limour
/
limour-blog.github.io
mirror da https://dl.limour.top/cf/https://github.com/limour-blog/limour-blog.github.io.git


			
							123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741742
							

<!DOCTYPE html>
<html lang="en" data-default-color-scheme=auto>


<head><!-- hexo injector head_begin start -->
<script defer src="https://api.limour.top/vue/0d2f95c1-755d-436b-adf8-eee12a80ed32/script.js"></script>
<!-- hexo injector head_begin end -->
  <meta charset="UTF-8">
  <link rel="apple-touch-icon" sizes="76x76" href="https://img.limour.top/2023/08/29/64ee07361815a.webp">
  <link rel="icon" href="https://img.limour.top/2023/08/29/64ee07361815a.webp">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=5.0, shrink-to-fit=no">
  <meta http-equiv="x-ua-compatible" content="ie=edge">
  
  <meta name="theme-color" content="#2f4154">
  <meta name="author" content="Limour">
  <meta name="keywords" content="">
  
    <meta name="description" content="更新：不用编译了，直接装最新版就行，cuda 是可以兼容低版本驱动的，cuda-compat 似乎可以不装。之前运行检测失败是因为 HPC 上的环境变量没有指定驱动的 bin 和 lib 的路径。            先说结论，没有 root 权限的 HPC，千万不要想着用最新版本的 pytorch。自己编译也不行，因为已经不支持 cuda11.7 以下的版本了，而没有">
  
  
  <title>【记录】在 Slurm 平台的GPU集群上使用 Pytorch - Limour&#39;s Blog</title>

  <link  rel="stylesheet" href="https://jscdn.limour.top/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" />


  <link  rel="stylesheet" href="https://jscdn.limour.top/npm/github-markdown-css@4.0.0/github-markdown.min.css" />

  <link  rel="stylesheet" href="https://jscdn.limour.top/npm/hint.css@2.7.0/hint.min.css" />


<!-- 主题依赖的图标库，不要自行修改 -->
<!-- Do not modify the link that theme dependent icons -->

<link rel="stylesheet" href="//at.alicdn.com/t/c/font_1749284_5i9bdhy70f8.css">


<link rel="stylesheet" href="//at.alicdn.com/t/font_1736178_lbnruvf0jn.css">


<link  rel="stylesheet" href="/css/main.css" />


  <link id="highlight-css" rel="stylesheet" href="/css/highlight.css" />
  
    <link id="highlight-css-dark" rel="stylesheet" href="/css/highlight-dark.css" />
  

<link rel="stylesheet" href="/theme-inject/custom.css">
<link rel="stylesheet" href="/theme-inject/iconfont.css">


  <script id="fluid-configs">
    var Fluid = window.Fluid || {};
    Fluid.ctx = Object.assign({}, Fluid.ctx)
    var CONFIG = {"hostname":"hexo.limour.top","root":"/","version":"1.9.8","typing":{"enable":false,"typeSpeed":70,"cursorChar":"_","loop":false,"scope":[]},"anchorjs":{"enable":true,"element":"h1,h2,h3,h4,h5,h6","placement":"left","visible":"hover","icon":"§"},"progressbar":{"enable":true,"height_px":3,"color":"#29d","options":{"showSpinner":false,"trickleSpeed":100}},"code_language":{"enable":true,"default":"TEXT"},"copy_btn":true,"image_caption":{"enable":true},"image_zoom":{"enable":false,"img_url_replace":["",""]},"toc":{"enable":true,"placement":"right","headingSelector":"h1,h2,h3,h4,h5,h6","collapseDepth":0},"lazyload":{"enable":true,"loading_img":"https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg","onlypost":false,"offset_factor":2},"web_analytics":{"enable":false,"follow_dnt":true,"baidu":null,"google":{"measurement_id":null},"tencent":{"sid":null,"cid":null},"leancloud":{"app_id":null,"app_key":null,"server_url":null,"path":"window.location.pathname","ignore_local":false},"umami":{"src":null,"website_id":null,"domains":null,"start_time":"2024-01-01T00:00:00.000Z","token":null,"api_server":null},"woyaola":null,"cnzz":null},"search_path":"/local-search.xml","include_content_in_search":true};

    if (CONFIG.web_analytics.follow_dnt) {
      var dntVal = navigator.doNotTrack || window.doNotTrack || navigator.msDoNotTrack;
      Fluid.ctx.dnt = dntVal && (dntVal.startsWith('1') || dntVal.startsWith('yes') || dntVal.startsWith('on'));
    }
  </script>
  <script  src="/js/utils.js" ></script>
  <script  src="/js/color-schema.js" ></script>
  

  <link rel="canonical" href="https://hexo.limour.top/-ji-lu--zai--Slurm--ping-tai-de-GPU-ji-qun-shang-shi-yong--Pytorch"/>
<meta name="generator" content="Hexo 7.1.1"><link rel="alternate" href="/atom.xml" title="Limour's Blog" type="application/atom+xml">
<link rel="alternate" href="/rss2.xml" title="Limour's Blog" type="application/rss+xml">
</head>


<body>
  

  <header>
    

<div class="header-inner" style="height: 70vh;">
  <nav id="navbar" class="navbar fixed-top  navbar-expand-lg navbar-dark scrolling-navbar">
  <div class="container">
    <a class="navbar-brand" href="/">
      <strong>Limour&#39;s Blog</strong>
    </a>

