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  • PyG Documentation — pytorch_geometric documentation
    PyG 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 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
  • Installation — pytorch_geometric documentation
    Installation via PyPI From PyG 2 3 onwards, you can install and use PyG without any external library required except for PyTorch For this, simply run:
  • Introduction by Example — pytorch_geometric documentation
    Introduction by Example We shortly introduce the fundamental concepts of PyG through self-contained examples For an introduction to Graph Machine Learning, we refer the interested reader to the Stanford CS224W: Machine Learning with Graphs lectures For an interactive introduction to PyG, we recommend our carefully curated Google Colab notebooks At its core, PyG provides the following main
  • torch_geometric. nn — pytorch_geometric documentation
    It is recommended to use torch nn parallel DistributedDataParallel instead of DataParallel for multi-GPU training DataParallel is usually much slower than DistributedDataParallel even on a single machine Take a look here for an example on how to use PyG in combination with DistributedDataParallel
  • Explaining Graph Neural Networks — pytorch_geometric documentation
    Explaining Graph Neural Networks Interpreting GNN models is crucial for many use cases PyG (2 3 and beyond) provides the torch_geometric explain package for first-class GNN explainability support that currently includes a flexible interface to generate a variety of explanations via the Explainer class, several underlying explanation algorithms including, e g , GNNExplainer, PGExplainer and
  • Design of Graph Neural Networks — pytorch_geometric documentation
    Design of Graph Neural Networks Creating Message Passing Networks Heterogeneous Graph Learning Working with Graph Datasets Use-Cases Applications Distributed Training Advanced Concepts Advanced Mini-Batching Memory-Efficient Aggregations Hierarchical Neighborhood Sampling Compiled Graph Neural Networks TorchScript Support Scaling Up GNNs via Remote Backends Managing Experiments with GraphGym
  • Colab Notebooks and Video Tutorials — pytorch_geometric documentation
    The Stanford CS224W course has collected a set of graph machine learning tutorial blog posts, fully realized with PyG Students worked on projects spanning all kinds of tasks, model architectures and applications
  • Creating Message Passing Networks — pytorch_geometric documentation
    PyG provides the MessagePassing base class, which helps in creating such kinds of message passing graph neural networks by automatically taking care of message propagation
  • Working with Graph Datasets — pytorch_geometric documentation
    Working with Graph Datasets Creating Graph Datasets Loading Graphs from CSV Dataset Splitting Use-Cases Applications Distributed Training Advanced Concepts Advanced Mini-Batching Memory-Efficient Aggregations Hierarchical Neighborhood Sampling Compiled Graph Neural Networks TorchScript Support Scaling Up GNNs via Remote Backends Managing Experiments with GraphGym CPU Affinity for PyG
  • Use-Cases Applications — pytorch_geometric documentation
    Working with Graph Datasets Use-Cases Applications Scaling GNNs via Neighbor Sampling Point Cloud Processing Explaining Graph Neural Networks Shallow Node Embeddings Graph Transformer Distributed Training Advanced Concepts Advanced Mini-Batching Memory-Efficient Aggregations Hierarchical Neighborhood Sampling Compiled Graph Neural Networks TorchScript Support Scaling Up GNNs via Remote





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