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win11在WSL2虚拟环境中使用GPU开启训练任务

windows11更新

  • 点击设置->Windows更新->检查更新，先把系统更新到最新状态。

wsl指令知识总结

# 升级`wsl`软件至`wsl2`版本
wsl --install #升级后需要更新系统并重启

# 查看`wsl`软件版本以及`linux`内核版本：
wsl cat /proc/version
#Linux version 5.10.102.1-microsoft-standard-WSL2

#关闭系统：
wsl --shutdown
#取消注册或者卸载系统：
wsl --unregister Ubuntu-20.04
#重装：
wsl --install --distribution Ubuntu-20.04
#或者点击：开始->ubuntu图标

# 将分发系统导出后缀名为`TAR`的文件
wsl --export <Distribution Name> <FileName>
# wsl --export Ubuntu-20.04 Ubuntu-20.04.tar

# 将指定的tar包文件作为新发行版导入
wsl --import <Distribution Name> <InstallLocation> <FileName>
#wsl --import Ubuntu-20.04 D:\from_linux\wsl-ubuntu-tar Ubuntu-20.04.tar
  • wsl2安装过程中可能出现的问题以及解决方案
如果出现WslRegisterDistribution failed with error: 0x800701bc Error: 0x800701bc WSL 2 ?????????????????? https://aka.ms/wsl2kernel问题，
参考如下链接https://blog.csdn.net/qq_18625805/article/details/109732122
下载链接：https://wslstorestorage.blob.core.windows.net/wslblob/wsl_update_x64.msi，更新WSL2 Linux 内核

Ubuntu20.04软件更新步骤

  • 修改镜像源
#第一步：备份源文件：
sudo cp /etc/apt/sources.list /etc/apt/sources.list.backup

#第二步：编辑/etc/apt/sources.list文件
sudo vim /etc/apt/sources.list
  • 拷贝粘贴如下的国内镜像源
deb https://mirrors.ustc.edu.cn/ubuntu/ focal main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal main restricted universe multiverse
deb https://mirrors.ustc.edu.cn/ubuntu/ focal-updates main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-updates main restricted universe multiverse
deb https://mirrors.ustc.edu.cn/ubuntu/ focal-backports main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-backports main restricted universe multiverse
deb https://mirrors.ustc.edu.cn/ubuntu/ focal-security main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-security main restricted universe multiverse
deb https://mirrors.ustc.edu.cn/ubuntu/ focal-proposed main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-proposed main restricted universe multiverse
  • 软件更新升级
sudo apt update && sudo apt upgrade -y && sudo apt autoremove -y

wsl2虚拟机的Ubuntu20.04系统安装可视化的应用程序

  • 参考安装教程进行操作

vscode安装路径

C:\Users\zhuji\AppData\Local\Programs\Microsoft VS Code
#彻底卸载vscode方法：https://bbs.huaweicloud.com/blogs/254150

安装google chrome浏览器

sudo wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
sudo apt --fix-broken  -y install ./google-chrome-stable_current_amd64.deb

win11安装最新的nvidia驱动软件

参考https://www.nvidia.com/download/index.aspx选取516.59-notebook-win10-win11-64bit-international-dch-whql.exe软件进行安装升级。

在wsl2虚拟环境系统中安装不带显卡驱动的cuda软件包

  • 参考安装nvidia cuda
  • 其他cuda版本不带显卡的软件包安装教程参考
  • 以cuda11.7举例在wsl2环境中安装cuda软件包
sudo apt-key del 7fa2af80
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda-repo-wsl-ubuntu-11-7-local_11.7.0-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-11-7-local_11.7.0-1_amd64.deb
sudo apt-get update
sudo apt-get -y install cuda

  • 参考安装带nvidia-docker功能的docker软件

  • 赋予docker管理员权限

sudo usermod -aG docker ${USER}
# 该命令需要重启系统后才能生效
# sudo docker rm $(sudo docker ps -a -q) #删除已停止运行的容器。

