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DGL - Deep Graph Library 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
Deep Graph Library - DGL Amazon SageMaker now supports DGL, simplifying implementation of DGL models A Deep Learning container (MXNet 1 6 and PyTorch 1 3) bundles all the software dependencies and the SageMaker API automatically sets up and scales the infrastructure required to train graphs
Deep Graph Library - DGL The DGL 2 1 introduces GPU acceleration for the whole GNN data loading pipeline in GraphBolt, including the graph sampling and feature fetching stages Read more
1. 1 关于图的基本概念 — DGL 2. 5 documentation 1 1 关于图的基本概念 (English Version) 图是用以表示实体及其关系的结构，记为 G = (V, E) 。图由两个集合组成，一是节点的集合 V ，一个是边的集合 E 。 在边集 E 中，一条边 (u, v) 连接一对节点 u 和 v ，表明两节点间存在关系。关系可以是无向的， 如描述节点之间的对称关系；也可以是有向的，如描述
Deep Graph Library - DGL DGL 1 0: Empowering Graph Machine Learning for Everyone We are thrilled to announce the arrival of DGL 1 0, a cutting-edge machine learning framework for deep learning on graphs Over the past three years, there has been growing interest from both academia and industry in this technology
Deep Graph Library - DGL Why DGL? In the last few years, deep learning has enjoyed plenty of extraordinary successes Many challenging tasks have been solved or close to being solved by Deep Learning, such as image recognition, rich-resource machine translation, game playing
Deep Graph Library - DGL You can easily install DGL 2 0 with dgl graphbolt on any platform using pip or conda To jump right in, dive into our brand-new Stochastic Training of GNNs with GraphBolt tutorial and experiment with our node classification and link prediction examples in Google Colab