Comparison
Auto-PyTorch vs hub
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick hub if hub is specifically tailored to Python developers who wish to incorporate transfer learning into their TensorFlow projects with pre-trained model components for applications such as image classification.
Markdown twin · Auto-PyTorch alternatives · hub alternatives
GraphCanon updated 2d
Trust & integrity
| Signal | Auto-PyTorch | hub |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Dormant (581d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2d · 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
- hub
- A library for transfer learning by reusing parts of TensorFlow models.
Stars
- Auto-PyTorch
- 2.5k
- hub
- 3.5k
Forks
- Auto-PyTorch
- 303
- hub
- 1.6k
Open issues
- Auto-PyTorch
- 75
- hub
- 6
Language
- Auto-PyTorch
- Python
- hub
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- hub
- hub is specifically tailored to Python developers who wish to incorporate transfer learning into their TensorFlow projects with pre-trained model components for applications such as image classification.
Persona
- Auto-PyTorch
- -
- hub
- -
Runtime
- Auto-PyTorch
- -
- hub
- -
License
- Auto-PyTorch
- Apache-2.0
- hub
- hub is licensed under Apache-2.0, allowing for broad use in both open source and commercial projects.
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- hub
- Jan 17, 2025
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- hub
- Data & Retrieval, Model Training
Trust and health
Days since push
- Auto-PyTorch
- 846d
- hub
- 581d
Open issues (now)
- Auto-PyTorch
- 75
- hub
- 6
Stars delta
- Auto-PyTorch
- Unknown
- hub
- +1 (30d)
Open issues delta
- Auto-PyTorch
- Unknown
- hub
- -5 (30d)
OSV dependency advisories
- Auto-PyTorch
- Published findings
- hub
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- hub
- Trust report
Choose Auto-PyTorch if…
- Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data.
- 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 hub if…
- Pricing: The core functionalities of hub are free to use with an open-source license; however, additional services or enterprise support might incur costs..
- Requirements: Requires a Python environment and TensorFlow installation to operate..
- Tags unique to hub: embeddings, image-classification, machine-learning, ml.
- When you need to leverage existing TensorFlow models and integrate specific parts of them for tasks like embedding or image-classification without retraining the entire model from scratch.
When NOT to use hub
- When working strictly with non-TensorFlow frameworks such as PyTorch or MXNet, as hub is built specifically for enhancing and reusing models within TensorFlow.
- If your project requires a more generalized approach to machine-learning without reliance on pre-existing model components, focusing instead on training models from the ground up.
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 (tensorflow/hub) · observed Aug 22, 2026
- GitHub forks (tensorflow/hub) · observed Aug 22, 2026
- Last push (tensorflow/hub) · observed Jan 17, 2025
- License file (Apache-2.0) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Auto-PyTorch 2.5k · hub 3.5k (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and hub?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. hub: A library for transfer learning by reusing parts of TensorFlow models.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over hub?
- Choose Auto-PyTorch over hub when Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data; 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 hub over Auto-PyTorch?
- Choose hub over Auto-PyTorch when Pricing: The core functionalities of hub are free to use with an open-source license; however, additional services or enterprise support might incur costs.; Requirements: Requires a Python environment and TensorFlow installation to operate.; Tags unique to hub: embeddings, image-classification, machine-learning, ml; When you need to leverage existing TensorFlow models and integrate specific parts of them for tasks like embedding or image-classification without retraining the entire model from scratch.
- 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 hub?
- When working strictly with non-TensorFlow frameworks such as PyTorch or MXNet, as hub is built specifically for enhancing and reusing models within TensorFlow. If your project requires a more generalized approach to machine-learning without reliance on pre-existing model components, focusing instead on training models from the ground up.
- Is Auto-PyTorch or hub more popular on GitHub?
- hub has more GitHub stars (3,523 vs 2,541). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and hub open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, hub: Apache-2.0).
- Where can I find alternatives to Auto-PyTorch or hub?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and hub alternatives (Auto-PyTorch markdown twin, hub 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 hub?
- Auto-PyTorch: Dormant. hub: 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 hub?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; hub trust report.