    <button id="navbar-toggler-btn" class="navbar-toggler" type="button" data-toggle="collapse"
            data-target="#navbarSupportedContent"
            aria-controls="navbarSupportedContent" aria-expanded="false" aria-label="Toggle navigation">
      <div class="animated-icon"><span></span><span></span><span></span></div>
    </button>

    <!-- Collapsible content -->
    <div class="collapse navbar-collapse" id="navbarSupportedContent">
      <ul class="navbar-nav ml-auto text-center">
        
          
            <li class="nav-item">
              <a class="nav-link" href="https://hexo.limour.top/" target="_self">
                <i class="iconfont icon-home-fill"></i>
                <span>Home</span>
              </a>
            </li>
          
        
            <li class="nav-item">
              <a class="nav-link" href="/archives/" target="_self">
                <i class="iconfont icon-archive-fill"></i>
                <span>Archive1</span>
              </a>
            </li>
          
        
            <li class="nav-item">
              <a class="nav-link" href="https://occdn.limour.top/archives/" target="_self">
                <i class="iconfont icon-archive-fill"></i>
                <span>Archive2</span>
              </a>
            </li>
          
        
            <li class="nav-item">
              <a class="nav-link" href="https://b.limour.top/archives/" target="_self">
                <i class="iconfont icon-archive-fill"></i>
                <span>Archive3</span>
              </a>
            </li>
          
        
            <li class="nav-item">
              <a class="nav-link" href="https://od.limour.top/" target="_self">
                <i class="iconfont icon-onedrive"></i>
                <span>Alist</span>
              </a>
            </li>
          
        
            <li class="nav-item">
              <a class="nav-link" href="https://orcid.org/0000-0001-8897-1685" target="_self">
                <i class="iconfont icon-orcid"></i>
                <span>Orcid</span>
              </a>
            </li>
          
        
            <li class="nav-item">
              <a class="nav-link" href="/links/" target="_self">
                <i class="iconfont icon-link-fill"></i>
                <span>Links</span>
              </a>
            </li>
          
        
            <li class="nav-item">
              <a class="nav-link" href="/atom.xml" target="_self">
                <i class="iconfont icon-rss"></i>
                <span>RSS</span>
              </a>
            </li>
          
        
          <li class="nav-item" id="search-btn">
            <a class="nav-link" target="_self" href="javascript:;" data-toggle="modal" data-target="#modalSearch" aria-label="Search">
              <i class="iconfont icon-search"></i>
            </a>
          </li>
          
        
          <li class="nav-item" id="color-toggle-btn">
            <a class="nav-link" target="_self" href="javascript:;" aria-label="Color Toggle">
              <i class="iconfont icon-dark" id="color-toggle-icon"></i>
            </a>
          </li>
        
      </ul>
    </div>
  </div>
</nav>

  
<div id="banner" class="banner" parallax=true
     style="background: url('https://img.limour.top/2023/08/29/64ee08e108638.webp') no-repeat center center; background-size: cover;">
  <div class="full-bg-img">
    <div class="mask flex-center" style="background-color: rgba(0, 0, 0, 0.3)">
      <div class="banner-text text-center fade-in-up">
        <div class="h2">
          
            <span id="subtitle">【记录】在 Slurm 平台的GPU集群上使用 Pytorch</span>
          
        </div>

        
  <div class="mt-3">
    
      <span class="post-meta mr-2">
        <i class="iconfont icon-author" aria-hidden="true"></i>
        Limour
      </span>
    
    
      <span class="post-meta">
        <i class="iconfont icon-date-fill" aria-hidden="true"></i>
        <time datetime="2023-09-06 20:24" pubdate>
          September 6, 2023 pm
        </time>
      </span>
    
  </div>

  <div class="mt-1">
    
      <span class="post-meta mr-2">
        <i class="iconfont icon-chart"></i>
        
          1.9k words
        
      </span>
    

      <span class="post-meta mr-2">
        <i class="iconfont icon-clock-fill"></i>
        
        
          16 mins
        
      </span>
    

  </div>


      </div>

      
    </div>
  </div>
</div>

</div>

  </header>

  <main>
    
      
<div class="container-fluid nopadding-x">
  <div class="row nomargin-x">
    <div class="side-col d-none d-lg-block col-lg-2">
      

    </div>

    <div class="col-lg-8 nopadding-x-md">
      <div class="container nopadding-x-md" id="board-ctn">
        <div id="board">
          <article class="post-content mx-auto">
            <h1 id="seo-header">【记录】在 Slurm 平台的GPU集群上使用 Pytorch</h1>
            
