--- title: "Auto-PyTorch vs auto-sklearn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-automl-auto-sklearn" tools: ["automl-auto-pytorch", "automl-auto-sklearn"] --- # Auto-PyTorch vs auto-sklearn *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 auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [auto-sklearn](https://automl.github.io/auto-sklearn) has 8.1k stars, 1.3k forks, and 209 open issues, last pushed Jun 29, 2026. Figures are from public GitHub metadata via [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [auto-sklearn's repository](https://github.com/automl/auto-sklearn). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | Automated Machine Learning with scikit-learn | | Stars | 2,541 | 8,127 | | Forks | 303 | 1,327 | | Open issues | 75 | 209 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-3-Clause | | 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) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 846d | 35d | | Open issues (now) | 75 | 209 | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/automl-auto-sklearn/trust.md) | ## Shared compatibility - **Python**: [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime; [auto-sklearn](/tools/automl-auto-sklearn.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: auto-sklearn - **Adopt for:** auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. ## Choose when ### Choose Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, auto-sklearn is BSD-3-Clause. - Tags unique to Auto-PyTorch: deep-learning, pytorch, tabular-data, time-series-forecasting. - Also covers Data & Retrieval. - Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models. ### Choose auto-sklearn if… - License: auto-sklearn is BSD-3-Clause, Auto-PyTorch is Apache-2.0. - Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search. - When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically. ## 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 auto-sklearn - If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. - In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library. ## Common questions ### What is the difference between Auto-PyTorch and auto-sklearn? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. auto-sklearn: Automated Machine Learning with scikit-learn. See the comparison table for live GitHub stats and shared categories. ### When should I choose Auto-PyTorch over auto-sklearn? Choose Auto-PyTorch over auto-sklearn when License: Auto-PyTorch is Apache-2.0, auto-sklearn is BSD-3-Clause; Tags unique to Auto-PyTorch: deep-learning, pytorch, tabular-data, time-series-forecasting; Also covers Data & Retrieval; Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models. ### When should I choose auto-sklearn over Auto-PyTorch? Choose auto-sklearn over Auto-PyTorch when License: auto-sklearn is BSD-3-Clause, Auto-PyTorch is Apache-2.0; Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically. ### 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 auto-sklearn? If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library. ### Is Auto-PyTorch or auto-sklearn more popular on GitHub? auto-sklearn has more GitHub stars (8,127 vs 2,541). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and auto-sklearn open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, auto-sklearn: BSD-3-Clause). ### Where can I find alternatives to Auto-PyTorch or auto-sklearn? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md), [auto-sklearn markdown twin](/tools/automl-auto-sklearn/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-automl-auto-sklearn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Auto-PyTorch or auto-sklearn? Auto-PyTorch: Dormant. auto-sklearn: Steady. 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 auto-sklearn? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [auto-sklearn trust report](/tools/automl-auto-sklearn/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/_