MindSpore Graph Learning Documents

MindSpore Graph Learning is a graph learning suite, which supports point-centered programming for graph neural networks and efficient training inference.

Thanks to the MindSpore graph kernel fusion, MindSpore Graph Learning can optimize the build of execution patterns specific to graph models, helping developers shorten the training time. MindSpore Graph Learning also proposes an innovative vertex-centric programming paradigm, which provides native graph neural network expressions and built-in models covering most application scenarios, enabling developers to easily build graph neural networks.

Design Features

1. Vertex-centric programming paradigm

   A graph neural network model transfers and aggregates information on a given graph structure, which cannot be intuitively expressed through entire graph computing. MindSpore Graph Learning provides a vertex-centric programming paradigm that better complies with the graph learning algorithm logic and Python language style. It can directly translate formulas into code, reducing the gap between algorithm design and implementation.

2. Accelerated graph models

   MindSpore Graph Learning combines the features of MindSpore graph kernel fusion and auto kernel generator (AKG) to automatically identify the specific execution pattern of graph neural network tasks for fusion and kernel-level optimization, covering the fusion of existing operators and new combined operators in the existing framework. The performance is improved by 3 to 4 times compared with that of the existing popular frameworks.

Future Roadmap

The initial version of MindSpore Graph Learning includes the point-centric programming paradigm, provides implementation of typical graph models, and provides cases and performance evaluation for single-node training on small datasets. The initial version does not support performance evaluation and distributed training on large datasets, and does not support interconnection with graph databases. These features will be included in later versions of MindSpore Graph Learning.

Typical MindSpore Graph Learning Application Scenarios

• Graph convolutional neural network (GCN) for entire graph training

  Entire graph training requires that the graph nodes and edge features of the user be stored in the GPU. The GCN is a type of graph neural network that uses convolution operations.

Deployment

• Install Graph Learning

Guide

• Entire Graph Training Using Graph Convolutional Network (GCN)

API References

• mindspore_gl.dataloader
• mindspore_gl.dataset
• mindspore_gl.graph
• mindspore_gl.nn
• mindspore_gl.sampling
• mindspore_gl.utils

References

• FAQ

RELEASE NOTES

• Release Notes