This document describes how to quickly install MindSpore in a Linux system with an Ascend 910 environment by pip.
/usr/local/Ascend
of Ascend 910 AI processor software package, If not, the root user needs to add the current user to the user group where /usr/local/Ascend
is located. For the specific configuration, please refer to the software package instruction document.Install the .whl package provided in Ascend 910 AI processor software package. The .whl package is released with the software package. After software package is upgraded, reinstall the .whl package.
pip install /usr/local/Ascend/ascend-toolkit/latest/fwkacllib/lib64/topi-{version}-py3-none-any.whl
pip install /usr/local/Ascend/ascend-toolkit/latest/fwkacllib/lib64/te-{version}-py3-none-any.whl
pip install /usr/local/Ascend/ascend-toolkit/latest/fwkacllib/lib64/hccl-{version}-py3-none-any.whl
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindSpore/ascend/{system}/mindspore_ascend-{version}-cp37-cp37m-linux_{arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
Of which,
{version}
denotes the version of MindSpore. For example, when you are installing MindSpore 1.1.0, {version}
should be 1.1.0.{arch}
denotes the system architecture. For example, the Linux system you are using is x86 architecture 64-bit, {arch}
should be x86_64
. If the system is ARM architecture 64-bit, then it should be aarch64
.{system}
denotes the system version. For example, if you are using EulerOS ARM architecture, {system}
should be euleros_aarch64
. Currently, the following systems are supported by Ascend: euleros_aarch64
/euleros_x86
/centos_x86
/ubuntu_aarch64
/ubuntu_x86
.If Ascend 910 AI processor software is installed in a non-default path, after MindSpore is installed, export runtime-related environment variables. /usr/local/Ascend
in the following command LOCAL_ASCEND=/usr/local/Ascend
denotes the installation path of the software package, please replace it as your actual installation path.
# control log level. 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR, default level is WARNING.
export GLOG_v=2
# Conda environmental options
LOCAL_ASCEND=/usr/local/Ascend # the root directory of run package
# lib libraries that the run package depends on
export LD_LIBRARY_PATH=${LOCAL_ASCEND}/add-ons/:${LOCAL_ASCEND}/ascend-toolkit/latest/fwkacllib/lib64:${LOCAL_ASCEND}/driver/lib64:${LOCAL_ASCEND}/opp/op_impl/built-in/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}
# Environment variables that must be configured
export TBE_IMPL_PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe # TBE operator implementation tool path
export ASCEND_OPP_PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/opp # OPP path
export PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin/:${PATH} # TBE operator compilation tool path
export PYTHONPATH=${TBE_IMPL_PATH}:${PYTHONPATH}
# Python library that TBE implementation depends on
After configuring the environment variables, execute the following Python script:
import numpy as np
from mindspore import Tensor
import mindspore.ops as ops
import mindspore.context as context
context.set_context(device_target="Ascend")
x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
print(ops.tensor_add(x, y))
The outputs should be the same as:
[[[ 2. 2. 2. 2.],
[ 2. 2. 2. 2.],
[ 2. 2. 2. 2.]],
[[ 2. 2. 2. 2.],
[ 2. 2. 2. 2.],
[ 2. 2. 2. 2.]],
[[ 2. 2. 2. 2.],
[ 2. 2. 2. 2.],
[ 2. 2. 2. 2.]]]
It means MindSpore has been installed successfully.
Using the following command if you need to update the MindSpore version:
pip install --upgrade mindspore-ascend
If you need to analyze information such as model scalars, graphs, computation graphs and model traceback, you can install MindInsight.
For more details, please refer to MindInsight.
If you need to conduct AI model security research or enhance the security of the model in you applications, you can install MindArmour.
For more details, please refer to MindArmour.
If you need to access and experience MindSpore pre-trained models quickly, you can install MindSpore Hub.
For more details, please refer to MindSpore Hub.
If you need to access and experience MindSpore online inference services quickly, you can install MindSpore Serving.
For more details, please refer to MindSpore Serving.
Provides videos for installation, helping users install MindSpore on different hardware platforms.
ModelArts helps users quickly create and deploy models and manage the entire AI workflow. Click ModelArts to use MindSpore.