Comparison
Awesome-AutoDL vs optuna
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick optuna if optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks.
Markdown twin · Awesome-AutoDL alternatives · optuna alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | optuna |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Very active (1d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- optuna
- A hyperparameter optimization framework
Stars
- Awesome-AutoDL
- 2.3k
- optuna
- 15k
Forks
- Awesome-AutoDL
- 319
- optuna
- 1.4k
Open issues
- Awesome-AutoDL
- 2
- optuna
- 16
Language
- Awesome-AutoDL
- Python
- optuna
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- optuna
- Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks.
Persona
- Awesome-AutoDL
- -
- optuna
- -
Runtime
- Awesome-AutoDL
- -
- optuna
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- optuna
- MIT
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- optuna
- Aug 3, 2026
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- optuna
- Model Training
Trust and health
Maintenance
- Awesome-AutoDL
- Dormant (18%)
- optuna
- Very active (96%)
Days since push
- Awesome-AutoDL
- 1408d
- optuna
- 1d
Open issues (now)
- Awesome-AutoDL
- 2
- optuna
- 16
Owner type
- Awesome-AutoDL
- User
- optuna
- Organization
Full report
- Awesome-AutoDL
- Trust report
- optuna
- 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 optuna if…
- Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel.
- When you need to streamline the hyperparameter tuning process for machine learning models built in Python.
- More GitHub stars (15k vs 2.3k) - visibility, not fit.
When NOT to use optuna
- If your project is not compatible with Python, as Optuna does not support other languages directly out of box.
- Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs.
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 (optuna/optuna) · observed Aug 4, 2026
- GitHub forks (optuna/optuna) · observed Aug 4, 2026
- Last push (optuna/optuna) · observed Aug 3, 2026
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · optuna 15k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and optuna?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. optuna: A hyperparameter optimization framework. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over optuna?
- Choose Awesome-AutoDL over optuna 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 optuna over Awesome-AutoDL?
- Choose optuna over Awesome-AutoDL when Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel; When you need to streamline the hyperparameter tuning process for machine learning models built in Python; More GitHub stars (15k vs 2.3k) - visibility, not fit.
- 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 optuna?
- If your project is not compatible with Python, as Optuna does not support other languages directly out of box. Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs.
- Is Awesome-AutoDL or optuna more popular on GitHub?
- optuna has more GitHub stars (14,603 vs 2,339). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and optuna open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, optuna: MIT).
- Where can I find alternatives to Awesome-AutoDL or optuna?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and optuna alternatives (Awesome-AutoDL markdown twin, optuna 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 optuna?
- Awesome-AutoDL: Dormant. optuna: Very active. 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 optuna?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; optuna trust report.