              <p id="updated-time" class="note note-info" style="">
                
                  
                    Last updated on March 19, 2024 pm
                  
                
              </p>
            
            
              <div class="markdown-body">
                
                <div class="note note-info">
            <p>更新：不用编译了，直接装最新版就行，cuda 是可以<a href="https://hexo.limour.top/go/#aHR0cHM6Ly9kb2NzLm52aWRpYS5jb20vZGVwbG95L2N1ZGEtY29tcGF0aWJpbGl0eS8jdXNlLXRoZS1yaWdodC1jb21wYXQtcGFja2FnZQ==" rel="noopener external nofollow noreferrer">兼容</a>低版本驱动的，cuda-compat 似乎可以不装。之前运行检测失败是因为 HPC 上的环境变量没有指定驱动的 bin 和 lib 的路径。</p>
          </div>
<p>先说结论，没有 root 权限的 HPC，千万不要想着用最新版本的 pytorch。自己编译也不行，因为已经不支持 cuda11.7 以下的版本了，而没有 root 权限，既改不了驱动，也装不了 cuda-compat，不要折腾了。</p>
<h2 id="更换-conda">更换 conda</h2>
<ul>
<li>安装 <a href="/-ji-lu--an-zhuang-conda-bing-geng-huan-qing-hua-yuan">conda</a></li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-comment"># 千万不要用系统提供的 conda</span><br><span class="hljs-comment"># 一定要自己重新 init 一个</span><br><span class="hljs-comment"># 因为除了自己的 home 目录会正确映射到集群的节点上</span><br><span class="hljs-comment"># 其他的目录很可能不正确</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br>conda clean --all<br>conda install -y nano<br></code></pre></td></tr></table></figure>
<h4 id="更换编译器">更换编译器</h4>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=install-nvidia-gxx</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 每个节点所运行的进程数为1</span><br><span class="hljs-comment">#SBATCH --ntasks-per-node=1</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-comment">## https://gist.github.com/ax3l/9489132 查看对应 nvcc 支持的 g++ 版本</span><br>conda create -y -n gcc -c conda-forge gcc=9.5.0<br><span class="hljs-built_in">source</span> activate gcc<br>conda install -c conda-forge gxx=9.5.0<br>gcc -v<br>g++ -v<br></code></pre></td></tr></table></figure>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-built_in">source</span> ~/.bashrc<br>conda run -n gcc whereis gcc<br><span class="hljs-comment"># ~/miniconda3/envs/gcc/bin/gcc</span><br>conda run -n gcc whereis g++<br><span class="hljs-comment"># ~/miniconda3/envs/gcc/bin/g++</span><br></code></pre></td></tr></table></figure>
<h2 id="检测驱动版本">检测驱动版本</h2>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=test-nvidia</span><br><br><span class="hljs-comment">### 指定该作业的运行分区，sinfo 获取分区列表</span><br><span class="hljs-comment">#SBATCH --partition=body</span><br><br><span class="hljs-comment">### 指定申请的节点</span><br><span class="hljs-comment">#SBATCH --nodelist=gpu4</span><br><br><span class="hljs-comment">### 排除指定的节点；</span><br><span class="hljs-comment">#SBATCH --exclude=gpu1</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 该作业需要1个CPU</span><br><span class="hljs-comment">#SBATCH --ntasks=1</span><br><br><span class="hljs-comment">### 申请1块GPU卡</span><br><span class="hljs-comment">#SBATCH --gres=gpu:1</span><br><br><span class="hljs-comment">### 作业最大的运行时间，超过时间后作业资源会被SLURM回收</span><br><span class="hljs-comment">#SBATCH --time=0:05:00</span><br><br><span class="hljs-comment">### 指定从哪个项目扣费。如果没有这条参数，则从个人账户扣费</span><br><span class="hljs-comment">#SBATCH --comment public_cluster</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br>lspci | grep VGA<br><span class="hljs-comment">### modinfo nvidia</span><br><span class="hljs-built_in">cat</span> /proc/driver/nvidia/version <br><span class="hljs-comment">### /opt/app/cuda/11.2/extras/demo_suite/bandwidthTest</span><br></code></pre></td></tr></table></figure>
<h2 id="安装-pytorch">安装 pytorch</h2>
<h3 id="方式一-conda">方式一 conda</h3>
<h4 id="新版本">新版本</h4>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=install-nvidia</span><br><br><span class="hljs-comment">### 指定该作业的运行分区，sinfo 获取分区列表</span><br><span class="hljs-comment">#SBATCH --partition=body</span><br><br><span class="hljs-comment">### 指定申请的节点</span><br><span class="hljs-comment">#SBATCH --nodelist=gpu4</span><br><br><span class="hljs-comment">### 排除指定的节点；</span><br><span class="hljs-comment">#SBATCH --exclude=gpu1</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 该作业需要1个CPU</span><br><span class="hljs-comment">#SBATCH --ntasks=1</span><br><br><span class="hljs-comment">### 申请1块GPU卡</span><br><span class="hljs-comment">#SBATCH --gres=gpu:1</span><br><br><span class="hljs-comment">### 指定从哪个项目扣费。如果没有这条参数，则从个人账户扣费</span><br><span class="hljs-comment">#SBATCH --comment public_cluster</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-built_in">source</span> activate gcc<br>conda create -y -n gpu pytorch-cuda=11.8 -c pytorch -c nvidia<br>conda install -y -n gpu pytorch -c pytorch -c nvidia<br>conda install -y -n gpu torchvision torchaudio -c pytorch -c nvidia<br></code></pre></td></tr></table></figure>
