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<h1 class="entry-title"><a href="/blog/2024/01/15/aigc-setup-on-windows-wsl-2/">AIGC Setup on Win11 WSL2</a></h1>
<p class="meta">
<time datetime="2024-01-15T01:25:22+08:00" pubdate data-updated="true">Mon 2024-01-15 01:25</time>
</p>
</header>
<div class="entry-content"><p>看 <a href="https://github.com/01-ai/Yi">Yi</a>官方文档，一开始摸不着头脑，不知道从哪里入手。 网上找了一些资料，查到了苏洋的博客，先把环境搭建起来。</p>
<ul>
<li><a href="https://soulteary.com/2023/07/29/docker-based-deep-learning-environment-under-windows.html">基于 Docker 的深度学习环境：Windows</a></li>
<li><a href="https://soulteary.com/2023/07/29/get-started-with-stability-ai-sdxl-1-0-release-using-docker.html">使用 Docker 快速上手 Stability AI 的 SDXL 1.0 正式版-Linux</a></li>
</ul>
<p>为了在 Windows11 机器方便使用GPU，以及开源很多工程都提供docker入门，但WSL2慢，考虑本地已经搞了一个WSL1了会不会冲突，同时虚拟机里面也安装不了WSL2，VMWare桌面虚拟机的话直接使用GPU没有很好的方式等等，纠结了一天，最终还是选了安装 WSL2+Docker Desktop。</p>
<p>跟着文章，你将会了解Windows+WLS2+Docker怎么跑GPU模型，以及在国内怎么下载模型文件。</p>
<h2>使用WSL2</h2>
<p>在 启用或关闭Windows功能 中选择 虚拟机平台。</p>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
<span class='line-number'>2</span>
</pre></td><td class='code'><pre><code class=''><span class='line'>wsl --update
</span><span class='line'>wsl --set-default-version 2</span></code></pre></td></tr></table></div></figure>
<p>然后在微软商店Microsoft Store里面安装 <strong>Ubuntu-20.04</strong> （版本选20或者22）的系统（通过应用商店的话就规避了可能安装同一个的Linux的问题：已经安装 在应用商店的按钮不是[获取]是[打开]）。</p>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
<span class='line-number'>2</span>
<span class='line-number'>3</span>
<span class='line-number'>4</span>
<span class='line-number'>5</span>
<span class='line-number'>6</span>
<span class='line-number'>7</span>
<span class='line-number'>8</span>
<span class='line-number'>9</span>
<span class='line-number'>10</span>
<span class='line-number'>11</span>
<span class='line-number'>12</span>
<span class='line-number'>13</span>
<span class='line-number'>14</span>
<span class='line-number'>15</span>
<span class='line-number'>16</span>
</pre></td><td class='code'><pre><code class=''><span class='line'>winse@DESKTOP-BR4MG38:~$ cat /etc/os-release
</span><span class='line'>NAME="Ubuntu"
</span><span class='line'>VERSION="20.04.6 LTS (Focal Fossa)"
</span><span class='line'>ID=ubuntu
</span><span class='line'>ID_LIKE=debian
</span><span class='line'>PRETTY_NAME="Ubuntu 20.04.6 LTS"
</span><span class='line'>VERSION_ID="20.04"
</span><span class='line'>HOME_URL="https://www.ubuntu.com/"
</span><span class='line'>SUPPORT_URL="https://help.ubuntu.com/"
</span><span class='line'>BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
</span><span class='line'>PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
</span><span class='line'>VERSION_CODENAME=focal
</span><span class='line'>UBUNTU_CODENAME=focal
</span><span class='line'>
</span><span class='line'>winse@DESKTOP-BR4MG38:~$ uname -a
</span><span class='line'>Linux DESKTOP-BR4MG38 5.15.133.1-microsoft-standard-WSL2 #1 SMP Thu Oct 5 21:02:42 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux</span></code></pre></td></tr></table></div></figure>
<p>对比WSL1，WSL2的 <code>ip a</code> ，WSL2还是干净很多，把宿主机的一些信息合并到linux里面了（如：hosts）。</p>
<h2>Docker Desktop + WSL2</h2>
<ul>
<li><p><a href="https://docs.docker.com/desktop/install/windows-install/">https://docs.docker.com/desktop/install/windows-install/</a></p></li>
<li><p>微软的安装内容讲的差不多，增加了使用vscode的内容
