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            <h1 id="seo-header">【记录】在 Slurm 平台的GPU集群上使用 Pytorch</h1>
            
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                    Last updated on March 19, 2024 pm
                  
                
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            <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>
                
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