--- title: "Awesome-AutoDL vs nas-env" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-gomerudo-nas-env" tools: ["d-x-y-awesome-autodl", "gomerudo-nas-env"] --- # Awesome-AutoDL vs nas-env *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 nas-env if nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. [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. [nas-env](https://github.com/gomerudo/nas-env) has 31 stars, 3 forks, and 0 open issues, last pushed May 4, 2020. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [nas-env's repository](https://github.com/gomerudo/nas-env). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [nas-env](/tools/gomerudo-nas-env.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | Simple OpenAI Gym environment for Neural Architecture Search (NAS) | | Stars | 2,339 | 31 | | Forks | 319 | 3 | | 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. | nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | MIT | | 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) | [nas-env](/tools/gomerudo-nas-env.md) | | --- | --- | --- | | Days since push | 1408d | 2282d | | Open issues (now) | 2 | 0 | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/gomerudo-nas-env/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: nas-env - **Adopt for:** nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. ## Choose when ### Choose Awesome-AutoDL if… - Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning. - 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 nas-env if… - Tags unique to nas-env: openai-gym, python, reinforcement-learning. - When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym - Leaner open-issue backlog (0). ## 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 nas-env - If you require a fully documented package as documentation for nas-env remains under development - During production phases when stability is crucial because nas-env is still undergoing architectural changes ## Common questions ### What is the difference between Awesome-AutoDL and nas-env? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. nas-env: Simple OpenAI Gym environment for Neural Architecture Search (NAS). See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over nas-env? Choose Awesome-AutoDL over nas-env when Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; 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 nas-env over Awesome-AutoDL? Choose nas-env over Awesome-AutoDL when Tags unique to nas-env: openai-gym, python, reinforcement-learning; When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym; Leaner open-issue backlog (0). ### 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 nas-env? If you require a fully documented package as documentation for nas-env remains under development During production phases when stability is crucial because nas-env is still undergoing architectural changes ### Is Awesome-AutoDL or nas-env more popular on GitHub? Awesome-AutoDL has more GitHub stars (2,339 vs 31). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and nas-env open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, nas-env: MIT). ### Where can I find alternatives to Awesome-AutoDL or nas-env? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [nas-env alternatives](/tools/gomerudo-nas-env/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [nas-env markdown twin](/tools/gomerudo-nas-env/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-gomerudo-nas-env.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or nas-env? Awesome-AutoDL: Dormant. nas-env: 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 nas-env? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [nas-env trust report](/tools/gomerudo-nas-env/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/_