<a href="https://learn.microsoft.com/en-us/windows/wsl/tutorials/wsl-containers#develop-in-remote-containers-using-vs-code">https://learn.microsoft.com/en-us/windows/wsl/tutorials/wsl-containers#develop-in-remote-containers-using-vs-code</a></p></li>
</ul>
<p>通过exe安装，安装过程中选择使用WSL2，装好后wsl显示多出了两个linux。</p>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
<span class='line-number'>2</span>
<span class='line-number'>3</span>
<span class='line-number'>4</span>
<span class='line-number'>5</span>
<span class='line-number'>6</span>
</pre></td><td class='code'><pre><code class=''><span class='line'>C:\Users\P15>wsl -l -v
</span><span class='line'> NAME STATE VERSION
</span><span class='line'>* Ubuntu Stopped 1
</span><span class='line'> Ubuntu-20.04 Running 2
</span><span class='line'> docker-desktop-data Running 2
</span><span class='line'> docker-desktop Running 2</span></code></pre></td></tr></table></div></figure>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
<span class='line-number'>2</span>
<span class='line-number'>3</span>
<span class='line-number'>4</span>
</pre></td><td class='code'><pre><code class=''><span class='line'>winse@DESKTOP-BR4MG38:~$ su -
</span><span class='line'>root@DESKTOP-BR4MG38:~# echo "winse ALL=(ALL:ALL) NOPASSWD: ALL" >>/etc/sudoers
</span><span class='line'>
</span><span class='line'>root@DESKTOP-BR4MG38:~# sed -i.bak -e 's|archive.ubuntu.com/ubuntu/|mirrors.aliyun.com/ubuntu/|' -e 's|security.ubuntu.com/ubuntu/|mirrors.aliyun.com/ubuntu/|' /etc/apt/sources.list
</span></code></pre></td></tr></table></div></figure>
<p>在 WSL-Ubuntu 里面可以直接用 Win11 的程序，直接查看docker的信息：</p>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
<span class='line-number'>2</span>
<span class='line-number'>3</span>
<span class='line-number'>4</span>
<span class='line-number'>5</span>
<span class='line-number'>6</span>
<span class='line-number'>7</span>
<span class='line-number'>8</span>
<span class='line-number'>9</span>
<span class='line-number'>10</span>
<span class='line-number'>11</span>
<span class='line-number'>12</span>
<span class='line-number'>13</span>
<span class='line-number'>14</span>
<span class='line-number'>15</span>
<span class='line-number'>16</span>
<span class='line-number'>17</span>
<span class='line-number'>18</span>
<span class='line-number'>19</span>
<span class='line-number'>20</span>
<span class='line-number'>21</span>
<span class='line-number'>22</span>
<span class='line-number'>23</span>
<span class='line-number'>24</span>
<span class='line-number'>25</span>
<span class='line-number'>26</span>
<span class='line-number'>27</span>
<span class='line-number'>28</span>
<span class='line-number'>29</span>
<span class='line-number'>30</span>
<span class='line-number'>31</span>
<span class='line-number'>32</span>
<span class='line-number'>33</span>
<span class='line-number'>34</span>
</pre></td><td class='code'><pre><code class=''><span class='line'>winse@DESKTOP-BR4MG38:~$ docker version
</span><span class='line'>Client: Docker Engine - Community
</span><span class='line'> Cloud integration: v1.0.35+desktop.5
</span><span class='line'> Version: 24.0.7
</span><span class='line'> API version: 1.43
</span><span class='line'> Go version: go1.20.10
</span><span class='line'> Git commit: afdd53b
</span><span class='line'> Built: Thu Oct 26 09:08:17 2023
</span><span class='line'> OS/Arch: linux/amd64
</span><span class='line'> Context: default
</span><span class='line'>
</span><span class='line'>Server: Docker Desktop