深度学习训练任务在不同环境下的训练速度对比

  • wsl2虚拟机vslinuxvs本地win11

  • mobilenet v2

  • vgg-19

wsl2虚拟机本地gpu显卡性能benchmark测试

sudo docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
  • 输出结果
Unable to find image 'nvcr.io/nvidia/k8s/cuda-sample:nbody' locally
nbody: Pulling from nvidia/k8s/cuda-sample
11323ed2c653: Pull complete 
b6166589502e: Pull complete 
df6d4a51da82: Pull complete 
a65da20ce53d: Pull complete 
f02d6169d353: Pull complete 
56e9fab00773: Pull complete 
af3342639518: Pull complete 
95e5f8cb48e9: Pull complete 
ba0cb6713727: Pull complete 
Digest: sha256:fa0c8b471d223df44b82795dee54a7bc36d372fc5a2c7197f8df89e30f2abf48
Status: Downloaded newer image for nvcr.io/nvidia/k8s/cuda-sample:nbody
Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
        -fullscreen       (run n-body simulation in fullscreen mode)
        -fp64             (use double precision floating point values for simulation)
        -hostmem          (stores simulation data in host memory)
        -benchmark        (run benchmark to measure performance) 
        -numbodies=<N>    (number of bodies (>= 1) to run in simulation) 
        -device=<d>       (where d=0,1,2.... for the CUDA device to use)
        -numdevices=<i>   (where i=(number of CUDA devices > 0) to use for simulation)
        -compare          (compares simulation results running once on the default GPU and once on the CPU)
        -cpu              (run n-body simulation on the CPU)
        -tipsy=<file.bin> (load a tipsy model file for simulation)

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
GPU Device 0: "Ampere" with compute capability 8.6

> Compute 8.6 CUDA device: [NVIDIA GeForce RTX 3080 Laptop GPU]
49152 bodies, total time for 10 iterations: 46.505 ms
= 519.497 billion interactions per second
= 10389.942 single-precision GFLOP/s at 20 flops per interaction

训练yolov5

git clone https://github.com/ultralytics/yolov5.git
sudo apt install python3.8-venv
cd ~/workspace/algorithm/object_detection/yolo/yolov5
python3 -m venv ./venv_yolov5
source venv_yolov5/bin/activate #进入虚拟环境
python3 train.py --batch-size=16
deactivate #退出虚拟环境

wsl2高级选项配置

  • 编辑C:\Users\zhuji\.wslconfig配置文件
# Settings apply across all Linux distros running on WSL 2
[wsl2]
# guiapplications=false
# Limits VM memory to use no more than 4 GB, this can be set as whole numbers using GB or MB
memory=16GB 

# Sets the VM to use two virtual processors
processors=4

# Specify a custom Linux kernel to use with your installed distros. The default kernel used can be found at https://github.com/microsoft/WSL2-Linux-Kernel
# kernel=C:\\temp\\myCustomKernel

# Sets additional kernel parameters, in this case enabling older Linux base images such as Centos 6
kernelCommandLine = vsyscall=emulate

# Sets amount of swap storage space to 8GB, default is 25% of available RAM
swap=16GB

# Sets swapfile path location, default is %USERPROFILE%\AppData\Local\Temp\swap.vhdx
# swapfile=C:\\temp\\wsl-swap.vhdx

# Disable page reporting so WSL retains all allocated memory claimed from Windows and releases none back when free
pageReporting=false

# Turn off default connection to bind WSL 2 localhost to Windows localhost
localhostforwarding=true

# Disables nested virtualization
nestedVirtualization=false

# Turns on output console showing contents of dmesg when opening a WSL 2 distro for debugging
debugConsole=false
  • 参考编辑\\wsl.localhost\Ubuntu-20.04\etc\wsl.conf具体的虚拟机系统启动配置文件
# Set a command to run when a new WSL instance launches. This example starts the Docker container service.
[boot]
command = service docker start
  • 关闭所有wsl2开启的虚拟机系统，打开powershell终端，
# 查看目前正在运行的虚拟机系统
wsl -l --running
# 关闭所有的虚拟机系统
wsl --shutdow
# 再输入wsl -l --running查看是否还有虚拟机系统在运行，如果没有再启动虚拟机系统，则配置文件生效。

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