<h4 id="旧版本">旧版本</h4>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=install-nvidia</span><br><br><span class="hljs-comment">### 指定该作业的运行分区，sinfo 获取分区列表</span><br><span class="hljs-comment">#SBATCH --partition=body</span><br><br><span class="hljs-comment">### 指定申请的节点</span><br><span class="hljs-comment">#SBATCH --nodelist=gpu4</span><br><br><span class="hljs-comment">### 排除指定的节点；</span><br><span class="hljs-comment">#SBATCH --exclude=gpu1</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 该作业需要1个CPU</span><br><span class="hljs-comment">#SBATCH --ntasks=1</span><br><br><span class="hljs-comment">### 申请1块GPU卡</span><br><span class="hljs-comment">#SBATCH --gres=gpu:1</span><br><br><span class="hljs-comment">### 指定从哪个项目扣费。如果没有这条参数，则从个人账户扣费</span><br><span class="hljs-comment">#SBATCH --comment public_cluster</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-built_in">source</span> activate gcc<br>conda create -y -n gpu python=3.9 pytorch=1.10.2=py3.9_cuda11.1_cudnn8.0.5_0 torchvision=*=py39_cu111 torchaudio=*=py39_cu111 cudatoolkit=11.1 -c pytorch<br></code></pre></td></tr></table></figure>
<h3 id="方式二-编译安装">方式二 编译安装</h3>
<p>折腾了几天，发现 cuda11.2 不支持高于 10.0 的 gcc，而低版本的 gcc 在 ninja 上会报错，以后再折腾吧。</p>
<h4 id="获取源码">获取源码</h4>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-comment"># wget cuda -O -| tar -xf -</span><br><span class="hljs-comment"># wget cudnn -O -| tar -xf -</span><br>git <span class="hljs-built_in">clone</span> --depth=1 --recursive https://github.com/pytorch/pytorch<br></code></pre></td></tr></table></figure>
<blockquote>
<p>对于子模块，可以先不要在<code>git clone</code>的时候加上<code>--recursive</code>，等主体部分下载完之后，该文件夹中有个隐藏文件称为：<code>.gitmodules</code>，把子项目中的<code>url</code>地址同样加上<code>.cnpmjs.org</code>后缀，然后利用<code>git submodule sync</code>更新子项目对应的url，最后再<code>git submodule update --init --recursive</code>，即可正常网速clone完所有子项目</p>
</blockquote>
<blockquote>
<p>如果集群无法访问 GitHub，可以先获取源码后，<code>tar -zcPf /root/tmp/pytorch.tar.gz pytorch</code> 打包，<a href="/Rclone-bei-fen-VPS-shu-ju-dao-onedrive">上传到集群</a>，<code>tar -zxf pytorch.tar.gz</code> 解包。</p>
</blockquote>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=ungzip-nvidia</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 每个节点所运行的进程数为1</span><br><span class="hljs-comment">#SBATCH --ntasks-per-node=1</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-built_in">cd</span> ~<br>tar -zxf pytorch.tar.gz<br></code></pre></td></tr></table></figure>
<h4 id="进行编译">进行编译</h4>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=install-nvidia</span><br><br><span class="hljs-comment">### 指定该作业的运行分区，sinfo 获取分区列表</span><br><span class="hljs-comment">#SBATCH --partition=body</span><br><br><span class="hljs-comment">### 指定申请的节点</span><br><span class="hljs-comment">#SBATCH --nodelist=gpu4</span><br><br><span class="hljs-comment">### 排除指定的节点；</span><br><span class="hljs-comment">#SBATCH --exclude=gpu1</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 该作业需要4个CPU</span><br><span class="hljs-comment">#SBATCH --ntasks=4</span><br><br><span class="hljs-comment">### 申请1块GPU卡</span><br><span class="hljs-comment">#SBATCH --gres=gpu:1</span><br><br><span class="hljs-comment">### 指定从哪个项目扣费。如果没有这条参数，则从个人账户扣费</span><br><span class="hljs-comment">#SBATCH --comment public_cluster</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br>conda create -y -n gpu python=3.10 -c conda-forge<br><span class="hljs-built_in">source</span> activate gpu<br>conda install mkl mkl-include -c conda-forge<br>conda install cmake -c conda-forge<br>conda install ninja=1.11.1 -c conda-forge <span class="hljs-comment">## 确保是最新版本，不然报错</span><br><span class="hljs-built_in">cd</span> ~/pytorch/<br>pip install -r requirements.txt<br><span class="hljs-built_in">export</span> CMAKE_PREFIX_PATH=<span class="hljs-variable">$&#123;CONDA_PREFIX:-&quot;$(dirname $(which conda))/../&quot;&#125;</span><br><span class="hljs-built_in">export</span> USE_CUDA=1<br><span class="hljs-built_in">export</span> USE_CUDNN=1<br>CUDNN_HOME=~/cuda/cudnn-linux-x86_64-8.9.4.25_cuda11-archive<br><span class="hljs-built_in">export</span> CUDNN_LIB_DIR=<span class="hljs-variable">$CUDNN_HOME</span>/lib<br><span class="hljs-built_in">export</span> CUDNN_INCLUDE_DIR=<span class="hljs-variable">$CUDNN_HOME</span>/include<br><span