</span><span class='line'> Engine:
</span><span class='line'> Version: 24.0.7
</span><span class='line'> API version: 1.43 (minimum version 1.12)
</span><span class='line'> Go version: go1.20.10
</span><span class='line'> Git commit: 311b9ff
</span><span class='line'> Built: Thu Oct 26 09:08:02 2023
</span><span class='line'> OS/Arch: linux/amd64
</span><span class='line'> Experimental: false
</span><span class='line'> containerd:
</span><span class='line'> Version: 1.6.25
</span><span class='line'> GitCommit: d8f198a4ed8892c764191ef7b3b06d8a2eeb5c7f
</span><span class='line'> runc:
</span><span class='line'> Version: 1.1.10
</span><span class='line'> GitCommit: v1.1.10-0-g18a0cb0
</span><span class='line'> docker-init:
</span><span class='line'> Version: 0.19.0
</span><span class='line'> GitCommit: de40ad0
</span><span class='line'>
</span><span class='line'>winse@DESKTOP-BR4MG38:~$ which docker
</span><span class='line'>/usr/bin/docker
</span><span class='line'>winse@DESKTOP-BR4MG38:~$ ll /usr/bin/docker
</span><span class='line'>lrwxrwxrwx 1 root root 48 Jan 13 11:03 /usr/bin/docker -> /mnt/wsl/docker-desktop/cli-tools/usr/bin/docker*
</span></code></pre></td></tr></table></div></figure>
<p>其实用的就是windows的docker</p>
<p><img src="/images/blogs/ai/wsl2-docker-cli.png" alt="" /></p>
<p>镜像加速</p>
<p><img src="/images/blogs/ai/docker-mirror.png" alt="" /></p>
<p>保存会重启docker，再查看docker的信息，确认Registry Mirrors：</p>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
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</pre></td><td class='code'><pre><code class=''><span class='line'>winse@DESKTOP-BR4MG38:~$ docker info
</span><span class='line'>Client: Docker Engine - Community
</span><span class='line'> Version: 24.0.7
</span><span class='line'> Context: default
</span><span class='line'> Debug Mode: false
</span><span class='line'> Plugins:
</span><span class='line'> buildx: Docker Buildx (Docker Inc.)
</span><span class='line'> Version: v0.12.0-desktop.2
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-buildx
</span><span class='line'> compose: Docker Compose (Docker Inc.)
</span><span class='line'> Version: v2.23.3-desktop.2
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-compose
</span><span class='line'> dev: Docker Dev Environments (Docker Inc.)
</span><span class='line'> Version: v0.1.0
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-dev
</span><span class='line'> extension: Manages Docker extensions (Docker Inc.)
</span><span class='line'> Version: v0.2.21
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-extension
</span><span class='line'> feedback: Provide feedback, right in your terminal! (Docker Inc.)
</span><span class='line'> Version: 0.1
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-feedback
</span><span class='line'> init: Creates Docker-related starter files for your project (Docker Inc.)
</span><span class='line'> Version: v0.1.0-beta.10
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-init
</span><span class='line'> sbom: View the packaged-based Software Bill Of Materials (SBOM) for an image (Anchore Inc.)
</span><span class='line'> Version: 0.6.0
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-sbom
</span><span class='line'> scan: Docker Scan (Docker Inc.)
</span><span class='line'> Version: v0.26.0
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-scan
</span><span class='line'> scout: Docker Scout (Docker Inc.)
