--- title: "Awesome-AutoDL vs guildai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-guildai-guildai" tools: ["d-x-y-awesome-autodl", "guildai-guildai"] --- # Awesome-AutoDL vs guildai *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 guildai if guild AI is geared towards Python developers who need to manage various types of experiments and utilize optimization methods like grid search, random search, and Bayesian optimization. [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. [guildai](https://guild.ai) has 904 stars, 93 forks, and 237 open issues, last pushed Apr 29, 2025. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [guildai's repository](https://github.com/guildai/guildai). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [guildai](/tools/guildai-guildai.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | Experiment tracking, ML developer tools | | Stars | 2,339 | 904 | | Forks | 319 | 93 | | Open issues | 2 | 237 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | Guild AI is geared towards Python developers who need to manage various types of experiments and utilize optimization methods like grid search, random search, and Bayesian optimization. | | 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) | [guildai](/tools/guildai-guildai.md) | | --- | --- | --- | | Days since push | 1408d | 460d | | Open issues (now) | 2 | 237 | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/guildai-guildai/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: guildai - **Adopt for:** Guild AI is geared towards Python developers who need to manage various types of experiments and utilize optimization methods like grid search, random search, and Bayesian optimization. ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, guildai is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ### Choose guildai if… - License: guildai is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to guildai: automation, bayesian-optimization, experiment tracking, grid search. - You require automation for running multiple experiment configurations to compare different models effectively. ## 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 guildai - If your project exclusively uses languages other than Python for machine learning tasks, Guild AI's capabilities may not be fully leveraged due to language-specific functionalities. - Your model development does not require intricate optimization methods or trial automation provided by this toolkit. - The need for experimentation tracking and archiving on cloud solutions is limited or you prefer manual handling of experiment data. ## Common questions ### What is the difference between Awesome-AutoDL and guildai? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. guildai: Experiment tracking, ML developer tools. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over guildai? Choose Awesome-AutoDL over guildai when License: Awesome-AutoDL is MIT, guildai is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; 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 guildai over Awesome-AutoDL? Choose guildai over Awesome-AutoDL when License: guildai is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to guildai: automation, bayesian-optimization, experiment tracking, grid search; You require automation for running multiple experiment configurations to compare different models effectively. ### 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 guildai? If your project exclusively uses languages other than Python for machine learning tasks, Guild AI's capabilities may not be fully leveraged due to language-specific functionalities. Your model development does not require intricate optimization methods or trial automation provided by this toolkit. The need for experimentation tracking and archiving on cloud solutions is limited or you prefer manual handling of experiment data. ### Is Awesome-AutoDL or guildai more popular on GitHub? Awesome-AutoDL has more GitHub stars (2,339 vs 904). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and guildai open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, guildai: Apache-2.0). ### Where can I find alternatives to Awesome-AutoDL or guildai? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [guildai alternatives](/tools/guildai-guildai/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [guildai markdown twin](/tools/guildai-guildai/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-guildai-guildai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or guildai? Awesome-AutoDL: Dormant. guildai: 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 Awesome-AutoDL and guildai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [guildai trust report](/tools/guildai-guildai/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/_