Comparison
Auto-PyTorch vs optuna
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick optuna if optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks.
Markdown twin · Auto-PyTorch alternatives · optuna alternatives
GraphCanon updated Oct 2, 2026
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Trust & integrity
| Signal | Auto-PyTorch | optuna |
|---|---|---|
| Maintenance | Dormant (903d since push) As of Sep 30, 2026 · github_public_v1 | Very active (1d since push) As of Oct 2, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 2, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- optuna
- A hyperparameter optimization framework
Stars
- Auto-PyTorch
- 2.5k
- optuna
- 15k
Forks
- Auto-PyTorch
- 298
- optuna
- 1.4k
Open issues
- Auto-PyTorch
- 75
- optuna
- 19
Language
- Auto-PyTorch
- Python
- optuna
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- optuna
- Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks.
Persona
- Auto-PyTorch
- -
- optuna
- -
Runtime
- Auto-PyTorch
- -
- optuna
- -
License
- Auto-PyTorch
- Apache-2.0
- optuna
- MIT
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- optuna
- Sep 30, 2026
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- optuna
- Model Training
Trust and health
Maintenance
- Auto-PyTorch
- Dormant (18%)
- optuna
- Very active (96%)
Days since push
- Auto-PyTorch
- 903d
- optuna
- 1d
Open issues (now)
- Auto-PyTorch
- 75
- optuna
- 19
Stars delta
- Auto-PyTorch
- -1 (30d)
- optuna
- +267 (30d)
Open issues delta
- Auto-PyTorch
- 0 (30d)
- optuna
- +3 (30d)
OSV dependency advisories
- Auto-PyTorch
- Published findings
- optuna
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- optuna
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · optuna: Python runtime
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, optuna is MIT.
- 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 optuna if…
- License: optuna is MIT, Auto-PyTorch is Apache-2.0.
- Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel.
- When you need to streamline the hyperparameter tuning process for machine learning models built in Python.
When NOT to use optuna
- If your project is not compatible with Python, as Optuna does not support other languages directly out of box.
- Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs.
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 Sep 30, 2026
- GitHub forks (automl/Auto-PyTorch) · observed Sep 30, 2026
- Last push (automl/Auto-PyTorch) · observed Apr 9, 2024
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (optuna/optuna) · observed Oct 2, 2026
- GitHub forks (optuna/optuna) · observed Oct 2, 2026
- Last push (optuna/optuna) · observed Sep 30, 2026
- License file (MIT) · observed Oct 2, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Auto-PyTorch 2.5k · optuna 15k (synced Sep 30, 2026).
Common questions
- What is the difference between Auto-PyTorch and optuna?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. optuna: A hyperparameter optimization framework. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over optuna?
- Choose Auto-PyTorch over optuna when License: Auto-PyTorch is Apache-2.0, optuna is MIT; 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 optuna over Auto-PyTorch?
- Choose optuna over Auto-PyTorch when License: optuna is MIT, Auto-PyTorch is Apache-2.0; Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel; When you need to streamline the hyperparameter tuning process for machine learning models built in Python.
- 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 optuna?
- If your project is not compatible with Python, as Optuna does not support other languages directly out of box. Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs.
- Is Auto-PyTorch or optuna more popular on GitHub?
- optuna has more GitHub stars (14,870 vs 2,540). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and optuna open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, optuna: MIT).
- Where can I find alternatives to Auto-PyTorch or optuna?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and optuna alternatives (Auto-PyTorch markdown twin, optuna 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 optuna?
- Auto-PyTorch: Dormant. optuna: 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 optuna?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; optuna trust report.