Comparison
Auto-PyTorch vs hyperband
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Markdown twin · Auto-PyTorch alternatives · hyperband alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | hyperband |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Dormant (2910d 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
- hyperband
- Tuning hyperparams fast with Hyperband
Stars
- Auto-PyTorch
- 2.5k
- hyperband
- 599
Forks
- Auto-PyTorch
- 303
- hyperband
- 73
Open issues
- Auto-PyTorch
- 75
- hyperband
- 9
Language
- Auto-PyTorch
- Python
- hyperband
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- hyperband
- Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Persona
- Auto-PyTorch
- -
- hyperband
- -
Runtime
- Auto-PyTorch
- -
- hyperband
- -
License
- Auto-PyTorch
- Apache-2.0
- hyperband
- Other
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- hyperband
- Aug 15, 2018
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- hyperband
- Model Training
Trust and health
Days since push
- Auto-PyTorch
- 846d
- hyperband
- 2910d
Open issues (now)
- Auto-PyTorch
- 75
- hyperband
- 9
Owner type
- Auto-PyTorch
- Organization
- hyperband
- User
OSV dependency advisories
- Auto-PyTorch
- Published findings
- hyperband
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- hyperband
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · hyperband: Python runtime
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, hyperband is Other.
- Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data.
- 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 hyperband if…
- License: hyperband is Other, Auto-PyTorch is Apache-2.0.
- Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning.
- Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
When NOT to use hyperband
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules.
- Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
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 (zygmuntz/hyperband) · observed Aug 4, 2026
- GitHub forks (zygmuntz/hyperband) · observed Aug 4, 2026
- Last push (zygmuntz/hyperband) · observed Aug 15, 2018
- License file (Other) · 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 · hyperband 599 (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and hyperband?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over hyperband?
- Choose Auto-PyTorch over hyperband when License: Auto-PyTorch is Apache-2.0, hyperband is Other; Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data; 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 hyperband over Auto-PyTorch?
- Choose hyperband over Auto-PyTorch when License: hyperband is Other, Auto-PyTorch is Apache-2.0; Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning; Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
- 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 hyperband?
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
- Is Auto-PyTorch or hyperband more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,541 vs 599). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and hyperband open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, hyperband: Other).
- Where can I find alternatives to Auto-PyTorch or hyperband?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and hyperband alternatives (Auto-PyTorch markdown twin, hyperband 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 hyperband?
- Auto-PyTorch: Dormant. hyperband: 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 hyperband?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; hyperband trust report.