Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.
fengxun dac5b74954 | 1 year ago | |
---|---|---|
.jenkins/test/config | 1 year ago | |
docs | 1 year ago | |
examples | 1 year ago | |
images | 2 years ago | |
mindspore_gl | 1 year ago | |
model_zoo | 1 year ago | |
recommendation | 1 year ago | |
scripts | 2 years ago | |
tests | 1 year ago | |
tools | 1 year ago | |
.gitignore | 2 years ago | |
FAQ_CN.md | 1 year ago | |
LICENSE | 2 years ago | |
OWNERS | 1 year ago | |
README.md | 1 year ago | |
README_CN.md | 1 year ago | |
RELEASE.md | 1 year ago | |
RELEASE_CN.md | 1 year ago | |
build.sh | 1 year ago | |
faq.md | 1 year ago | |
requirements.txt | 1 year ago | |
setup.py | 1 year ago |
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 | >=1.10.0 |
0.2.0a0 | r0.2.0-alpha | >=1.10.0 |
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/2.0.0a0/GraphLearning/cpu/{system_structure}/mindspore_gl-0.2.0a0-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/2.0.0a0/GraphLearning/gpu/x86_64/cuda-{cuda_verison}/mindspore_gl-0.2.0a0-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'
MindSpore Graph Learning is an efficient and easy-to-use graph learning framework, which allows researchers and developers to implement graph models according to formula easily and train efficiently.
https://gitee.com/mindspore/graphlearning
Python Vue TypeScript Markdown Shell other
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》