Comparison
Awesome-AutoDL vs hyperopt
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
Markdown twin · Awesome-AutoDL alternatives · hyperopt alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | Awesome-AutoDL | hyperopt |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 3w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · 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
- hyperopt
- Distributed Asynchronous Hyperparameter Optimization in Python
Stars
- Awesome-AutoDL
- 2.3k
- hyperopt
- 7.6k
Forks
- Awesome-AutoDL
- 319
- hyperopt
- 1.1k
Open issues
- Awesome-AutoDL
- 2
- hyperopt
- 9
Language
- Awesome-AutoDL
- Python
- hyperopt
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- hyperopt
- Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
Persona
- Awesome-AutoDL
- -
- hyperopt
- -
Runtime
- Awesome-AutoDL
- -
- hyperopt
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- hyperopt
- Other
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- hyperopt
- Aug 3, 2026
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- hyperopt
- Model Training
Trust and health
Maintenance
- Awesome-AutoDL
- Dormant (18%)
- hyperopt
- Very active (96%)
Days since push
- Awesome-AutoDL
- 1408d
- hyperopt
- 0d
Open issues (now)
- Awesome-AutoDL
- 2
- hyperopt
- 9
Owner type
- Awesome-AutoDL
- User
- hyperopt
- Organization
Full report
- Awesome-AutoDL
- Trust report
- hyperopt
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, hyperopt is Other.
- 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 hyperopt if…
- License: hyperopt is Other, Awesome-AutoDL is MIT.
- Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization.
- When you need to optimize machine learning model parameters on a distributed system asynchronously.
When NOT to use hyperopt
- If your project does not support asynchronous execution, opting for synchronous tools might be more suitable.
- Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options.
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 (hyperopt/hyperopt) · observed Aug 4, 2026
- GitHub forks (hyperopt/hyperopt) · observed Aug 4, 2026
- Last push (hyperopt/hyperopt) · observed Aug 3, 2026
- License file (Other) · 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 · hyperopt 7.6k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and hyperopt?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over hyperopt?
- Choose Awesome-AutoDL over hyperopt when License: Awesome-AutoDL is MIT, hyperopt is Other; 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 hyperopt over Awesome-AutoDL?
- Choose hyperopt over Awesome-AutoDL when License: hyperopt is Other, Awesome-AutoDL is MIT; Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization; When you need to optimize machine learning model parameters on a distributed system asynchronously.
- 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 hyperopt?
- If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options.
- Is Awesome-AutoDL or hyperopt more popular on GitHub?
- hyperopt has more GitHub stars (7,598 vs 2,339). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and hyperopt open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, hyperopt: Other).
- Where can I find alternatives to Awesome-AutoDL or hyperopt?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and hyperopt alternatives (Awesome-AutoDL markdown twin, hyperopt 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 hyperopt?
- Awesome-AutoDL: Dormant. hyperopt: 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 hyperopt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; hyperopt trust report.