</span><span class='line'> Version: v1.2.0
</span><span class='line'> Path: /usr/local/lib/docker/cli-plugins/docker-scout
</span><span class='line'>
</span><span class='line'>Server:
</span><span class='line'> Containers: 1
</span><span class='line'> Running: 1
</span><span class='line'> Paused: 0
</span><span class='line'> Stopped: 0
</span><span class='line'> Images: 5
</span><span class='line'> Server Version: 24.0.7
</span><span class='line'> Storage Driver: overlay2
</span><span class='line'> Backing Filesystem: extfs
</span><span class='line'> Supports d_type: true
</span><span class='line'> Using metacopy: false
</span><span class='line'> Native Overlay Diff: true
</span><span class='line'> userxattr: false
</span><span class='line'> Logging Driver: json-file
</span><span class='line'> Cgroup Driver: cgroupfs
</span><span class='line'> Cgroup Version: 1
</span><span class='line'> Plugins:
</span><span class='line'> Volume: local
</span><span class='line'> Network: bridge host ipvlan macvlan null overlay
</span><span class='line'> Log: awslogs fluentd gcplogs gelf journald json-file local logentries splunk syslog
</span><span class='line'> Swarm: inactive
</span><span class='line'> Runtimes: io.containerd.runc.v2 runc
</span><span class='line'> Default Runtime: runc
</span><span class='line'> Init Binary: docker-init
</span><span class='line'> containerd version: d8f198a4ed8892c764191ef7b3b06d8a2eeb5c7f
</span><span class='line'> runc version: v1.1.10-0-g18a0cb0
</span><span class='line'> init version: de40ad0
</span><span class='line'> Security Options:
</span><span class='line'> seccomp
</span><span class='line'> Profile: unconfined
</span><span class='line'> Kernel Version: 5.15.133.1-microsoft-standard-WSL2
</span><span class='line'> Operating System: Docker Desktop
</span><span class='line'> OSType: linux
</span><span class='line'> Architecture: x86_64
</span><span class='line'> CPUs: 16
</span><span class='line'> Total Memory: 31.26GiB
</span><span class='line'> Name: docker-desktop
</span><span class='line'> ID: 340fee1c-e22a-485c-a973-f0e26d7535c9
</span><span class='line'> Docker Root Dir: /var/lib/docker
</span><span class='line'> Debug Mode: false
</span><span class='line'> HTTP Proxy: http.docker.internal:3128
</span><span class='line'> HTTPS Proxy: http.docker.internal:3128
</span><span class='line'> No Proxy: hubproxy.docker.internal
</span><span class='line'> Experimental: false
</span><span class='line'> Insecure Registries:
</span><span class='line'> hubproxy.docker.internal:5555
</span><span class='line'> 127.0.0.0/8
</span><span class='line'> Registry Mirrors:
</span><span class='line'> https://us69kjun.mirror.aliyuncs.com/
</span><span class='line'> https://docker.mirrors.ustc.edu.cn/
</span><span class='line'> https://hub-mirror.c.163.com/
</span><span class='line'> https://mirror.baidubce.com/
</span><span class='line'> Live Restore Enabled: false
</span><span class='line'>
</span><span class='line'>WARNING: No blkio throttle.read_bps_device support
</span><span class='line'>WARNING: No blkio throttle.write_bps_device support
</span><span class='line'>WARNING: No blkio throttle.read_iops_device support
</span><span class='line'>WARNING: No blkio throttle.write_iops_device support
</span><span class='line'>WARNING: daemon is not using the default seccomp profile</span></code></pre></td></tr></table></div></figure>
<h2>GPU</h2>
<h3>Driver</h3>
<p>根据Win11机器的显卡安装最新版本驱动（不要在WSL中安装任何Linux版的Nvidia驱动！）</p>
<p><a href="https://www.nvidia.com/Download/index.aspx">https://www.nvidia.com/Download/index.aspx</a></p>
<p>输入nvidia-smi，查验是否安装成功。WSL2里面啥都不用做，在WSL2命令行直接就能查看nvidia-smi。</p>
<p>启动docker也能一样查看</p>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
</pre></td><td class='code'><pre><code class=''><span class='line'>winse@DESKTOP-BR4MG38:stable-diffusion-taiyi$ docker run -it --rm --gpus all ubuntu nvidia-smi
</span></code></pre></td></tr></table></div></figure>
<p>其实这个启动的container也是一个WSL2。注意：WSL中不需要安装任何Linux版的Nvidia驱动！</p>
<p>验证 WLS2中Docker跑起来的容器 是否能够正常调用GPU：</p>
<ul>
<li><a href="https://soulteary.com/2023/07/29/docker-based-deep-learning-environment-under-windows.html">https://soulteary.com/2023/07/29/docker-based-deep-learning-environment-under-windows.html</a></li>
</ul>
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</pre></td><td class='code'><pre><code class=''><span class='line'>winse@DESKTOP-BR4MG38:~$ docker pull nvcr.io/nvidia/pytorch:23.07-py3
</span><span class='line'>23.07-py3: Pulling from nvidia/pytorch
</span><span class='line'>3153aa388d02: Pulling fs layer
</span><span class='line'>...
