Comparison
Awesome-AutoDL vs nas-env
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.
Markdown twin · Awesome-AutoDL alternatives · nas-env alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | nas-env |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Dormant (2282d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Awesome-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
- nas-env
- Simple OpenAI Gym environment for Neural Architecture Search (NAS)
Stars
- Awesome-AutoDL
- 2.3k
- nas-env
- 31
Forks
- Awesome-AutoDL
- 319
- nas-env
- 3
Open issues
- Awesome-AutoDL
- 2
- nas-env
- 0
Language
- Awesome-AutoDL
- Python
- nas-env
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- nas-env
- nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license.
Persona
- Awesome-AutoDL
- -
- nas-env
- -
Runtime
- Awesome-AutoDL
- -
- nas-env
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- nas-env
- MIT
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- nas-env
- May 4, 2020
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- nas-env
- Model Training
Trust and health
Days since push
- Awesome-AutoDL
- 1408d
- nas-env
- 2282d
Open issues (now)
- Awesome-AutoDL
- 2
- nas-env
- 0
Full report
- Awesome-AutoDL
- Trust report
- nas-env
- Trust report
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).
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (gomerudo/nas-env) · observed Aug 4, 2026
- GitHub forks (gomerudo/nas-env) · observed Aug 4, 2026
- Last push (gomerudo/nas-env) · observed May 4, 2020
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · nas-env 31 (synced Aug 4, 2026).
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 and nas-env alternatives (Awesome-AutoDL markdown twin, nas-env markdown twin), 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 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; nas-env trust report.