pytorch_geometric and GiGL
PyTorch Geometric is a mature, general-purpose GNN library that GiGL builds upon as a foundational dependency for its specialized large-scale distributed training framework, making them complements rather than competitors.
About pytorch_geometric
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
Provides a message-passing API for implementing custom GNN layers and pre-built convolution operators (GCNConv, EdgeConv, etc.) that handle node aggregation and feature propagation. Supports heterogeneous graphs, dynamic temporal graphs, and large-scale models with millions of nodes, alongside specialized data loaders for mini-batch training on both small and giant graphs. Includes built-in benchmark datasets and graph transforms for point clouds and 3D meshes, with `torch.compile` and multi-GPU support for production deployments.
About GiGL
Snapchat/GiGL
Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks
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