</span><span class='line'>ee3f0ae6e80f: Pull complete
</span><span class='line'>d4528227b5b8: Pull complete
</span><span class='line'>Digest: sha256:c53e8702a4ccb3f55235226dab29ef5d931a2a6d4d003ab47ca2e7e670f7922b
</span><span class='line'>Status: Downloaded newer image for nvcr.io/nvidia/pytorch:23.07-py3
</span><span class='line'>nvcr.io/nvidia/pytorch:23.07-py3
</span><span class='line'>
</span><span class='line'>What's Next?
</span><span class='line'> 1. Sign in to your Docker account → docker login
</span><span class='line'> 2. View a summary of image vulnerabilities and recommendations → docker scout quickview nvcr.io/nvidia/pytorch:23.07-py3
</span><span class='line'>
</span><span class='line'>
</span><span class='line'>winse@DESKTOP-BR4MG38:~$ docker run -it --gpus=all --rm nvcr.io/nvidia/pytorch:23.07-py3 nvidia-smi
</span><span class='line'>
</span><span class='line'>=============
</span><span class='line'>== PyTorch ==
</span><span class='line'>=============
</span><span class='line'>
</span><span class='line'>NVIDIA Release 23.07 (build 63867923)
</span><span class='line'>PyTorch Version 2.1.0a0+b5021ba
</span><span class='line'>
</span><span class='line'>Container image Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
</span><span class='line'>
</span><span class='line'>Copyright (c) 2014-2023 Facebook Inc.
</span><span class='line'>Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
</span><span class='line'>Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
</span><span class='line'>Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
</span><span class='line'>Copyright (c) 2011-2013 NYU (Clement Farabet)
</span><span class='line'>Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
</span><span class='line'>Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
</span><span class='line'>Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
</span><span class='line'>Copyright (c) 2015 Google Inc.
</span><span class='line'>Copyright (c) 2015 Yangqing Jia
</span><span class='line'>Copyright (c) 2013-2016 The Caffe contributors
</span><span class='line'>All rights reserved.
</span><span class='line'>
</span><span class='line'>Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved.
</span><span class='line'>
</span><span class='line'>This container image and its contents are governed by the NVIDIA Deep Learning Container License.
</span><span class='line'>By pulling and using the container, you accept the terms and conditions of this license:
</span><span class='line'>https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
</span><span class='line'>
</span><span class='line'>NOTE: The SHMEM allocation limit is set to the default of 64MB. This may be
</span><span class='line'> insufficient for PyTorch. NVIDIA recommends the use of the following flags:
</span><span class='line'> docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 ...
