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id="pyg-documentation"> <h1>PyG Documentation<a class="headerlink" href="#pyg-documentation" title="Permalink to this heading"></a></h1> <p><span class="inline-logo pyg empty"></span> <strong>PyG</strong> <em>(PyTorch Geometric)</em> is a library built upon <span class="inline-logo pytorch empty"></span> <a class="reference external" href="https://pytorch.org">PyTorch</a> to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.</p> <p>It consists of various methods for deep learning on graphs and other irregular structures, also known as <a class="reference external" href="http://geometricdeeplearning.com/">geometric deep learning</a>, 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, <a class="reference external" href="https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu">multi GPU-support</a>, <a class="reference external" href="https://pytorch-geometric.readthedocs.io/en/latest/advanced/compile.html">torch.compile</a> support, <a class="reference external" href="https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py">DataPipe</a> support, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.</p> <p><a class="pyg-button" href="https://data.pyg.org/slack.html" target="_blank" title="Join our Slack community!"> <svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M5.042 15.165a2.528 2.528 0 0 1-2.52 2.523A2.528 2.528 0 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