Scaling up Graph Neural Networks with PyTorch Geometric

Announcing distributed graph neural network (GNN) training solution for PyG via torch_geometric.distributed

Graph Neural Networks (GNNs) have revolutionized the field of machine learning by enabling the modeling of data with complex relational structures in domains as diverse as e-commerce, social media and drug discovery. However, as the scale of graph data continues to grow, the demand for scalable GNN implementations becomes increasingly critical. In response to this need, the latest announcement from contributors to PyTorch Geometric, introduces distributed training capabilities, unlocking new avenues for scalability and efficiency in graph-based machine learning tasks. 

Distributed training is a technique used to accelerate model training by distributing computation across multiple devices or machines. PyTorch Geometric, a library dedicated to Graph Neural Networks, now offers native support for distributed training, leveraging PyTorch’s powerful distributed computing framework. The new technique was a collaboration by engineers from Kumo AI and Intel.

By enabling distributed training, PyTorch Geometric allows practitioners to train GNNs on massive graph datasets efficiently. This capability is particularly advantageous in scenarios where graph data is too large to fit into the memory of a single machine or GPU. With distributed training, computations are distributed across multiple devices or machines, enabling researchers to leverage parallelism and scale their models to handle large-scale graph datasets effectively.

Read the full announcement here. 

Interested in learning more? Join our PyG 2.5 – Distributed Training of GNNs webinar as we unveil the latest advancement in distributed training on Graph Neural Networks for PyG.

– Jure Leskovec