dgl
A Python package for deep learning on graphs, framework-agnostic and optimized for performance and scalability.
What is dgl?
DGL (Deep Graph Library) is a Python package built to ease deep learning on graphs, providing a high-performance and scalable framework for developing Graph Neural Networks (GNNs). It solves the challenge of applying deep learning to graph-structured data by offering efficient graph computation primitives and seamless integration with major deep learning frameworks.
Machine learning researchers, data scientists, and practitioners working with graph-structured data, such as social networks, molecular structures, or knowledge graphs, who need to build and train GNN models.
Developers choose DGL for its framework-agnostic design, allowing flexibility in model development, combined with optimized performance for large-scale graphs and a rich ecosystem of pre-built models and learning resources.
Overview
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Use Cases
Best For
- Building and training Graph Neural Networks for research or production
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