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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
Deglycyrrhizinated Licorice (DGL): Gut Benefits and Beyond One of my all-time favorites is deglycyrrhizinated licorice, or DGL The reason I love this supplement is that it addresses many health issues, and I am a big fan of addressing many issues with one stone, so to speak
GitHub - dmlc dgl: Python package built to ease deep learning on graph . . . 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 Figure: DGL Overall Architecture
DGL Overview - NVIDIA Docs - NVIDIA Documentation Hub 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
[1909. 01315] Deep Graph Library: A Graph-Centric, Highly-Performant . . . In this paper, we present the design principles and implementation of Deep Graph Library (DGL) DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable for extensive parallelization By advocating graph as the central programming abstraction, DGL can perform optimizations transparently
Deep Graph Library - DGL Learning DGL Check out our tutorials and documentations Using DGL with SageMaker Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at any scale Amazon SageMaker now supports DGL, simplifying implementation of DGL models