Comparison
Auto-PyTorch vs hyperopt
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
Markdown twin · Auto-PyTorch alternatives · hyperopt alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | hyperopt |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization 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
- hyperopt
- Distributed Asynchronous Hyperparameter Optimization in Python
Stars
- Auto-PyTorch
- 2.5k
- hyperopt
- 7.6k
Forks
- Auto-PyTorch
- 303
- hyperopt
- 1.1k
Open issues
- Auto-PyTorch
- 75
- hyperopt
- 9
Language
- Auto-PyTorch
- Python
- hyperopt
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- hyperopt
- Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
Persona
- Auto-PyTorch
- -
- hyperopt
- -
Runtime
- Auto-PyTorch
- -
- hyperopt
- -
License
- Auto-PyTorch
- Apache-2.0
- hyperopt
- Other
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- hyperopt
- Aug 3, 2026
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- hyperopt
- Model Training
Trust and health
Maintenance
- Auto-PyTorch
- Dormant (18%)
- hyperopt
- Very active (96%)
Days since push
- Auto-PyTorch
- 846d
- hyperopt
- 0d
Open issues (now)
- Auto-PyTorch
- 75
- hyperopt
- 9
OSV dependency advisories
- Auto-PyTorch
- Published findings
- hyperopt
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- hyperopt
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · hyperopt: Python runtime
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, hyperopt 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 hyperopt if…
- License: hyperopt is Other, Auto-PyTorch is Apache-2.0.
- Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization.
- When you need to optimize machine learning model parameters on a distributed system asynchronously.
When NOT to use hyperopt
- If your project does not support asynchronous execution, opting for synchronous tools might be more suitable.
- Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options.
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 (hyperopt/hyperopt) · observed Aug 4, 2026
- GitHub forks (hyperopt/hyperopt) · observed Aug 4, 2026
- Last push (hyperopt/hyperopt) · observed Aug 3, 2026
- 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 · hyperopt 7.6k (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and hyperopt?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over hyperopt?
- Choose Auto-PyTorch over hyperopt when License: Auto-PyTorch is Apache-2.0, hyperopt 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 hyperopt over Auto-PyTorch?
- Choose hyperopt over Auto-PyTorch when License: hyperopt is Other, Auto-PyTorch is Apache-2.0; Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization; When you need to optimize machine learning model parameters on a distributed system asynchronously.
- 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 hyperopt?
- If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options.
- Is Auto-PyTorch or hyperopt more popular on GitHub?
- hyperopt has more GitHub stars (7,598 vs 2,541). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and hyperopt open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, hyperopt: Other).
- Where can I find alternatives to Auto-PyTorch or hyperopt?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and hyperopt alternatives (Auto-PyTorch markdown twin, hyperopt 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 hyperopt?
- Auto-PyTorch: Dormant. hyperopt: Very active. 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 hyperopt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; hyperopt trust report.