Getting Started with Large-Scale GNNs using cuGraph Packages for DGL and PyG

Graph neural networks (GNNs) are an increasingly popular class of artificial neural networks designed to process data that can be represented as graphs. The two prominent GNN frameworks are the Deep Graph Library (DGL) and PyTorch Geometric (PyG). The RAPIDS cuGraph effort has been working on extending those packages, via cuGraph-DGL and cuGraph-PyG, to provide greater performance and scalability. Learn the basic concepts, implementations, and applications of graph neural networks using the cuGraph packages for DGL and PyG. We cover both fundamental knowledge and hands-on interactive activities for anyone who wants to get started using GNNs for graph analysis. The course will cover scalability into the billions of edges and highlight performance gains over the base frameworks.
Prerequisite(s):

Competency in the Python programming language
Experience with deep neural networks (specifically variations of CNNs)

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