Guide and ExamplesΒΆ

This section contains a guide for the learning engine and a number of examples.


We assume you has read the getting_started section and know how to launch a GraphScope session.

We present an end-to-end example, demonstrating how GLE trains a node classification model on a citation network using the local mode of GraphScope.

Training a Node Classification Model on Your Local Machine.

GraphScope is designed for processing large graphs, which are usually hard to fit in the memory of a single machine. With vineyard as the distributed in-memory data manager, GraphScope supports run on a cluster managed by Kubernetes(k8s). Next, we revisit the example we present in the first tutorial, showing how GraphScope process the node classification task on a Kubernetes cluster.

Training a Node Classification Model on K8s Cluster

GraphScope is also compatible with PyG models, the following examples shows ho2 to train a PyG model using GraphScope on your local machine.

Training a Node Classification Model(PyG) on Your Local Machine

Tutorial: Training a Node Classification Model (PyG) on a K8S Cluster