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- PyG Documentation — pytorch_geometric documentation
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
- Home - PyG
What is PyG? PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data PyG is both friendly to machine learning researchers and first-time users of machine learning toolkits
- PyG - GitHub
Graph Neural Network Library for PyTorch PyG has 5 repositories available Follow their code on GitHub
- PyG 2. 0: Scalable Learning on Real World Graphs
In this paper, we present Pyg 2 0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities
- Installation — pytorch_geometric documentation
For earlier PyTorch versions (torch<=2 5 0), you can install PyG via Anaconda for all major OS, and CUDA combinations If you have not yet installed PyTorch, install it via conda install as described in its official documentation
- Community - PyG
GraphGym is a platform for designing and evaluating Graph Neural Networks (GNNs), as originally proposed in the “Design Space for Graph Neural Networks” paper We now officially support GraphGym as part of of PyG
- pyg-team pytorch_geometric - GitHub
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
- torch-geometric · PyPI
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
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