MindSpore Graph Learning

• MindSpore Graph Learning Introduction
• Installation
  • System Environment Information Confirmation
  • Installation Methods
    • Installation by pip
    • Installation by Source Code
  • Installation Verification

MindSpore Graph Learning Introduction

MindSpore Graph Learning is an efficient and easy-to-use graph learning framework.

Compared to the normal model, 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 point-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.

Meanwhile, 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.

Combined with the MindSpore deep learning framework, the framework can basically cover most graph neural network applications. For more details, please refer to https://gitee.com/mindspore/graphlearning/tree/master/model_zoo.

Installation

System Environment Information Confirmation

• Ensure that the hardware platform is the Linux system, Windows and Mac are not supported.
• Refer to MindSpore Installation Guide to complete the installation of MindSpore, which requires at least version 1.6.1.
• For other dependencies, please refer to requirements.txt.

Version dependency

Due the dependency between MindSpore Graph Learning and MindSpore, please follow the table below and install the corresponding MindSpore verision from MindSpore download page.

MindSpore Graph Learning Version	Branch	MindSpore Minimum Version Requirements
master	master	>=2.0.0
0.2.0	r0.2.0	>=2.0.0

Installation Methods

You can install MindSpore Graph Learning either by pip or by source code.

Installation by pip

• Ascend/CPU

  pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.0.0rc1/GraphLearning/cpu/{system_structure}/mindspore_gl-0.2-cp37-cp37m-linux_{system_structure}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple

• GPU

  pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.0.0rc1/GraphLearning/gpu/x86_64/cuda-{cuda_verison}/mindspore_gl-0.2-cp37-cp37m-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple

• When the network is connected, dependency items are automatically downloaded during .whl package installation. For details about other dependency items, see requirements.txt. In other cases, you need to manually install dependency items.
• {system_structure} denotes the Linux system architecture, and the option is x86_64 and arrch64.
• {cuda_verison} denotes the CUDA version, and the option is 10.1, 11.1 and 11.6。

Installation by Source Code

1. Download source code from Gitee.

   git clone https://gitee.com/mindspore/graphlearning.git

2. Compile and install in MindSpore Graph Learning directory.

   cd graphlearning
   bash build.sh
   pip install ./output/mindspore_gl*.whl

Installation Verification

Successfully installed, if there is no error message such as No module named 'mindspore_gl' when execute the following command:

python -c 'import mindspore_gl'