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- DGL - Deep Graph Library
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
- 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
- dgl — DGL 2. 5 documentation
The dgl package contains data structure for storing structural and feature data (i e , the DGLGraph class) and also utilities for generating, manipulating and transforming graphs
- Install and Setup — DGL 2. 5 documentation
Go to root directory of the DGL repository, build a shared library, and install the Python binding for DGL
- 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
The new release makes it easier to compose and apply various graph augmentation and transformation algorithms to all DGL’s built-in dataset The new dgl transforms package follows the style of the PyTorch Dataset Transforms Users can specify the transforms to use with the transform keyword argument of all DGL datasets:
- 用户指南 — DGL 1. 1. 3 documentation
2020年9月，DGL社区的一群热心贡献者把DGL用户指南译成了中文，方便广大中文用户群学习和使用DGL。 特此致谢下述贡献者：
- 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
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