Comparison
Awesome-AutoDL vs Hypernets
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick Hypernets if hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains.
Markdown twin · Awesome-AutoDL alternatives · Hypernets alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | Hypernets |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Slowing (106d 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 | Published findings 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
- Hypernets
- A General Automated Machine Learning framework for building domain-specific AutoML toolkits.
Stars
- Awesome-AutoDL
- 2.3k
- Hypernets
- 265
Forks
- Awesome-AutoDL
- 319
- Hypernets
- 39
Open issues
- Awesome-AutoDL
- 2
- Hypernets
- 0
Language
- Awesome-AutoDL
- Python
- Hypernets
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- Hypernets
- Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains.
Persona
- Awesome-AutoDL
- -
- Hypernets
- -
Runtime
- Awesome-AutoDL
- -
- Hypernets
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- Hypernets
- Licensed under the Apache-2.0 license, allowing free use and distribution as long as copyright and licensing notices are preserved.
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- Hypernets
- Apr 20, 2026
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- Hypernets
- Developer Tools, Model Training
Trust and health
Maintenance
- Awesome-AutoDL
- Dormant (18%)
- Hypernets
- Slowing (36%)
Days since push
- Awesome-AutoDL
- 1408d
- Hypernets
- 106d
Open issues (now)
- Awesome-AutoDL
- 2
- Hypernets
- 0
Owner type
- Awesome-AutoDL
- User
- Hypernets
- Organization
OSV dependency advisories
- Awesome-AutoDL
- No lockfile (source not queried)
- Hypernets
- Published findings
Full report
- Awesome-AutoDL
- Trust report
- Hypernets
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, Hypernets is Apache-2.0.
- Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization.
- 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 Hypernets if…
- License: Hypernets is Apache-2.0, Awesome-AutoDL is MIT.
- Tags unique to Hypernets: hyperparameter-optimization, keras, lightgbm, pytorch.
- If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline
When NOT to use Hypernets
- If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus
- Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer
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 (DataCanvasIO/Hypernets) · observed Aug 4, 2026
- GitHub forks (DataCanvasIO/Hypernets) · observed Aug 4, 2026
- Last push (DataCanvasIO/Hypernets) · observed Apr 20, 2026
- License file (Apache-2.0) · 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 · Hypernets 265 (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and Hypernets?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. Hypernets: A General Automated Machine Learning framework for building domain-specific AutoML toolkits.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over Hypernets?
- Choose Awesome-AutoDL over Hypernets when License: Awesome-AutoDL is MIT, Hypernets is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization; 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 Hypernets over Awesome-AutoDL?
- Choose Hypernets over Awesome-AutoDL when License: Hypernets is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to Hypernets: hyperparameter-optimization, keras, lightgbm, pytorch; If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline.
- 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 Hypernets?
- If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer
- Is Awesome-AutoDL or Hypernets more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 265). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and Hypernets open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, Hypernets: Apache-2.0).
- Where can I find alternatives to Awesome-AutoDL or Hypernets?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and Hypernets alternatives (Awesome-AutoDL markdown twin, Hypernets 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 Hypernets?
- Awesome-AutoDL: Dormant. Hypernets: Slowing. 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 Hypernets?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; Hypernets trust report.