|
- 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
- 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
- Welcome to Deep Graph Library Tutorials and Documentation — DGL 2. 5 . . .
For absolute beginners, start with the Blitz Introduction to DGL It covers the basic concepts of common graph machine learning tasks and a step-by-step on building Graph Neural Networks (GNNs) to solve them
- 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
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
- 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
|
|
|