</span><span class='line'>
</span><span class='line'>Sat Jan 13 14:01:37 2024
</span><span class='line'>+---------------------------------------------------------------------------------------+
</span><span class='line'>| NVIDIA-SMI 535.146.01 Driver Version: 537.99 CUDA Version: 12.2 |
</span><span class='line'>|-----------------------------------------+----------------------+----------------------+
</span><span class='line'>| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
</span><span class='line'>| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
</span><span class='line'>| | | MIG M. |
</span><span class='line'>|=========================================+======================+======================|
</span><span class='line'>| 0 Quadro T2000 On | 00000000:01:00.0 On | N/A |
</span><span class='line'>| N/A 43C P8 6W / 60W | 856MiB / 4096MiB | 9% Default |
</span><span class='line'>| | | N/A |
</span><span class='line'>+-----------------------------------------+----------------------+----------------------+
</span><span class='line'>
</span><span class='line'>+---------------------------------------------------------------------------------------+
</span><span class='line'>| Processes: |
</span><span class='line'>| GPU GI CI PID Type Process name GPU Memory |
</span><span class='line'>| ID ID Usage |
</span><span class='line'>|=======================================================================================|
</span><span class='line'>| 0 N/A N/A 27 G /Xwayland N/A |
</span><span class='line'>| 0 N/A N/A 41 G /Xwayland N/A |
</span><span class='line'>| 0 N/A N/A 42 G /Xwayland N/A |
</span><span class='line'>+---------------------------------------------------------------------------------------+
</span><span class='line'>
</span><span class='line'>
</span><span class='line'>winse@DESKTOP-BR4MG38:~$ docker run --rm --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
</span><span class='line'>Unable to find image 'nvcr.io/nvidia/k8s/cuda-sample:nbody' locally
</span><span class='line'>nbody: Pulling from nvidia/k8s/cuda-sample
</span><span class='line'>22c5ef60a68e: Pull complete
</span><span class='line'>1939e4248814: Pull complete
</span><span class='line'>548afb82c856: Pull complete
</span><span class='line'>a424d45fd86f: Pull complete
</span><span class='line'>207b64ab7ce6: Pull complete
</span><span class='line'>f65423f1b49b: Pull complete
</span><span class='line'>2b60900a3ea5: Pull complete
</span><span class='line'>e9bff09d04df: Pull complete
</span><span class='line'>edc14edf1b04: Pull complete
</span><span class='line'>1f37f461c076: Pull complete
</span><span class='line'>9026fb14bf88: Pull complete
</span><span class='line'>Digest: sha256:59261e419d6d48a772aad5bb213f9f1588fcdb042b115ceb7166c89a51f03363
</span><span class='line'>Status: Downloaded newer image for nvcr.io/nvidia/k8s/cuda-sample:nbody
</span><span class='line'>Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
</span><span class='line'> -fullscreen (run n-body simulation in fullscreen mode)
</span><span class='line'> -fp64 (use double precision floating point values for simulation)
</span><span class='line'> -hostmem (stores simulation data in host memory)
</span><span class='line'> -benchmark (run benchmark to measure performance)
</span><span class='line'> -numbodies=<N> (number of bodies (>= 1) to run in simulation)
</span><span class='line'> -device=<d> (where d=0,1,2.... for the CUDA device to use)
</span><span class='line'> -numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
</span><span class='line'> -compare (compares simulation results running once on the default GPU and once on the CPU)
</span><span class='line'> -cpu (run n-body simulation on the CPU)
</span><span class='line'> -tipsy=<file.bin> (load a tipsy model file for simulation)
</span><span class='line'>
</span><span class='line'>NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
</span><span class='line'>
</span><span class='line'>> Windowed mode
</span><span class='line'>> Simulation data stored in video memory
</span><span class='line'>> Single precision floating point simulation
</span><span class='line'>> 1 Devices used for simulation
</span><span class='line'>GPU Device 0: "Turing" with compute capability 7.5
</span><span class='line'>
</span><span class='line'>> Compute 7.5 CUDA device: [Quadro T2000]
</span><span class='line'>16384 bodies, total time for 10 iterations: 64.071 ms
</span><span class='line'>= 41.897 billion interactions per second
</span><span class='line'>= 837.937 single-precision GFLOP/s at 20 flops per interaction
