# MindQuantum Installation [](https://gitee.com/mindspore/docs/blob/r1.7/docs/mindquantum/docs/source_en/mindquantum_install.md) ## Confirming System Environment Information - Refer to [MindSpore Installation Guide](https://www.mindspore.cn/install/en), install MindSpore, version 1.4.0 or later is required. - See [setup.py](https://gitee.com/mindspore/mindquantum/blob/r0.6/setup.py) for the remaining dependencies. ## Installation Methods You can install MindInsight either by pip or by source code. ### Install by pip Linux-x86_64 Python3.7 ```bash pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.7.0/MindQuantum/x86_64/mindquantum-0.6.0-cp37-cp37m-linux_x86_64.whl ``` Windows-x64 Python3.7 ```bash pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.7.0/MindQuantum/x86_64/mindquantum-0.6.0-cp37-cp37m-win_amd64.whl ``` Windows-x64 Python3.9 ```bash pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.7.0/MindQuantum/x86_64/mindquantum-0.6.0-cp39-cp39-win_amd64.whl ``` > - Refers to [MindSpore](https://www.mindspore.cn/versions) to find different version of packages。 ### Install by Source Code 1.Download Source Code from Gitee ```bash cd ~ git clone https://gitee.com/mindspore/mindquantum.git -b r0.6 ``` 2.Compiling MindQuantum ```bash cd ~/mindquantum python setup.py install --user ``` ## Verifying Successful Installation Successfully installed, if there is no error message such as No module named 'mindquantum' when execute the following command: ```bash python -c 'import mindquantum' ``` ## Install with Docker Mac or Windows users can install MindQuantum through Docker. Please refer to [Docker installation guide](https://gitee.com/mindspore/mindquantum/blob/r0.6/install_with_docker_en.md#). ## Note Please set the parallel core number before running MindQuantum scripts. For example, if you want to set the parallel core number to 4, please run the command below: ```bash export OMP_NUM_THREADS=4 ``` For large servers, please set the number of parallel kernels appropriately according to the size of the model to achieve optimal results.