--- title: "Auto-PyTorch vs dragonfly" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-dragonfly-dragonfly" tools: ["automl-auto-pytorch", "dragonfly-dragonfly"] --- # Auto-PyTorch vs dragonfly *GraphCanon updated Aug 4, 2026* ## 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. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [dragonfly](https://github.com/dragonfly/dragonfly) has 894 stars, 238 forks, and 43 open issues, last pushed Jun 19, 2023. Figures are from public GitHub metadata via [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [dragonfly's repository](https://github.com/dragonfly/dragonfly). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [dragonfly](/tools/dragonfly-dragonfly.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | An open source Python library for scalable Bayesian optimisation. | | Stars | 2,541 | 894 | | Forks | 303 | 238 | | Open issues | 75 | 43 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [dragonfly](/tools/dragonfly-dragonfly.md) | | --- | --- | --- | | Days since push | 846d | 1141d | | Open issues (now) | 75 | 43 | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/dragonfly-dragonfly/trust.md) | ## Shared compatibility - **Python**: [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime; [dragonfly](/tools/dragonfly-dragonfly.md) - Python runtime ## Decision facts: Auto-PyTorch - **Adopt for:** Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. ## Decision facts: dragonfly - **Pricing:** freemium - 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. - **Adopt for:** Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization ## Choose when ### 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. ### 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 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 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. ## 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 `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 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](/tools/automl-auto-pytorch/alternatives) and [dragonfly alternatives](/tools/dragonfly-dragonfly/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md), [dragonfly markdown twin](/tools/dragonfly-dragonfly/alternatives.md)), 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](/compare/automl-auto-pytorch-vs-dragonfly-dragonfly.md) 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](/tools/automl-auto-pytorch/trust); [dragonfly trust report](/tools/dragonfly-dragonfly/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-pytorch`](/api/graphcanon/graph?tool=automl-auto-pytorch) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_