--- title: "Auto-PyTorch vs Awesome-AutoDL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-d-x-y-awesome-autodl" tools: ["automl-auto-pytorch", "d-x-y-awesome-autodl"] --- # Auto-PyTorch vs Awesome-AutoDL *GraphCanon updated Aug 4, 2026* ## Verdict Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [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 [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | | Stars | 2,541 | 2,339 | | Forks | 303 | 319 | | Open issues | 75 | 2 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | | Categories | Data & Retrieval, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | | --- | --- | --- | | Days since push | 846d | 1408d | | Open issues (now) | 75 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | ## Decision facts: Auto-PyTorch - **Adopt for:** Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. ## 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 Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to Auto-PyTorch: pytorch, tabular-data, time-series-forecasting. - Also covers Data & Retrieval. - Auto-PyTorch ships Docker support for self-hosted deployment. - Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models. ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, Auto-PyTorch 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 NOT to use Auto-PyTorch - Avoid using it if your AI development focuses on frameworks other than PyTorch. - Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning. ## 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 Auto-PyTorch and Awesome-AutoDL? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. 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 Auto-PyTorch over Awesome-AutoDL? Choose Auto-PyTorch over Awesome-AutoDL when License: Auto-PyTorch is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to Auto-PyTorch: pytorch, tabular-data, time-series-forecasting; Also covers Data & Retrieval; Auto-PyTorch ships Docker support for self-hosted deployment; Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models. ### When should I choose Awesome-AutoDL over Auto-PyTorch? Choose Awesome-AutoDL over Auto-PyTorch when License: Awesome-AutoDL is MIT, Auto-PyTorch 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 avoid Auto-PyTorch? Avoid using it if your AI development focuses on frameworks other than PyTorch. Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning. ### 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 Auto-PyTorch or Awesome-AutoDL more popular on GitHub? Auto-PyTorch has more GitHub stars (2,541 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and Awesome-AutoDL open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, Awesome-AutoDL: MIT). ### Where can I find alternatives to Auto-PyTorch or Awesome-AutoDL? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/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/automl-auto-pytorch-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, Auto-PyTorch or Awesome-AutoDL? Auto-PyTorch: Dormant. 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 Auto-PyTorch and Awesome-AutoDL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-pytorch`](/api/graphcanon/graph?tool=automl-auto-pytorch) - 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/_