Comparison
Auto-PyTorch vs pytorch-meta
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick pytorch-meta if pyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks.
Markdown twin · Auto-PyTorch alternatives · pytorch-meta alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | pytorch-meta |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Dormant (1113d 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
- pytorch-meta
- Extensions and data-loaders for few-shot learning & meta-learning in PyTorch
Stars
- Auto-PyTorch
- 2.5k
- pytorch-meta
- 2.1k
Forks
- Auto-PyTorch
- 303
- pytorch-meta
- 264
Open issues
- Auto-PyTorch
- 75
- pytorch-meta
- 61
Language
- Auto-PyTorch
- Python
- pytorch-meta
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- pytorch-meta
- PyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks.
Persona
- Auto-PyTorch
- -
- pytorch-meta
- -
Runtime
- Auto-PyTorch
- -
- pytorch-meta
- -
License
- Auto-PyTorch
- Apache-2.0
- pytorch-meta
- MIT
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- pytorch-meta
- Jul 17, 2023
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- pytorch-meta
- Model Training
Trust and health
Days since push
- Auto-PyTorch
- 846d
- pytorch-meta
- 1113d
Open issues (now)
- Auto-PyTorch
- 75
- pytorch-meta
- 61
Owner type
- Auto-PyTorch
- Organization
- pytorch-meta
- User
OSV dependency advisories
- Auto-PyTorch
- Published findings
- pytorch-meta
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- pytorch-meta
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · pytorch-meta: Python runtime
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, pytorch-meta is MIT.
- Tags unique to Auto-PyTorch: automl, deep-learning, 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 pytorch-meta if…
- License: pytorch-meta is MIT, Auto-PyTorch is Apache-2.0.
- Tags unique to pytorch-meta: data-loaders, extensions, few-shot-learning, meta-learning.
- When developing models that require handling few-shot learning scenarios where only a small amount of labeled data is available.
When NOT to use pytorch-meta
- If your project requires extensive support for traditional deep learning tasks, as PyTorch-Meta does not offer comprehensive utilities beyond few-shot learning and meta-learning.
- For those strictly adhering to a single ecosystem that does not include the PyTorch framework or its specific versions below 1.4.
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 (tristandeleu/pytorch-meta) · observed Aug 4, 2026
- GitHub forks (tristandeleu/pytorch-meta) · observed Aug 4, 2026
- Last push (tristandeleu/pytorch-meta) · observed Jul 17, 2023
- License file (MIT) · 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 · pytorch-meta 2.1k (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and pytorch-meta?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. pytorch-meta: Extensions and data-loaders for few-shot learning & meta-learning in PyTorch. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over pytorch-meta?
- Choose Auto-PyTorch over pytorch-meta when License: Auto-PyTorch is Apache-2.0, pytorch-meta is MIT; Tags unique to Auto-PyTorch: automl, deep-learning, 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 pytorch-meta over Auto-PyTorch?
- Choose pytorch-meta over Auto-PyTorch when License: pytorch-meta is MIT, Auto-PyTorch is Apache-2.0; Tags unique to pytorch-meta: data-loaders, extensions, few-shot-learning, meta-learning; When developing models that require handling few-shot learning scenarios where only a small amount of labeled data is available.
- 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 pytorch-meta?
- If your project requires extensive support for traditional deep learning tasks, as PyTorch-Meta does not offer comprehensive utilities beyond few-shot learning and meta-learning. For those strictly adhering to a single ecosystem that does not include the PyTorch framework or its specific versions below 1.4.
- Is Auto-PyTorch or pytorch-meta more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,541 vs 2,062). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and pytorch-meta open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, pytorch-meta: MIT).
- Where can I find alternatives to Auto-PyTorch or pytorch-meta?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and pytorch-meta alternatives (Auto-PyTorch markdown twin, pytorch-meta 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 pytorch-meta?
- Auto-PyTorch: Dormant. pytorch-meta: 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 pytorch-meta?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; pytorch-meta trust report.