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Welcome to the GraphStorm v0.1.2 Documentation and Tutorials#

GraphStorm is a graph machine learning (GML) framework designed for enterprise use cases. It simplifies the development, training and deployment of GML models on industry-scale graphs (measured in billons of nodes and edges) by providing scalable training and inference pipelines of GML models. GraphStorm comes with a collection of built-in GML models, allowing users to train a GML model with a single command, eliminating the need to write any code. Moreover, GraphStorm provides a wide range of configurations to customiz model implementations and training pipelines, enhancing model performance. In addition, GraphStorm offers a programming interface that enables users to train custom GML models in a distributed manner. Users can bring their own model implementations and leverage the GraphStorm training pipeline for scalability.

Getting Started#

For beginners, please first start with the GraphStorm Docker environment setup. This tutorial covers how to set up a Docker environment and build a GraphStorm Docker image, which serves as the Standalone running environment for GraphStorm. We are working on supporting more running environments for GraphStorm.

Once successfully set up the GraphStorm Docker running environment,

• follow the GraphStorm Standalone Mode Quick-Start Tutorial to run examples using GraphStorm built-in data and models, hence getting familiar with GraphStorm’s usage of training and inference.

• follow the Use Your Own Graph Data Tutorial to prepare your own graph data for using GraphStorm.

• read the GraphStorm Training and Inference Configurations to learn the various configurations provided by GraphStorm that can help to achieve the best performance.

Scale to Giant Graphs#

For experienced users who wish to train and run infernece on very large graphs,

• follow the Use GraphStorm in a Distributed Cluster tutorial to use GraphStorm in the Distributed mode.

• follow the Use GraphStorm on SageMaker tutorial to use GraphStorm in the Distribute mode based on Amazon SageMaker.

Advanced Topics#

For users who want to use their own GML models in GraphStorm,

• follow the Use Your Own GNN Models tutorial to learn the programming interfaces and the steps of how to modify users’ own models.

• For users who want to use text as node features, follow the Use Text as Node Features tutorial to learn how to leverage BERT models to use text as node features in GraphStorm.

• There are various usages of GraphStorm helping to both speed up training process and boost model performance. Users can find these usages in the Advanced Usages page.

Contribution#

GraphStorm is free software; you can redistribute it and/or modify it under the terms of the Apache License 2.0. We welcome contributions. Join us on GitHub.