: Hypernets is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to Hypernets: hyperparameter-optimization, keras, lightgbm, pytorch. - If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline ## 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 Hypernets - If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus - Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer ## Common questions ### What is the difference between Awesome-AutoDL and Hypernets? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. Hypernets: A General Automated Machine Learning framework for building domain-specific AutoML toolkits.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over Hypernets? Choose Awesome-AutoDL over Hypernets when License: Awesome-AutoDL is MIT, Hypernets is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization; 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 Hypernets over Awesome-AutoDL? Choose Hypernets over Awesome-AutoDL when License: Hypernets is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to Hypernets: hyperparameter-optimization, keras, lightgbm, pytorch; If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline. ### 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 Hypernets? If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer ### Is Awesome-AutoDL or Hypernets more popular on GitHub? Awesome-AutoDL has more GitHub stars (2,339 vs 265). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and Hypernets open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, Hypernets: Apache-2.0). ### Where can I find alternatives to Awesome-AutoDL or Hypernets? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [Hypernets alternatives](/tools/datacanvasio-hypernets/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [Hypernets markdown twin](/tools/datacanvasio-hypernets/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-datacanvasio-hypernets.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or Hypernets? Awesome-AutoDL: Dormant. Hypernets: 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 Hypernets? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [Hypernets trust report](/tools/datacanvasio-hypernets/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/_