Deep Graph Library (DGL) is a Python package built for the implementation and training of graph neural networks on top of existing DL frameworks. NGC Containers are the easiest way to get started with DGL.

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.

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 is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.

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Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (currently supporting PyTorch, MXNet and TensorFlow).

The folder contains example implementations of selected research papers related to Graph Neural Networks. Note that the examples may not work with incompatible DGL versions. For …

Aug 31, 2023 · In this post, I introduce how to use cuGraph-DGL, a GPU-accelerated library for graph computations. It extends Deep Graph Library (DGL), a popular framework for GNNs that enables large-scale applications. Before I dive into cuGraph-DGL, I …

The NVIDIA® Deep Learning SDK accelerates widely-used deep learning frameworks such as DGL. DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs.

The potential for graph networks in practical AI applications is highlighted in the Amazon SageMaker AI tutorials for Deep Graph Library (DGL). Examples for training models on graph datasets include social networks, knowledge bases, biology, and chemistry.