class="hljs-built_in">export</span> CUDNN_LIBRARY=<span class="hljs-variable">$CUDNN_HOME</span>/lib/libcudnn.so<br><span class="hljs-built_in">export</span> CUDNN_LIBRARY_PATH=<span class="hljs-variable">$CUDNN_LIBRARY</span><br><span class="hljs-built_in">export</span> CUDA_HOME=/opt/app/cuda/11.2<br><span class="hljs-built_in">export</span> CMAKE_CUDA_COMPILER=<span class="hljs-variable">$CUDA_HOME</span>/bin/nvcc<br><span class="hljs-built_in">export</span> CMAKE_CUDA_ARCHITECTURES=<span class="hljs-string">&quot;60;70;75;80&quot;</span><br><span class="hljs-built_in">export</span> TORCH_CUDA_ARCH_LIST=<span class="hljs-string">&quot;6.0;7.0;7.5;8.0&quot;</span><br>GCC_HOME=~/miniconda3/envs/gcc<br><span class="hljs-built_in">export</span> CUDAHOSTCXX=<span class="hljs-variable">$GCC_HOME</span>/bin/g++<br><span class="hljs-built_in">export</span> CC=<span class="hljs-variable">$GCC_HOME</span>/bin/gcc<br><span class="hljs-built_in">export</span> CXX=<span class="hljs-variable">$GCC_HOME</span>/bin/g++<br><span class="hljs-built_in">export</span> PATH=<span class="hljs-variable">$CUDA_HOME</span>/bin:<span class="hljs-variable">$PATH</span><br><span class="hljs-built_in">export</span> LD_LIBRARY_PATH=<span class="hljs-variable">$CUDA_HOME</span>/lib64:<span class="hljs-variable">$CUDA_HOME</span>/extras/CUPTI/lib64:<span class="hljs-variable">$CUDNN_LIB_DIR</span>:<span class="hljs-variable">$LD_LIBRARY_PATH</span><br><span class="hljs-built_in">rm</span> -rf build<br>python setup.py develop -allow-unsupported-compiler<br></code></pre></td></tr></table></figure>
<h2 id="检测-pytorch">检测 pytorch</h2>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-built_in">source</span> ~/.bashrc<br>conda run -n gpu whereis python<br><span class="hljs-comment">## /home/uxxx/miniconda3/envs/gpu/bin/python</span><br>nano test.py &amp;&amp; <span class="hljs-built_in">chmod</span> +x test.py<br></code></pre></td></tr></table></figure>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><code class="hljs python"><span class="hljs-comment">#!/home/uxxx/miniconda3/envs/gpu/bin/python</span><br><br><span class="hljs-comment"># 检测CUDA是否安装正确并能被Pytorch检测</span><br><span class="hljs-keyword">import</span> torch <span class="hljs-comment"># 如果pytorch安装成功即可导入</span><br><span class="hljs-built_in">print</span>(torch.cuda.is_available()) <span class="hljs-comment"># 查看CUDA是否可用</span><br><span class="hljs-built_in">print</span>(torch.cuda.device_count()) <span class="hljs-comment"># 查看可用的CUDA数量</span><br><span class="hljs-built_in">print</span>(torch.version.cuda) <span class="hljs-comment"># 查看CUDA的版本号</span><br><br><span class="hljs-comment"># 检测能否调用CUDA加速</span><br>a = torch.Tensor(<span class="hljs-number">5</span>,<span class="hljs-number">3</span>)<br>a = a.cuda()<br><span class="hljs-built_in">print</span>(a)<br></code></pre></td></tr></table></figure>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=test-nvidia</span><br><br><span class="hljs-comment">### 指定该作业的运行分区，sinfo 获取分区列表</span><br><span class="hljs-comment">#SBATCH --partition=body</span><br><br><span class="hljs-comment">### 指定申请的节点</span><br><span class="hljs-comment">#SBATCH --nodelist=gpu4</span><br><br><span class="hljs-comment">### 排除指定的节点；</span><br><span class="hljs-comment">#SBATCH --exclude=gpu1</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 该作业需要1个CPU</span><br><span class="hljs-comment">#SBATCH --ntasks=1</span><br><br><span class="hljs-comment">### 申请1块GPU卡</span><br><span class="hljs-comment">#SBATCH --gres=gpu:1</span><br><br><span class="hljs-comment">### 指定从哪个项目扣费。如果没有这条参数，则从个人账户扣费</span><br><span class="hljs-comment">#SBATCH --comment public_cluster</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-built_in">source</span> activate gpu<br><span class="hljs-built_in">cd</span> ~<br>GCC_HOME=~/miniconda3/envs/gcc<br><span class="hljs-built_in">export</span> CC=<span class="hljs-variable">$GCC_HOME</span>/bin/gcc<br><span class="hljs-built_in">export</span> CXX=<span class="hljs-variable">$GCC_HOME</span>/bin/g++<br>DRIVER_HOME=/opt/app/nvidia/460.91.03<br><span class="hljs-built_in">export</span> PATH=<span class="hljs-variable">$DRIVER_HOME</span>/bin:<span class="hljs-variable">$PATH</span> <span class="hljs-comment"># 重要</span><br><span class="hljs-built_in">export</span> LD_LIBRARY_PATH=<span class="hljs-variable">$DRIVER_HOME</span>/lib:<span class="hljs-variable">$LD_LIBRARY_PATH</span> <span class="hljs-comment"># 重要</span><br><br>python ./test.py<br></code></pre></td></tr></table></figure>
