{"data":{"slug":"thumnlab-autogl","name":"AutoGL","tagline":"AutoML framework & toolkit for machine learning on graphs","github_url":"https://github.com/THUMNLab/AutoGL","owner":"THUMNLab","repo":"AutoGL","owner_avatar_url":"https://avatars.githubusercontent.com/u/68025303?v=4","primary_language":"Python","stars":1138,"forks":123,"topics":["automl","deep-learning","graph-neural-networks","hyper-parameter-optimization","machine-learning","neural-architecture-search","pytorch","pytorch-geometric"],"archived":false,"github_pushed_at":"2025-11-20T02:46:56+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/thumnlab-autogl","markdown_url":"https://www.graphcanon.com/tools/thumnlab-autogl.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/thumnlab-autogl","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=thumnlab-autogl","description":"An autoML framework & toolkit for machine learning on graphs.","homepage_url":"http://mn.cs.tsinghua.edu.cn/AutoGL/","license":"Apache-2.0","open_issues":20,"watchers":29,"ai_summary":"Supports automated hyperparameter optimization and neural architecture search for graph data.","readme_excerpt":"### Requirements\n\nPlease make sure you meet the following requirements before installing AutoGL.\n\n1. Python >= 3.6.0\n\n2. PyTorch (>=1.6.0)\n\n    see <https://pytorch.org/> for installation.\n\n3. Graph Library Backend\n\n    You will need either PyTorch Geometric (PyG) or Deep Graph Library (DGL) as the backend. You can select a backend following [here](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_backend.html) if you install both.\n\n    3.1 PyTorch Geometric (>=1.7.0)\n\n    See <https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html/> for installation.\n\n    3.2 Deep Graph Library (>=0.7.0)\n\n    See <https://dgl.ai/> for installation.\n\n---\n\n### Installation\n\n#### Install from pip\n\nRun the following command to install this package through `pip`.\n\n```\npip install autogl\n```\n\n#### Install from source\n\nRun the following command to install this package from the source.\n\n```\ngit clone https://github.com/THUMNLab/AutoGL.git\ncd AutoGL\npython setup.py install\n```\n\n#### Install for development\n\nIf you are a developer of the AutoGL project, please use the following command to create a soft link, then you can modify the local package without install them again.\n\n```\npip install -e .\n```\n\n---\n\n## License\nWe follow [Apache license](LICENSE) across the entire codebase from v0.2.","github_created_at":"2020-11-30T14:26:22+00:00","created_at":"2026-07-11T23:32:46.4244+00:00","updated_at":"2026-08-04T00:02:02.865349+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"automl","name":"automl"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"graph-neural-networks","name":"graph-neural-networks"},{"slug":"hyper-parameter-optimization","name":"hyper-parameter-optimization"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"neural-architecture-search","name":"neural-architecture-search"},{"slug":"pytorch","name":"pytorch"},{"slug":"pytorch-geometric","name":"pytorch-geometric"}],"trust":{"provenance":{"is_fork":false,"github_id":317248195,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T00:02:02.086Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":256,"last_release_at":"2022-12-30T06:11:04Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:32:49.747Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T00:02:02.564Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-04T00:02:02.564Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-04T00:02:02.564Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["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."],"min_ram_gb":8,"requires_docker":false},"constraints":{"min_ram_gb":8,"requires_docker":false},"when_to_use":["When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets.","If your project requires leveraging either PyTorch Geometric or Deep Graph Library as backend support, enabling seamless integration with these deep learning libraries."],"when_not_to_use":["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."],"source":"enrich:decision_facts","observed_at":"2026-07-16T22:10:02.091Z"},"constraint_facets":{"min_ram_gb":8,"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"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."},{"label":"Adopt for","value":"AutoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks."}]}}