Framework Agnostic
Build your models with PyTorch, TensorFlow or Apache MXNet.
Efficient and Scalable
Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure.
Diverse Ecosystem
DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others.
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v0.7 Release Highlights
Check out the new features in the latest DGL 0.7 release.
v0.6 Release Highlight
The recent DGL 0.6 release is a major update on many aspects of the project including documentation, APIs, system speed, and scalability. This article highlights some of the new features...
DGL Empowers Service for Predictions on Connected Datasets with Graph Neural Networks
Announcing Amazon Neptune ML, an easy, fast, and accurate approach for predictions on graphs powered by Deep Graph Library.
v0.5.3 Patch Update
This is a patch release mainly for supporting CUDA 11.0. Now DGL supports CUDA 11.0 and PyTorch 1.7 on Linux/Windows/Mac.
News
Latest Updates
Keep track of what's new in DGL, such as important bug fixes, new features, new releases, etc.
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Blogs
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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Discussion
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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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Brought to you by NYU, NYU-Shanghai, and Amazon AWS.
By far the cleanest and most elegant library for graph neural networks in PyTorch. Highly recommended! Unifies Capsule Nets (GNNs on bipartite graphs) and Transformers (GCNs with attention on fully-connected graphs) in a single API.
I taught my students Deep Graph Library (DGL) in my lecture on "Graph Neural Networks" today. It is a great resource to develop GNNs with PyTorch.
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