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Pytorch1.4 GPU环境.html
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Pytorch1.4 GPU环境.html
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<span>Pytorch1.4 GPU环境</span>
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发布 :
2020-04-05
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分类 :
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杂记
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<h1 id="Pytorch1-4-GPU-环境"><a href="#Pytorch1-4-GPU-环境" class="headerlink" title="Pytorch1.4 GPU 环境"></a>Pytorch1.4 GPU 环境</h1><p>首先确定你的电脑显卡是 NVIDIA 的，如果不是，请安装 CPU 版本。使用 pip 直接安装：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pip install torch==<span class="number">1.4</span><span class="number">.0</span>+cpu torchvision==<span class="number">0.5</span><span class="number">.0</span>+cpu -f https://download.pytorch.org/whl/torch_stable.html</span><br></pre></td></tr></table></figure>
<h3 id="驱动"><a href="#驱动" class="headerlink" title="驱动"></a>驱动</h3><ul>
<li>下载 CUDA 驱动<a href="https://developer.nvidia.com/cuda-10.1-download-archive-base" target="_blank" rel="external nofollow noopener noreferrer">CUDA10.1</a></li>
</ul>
<p><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092785684-8e01bc0c-88e7-46d7-ba7b-44e889c681f6.png#align=left&display=inline&height=794&name=1586089962427.png&originHeight=794&originWidth=1706&size=69628&status=done&style=none&width=1706" alt="1586089962427.png"></p>
<ul>
<li>双击安装：<br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092816038-0bed45c2-b1b3-4320-a9ee-e4392989d4a3.png#align=left&display=inline&height=203&name=1586090360189.png&originHeight=203&originWidth=519&size=29782&status=done&style=none&width=519" alt="1586090360189.png"><br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092823764-32956c52-c38c-40df-8a84-036aff32031d.png#align=left&display=inline&height=555&name=1586090532529.png&originHeight=555&originWidth=742&size=161351&status=done&style=none&width=742" alt="1586090532529.png"></li>
</ul>
<p>显卡驱动按照自己情况安装：<br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092845702-0e2c3372-5ce6-4479-ba70-fffa9332c0db.png#align=left&display=inline&height=687&name=1586090713816.png&originHeight=687&originWidth=919&size=204839&status=done&style=none&width=919" alt="1586090713816.png"></p>
<p>根据自己要求，选择安装位置<br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092860932-9ec1febf-63dc-41ea-abe4-371f78ed9792.png#align=left&display=inline&height=555&name=1586090591332.png&originHeight=555&originWidth=742&size=174385&status=done&style=none&width=742" alt="1586090591332.png"></p>
<h3 id="CUDNN"><a href="#CUDNN" class="headerlink" title="CUDNN"></a>CUDNN</h3><p>下载与 CUDA10.1 匹配的<a href="https://developer.nvidia.com/rdp/cudnn-archive" target="_blank" rel="external nofollow noopener noreferrer">CUDNN</a>。需要登录 NVIDIA 开发者社区<br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092889207-0b5edff3-2321-4fec-a99e-3fcb10dfeac0.png#align=left&display=inline&height=676&name=1586091216025.png&originHeight=676&originWidth=857&size=51033&status=done&style=none&width=857" alt="1586091216025.png"></p>
<ul>
<li>解压后复制到 CUDA Development 目录</li>
</ul>
<p><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092916265-81a7cb1f-4696-4efb-a522-e9ad620a81d8.png#align=left&display=inline&height=923&name=1586091415721.png&originHeight=923&originWidth=1175&size=105240&status=done&style=none&width=1175" alt="1586091415721.png"></p>
<h3 id="环境变量"><a href="#环境变量" class="headerlink" title="环境变量"></a>环境变量</h3><p>查看环境变量是否自动添加了，如果没有就自己添加一下 bin 目录<br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092929863-dadf999f-8bdb-4770-b7bf-32f8b3e17c49.png#align=left&display=inline&height=664&name=1586091834271.png&originHeight=664&originWidth=839&size=35470&status=done&style=none&width=839" alt="1586091834271.png"></p>
<h3 id="测试"><a href="#测试" class="headerlink" title="测试"></a>测试</h3><p>查看是否安装成功<br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092951492-8add2076-c2c0-4c14-bb38-0a3905a1b62a.png#align=left&display=inline&height=269&name=1586091899039.png&originHeight=269&originWidth=595&size=29362&status=done&style=none&width=595" alt="1586091899039.png"></p>
<h3 id="安装-Pytorch"><a href="#安装-Pytorch" class="headerlink" title="安装 Pytorch"></a>安装 Pytorch</h3><p>安装好 anaconda 后，用 pip 工具安装 pytorch：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pip install torch===<span class="number">1.4</span><span class="number">.0</span> torchvision===<span class="number">0.5</span><span class="number">.0</span> -f https://download.pytorch.org/whl/torch_stable.html</span><br></pre></td></tr></table></figure>
<p>安装好后，测试：<br><img src="https://cdn.nlark.com/yuque/0/2020/png/478234/1586092974459-3bd9f1d6-e587-4b6e-83de-0b71aad513e3.png#align=left&display=inline&height=154&name=1586092015000.png&originHeight=154&originWidth=1063&size=16845&status=done&style=none&width=1063" alt="1586092015000.png"><br>如果返回 True 则 Pytorch GPU 版安装完成。</p>
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本文作者 : HeoLis <br>
原文链接 : <a href="">https://ishero.net/Pytorch1.4%20GPU%E7%8E%AF%E5%A2%83.html</a><br>
版权声明 : 本博客所有文章除特别声明外，均采用 <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" rel="external nofollow noopener noreferrer">CC BY-NC-SA 4.0</a> 许可协议。转载请注明出处！
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