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itemtype="http://schema.org/Article" class="article post white-box reveal md shadow floatable blur article-type-post" id="post" itemprop="blogPost"><link itemprop="mainEntityOfPage" href="https://blog.mhuig.top/p/56213be8/"><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="MHuiG"></span><span hidden itemprop="post" itemscope itemtype="http://schema.org/Post"><meta itemprop="name" content="MHuiG"><meta itemprop="description" content="MHuiG&#39;s Blog (MHuiG的博客) MHuiG&#39;s Neverland（MHuiG的梦幻岛） —— MHuiG(@MHuiG) 随便写写画画的地方 - 技术博客"></span><div class="headimg-div"> <a class="headimg-a"><img itemprop="image" class="headimg lazyload" src="https://static.mhuig.top/npm/mhgoos@0.0.1651994754992/2020116102711.webp" data-srcset="https://static.mhuig.top/npm/mhgoos@0.0.1651994754992/2020116102711.webp" srcset="data:image/gif;base64,R0lGODlhAQABAIAAAP///////yH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="></a></div><div class="article-meta" id="top"><h1 class="title" itemprop="name headline"> 尝试使用 GPU 加速计算</h1><div class="new-meta-box"><div class="new-meta-item author" itemprop="author" itemscope itemtype="http://schema.org/Person"> <a itemprop="url" class="author" target="_blank" href="https://mhuig.top/" rel="nofollow noopener"><img itemprop="image" src="/lib/avatar/avatar-16.webp?v=2199236952" class="lazyload" data-srcset="/lib/avatar/avatar-16.webp" srcset="data:image/gif;base64,R0lGODlhAQABAIAAAP///////yH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="><p itemprop="name">MHuiG</p></a></div><div class="new-meta-item wordcount"><a class="notlink"><i class="fa-duotone fa-keyboard fa-fw" aria-hidden="true"></i><p>字数：1.9k 字</p></a></div><div class="new-meta-item readtime"><a class="notlink"><i class="fa-duotone fa-hourglass-half fa-fw" aria-hidden="true"></i><p>时长：10 分钟</p></a></div></div></div><div id="layoutHelper-page-plugins"></div><div id="post-body" itemprop="articleBody"><p>大规模训练，gpu 和 cpu 速度差别很大。</p><span id="more"></span><div class="story post-story"><h2 id="概述"><a href="#概述" class="headerlink" title="概述"></a>概述</h2><h3 id="GPU"><a href="#GPU" class="headerlink" title="GPU"></a>GPU</h3><ul><li>CPU (Central Processing Unit) 即中央处理器。</li><li>GPU (Graphics Processing Unit) 即图形处理器。</li></ul><p>当程序员为 CPU 编写程序时，他们倾向于利用复杂的逻辑结构优化算法从而减少计算任务的运行时间，即 Latency。<br>当程序员为 GPU 编写程序时，则利用其处理海量数据的优势，通过提高总的数据吞吐量（Throughput）来掩盖 Lantency。</p><p><img src="data:image/jpeg;base64,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" alt="GPU"></p><p>其中绿色的是计算单元，橙红色的是存储单元，橙黄色的是控制单元。</p><p>首先你需要硬件支持，一块能够支持 GPU 加速计算的显卡，这里以 NVIDIA 的 GPU 为例。</p><h3 id="CUDA"><a href="#CUDA" class="headerlink" title="CUDA"></a>CUDA</h3><p>CUDA（Compute Unified Device Architecture），是显卡厂商 NVIDIA 推出的运算平台。 CUDA™是一种由 NVIDIA 推出的通用并行计算架构，该架构使 GPU 能够解决复杂的计算问题。</p><p>CUDA 提供了一种可扩展的编程模型，使得已经写好的 CUDA 代码可以在任意数量核心的 GPU 上运行。只有运行时，系统才知道物理处理器的数量。</p><p><img src="https://static.mhuig.top/npm/imbox@0.0.3/data/2020101183832.png" class="lazyload" data-srcset="https://static.mhuig.top/npm/imbox@0.0.3/data/2020101183832.png" srcset="data:image/gif;base64,R0lGODlhAQABAIAAAP///////yH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" alt="CUDA"></p><h3 id="CUDNN"><a href="#CUDNN" class="headerlink" title="CUDNN"></a>CUDNN</h3><p>NVIDIA cuDNN 是用于深度神经网络的 GPU 加速库。它强调性能、易用性和低内存开销。NVIDIA cuDNN 可以集成到更高级别的机器学习框架中，如加州大学伯克利分校的流行 CAFFE 软件。简单的，插入式设计可以让开发人员专注于设计和实现神经网络模型，而不是调整性能，同时还可以在 GPU 上实现高性能现代并行计算。</p></div><div class="story post-story"><h2 id="支持GPU的计算框架"><a href="#支持GPU的计算框架" class="headerlink" title="支持GPU的计算框架"></a>支持 GPU 的计算框架</h2><h3 id="Tensorflow-GPU"><a href="#Tensorflow-GPU" class="headerlink" title="Tensorflow-GPU"></a>Tensorflow-GPU</h3><p>安装教程参考 <a target="_blank" rel="external nofollow noopener noreferrer" href="https://tensorflow.google.cn/install">https://tensorflow.google.cn/install</a></p><p>需要注意的是 严格对应 tensorflow_gpu、Python、 编译器、 cuDNN、CUDA 的版本关系。