Comparison
Awesome-AutoDL vs hyperband
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Markdown twin · Awesome-AutoDL alternatives · hyperband alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | hyperband |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Dormant (2910d 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
- hyperband
- Tuning hyperparams fast with Hyperband
Stars
- Awesome-AutoDL
- 2.3k
- hyperband
- 599
Forks
- Awesome-AutoDL
- 319
- hyperband
- 73
Open issues
- Awesome-AutoDL
- 2
- hyperband
- 9
Language
- Awesome-AutoDL
- Python
- hyperband
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- hyperband
- Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Persona
- Awesome-AutoDL
- -
- hyperband
- -
Runtime
- Awesome-AutoDL
- -
- hyperband
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- hyperband
- Other
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- hyperband
- Aug 15, 2018
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- hyperband
- Model Training
Trust and health
Days since push
- Awesome-AutoDL
- 1408d
- hyperband
- 2910d
Open issues (now)
- Awesome-AutoDL
- 2
- hyperband
- 9
Full report
- Awesome-AutoDL
- Trust report
- hyperband
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, hyperband 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 hyperband if…
- License: hyperband is Other, Awesome-AutoDL is MIT.
- Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning.
- Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
When NOT to use hyperband
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules.
- Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
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 (zygmuntz/hyperband) · observed Aug 4, 2026
- GitHub forks (zygmuntz/hyperband) · observed Aug 4, 2026
- Last push (zygmuntz/hyperband) · observed Aug 15, 2018
- 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 · hyperband 599 (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and hyperband?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over hyperband?
- Choose Awesome-AutoDL over hyperband when License: Awesome-AutoDL is MIT, hyperband 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 hyperband over Awesome-AutoDL?
- Choose hyperband over Awesome-AutoDL when License: hyperband is Other, Awesome-AutoDL is MIT; Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning; Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
- 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 hyperband?
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
- Is Awesome-AutoDL or hyperband more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 599). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and hyperband open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, hyperband: Other).
- Where can I find alternatives to Awesome-AutoDL or hyperband?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and hyperband alternatives (Awesome-AutoDL markdown twin, hyperband 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 hyperband?
- Awesome-AutoDL: Dormant. hyperband: 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 hyperband?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; hyperband trust report.