<h2 id="附加-安装-ComfyUI">附加: 安装 ComfyUI</h2>
<h3 id="安装-xformers">安装 xformers</h3>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=install-nvidia-xformers</span><br><br><span class="hljs-comment">### 指定该作业的运行分区，sinfo 获取分区列表</span><br><span class="hljs-comment">#SBATCH --partition=body</span><br><br><span class="hljs-comment">### 指定申请的节点</span><br><span class="hljs-comment">#SBATCH --nodelist=gpu4</span><br><br><span class="hljs-comment">### 排除指定的节点；</span><br><span class="hljs-comment">#SBATCH --exclude=gpu1</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 该作业需要1个CPU</span><br><span class="hljs-comment">#SBATCH --ntasks=1</span><br><br><span class="hljs-comment">### 申请1块GPU卡</span><br><span class="hljs-comment">#SBATCH --gres=gpu:1</span><br><br><span class="hljs-comment">### 指定从哪个项目扣费。如果没有这条参数，则从个人账户扣费</span><br><span class="hljs-comment">#SBATCH --comment public_cluster</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-built_in">source</span> activate gpu<br><span class="hljs-built_in">cd</span> ~<br>GCC_HOME=~/miniconda3/envs/gcc<br><span class="hljs-built_in">export</span> CC=<span class="hljs-variable">$GCC_HOME</span>/bin/gcc<br><span class="hljs-built_in">export</span> CXX=<span class="hljs-variable">$GCC_HOME</span>/bin/g++<br>DRIVER_HOME=/opt/app/nvidia/460.91.03<br><span class="hljs-built_in">export</span> PATH=<span class="hljs-variable">$DRIVER_HOME</span>/bin:<span class="hljs-variable">$PATH</span> <span class="hljs-comment"># 重要</span><br><span class="hljs-built_in">export</span> LD_LIBRARY_PATH=<span class="hljs-variable">$DRIVER_HOME</span>/lib:<span class="hljs-variable">$LD_LIBRARY_PATH</span> <span class="hljs-comment"># 重要</span><br><br>wget https://github.com/comfyanonymous/ComfyUI/archive/refs/heads/master.zip -O comfyUI.zip<br>unzip comfyUI.zip <br><span class="hljs-built_in">cd</span> ComfyUI-master<br>pip install xformers<br>pip install -r requirements.txt<br></code></pre></td></tr></table></figure>
<h3 id="启动-comfyUI">启动 comfyUI</h3>
<ul>
<li><a href="/-ji-lu--an-zhuang-npsfrp-fu-wu-duan-yu-ke-hu-duan">安装内网穿透</a></li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><br><span class="hljs-comment">### 设置该作业的作业名</span><br><span class="hljs-comment">#SBATCH --job-name=run-nvidia</span><br><br><span class="hljs-comment">### 指定该作业的运行分区，sinfo 获取分区列表</span><br><span class="hljs-comment">#SBATCH --partition=body</span><br><br><span class="hljs-comment">### 指定申请的节点</span><br><span class="hljs-comment">#SBATCH --nodelist=gpu4</span><br><br><span class="hljs-comment">### 排除指定的节点；</span><br><span class="hljs-comment">#SBATCH --exclude=gpu1</span><br><br><span class="hljs-comment">### 指定该作业需要1个节点数</span><br><span class="hljs-comment">#SBATCH --nodes=1</span><br><br><span class="hljs-comment">### 该作业需要4个CPU</span><br><span class="hljs-comment">#SBATCH --ntasks=4</span><br><br><span class="hljs-comment">### 申请1块GPU卡</span><br><span class="hljs-comment">#SBATCH --gres=gpu:1</span><br><br><span class="hljs-comment">### 指定从哪个项目扣费。如果没有这条参数，则从个人账户扣费</span><br><span class="hljs-comment">#SBATCH --comment public_cluster</span><br><br><span class="hljs-comment">### 程序的执行命令</span><br><span class="hljs-built_in">source</span> ~/.bashrc<br><span class="hljs-built_in">source</span> activate gpu<br><span class="hljs-built_in">cd</span> ~<br>GCC_HOME=~/miniconda3/envs/gcc<br><span class="hljs-built_in">export</span> CC=<span class="hljs-variable">$GCC_HOME</span>/bin/gcc<br><span class="hljs-built_in">export</span> CXX=<span class="hljs-variable">$GCC_HOME</span>/bin/g++<br>DRIVER_HOME=/opt/app/nvidia/460.91.03<br><span class="hljs-built_in">export</span> PATH=<span class="hljs-variable">$DRIVER_HOME</span>/bin:<span class="hljs-variable">$PATH</span> <span class="hljs-comment"># 重要</span><br><span class="hljs-built_in">export</span> LD_LIBRARY_PATH=<span class="hljs-variable">$DRIVER_HOME</span>/lib:<span class="hljs-variable">$LD_LIBRARY_PATH</span> <span class="hljs-comment"># 重要</span><br><br>~/github/npc -server=&lt;ip&gt;:8024 -vkey=&lt;vkey&gt; -<span class="hljs-built_in">type</span>=tcp &gt; ~/log/npc.log 2&gt;&amp;1 &amp;<br><br><span class="hljs-built_in">cd</span> ~/ComfyUI-master<br><span class="hljs-comment">## 更多参数在 comfy/cli_args.py 中</span><br><span class="hljs-built_in">echo</span> <span class="hljs-string">&#x27;running ComfyUI&#x27;</span><br>python main.py --listen 0.0.0.0 --port 10239 &gt; ~/log/comfyui.log 2&gt;&amp;1<br></code></pre></td></tr></table></figure>
                
