Install from source
Check out our guide on build from source.
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. 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. Please refer to the SageMaker documentation for more information. The best way to get stated is with our sample Notebooks below:
- Semi-supervised classification of a knowledge base using a GCN (https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/dgl_gcn)
- Learning embeddings of large-scale knowledge graphs using a dataset of scientific publications (https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/dgl_kge)
- Molecular property prediction of toxicity using a GCN (https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/dgl_gcn_tox21)
- Recommender system for movies using a GCMC implementation (https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/dgl_gcmc)