MindSpore
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About
MindSpore is an open-source AI framework designed to provide development-friendly, efficient execution, and flexible deployment for AI engineers and data scientists. It supports multiple processor architectures and aims to foster a thriving AI software and hardware application ecosystem through community collaboration. MindSpore offers features such as distributed training, AI4S fusion computing, and full-scenario fast deployment.
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FAQs
安装MindSpore版本: GPU、CUDA 10.1、0.5.0-beta，出现问题: `cannot open shared object file:No such file or directory`。
从报错情况来看，是cuBLAS库没有找到。一般的情况下是cuBLAS库没有安装，或者是因为没有加入到环境变量中去。通常cuBLAS是随着CUDA以及驱动一起安装的，确认安装后把cuBLAS所在的目录加入`LD_LIBRARY_PATH`环境变量中即可。
使用pip安装时报错: `ERROR: mindspore_{VERSION}.whl is not a supported wheel on this platform`应该怎么办？
pip会通过wheel安装包的文件名来判断该安装包是否与当前Python环境兼容，因此，如果出现 `is not a supported wheel on this platform` 问题，请检查当前环境是否满足MindSpore安装要求，以及该MindSpore安装包版本是否正确。
macOS系统源码编译后使用pip安装报错: `ERROR: mindspore-{VERSION}.whl is not a supported wheel on this platform`应该怎么办？
首先检查output目录下编译得到的安装包名，类似mindspore-1.6.0-cp37-cp37m-macosx_11_1_x84_64.whl。包名中“11_1”的意思是编译时使用的SDK版本是11.1。如果使用的SDK版本为11.x，则可能是因为编译时使用的SDK版本过高导致无法安装。
使用pip安装时报错: `SSL:CERTIFICATE_VERIFY_FATLED`应该怎么办？
在pip安装命令后添加参数 `--trusted-host=ms-release.obs.cn-north-4.myhuaweicloud.com`重试即可。
MindSpore对Protobuf版本是否有特别要求？
MindSpore默认安装Protobuf的3.13.0版本，如果不是该版本，在使用pytest测试代码时日志中会产生很多告警，建议您使用命令`pip install protobuf==3.13.0`重新安装3.13.0版本。
使用pip安装依赖库时提示`No matching distribution found for XXX`错误，应该怎么办？
请执行`pip config list`，查看当前软件库索引路径`index-url`。某些情况下，软件库索引会出现更新滞后，可尝试设置其他软件库索引路径。
MindSpore网站安装页面找不到MindSpore Armour的whl包，无法安装怎么办？
您可以从[MindSpore网站下载地址](https://www.mindspore.cn/versions)下载whl包，通过`pip install`命令进行安装。
在ARM架构的环境上使用pip安装MindSpore时报错: `Could not find a version that satisfies the requirement`应该怎么办？
大概率是因为pip版本低于19.3，无法识别`manylinux2014`标签，导致pip install阶段下载了错误版本的`numpy`或`scipy`等python软件包，进而引发了无法找到构建依赖的问题，请执行`pip install --upgrade pip`将环境中的pip升级到19.3以上，重新安装MindSpore。
pip安装MindSpore时，报错 `Running setup.py install for pillow: finished with status 'error' ... The headers or library files could not be found for jpeg, ...`，应该怎么办？
MindSpore依赖三方库`pillow`进行部分的数据处理操作，而`pillow`需要依赖环境上已经安装`libjpeg`库，以Ubuntu环境为例，可以使用`sudo apt-get install libjpeg8-dev`来安装`libjpeg`库，然后再安装MindSpore。
ARM版macOS在Python3.8环境中编译的mindspore包安装后执行报错 `ImportError: dlopen ... no suitable image found. Did find:..._psutil_osx.cpython-38-darwin.so: mach-o, but wrong architecture`怎么办？
ARM版macOS上的Python3.8包含的psutil无法正确识别当前系统的架构，会自编译为适配x86架构的二进制，因此出现冲突问题。如果在Conda环境则执行`pip uninstall psutil; conda install psutil`，如果在非Conda环境中则执行`pip uninstall psutil; pip install --no-binary :all: psutil`以正确安装psutil。
ARM版macOS在安装MindSpore时，安装依赖库scipy报错 `error: metadata-generation-failed` 怎么办？
对应ARM版macOS的scipy在pypi源的版本仅适配MacOS 12以上版本的操作系统，低版本操作系统会自动下载scipy源码包进行编译，大概率会遇到编译失败问题。如果使用Conda环境，建议执行`pip uninstall scipy; conda install scipy`，MacOS 11系统强烈建议使用Conda以规避类似的兼容性问题。
Ascend硬件平台，在个人的Conda环境中，有时候出现报错RuntimeError: json.exception.parse_error.101 parse error at line 1, column 1: syntax error while parsing value - invalid literal; last read: 'T'，该怎么处理？
出现这种类型的报错，大概率是昇腾AI处理器配套软件包更新后个人的Conda环境中没有更新te或hccl工具包，可以将当前Conda环境中的上述几个工具包卸载，然后执行软件包中提供的环境变量配置脚本，即可配置PYTHONPATH，自动找到安装目录中上述软件包的最新版本，无需手动安装到Conda环境: `source /usr/local/Ascend/ascend-toolkit/set_env.sh`。
ARM版macOS在Python3.9环境中安装mindspore包执行报错 `ImportError: dlopen ... Library not loaded: @rpath/libffi.7.dylib`怎么办？
ARM版macOS上配置python3.9的conda环境时，在部分设备上没有自动安装libffi，导致该问题出现。手动执行安装命令`conda install libffi`即可修复。
MindSpore安装: 版本0.6.0-beta + Ascend 910 + Ubuntu_aarch64 + Python3.7.5，手动下载对应版本的whl包，编译并安装gmp6.1.2。其他Python库依赖已经安装完成，执行样例失败，报错显示找不到so文件。
`libdatatransfer.so`动态库是`fwkacllib/lib64`目录下的，请先在`/usr/local`目录查找到这个库所在的路径，然后把这个路径加到`LD_LIBRARY_PATH`环境变量中，确认设置生效后，再执行。
在Linux中已经安装了交叉编译工具，但是编译命令要怎么写呢？
arm64版本编译: `bash build.sh -I arm64`；arm32版本编译: `bash build.sh -I arm32`；注意要先设置环境变量，指定Android NDK路径: `export ANDROID_NDK=/path/to/android-ndk`，编译成功后，在output目录可以找到编译出的包。
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