</span><span class='line'>
</span><span class='line'>
</span><span class='line'>#再跑一遍
</span><span class='line'>winse@DESKTOP-BR4MG38:~$ docker run --rm --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
</span><span class='line'>Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
</span><span class='line'> -fullscreen (run n-body simulation in fullscreen mode)
</span><span class='line'> -fp64 (use double precision floating point values for simulation)
</span><span class='line'> -hostmem (stores simulation data in host memory)
</span><span class='line'> -benchmark (run benchmark to measure performance)
</span><span class='line'> -numbodies=<N> (number of bodies (>= 1) to run in simulation)
</span><span class='line'> -device=<d> (where d=0,1,2.... for the CUDA device to use)
</span><span class='line'> -numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
</span><span class='line'> -compare (compares simulation results running once on the default GPU and once on the CPU)
</span><span class='line'> -cpu (run n-body simulation on the CPU)
</span><span class='line'> -tipsy=<file.bin> (load a tipsy model file for simulation)
</span><span class='line'>
</span><span class='line'>NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
</span><span class='line'>
</span><span class='line'>> Windowed mode
</span><span class='line'>> Simulation data stored in video memory
</span><span class='line'>> Single precision floating point simulation
</span><span class='line'>> 1 Devices used for simulation
</span><span class='line'>GPU Device 0: "Turing" with compute capability 7.5
</span><span class='line'>
</span><span class='line'>> Compute 7.5 CUDA device: [Quadro T2000]
</span><span class='line'>16384 bodies, total time for 10 iterations: 23.398 ms
</span><span class='line'>= 114.724 billion interactions per second
</span><span class='line'>= 2294.490 single-precision GFLOP/s at 20 flops per interaction
</span></code></pre></td></tr></table></div></figure>
<h3>WSL2 cuda-toolkit</h3>
<p>开发环境/运行环境
* <a href="https://zhuanlan.zhihu.com/p/555151725">https://zhuanlan.zhihu.com/p/555151725</a>
* <a href="https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-WSL2">https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-WSL2</a>
* <a href="https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_network">https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_network</a></p>
<p><img src="/images/blogs/ai/wsl2-cuda.png" alt="" /></p>
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</pre></td><td class='code'><pre><code class=''><span class='line'>wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
</span><span class='line'>sudo dpkg -i cuda-keyring_1.1-1_all.deb
</span><span class='line'>sudo apt-get update
</span><span class='line'>sudo apt-get -y install cuda-toolkit-12-3</span></code></pre></td></tr></table></div></figure>
<p>运行安装：</p>
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</pre></td><td class='code'><pre><code class=''><span class='line'>
</span><span class='line'>(demo_env) winse@DESKTOP-BR4MG38:ai$ wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
</span><span class='line'>--2024-01-14 23:53:22-- https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
</span><span class='line'>Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 152.199.39.144, 72.21.80.5, 72.21.80.6, ...
</span><span class='line'>Connecting to developer.download.nvidia.com (developer.download.nvidia.com)|152.199.39.144|:443... connected.
</span><span class='line'>HTTP request sent, awaiting response... 301 Moved Permanently
</span><span class='line'>Location: https://developer.download.nvidia.cn/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb [following]
</span><span class='line'>--2024-01-14 23:53:23-- https://developer.download.nvidia.cn/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
</span><span class='line'>Resolving developer.download.nvidia.cn (developer.download.nvidia.cn)... 59.36.216.26, 59.36.216.27, 175.4.58.180, ...
</span><span class='line'>Connecting to developer.download.nvidia.cn (developer.download.nvidia.cn)|59.36.216.26|:443... connected.
</span><span class='line'>HTTP request sent, awaiting response... 200 OK
</span><span class='line'>Length: 4328 (4.2K) [application/x-deb]
</span><span class='line'>Saving to: ‘cuda-keyring_1.1-1_all.deb’
</span><span class='line'>
</span><span class='line'>cuda-keyring_1.1-1_all.deb 100%[====================================================>] 4.23K --.-KB/s in 0s
</span><span class='line'>
