Training Industry-scale GNNs with GiGL

Recent advances in graph machine learning (GML) and Graph Neu- ral Networks (GNNs) have sparked significant practical interest given the ability to model complex relationships between entities. Despite rapid progress in GNN designs, scalability remains a major challenge. Industry applications require solutions that can handle graphs with billions of nodes and edges efficiently. GiGL (Gigantic Graph Learning) is an open-source library from Snapchat, designed for large-scale distributed training and inference with GNNs. It seamlessly integrates with popular open-source GNN libraries like PyTorch Geometric (PyG). GiGL provides simplified configurable interfaces with minimal modeling code requirements, providing in- dustrial practitioners a straightforward way to apply GNNs to large- scale applications and enabling academics to conduct large-scale experiments. At the same time, it enables complex modeling capabil- ities desirable for modeling iteration. In this hands-on tutorial, we will demonstrate how GiGL addresses the scalability challenge in GNNs and provide a step-by-step guide for attendees to complete end-to-end training and inference with GiGL on industry-scale graphs. By the end of our tutorial, participants will have hands-on experience in training GNNs on graphs with billions of nodes and edges - capabilities not easily achievable with open-source graph learning libraries like PyG alone. We anticipate strong interest and participation from both industrial practitioners working on GNN applications and academics conducting large-scale experiments.