--- title: "Auto-PyTorch vs AutoGL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-thumnlab-autogl" tools: ["automl-auto-pytorch", "thumnlab-autogl"] --- # Auto-PyTorch vs AutoGL *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 AutoGL if autoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [AutoGL](http://mn.cs.tsinghua.edu.cn/AutoGL/) has 1.1k stars, 123 forks, and 20 open issues, last pushed Nov 20, 2025. Figures are from public GitHub metadata via [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [AutoGL's repository](https://github.com/THUMNLab/AutoGL). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | AutoML framework & toolkit for machine learning on graphs | | Stars | 2,541 | 1,138 | | Forks | 303 | 123 | | Open issues | 75 | 20 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | AutoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | 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) | [AutoGL](/tools/thumnlab-autogl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 846d | 256d | | Open issues (now) | 75 | 20 | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/thumnlab-autogl/trust.md) | ## Shared compatibility - **Python**: [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime; [AutoGL](/tools/thumnlab-autogl.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: AutoGL - **Requirements:** Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0. - **Adopt for:** AutoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks. ## Choose when ### Choose Auto-PyTorch if… - Tags unique to Auto-PyTorch: tabular-data, time-series-forecasting. - 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 AutoGL if… - Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0.. - Tags unique to AutoGL: graph-neural-networks, hyper-parameter-optimization, machine-learning, neural-architecture-search. - When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets. ## 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 AutoGL - For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets. - If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies. ## Common questions ### What is the difference between Auto-PyTorch and AutoGL? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Auto-PyTorch over AutoGL? Choose Auto-PyTorch over AutoGL when Tags unique to Auto-PyTorch: tabular-data, time-series-forecasting; 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 AutoGL over Auto-PyTorch? Choose AutoGL over Auto-PyTorch when Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0.; Tags unique to AutoGL: graph-neural-networks, hyper-parameter-optimization, machine-learning, neural-architecture-search; When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets. ### 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 AutoGL? For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets. If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies. ### Is Auto-PyTorch or AutoGL more popular on GitHub? Auto-PyTorch has more GitHub stars (2,541 vs 1,138). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and AutoGL open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, AutoGL: Apache-2.0). ### Where can I find alternatives to Auto-PyTorch or AutoGL? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [AutoGL alternatives](/tools/thumnlab-autogl/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md), [AutoGL markdown twin](/tools/thumnlab-autogl/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-thumnlab-autogl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Auto-PyTorch or AutoGL? Auto-PyTorch: Dormant. AutoGL: Slowing. 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 AutoGL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [AutoGL trust report](/tools/thumnlab-autogl/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/_