Comparison
Auto-PyTorch vs awesome-AutoML
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · Auto-PyTorch alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Slowing (133d 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-AutoML
- Curating AutoML research and resources
Stars
- Auto-PyTorch
- 2.5k
- awesome-AutoML
- 941
Forks
- Auto-PyTorch
- 303
- awesome-AutoML
- 156
Open issues
- Auto-PyTorch
- 75
- awesome-AutoML
- 1
Language
- Auto-PyTorch
- Python
- awesome-AutoML
- -
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- Auto-PyTorch
- -
- awesome-AutoML
- -
Runtime
- Auto-PyTorch
- -
- awesome-AutoML
- -
License
- Auto-PyTorch
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- awesome-AutoML
- Mar 24, 2026
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- Auto-PyTorch
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- Auto-PyTorch
- 846d
- awesome-AutoML
- 133d
Open issues (now)
- Auto-PyTorch
- 75
- awesome-AutoML
- 1
Owner type
- Auto-PyTorch
- Organization
- awesome-AutoML
- User
OSV dependency advisories
- Auto-PyTorch
- Published findings
- awesome-AutoML
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- awesome-AutoML
- Trust report
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to Auto-PyTorch: deep-learning, 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-AutoML if…
- License: awesome-AutoML is GPL-3.0, Auto-PyTorch is Apache-2.0.
- Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
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 (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Auto-PyTorch 2.5k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and awesome-AutoML?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over awesome-AutoML?
- Choose Auto-PyTorch over awesome-AutoML when License: Auto-PyTorch is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to Auto-PyTorch: deep-learning, 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-AutoML over Auto-PyTorch?
- Choose awesome-AutoML over Auto-PyTorch when License: awesome-AutoML is GPL-3.0, Auto-PyTorch is Apache-2.0; Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- 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-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is Auto-PyTorch or awesome-AutoML more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,541 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to Auto-PyTorch or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and awesome-AutoML alternatives (Auto-PyTorch markdown twin, awesome-AutoML 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-AutoML?
- Auto-PyTorch: Dormant. awesome-AutoML: 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 Auto-PyTorch and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; awesome-AutoML trust report.