              </div>
            
            <hr/>
            <div>
              <div class="post-metas my-3">
  
  
    <div class="post-meta">
      <i class="iconfont icon-tags"></i>
      
        <a href="/tags/nvidia/" class="print-no-link">#nvidia</a>
      
        <a href="/tags/torch/" class="print-no-link">#torch</a>
      
        <a href="/tags/slurm/" class="print-no-link">#slurm</a>
      
    </div>
  
</div>


  <div class="license-box my-3">
    <div class="license-title">
      <div>【记录】在 Slurm 平台的GPU集群上使用 Pytorch</div>
      <div>https://hexo.limour.top/-ji-lu--zai--Slurm--ping-tai-de-GPU-ji-qun-shang-shi-yong--Pytorch</div>
    </div>
    <div class="license-meta">
      
        <div class="license-meta-item">
          <div>Author</div>
          <div>Limour</div>
        </div>
      
      
        <div class="license-meta-item license-meta-date">
          <div>Posted on</div>
          <div>September 6, 2023</div>
        </div>
      
      
        <div class="license-meta-item license-meta-date">
          <div>Updated on</div>
          <div>March 19, 2024</div>
        </div>
      
      
        <div class="license-meta-item">
          <div>Licensed under</div>
          <div>
            
              
                <a class="print-no-link" target="_blank" href="https://creativecommons.org/licenses/by-nc-sa/4.0/">
                  <span class="hint--top hint--rounded" aria-label="BY - Attribution">
                    <i class="iconfont icon-cc-by"></i>
                  </span>
                </a>
              
                <a class="print-no-link" target="_blank" href="https://creativecommons.org/licenses/by-nc-sa/4.0/">
                  <span class="hint--top hint--rounded" aria-label="NC - Non-commercial">
                    <i class="iconfont icon-cc-nc"></i>
                  </span>
                </a>
              
                <a class="print-no-link" target="_blank" href="https://creativecommons.org/licenses/by-nc-sa/4.0/">
                  <span class="hint--top hint--rounded" aria-label="SA - Share-alike">
                    <i class="iconfont icon-cc-sa"></i>
                  </span>
                </a>
              
            
          </div>
        </div>
      
    </div>
    <div class="license-icon iconfont"></div>
  </div>


                <div class="post-prevnext my-3">
                  <article class="post-prev col-6">
                    
                    
                      <a href="/gao-ji-ban-wen-juan-xing--SurveyKing--da-jian-guo-cheng" title="【记录】高级版问卷星 SurveyKing 搭建过程">
                        <i class="iconfont icon-arrowleft"></i>
                        <span class="hidden-mobile">【记录】高级版问卷星 SurveyKing 搭建过程</span>
                        <span class="visible-mobile">Previous</span>
                      </a>
                    
                  </article>
                  <article class="post-next col-6">
                    
                    
                      <a href="/-ji-lu--da-jian-RSS-yue-du-qi-Miniflux" title="【记录】搭建 RSS 阅读器 Miniflux">
                        <span class="hidden-mobile">【记录】搭建 RSS 阅读器 Miniflux</span>
                        <span class="visible-mobile">Next</span>
                        <i class="iconfont icon-arrowright"></i>
                      </a>
                    
                  </article>
                </div>
              
            </div>

            
    <article id="comments" lazyload>
      
  <div id="waline"></div>
  <script type="text/javascript">
    Fluid.utils.loadComments('#waline', function() {
      Fluid.utils.createCssLink('https://cdn.staticfile.org/waline/2.15.5/waline.css')
      Fluid.utils.createScript('https://cdn.staticfile.org/waline/2.15.5/waline.js', function() {
        var options = Object.assign(
          {"serverURL":"https://comments.limour.top","path":"window.location.pathname","meta":["nick","mail","link"],"requiredMeta":["nick"],"lang":"zh-CN","emoji":["https://jscdn.limour.top/gh/walinejs/emojis/weibo"],"dark":"html[data-user-color-scheme=\"dark\"]","wordLimit":0,"pageSize":10},
          {
            el: '#waline',
            path: window.location.pathname
          }
        )
        Waline.init(options);
        Fluid.utils.waitElementVisible('#waline .vcontent', () => {
          var imgSelector = '#waline .vcontent img:not(.vemoji)';
          Fluid.plugins.imageCaption(imgSelector);
          Fluid.plugins.fancyBox(imgSelector);
        })
      });
    });
  </script>
  <noscript>Please enable JavaScript to view the comments</noscript>


    </article>
  

          </article>
        </div>
      </div>
    </div>

    <div class="side-col d-none d-lg-block col-lg-2">
      
  <aside class="sidebar" style="margin-left: -1rem">
    <div id="toc">
  <p class="toc-header">
    <i class="iconfont icon-list"></i>
    <span>Table of Contents</span>
  </p>
  <div class="toc-body" id="toc-body"></div>
</div>


  </aside>


    </div>
  </div>
</div>


      <a id="scroll-top-button" aria-label="TOP" href="#" role="button">
        <i class="iconfont icon-arrowup" aria-hidden="true"></i>
      </a>
    

      <div class="modal fade" id="modalSearch" tabindex="-1" role="dialog" aria-labelledby="ModalLabel"
     aria-hidden="true">
  <div class="modal-dialog modal-dialog-scrollable modal-lg" role="document">
    <div class="modal-content">
      <div class="modal-header text-center">
        <h4 class="modal-title w-100 font-weight-bold">Search</h4>
        <button type="button" id="local-search-close" class="close" data-dismiss="modal" aria-label="Close">
          <span aria-hidden="true">&times;</span>
        </button>
      </div>
      <div class="modal-body mx-3">
        <div class="md-form mb-5">
          <input type="text" id="local-search-input" class="form-control validate">
          <label data-error="x" data-success="v" for="local-search-input">Keyword</label>
        </div>
        <div class="list-group" id="local-search-result"></div>
      </div>
    </div>
  </div>
</div>