</span><span class='line'>2024-01-14 23:53:23 (1.61 GB/s) - ‘cuda-keyring_1.1-1_all.deb’ saved [4328/4328]
</span><span class='line'>
</span><span class='line'>(demo_env) winse@DESKTOP-BR4MG38:ai$
</span><span class='line'>(demo_env) winse@DESKTOP-BR4MG38:ai$ sudo dpkg -i cuda-keyring_1.1-1_all.deb
</span><span class='line'>(demo_env) winse@DESKTOP-BR4MG38:ai$ sudo apt-get update
</span><span class='line'>(demo_env) winse@DESKTOP-BR4MG38:ai$ sudo apt-get -y install cuda-toolkit-12-3
</span></code></pre></td></tr></table></div></figure>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
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</pre></td><td class='code'><pre><code class=''><span class='line'>(base) winse@DESKTOP-BR4MG38:~$ vi .bashrc
</span><span class='line'>
</span><span class='line'>export PATH=/usr/local/cuda/bin:$PATH
</span></code></pre></td></tr></table></div></figure>
<p>新打开一个shell：</p>
<figure class='code'><div class="highlight"><table><tr><td class="gutter"><pre class="line-numbers"><span class='line-number'>1</span>
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</pre></td><td class='code'><pre><code class=''><span class='line'>(base) winse@DESKTOP-BR4MG38:~$ nvcc -V
</span><span class='line'>nvcc: NVIDIA (R) Cuda compiler driver
</span><span class='line'>Copyright (c) 2005-2023 NVIDIA Corporation
</span><span class='line'>Built on Wed_Nov_22_10:17:15_PST_2023
</span><span class='line'>Cuda compilation tools, release 12.3, V12.3.107
</span><span class='line'>Build cuda_12.3.r12.3/compiler.33567101_0</span></code></pre></td></tr></table></div></figure>
<h3>cuDNN</h3>
<p><a href="https://developer.nvidia.com/cudnn">https://developer.nvidia.com/cudnn</a></p>
<p>NVIDIA CUDA® Deep Neural Network library 支持神经网络的推理。</p>
<p>注册下载对应CUDA的版本 <a href="https://developer.nvidia.com/rdp/cudnn-download">https://developer.nvidia.com/rdp/cudnn-download</a></p>
<p>注意：如果不在WSL2-Ubuntu中直接使用cuDNN，后续通过容器直接拉取包含cuDNN的容器，就可以省略这一部分。</p>
<p><a href="https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-deb">https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-deb</a></p>
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</pre></td><td class='code'><pre><code class=''><span class='line'>#sudo dpkg -i cudnn-local-repo-ubuntu2004-8.9.6.50_1.0-1_amd64.deb
</span><span class='line'>#sudo dpkg -r cudnn-local-repo-ubuntu2004-8.9.6.50
</span><span class='line'>#sudo rm /etc/apt/sources.list.d/cudnn-local-ubuntu2004-8.9.6.50.list
</span><span class='line'>
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ sudo dpkg -i cudnn-local-repo-ubuntu2004-8.9.7.29_1.0-1_amd64.deb
</span><span class='line'>
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ sudo cp /var/cudnn-local-repo-ubuntu2004-8.9.7.29/cudnn-local-30472A84-keyring.gpg /usr/share/keyrings/
</span><span class='line'>
</span><span class='line'>
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ sudo apt install zlib1g
</span><span class='line'>
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ sudo apt update
</span><span class='line'>
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ apt search libcudnn8
</span><span class='line'>Sorting... Done
</span><span class='line'>Full Text Search... Done
</span><span class='line'>libcudnn8/unknown 8.9.7.29-1+cuda12.2 amd64
</span><span class='line'> cuDNN runtime libraries
</span><span class='line'>
</span><span class='line'>libcudnn8-dev/unknown 8.9.7.29-1+cuda12.2 amd64
</span><span class='line'> cuDNN development libraries and headers
</span><span class='line'>
</span><span class='line'>libcudnn8-samples/unknown 8.9.7.29-1+cuda12.2 amd64
</span><span class='line'> cuDNN samples
</span><span class='line'>
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ sudo apt install libcudnn8 libcudnn8-dev libcudnn8-samples
</span></code></pre></td></tr></table></div></figure>
<p>校验是否安装成功</p>
<p><a href="https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#verify">https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#verify</a></p>
<p>运行报错参考 <a href="https://forums.developer.nvidia.com/t/freeimage-is-not-set-up-correctly-please-ensure-freeimae-is-set-up-correctly/66950">https://forums.developer.nvidia.com/t/freeimage-is-not-set-up-correctly-please-ensure-freeimae-is-set-up-correctly/66950</a></p>
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</pre></td><td class='code'><pre><code class=''><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ cp -r /usr/src/cudnn_samples_v8 ./
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:i$ cd cudnn_samples_v8/mnistCUDNN/
</span><span class='line'>(base) winse@DESKTOP-BR4MG38:mnistCUDNN$