Deep Graph Library

Easy Deep Learning on Graphs

Framework Agnostic

Build your models with PyTorch, TensorFlow or MXNet.


High Performant

DGL adopts advanced optimization techniques like kernel fusion, multi-thread and multi-process acceleration, and automatic sparse format tuning. Compared with other popular GNN frameworks such as PyTorch Geometric, DGL is both faster and more memory-friendly.


Ecosystem of Domain specific toolkits

DGL supports a variety of domains. DGL-KE is an easy-to-use and highly scalable package for learning large-scale knowledge graph embeddings. DGL-LifeSci is a specialized package for applications in bioinformatics and cheminformatics powered by graph neural networks.

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Latest Updates

Keep track of what's new in DGL, such as important bug fixes, new features, new releases, etc.

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Visit Blogs

Deep learning on graphs is very new direction. We use blogs to introduce new ideas and researches of this area and explains how DGL can support them very easily.

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Join Discussion

Got questions? Interested in contributing? or simply want to know what others are playing with? Use our forum for all kinds of discussion.

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