</p><p>相关对应关系 windows 平台可参考:</p><p><a target="_blank" rel="external nofollow noopener noreferrer" href="https://tensorflow.google.cn/install/source_windows#gpu">Tensorflow 文档</a></p><p><a target="_blank" rel="external nofollow noopener noreferrer" href="https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html">显卡驱动与 Cuda 版本之间的对应关系</a></p><p><a target="_blank" rel="external nofollow noopener noreferrer" href="https://developer.nvidia.com/rdp/cudnn-archive">cuda 与 cudnn 版本之间的对应关系</a></p><p>打开 NVIDIA 控制面板进入系统信息，可查看当前支持的 CUDA 驱动版本。</p><p><img src="https://static.mhuig.top/npm/mhgoos@0.0.1651994863868/2020101194632.webp" class="lazyload" data-srcset="https://static.mhuig.top/npm/mhgoos@0.0.1651994863868/2020101194632.webp" srcset="data:image/gif;base64,R0lGODlhAQABAIAAAP///////yH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" alt="查看当前支持的CUDA驱动版本"></p><p>这里的 CUDA 驱动版本是指你只可以安装该版本及以下版本的 CUDA。</p><p>根据实际情况，笔者计划使用的环境是：</p><ul><li>tensorflow_gpu-1.14.0</li><li>python 3.7</li><li>MSVC 2017</li><li>cuDNN 7.4.2.24</li><li>CUDA 10.0.130</li></ul><h4 id="安装CUDA"><a href="#安装CUDA" class="headerlink" title="安装CUDA"></a>安装 CUDA</h4><p>进入 <a target="_blank" rel="external nofollow noopener noreferrer" href="https://developer.nvidia.com/cuda-toolkit-archive">cuda-toolkit-archive</a> 选择需要的 CUDA Toolkit 版本下载安装即可。</p><p>笔者选择 network 安装方式。注意第一个路径是选择临时解压路径。选择自定义安装，取消安装不需要的应用，如 NVIDIA GeForce Experience。</p><p>在 cmd 中执行：</p><figure class="highlight bash"><table><tbody><tr><td class="code"><pre><span class="line">nvcc -V</span><br></pre></td></tr></tbody></table></figure><p><img src="https://static.mhuig.top/npm/imbox@0.0.3/data/2020101194654.png" class="lazyload" data-srcset="https://static.mhuig.top/npm/imbox@0.0.3/data/2020101194654.png" srcset="data:image/gif;base64,R0lGODlhAQABAIAAAP///////yH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" alt="nvcc -V"></p><p>需要注意配置相关环境变量。</p><h4 id="安装CUDNN"><a href="#安装CUDNN" class="headerlink" title="安装CUDNN"></a>安装 CUDNN</h4><p>进入 <a target="_blank" rel="external nofollow noopener noreferrer" href="https://developer.nvidia.com/rdp/cudnn-archive">cudnn-archive</a> 选择需要的 CUDA Toolkit 版本下载解压即可。</p><p>笔者将 CUDA 默认安装在 <code>C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0</code></p><p>将 CUDNN 解压到 <code>C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0</code> 中即可。</p><p>或者配置单独的环境变量。</p><h4 id="安装Tensorflow-GPU"><a href="#安装Tensorflow-GPU" class="headerlink" title="安装Tensorflow-GPU"></a>安装 Tensorflow-GPU</h4><p>如果之前安装过 cpu 版本的 Tensorflow 需要进行卸载.</p><p>若在虚拟环境中使用 conda 安装，conda 会自动安装相关的 cuda 和 cudnn 依赖。</p><figure class="highlight bash"><table><tbody><tr><td class="code"><pre><span class="line">conda install tensorflow-gpu</span><br></pre></td></tr></tbody></table></figure><p><img src="https://static.mhuig.top/npm/imbox@0.0.3/data/2020101194713.png" class="lazyload" data-srcset="https://static.mhuig.top/npm/imbox@0.0.3/data/2020101194713.png" srcset="data:image/gif;base64,R0lGODlhAQABAIAAAP///////yH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" alt="安装Tensorflow-GPU"></p><p>下面给出一段 GPU 测试程序：</p><figure class="highlight python"><table><tbody><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> time</span><br><span class="line"><span class="keyword">import</span> tensorflow <span class="keyword">as</span> tf</span><br><span class="line">begin = time.time()</span><br><span class="line"><span class="keyword">with</span> tf.device(<span class="string">'/gpu:0'</span>):</span><br><span class="line"> rand_t = tf.random_uniform([<span