GraphStorm Model Training and Inference
Once your raw data are converted into partitioned DGL distributed graphs by using the GraphStorm Graph Construction user guide, you can use Graphstorm CLIs to train GML models and do inference on a signle machine if there is one partition only, or on a distributed environment, such as a Linux cluster, for multiple partition graphs.
This section provides guidelines of GraphStorm model training and inference on signle machine, distributed clusters, and Amazon SageMaker.
GraphStorm CLIs require less- or no-code operations for users to perform Graph Machine Learning (GML) tasks. In most cases, users only need to configure the parameters or arguments provided by GraphStorm to fulfill their GML tasks. Users can find the details of these configurations in the Model Training and Inference Configurations.
In addition, there are two node ID mapping operations during the graph construction procedure, and these mapping results are saved in a certain folder by which GraphStorm training and inference CLIs will automatically use to remap prediction results’ node IDs back to the original IDs. In case when such automatic remapping does not occur, you can find the details of outputs of model training and inference without remapping in GraphStorm Training and Inference Output. In addition, users can do the remapping mannually according to the GraphStorm Output Node ID Remapping guideline.