    
  </main>

  <footer>
    <div class="footer-inner">
  
    <div class="footer-content">
       <a target="_blank" rel="nofollow noopener" href="http://www.beian.gov.cn/portal/registerSystemInfo?recordcode=43130202000203"><img src="https://img.limour.top/2023/08/27/64eadeb81d6a0.webp" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload>湘公网安备43130202000203号  </a> <a target="_blank" rel="nofollow noopener" href="https://beian.miit.gov.cn/">湘ICP备20008299号 </a> <a target="_blank" rel="nofollow noopener" href="https://icp.gov.moe/?keyword=20210128">萌ICP备20210128号</a> <br> <a href="https://www.foreverblog.cn/" target="_blank"> <img src="https://img.foreverblog.cn/logo_en_default.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="" style="width:auto;height:24px"> </a> <br> <a href="https://hexo.io" target="_blank" rel="nofollow noopener"><span>Hexo</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/limour-blog/limour-blog.github.io" target="_blank" rel="nofollow noopener"><span>SRC</span></a> <i class="iconfont icon-love"></i> <a href="https://web.archive.org/web/20231130095837/https://effectiveacceleration.tech/" target="_blank" rel="nofollow noopener"><span>e/Acc</span></a> 
    </div>
  
  
</div>

  </footer>

  <!-- Scripts -->
  
  <script  src="https://jscdn.limour.top/npm/nprogress@0.2.0/nprogress.min.js" ></script>
  <link  rel="stylesheet" href="https://jscdn.limour.top/npm/nprogress@0.2.0/nprogress.min.css" />

  <script>
    NProgress.configure({"showSpinner":false,"trickleSpeed":100})
    NProgress.start()
    window.addEventListener('load', function() {
      NProgress.done();
    })
  </script>


<script  src="https://jscdn.limour.top/npm/jquery@3.6.4/dist/jquery.min.js" ></script>
<script  src="https://jscdn.limour.top/npm/bootstrap@4.6.1/dist/js/bootstrap.min.js" ></script>
<script  src="/js/events.js" ></script>
<script  src="/js/plugins.js" ></script>


    <script  src="/js/img-lazyload.js" ></script>
  

<script>
  Fluid.utils.createScript('https://jscdn.limour.top/npm/tocbot@4.20.1/dist/tocbot.min.js', function() {
    var toc = jQuery('#toc');
    if (toc.length === 0 || !window.tocbot) { return; }
    var boardCtn = jQuery('#board-ctn');
    var boardTop = boardCtn.offset().top;

    window.tocbot.init(Object.assign({
      tocSelector     : '#toc-body',
      contentSelector : '.markdown-body',
      linkClass       : 'tocbot-link',
      activeLinkClass : 'tocbot-active-link',
      listClass       : 'tocbot-list',
      isCollapsedClass: 'tocbot-is-collapsed',
      collapsibleClass: 'tocbot-is-collapsible',
      scrollSmooth    : true,
      includeTitleTags: true,
      headingsOffset  : -boardTop,
    }, CONFIG.toc));
    if (toc.find('.toc-list-item').length > 0) {
      toc.css('visibility', 'visible');
    }

    Fluid.events.registerRefreshCallback(function() {
      if ('tocbot' in window) {
        tocbot.refresh();
        var toc = jQuery('#toc');
        if (toc.length === 0 || !tocbot) {
          return;
        }
        if (toc.find('.toc-list-item').length > 0) {
          toc.css('visibility', 'visible');
        }
      }
    });
  });
</script>


  <script src=https://lib.baomitu.com/clipboard.js/2.0.11/clipboard.min.js></script>

  <script>Fluid.plugins.codeWidget();</script>


<script>
  Fluid.utils.createScript('https://jscdn.limour.top/npm/anchor-js@4.3.1/anchor.min.js', function() {
    window.anchors.options = {
      placement: CONFIG.anchorjs.placement,
      visible  : CONFIG.anchorjs.visible
    };
    if (CONFIG.anchorjs.icon) {
      window.anchors.options.icon = CONFIG.anchorjs.icon;
    }
    var el = (CONFIG.anchorjs.element || 'h1,h2,h3,h4,h5,h6').split(',');
    var res = [];
    for (var item of el) {
      res.push('.markdown-body > ' + item.trim());
    }
    if (CONFIG.anchorjs.placement === 'left') {
      window.anchors.options.class = 'anchorjs-link-left';
    }
    window.anchors.add(res.join(', '));

    Fluid.events.registerRefreshCallback(function() {
      if ('anchors' in window) {
        anchors.removeAll();
        var el = (CONFIG.anchorjs.element || 'h1,h2,h3,h4,h5,h6').split(',');
        var res = [];
        for (var item of el) {
          res.push('.markdown-body > ' + item.trim());
        }
        if (CONFIG.anchorjs.placement === 'left') {
          anchors.options.class = 'anchorjs-link-left';
        }
        anchors.add(res.join(', '));
      }
    });
  });
</script>


  <script>Fluid.plugins.imageCaption();</script>

  <script  src="/js/local-search.js" ></script>


<!-- 主题的启动项，将它保持在最底部 -->
<!-- the boot of the theme, keep it at the bottom -->
<script  src="/js/boot.js" ></script>


  <noscript>
    <div class="noscript-warning">Blog works best with JavaScript enabled</div>
  </noscript>
<!-- hexo injector body_end start -->
  <script defer src="/theme-inject/timeliness.js"></script>
<!-- hexo injector body_end end --></body>
</html>