Comparison
Awesome-AutoDL vs dragonfly
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick dragonfly if dragonfly is an open-source Python library that specializes in scalable Bayesian optimization.
Markdown twin · Awesome-AutoDL alternatives · dragonfly alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | dragonfly |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Dormant (1141d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 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
- dragonfly
- An open source Python library for scalable Bayesian optimisation.
Stars
- Awesome-AutoDL
- 2.3k
- dragonfly
- 894
Forks
- Awesome-AutoDL
- 319
- dragonfly
- 238
Open issues
- Awesome-AutoDL
- 2
- dragonfly
- 43
Language
- Awesome-AutoDL
- Python
- dragonfly
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- dragonfly
- Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization
Persona
- Awesome-AutoDL
- -
- dragonfly
- -
Runtime
- Awesome-AutoDL
- -
- dragonfly
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- dragonfly
- MIT
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- dragonfly
- Jun 19, 2023
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- dragonfly
- Model Training
Trust and health
Days since push
- Awesome-AutoDL
- 1408d
- dragonfly
- 1141d
Open issues (now)
- Awesome-AutoDL
- 2
- dragonfly
- 43
Owner type
- Awesome-AutoDL
- User
- dragonfly
- Organization
OSV dependency advisories
- Awesome-AutoDL
- No lockfile (source not queried)
- dragonfly
- No published findings from this source as of 2026-07-11
Full report
- Awesome-AutoDL
- Trust report
- dragonfly
- 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 dragonfly if…
- Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works..
- Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager..
- Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation.
- When dealing with large-scale problems where traditional optimization methods may not be efficient enough.
When NOT to use dragonfly
- If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management.
- In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations.
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 (dragonfly/dragonfly) · observed Aug 4, 2026
- GitHub forks (dragonfly/dragonfly) · observed Aug 4, 2026
- Last push (dragonfly/dragonfly) · observed Jun 19, 2023
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · dragonfly 894 (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and dragonfly?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. dragonfly: An open source Python library for scalable Bayesian optimisation.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over dragonfly?
- Choose Awesome-AutoDL over dragonfly 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 dragonfly over Awesome-AutoDL?
- Choose dragonfly over Awesome-AutoDL when Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.; Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the
pippackage manager.; Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation; When dealing with large-scale problems where traditional optimization methods may not be efficient enough. - 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 dragonfly?
- If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations.
- Is Awesome-AutoDL or dragonfly more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 894). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and dragonfly open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, dragonfly: MIT).
- Where can I find alternatives to Awesome-AutoDL or dragonfly?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and dragonfly alternatives (Awesome-AutoDL markdown twin, dragonfly 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 dragonfly?
- Awesome-AutoDL: Dormant. dragonfly: 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 dragonfly?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; dragonfly trust report.