class="number">50</span>,<span class="number">50</span>],<span class="number">0</span>,<span class="number">10</span>,dtype=tf.float32,seed=<span class="number">0</span>)</span><br><span class="line"> a = tf.Variable(rand_t)</span><br><span class="line"> b = tf.Variable(rand_t)</span><br><span class="line"> c = tf.matmul(a,b)</span><br><span class="line"> init = tf.global_variables_initializer()</span><br><span class="line">sess = tf.Session(config=tf.ConfigProto(log_device_placement=<span class="literal">True</span>)) <span class="comment">#强制使用GPU</span></span><br><span class="line">sess.run(init)</span><br><span class="line"><span class="built_in">print</span>(sess.run(c))</span><br><span class="line">end = time.time()</span><br><span class="line"><span class="built_in">print</span>(end-begin,<span class="string">'s'</span>)</span><br></pre></td></tr></tbody></table></figure><p>运行结果:</p><figure class="highlight plaintext"><table><tbody><tr><td class="code"><pre><span class="line">2020-10-11 09:36:56.713905: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2</span><br><span class="line">2020-10-11 09:36:56.742138: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library nvcuda.dll</span><br><span class="line">2020-10-11 09:36:57.193243: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:</span><br><span class="line">name: GeForce GTX 960M major: 5 minor: 0 memoryClockRate(GHz): 1.176</span><br><span class="line">pciBusID: 0000:01:00.0</span><br><span class="line">2020-10-11 09:36:57.199952: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.</span><br><span class="line">2020-10-11 09:36:57.204946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0</span><br><span class="line">2020-10-11 09:36:58.622428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:</span><br><span class="line">2020-10-11 09:36:58.626880: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0</span><br><span class="line">2020-10-11 09:36:58.630073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N</span><br><span class="line">2020-10-11 09:36:58.633910: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1382 MB memory) -> physical GPU (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0, compute capability: 5.0)</span><br><span class="line">Device mapping:</span><br><span class="line">/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0, compute capability: 5.0</span><br><span class="line">2020-10-11 09:36:58.657797: I tensorflow/core/common_runtime/direct_session.cc:296] Device mapping:</span><br><span class="line">/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0, compute capability: 5.0</span><br><span class="line"></span><br><span class="line">random_uniform/RandomUniform: (RandomUniform): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.683509: I tensorflow/core/common_runtime/placer.cc:54] random_uniform/RandomUniform: (RandomUniform)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">random_uniform/sub: (Sub): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.690684: I tensorflow/core/common_runtime/placer.cc:54] random_uniform/sub: (Sub)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">random_uniform/mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.705871: I tensorflow/core/common_runtime/placer.cc:54] random_uniform/mul: (Mul)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">random_uniform: (Add): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.719880: I tensorflow/core/common_runtime/placer.cc:54] random_uniform: (Add)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">Variable: (VariableV2): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.738611: I tensorflow/core/common_runtime/placer.cc:54] Variable: (VariableV2)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">Variable/Assign: (Assign): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.749906: I tensorflow/core/common_runtime/placer.cc:54] Variable/Assign: (Assign)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">Variable/read: (Identity): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.759648: I tensorflow/core/common_runtime/placer.cc:54] Variable/read: (Identity)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">Variable_1: (VariableV2): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.769854: I tensorflow/core/common_runtime/placer.cc:54] Variable_1: (VariableV2)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">Variable_1/Assign: (Assign): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.776819: I tensorflow/core/common_runtime/placer.cc:54] Variable_1/Assign: (Assign)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">Variable_1/read: (Identity): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.792460: I tensorflow/core/common_runtime/placer.cc:54] Variable_1/read: (Identity)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.808352: I tensorflow/core/common_runtime/placer.cc:54] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">init: (NoOp): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.823256: I tensorflow/core/common_runtime/placer.cc:54] init: (NoOp)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">random_uniform/shape: (Const): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.838355: I tensorflow/core/common_runtime/placer.cc:54] random_uniform/shape: (Const)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">random_uniform/min: (Const): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.853758: I tensorflow/core/common_runtime/placer.cc:54] random_uniform/min: (Const)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">random_uniform/max: (Const): /job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">2020-10-11 09:36:58.868493: I tensorflow/core/common_runtime/placer.cc:54] random_uniform/max: (Const)/job:localhost/replica:0/task:0/device:GPU:0</span><br><span class="line">[[1405.2429 1441.7413 1364.38 ... 1480.2251 1279.0061 1620.0938 ]</span><br><span class="line"> [1232.6589 1344.4458 1169.7095 ... 1205.1284 1040.5566 1421.967 ]</span><br><span class="line"> [1209.3164 1180.3206 1158.1396 ... 1200.0344 1014.03217 1222.5107 ]</span><br><span class="line"> ...</span><br><span class="line"> [1298.9648 1262.9236 1205.6918 ... 1396.479 1090.7253 1437.241 ]</span><br><span class="line"> [1118.2473 1209.0151 1077.7229 ... 1180.7025 1076.4694 1139.742 ]</span><br><span class="line"> [1200.8866 1297.2266 1260.01 ... 1289.4297 1165.2448 1433.4183 ]]</span><br><span class="line">2.6225805282592773 s</span><br></pre></td></tr></tbody></table></figure><p>可以看到 GPU 已经在工作了。</p><p>同时可以在 cmd 中运行 nvidia-smi【C:\Program Files\NVIDIA Corporation\NVSMInvidia-smi.exe】监控 GPU 使用情况和更改 GPU 状态。</p><h3 id="Keras-GPU"><a href="#Keras-GPU" class="headerlink" title="Keras-GPU"></a>Keras-GPU</h3><p>conda 直接安装即可，注意需要先安装 tensorflow-gpu。</p><figure class="highlight shell"><table><tbody><tr><td class="code"><pre><span class="line">conda install keras-gpu</span><br></pre></td></tr></tbody></table></figure><h3 id="Pytorch-GPU"><a href="#Pytorch-GPU" class="headerlink" title="Pytorch-GPU"></a>Pytorch-GPU</h3><p>再也找不到比官网更详尽的文档了，请直接参考 <a target="_blank" rel="external nofollow noopener noreferrer" href="https://pytorch.org/%E3%80%82">https://pytorch.org/。