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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
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 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
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
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: