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
This tool helps machine learning engineers and researchers build and train Graph Neural Networks (GNNs) for analyzing structured data. It takes graph-structured data (like social networks, molecular structures, or citation graphs) as input and produces trained GNN models capable of tasks such as classifying nodes, predicting links, or generating new graphs. It is designed for those already familiar with PyTorch.
About GiGL
Snapchat/GiGL
Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks
This project helps machine learning engineers and data scientists build and deploy Graph Neural Networks (GNNs) for extremely large datasets, often involving billions of nodes. It takes raw graph data and task configurations as input, then outputs trained GNN models capable of performing tasks like node classification or link prediction at scale. The primary users are ML practitioners dealing with massive, interconnected datasets.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work