Comparison
Auto-PyTorch vs dragonfly
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick dragonfly if dragonfly is an open-source Python library that specializes in scalable Bayesian optimization.
Markdown twin · Auto-PyTorch alternatives · dragonfly alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | dragonfly |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Dormant (1141d 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 published findings from this source as of 2026-07-11 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
- dragonfly
- An open source Python library for scalable Bayesian optimisation.
Stars
- Auto-PyTorch
- 2.5k
- dragonfly
- 894
Forks
- Auto-PyTorch
- 303
- dragonfly
- 238
Open issues
- Auto-PyTorch
- 75
- dragonfly
- 43
Language
- Auto-PyTorch
- Python
- dragonfly
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- dragonfly
- Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization
Persona
- Auto-PyTorch
- -
- dragonfly
- -
Runtime
- Auto-PyTorch
- -
- dragonfly
- -
License
- Auto-PyTorch
- Apache-2.0
- dragonfly
- MIT
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- dragonfly
- Jun 19, 2023
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- dragonfly
- Model Training
Trust and health
Days since push
- Auto-PyTorch
- 846d
- dragonfly
- 1141d
Open issues (now)
- Auto-PyTorch
- 75
- dragonfly
- 43
OSV dependency advisories
- Auto-PyTorch
- Published findings
- dragonfly
- No published findings from this source as of 2026-07-11
Full report
- Auto-PyTorch
- Trust report
- dragonfly
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · dragonfly: Python runtime
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, dragonfly 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 dragonfly if…
- License: dragonfly is MIT, Auto-PyTorch is Apache-2.0.
- Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works..
- Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager..
- Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation.
- When dealing with large-scale problems where traditional optimization methods may not be efficient enough.
When NOT to use dragonfly
- If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management.
- In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations.
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 (dragonfly/dragonfly) · observed Aug 4, 2026
- GitHub forks (dragonfly/dragonfly) · observed Aug 4, 2026
- Last push (dragonfly/dragonfly) · observed Jun 19, 2023
- License file (MIT) · 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 · dragonfly 894 (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and dragonfly?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. dragonfly: An open source Python library for scalable Bayesian optimisation.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over dragonfly?
- Choose Auto-PyTorch over dragonfly when License: Auto-PyTorch is Apache-2.0, dragonfly 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 dragonfly over Auto-PyTorch?
- Choose dragonfly over Auto-PyTorch when License: dragonfly is MIT, Auto-PyTorch is Apache-2.0; Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.; Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the
pippackage manager.; Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation; When dealing with large-scale problems where traditional optimization methods may not be efficient enough. - 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 dragonfly?
- If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations.
- Is Auto-PyTorch or dragonfly more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,541 vs 894). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and dragonfly open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, dragonfly: MIT).
- Where can I find alternatives to Auto-PyTorch or dragonfly?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and dragonfly alternatives (Auto-PyTorch markdown twin, dragonfly 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 dragonfly?
- Auto-PyTorch: Dormant. dragonfly: 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 dragonfly?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; dragonfly trust report.