</a></p><figure class="highlight shell"><table><tbody><tr><td class="code"><pre><span class="line">conda install pytorch torchvision cudatoolkit=10.0</span><br></pre></td></tr></tbody></table></figure><p>GPU 测试程序：</p><figure class="highlight python"><table><tbody><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> torch</span><br><span class="line">flag = torch.cuda.is_available()</span><br><span class="line"><span class="built_in">print</span>(flag)</span><br><span class="line"></span><br><span class="line">ngpu= <span class="number">1</span></span><br><span class="line"><span class="comment"># Decide which device we want to run on</span></span><br><span class="line">device = torch.device(<span class="string">"cuda:0"</span> <span class="keyword">if</span> (torch.cuda.is_available() <span class="keyword">and</span> ngpu > <span class="number">0</span>) <span class="keyword">else</span> <span class="string">"cpu"</span>)</span><br><span class="line"><span class="built_in">print</span>(device)</span><br><span class="line"><span class="built_in">print</span>(torch.cuda.get_device_name(<span class="number">0</span>))</span><br><span class="line"><span class="built_in">print</span>(torch.rand(<span class="number">3</span>,<span class="number">3</span>).cuda()) </span><br></pre></td></tr></tbody></table></figure><p>输出结果：</p><figure class="highlight python"><table><tbody><tr><td class="code"><pre><span class="line"><span class="literal">True</span></span><br><span class="line">cuda:<span class="number">0</span></span><br><span class="line">GeForce GTX 960M</span><br><span class="line">tensor([[<span class="number">0.0208</span>, <span class="number">0.2799</span>, <span class="number">0.4918</span>],</span><br><span class="line"> [<span class="number">0.0020</span>, <span class="number">0.1067</span>, <span class="number">0.8207</span>],</span><br><span class="line"> [<span class="number">0.5531</span>, <span class="number">0.0994</span>, <span class="number">0.2108</span>]], device=<span class="string">'cuda:0'</span>)</span><br></pre></td></tr></tbody></table></figure></div></div><div class="footer"><div class="copyright license"><div class="license-title">尝试使用 GPU 加速计算</div><div class="license-link"><a href="https://blog.mhuig.top/p/56213be8/">https://blog.mhuig.top/p/56213be8/</a></div><div class="license-meta"><div class="license-meta-item"><div class="license-meta-title">本文作者</div><div class="license-meta-text">MHuiG</div></div><div class="license-meta-item"><div class="license-meta-title">发布于</div><div class="license-meta-text">2020年10月11日</div></div><div class="license-meta-item"><div class="license-meta-title">许可协议</div><div class="license-meta-text"><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh#" target="_blank" rel="external nofollow noopener noreferrer">CC BY-NC-SA 4.0</a></div></div></div><div class="license-statement">署名-非商业性使用-相同方式共享 4.0 国际。</div></div></div><div class="article-meta" id="bottom"><div class="new-meta-box"><div class="new-meta-item category"><i class="fa-duotone fa-folder-open fa-fw" aria-hidden="true"></i> <a class="category-link" href="/categories/Data-mining/">Data mining</a> <span hidden itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/Data-mining/" itemprop="url"><span itemprop="name">Data mining</span></a></span></div><div class="new-meta-item meta-tags"><a class="tag" href="/tags/Data-mining/" rel="nofollow"><i class="fa-duotone fa-hashtag fa-fw" aria-hidden="true"></i><p>Data mining</p></a></div><div class="new-meta-item meta-tags"><a class="tag" href="/tags/Machine-Learning/" rel="nofollow"><i class="fa-duotone fa-hashtag fa-fw" aria-hidden="true"></i><p>Machine Learning</p></a></div> <span hidden itemprop="keywords">Data mining Machine Learning</span><div class="new-meta-item date" itemprop="dateCreated datePublished" datetime="2020-10-11T09:11:44+08:00"><a class="notlink"><i class="fa-duotone fa-calendar-alt fa-fw" 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