Pytorch Geometric Readme Md At Master Github

There was an error while loading. Please reload this page. Documentation | PyG 1.0 Paper | PyG 2.0 Paper | Colab Notebooks | External Resources | OGB Examples 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. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, torch.compile support, DataPipe support, a large number of common benchmark datasets (based on simple interfaces...

Whether you are a machine learning researcher or first-time user of machine learning toolkits, here are some reasons to try out PyG for machine learning on graph-structured data. In this quick tour, we highlight the ease of creating and training a GNN model with only a few lines of code. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

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