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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.
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 |
You can install MindSpore Graph Learning either by pip or by source code.
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 isx86_64
andarrch64
.{cuda_verison}
denotes the CUDA version, and the option is10.1
,11.1
and11.6
。
Download source code from Gitee.
git clone https://gitee.com/mindspore/graphlearning.git
Compile and install in MindSpore Graph Learning directory.
cd graphlearning
bash build.sh
pip install ./output/mindspore_gl*.whl
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'
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