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.
What is new in DGL v0.5 release?
The recent DGL 0.5 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...
Fighting COVID-19 with Deep Graph
Use the new Drug Repurposing Knowledge Graph (DRKG) for repurposing drugs for fighting COVID-19. A step-by-step tutorial on how to use knowledge graph embeddings learned by DGL-KE to make prediction...
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.
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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