Comparison
Auto-PyTorch vs Awesome-AutoDL
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Markdown twin · Auto-PyTorch alternatives · Awesome-AutoDL alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | Awesome-AutoDL |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Dormant (1408d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- Auto-PyTorch
- Automatic architecture search and hyperparameter optimization for PyTorch
- Awesome-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
Stars
- Auto-PyTorch
- 2.5k
- Awesome-AutoDL
- 2.3k
Forks
- Auto-PyTorch
- 303
- Awesome-AutoDL
- 319
Open issues
- Auto-PyTorch
- 75
- Awesome-AutoDL
- 2
Language
- Auto-PyTorch
- Python
- Awesome-AutoDL
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Persona
- Auto-PyTorch
- -
- Awesome-AutoDL
- -
Runtime
- Auto-PyTorch
- -
- Awesome-AutoDL
- -
License
- Auto-PyTorch
- Apache-2.0
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- Awesome-AutoDL
- Sep 26, 2022
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- Awesome-AutoDL
- Developer Tools, Model Training
Trust and health
Days since push
- Auto-PyTorch
- 846d
- Awesome-AutoDL
- 1408d
Open issues (now)
- Auto-PyTorch
- 75
- Awesome-AutoDL
- 2
Owner type
- Auto-PyTorch
- Organization
- Awesome-AutoDL
- User
OSV dependency advisories
- Auto-PyTorch
- Published findings
- Awesome-AutoDL
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- Awesome-AutoDL
- Trust report
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, Awesome-AutoDL is MIT.
- Tags unique to Auto-PyTorch: pytorch, tabular-data, time-series-forecasting.
- Also covers Data & Retrieval.
- Auto-PyTorch ships Docker support for self-hosted deployment.
- Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.
When NOT to use Auto-PyTorch
- Avoid using it if your AI development focuses on frameworks other than PyTorch.
- Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning.
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, Auto-PyTorch is Apache-2.0.
- Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (automl/Auto-PyTorch) · observed Aug 4, 2026
- GitHub forks (automl/Auto-PyTorch) · observed Aug 4, 2026
- Last push (automl/Auto-PyTorch) · observed Apr 9, 2024
- 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 (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 on cards: Auto-PyTorch 2.5k · Awesome-AutoDL 2.3k (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and Awesome-AutoDL?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over Awesome-AutoDL?
- Choose Auto-PyTorch over Awesome-AutoDL when License: Auto-PyTorch is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to Auto-PyTorch: pytorch, tabular-data, time-series-forecasting; Also covers Data & Retrieval; Auto-PyTorch ships Docker support for self-hosted deployment; Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.
- When should I choose Awesome-AutoDL over Auto-PyTorch?
- Choose Awesome-AutoDL over Auto-PyTorch when License: Awesome-AutoDL is MIT, Auto-PyTorch is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas; 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 avoid Auto-PyTorch?
- Avoid using it if your AI development focuses on frameworks other than PyTorch. Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning.
- 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.
- Is Auto-PyTorch or Awesome-AutoDL more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,541 vs 2,339). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and Awesome-AutoDL open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, Awesome-AutoDL: MIT).
- Where can I find alternatives to Auto-PyTorch or Awesome-AutoDL?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and Awesome-AutoDL alternatives (Auto-PyTorch markdown twin, Awesome-AutoDL 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, Auto-PyTorch or Awesome-AutoDL?
- Auto-PyTorch: Dormant. Awesome-AutoDL: 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 Auto-PyTorch and Awesome-AutoDL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; Awesome-AutoDL trust report.