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   <div class="section" id="device">
 <h1>设备(Device)<a class="headerlink" href="#device" title="Permalink to this headline">¶</a></h1>
 <p>设备抽象代表了任何基于内存和计算单元的硬件设备。所有<a class="reference external" href="tensor.html">Tensor操作</a>由寄宿的设备进行调度以执行。Tensor内存也由设备内存管理器进行管理。因此，内存优化和执行都由Device类进行实现。</p>
 <div class="section" id="id1">
 <h2>特定设备<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
 <p>目前，SINGA有三种Device实现，</p>
 <ol class="simple">
 <li>CudaGPU： 在Nvidia GPU卡上运行Cuda代码</li>
 <li>CppCPU：在CPU上运行Cpp代码</li>
 <li>OpenclGPU： GPU卡上运行OpenCL代码</li>
 </ol>
 </div>
 <div class="section" id="python-api">
 <h2>Python API<a class="headerlink" href="#python-api" title="Permalink to this headline">¶</a></h2>
 <p>此脚本包括Device类和它的子类，用户可以调用singa::Device和它的方法。</p>
 <hr class="docutils" />
 <div class="section" id="singa-device-create-cuda-gpus-num">
 <h3>singa.device.create_cuda_gpus(num)<a class="headerlink" href="#singa-device-create-cuda-gpus-num" title="Permalink to this headline">¶</a></h3>
 <p>创建一个列表的CudaGPU设备。</p>
 <p><strong>参数：</strong></p>
 <ul class="simple">
 <li><strong>num(int)</strong> - 创建的设备数目</li>
 </ul>
 <p><strong>返回值：</strong> 一个列表CudaGPU设备</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="singa-device-create-cuda-gpus-on-device-ids">
 <h3>singa.device.create_cuda_gpus_on(device_ids)<a class="headerlink" href="#singa-device-create-cuda-gpus-on-device-ids" title="Permalink to this headline">¶</a></h3>
 <p>创建一个列表的CudaGPU设备。</p>
 <p><strong>参数：</strong></p>
 <ul class="simple">
 <li><strong>device_ids(list)</strong> - 一个列表的显卡ID</li>
 </ul>
 <p><strong>返回值：</strong> 一个列表CudaGPU设备</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="singa-device-get-default-device">
 <h3>singa.device.get_default_device()<a class="headerlink" href="#singa-device-get-default-device" title="Permalink to this headline">¶</a></h3>
 <p>获取默认的CppCPU设备。</p>
 <hr class="docutils" />
 <p>下面的代码展示了创建设备的例子：</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">device</span>
 <span class="n">cuda</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu_on</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># use GPU card of ID 0</span>
 <span class="n">host</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">get_default_device</span><span class="p">()</span>  <span class="c1"># get the default host device (a CppCPU)</span>
 <span class="n">ary1</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># create 2 devices, starting from ID 0</span>
 <span class="n">ary2</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>  <span class="c1"># create 2 devices on ID 0 and 2</span>
 </pre></div>
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	<div class="section" id="device">
	<h1>设备(Device)<a class="headerlink" href="#device" title="Permalink to this headline">¶</a></h1>
	<p>设备抽象代表了任何基于内存和计算单元的硬件设备。所有<a class="reference external" href="tensor.html">Tensor操作</a>由寄宿的设备进行调度以执行。Tensor内存也由设备内存管理器进行管理。因此，内存优化和执行都由Device类进行实现。</p>
	<div class="section" id="id1">
	<h2>特定设备<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
	<p>目前，SINGA有三种Device实现，</p>
	<ol class="simple">
	<li>CudaGPU：在Nvidia GPU卡上运行Cuda代码</li>
	<li>CppCPU：在CPU上运行Cpp代码</li>
	<li>OpenclGPU： GPU卡上运行OpenCL代码</li>
	</ol>
	</div>
	<div class="section" id="python-api">
	<h2>Python API<a class="headerlink" href="#python-api" title="Permalink to this headline">¶</a></h2>
	<p>此脚本包括Device类和它的子类，用户可以调用singa::Device和它的方法。</p>
	<hr class="docutils" />
	<div class="section" id="singa-device-create-cuda-gpus-num">
	<h3>singa.device.create_cuda_gpus(num)<a class="headerlink" href="#singa-device-create-cuda-gpus-num" title="Permalink to this headline">¶</a></h3>
	<p>创建一个列表的CudaGPU设备。</p>
	<p><strong>参数：</strong></p>
	<ul class="simple">
	<li><strong>num(int)</strong> - 创建的设备数目</li>
	</ul>
	<p><strong>返回值：</strong> 一个列表CudaGPU设备</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="singa-device-create-cuda-gpus-on-device-ids">
	<h3>singa.device.create_cuda_gpus_on(device_ids)<a class="headerlink" href="#singa-device-create-cuda-gpus-on-device-ids" title="Permalink to this headline">¶</a></h3>
	<p>创建一个列表的CudaGPU设备。</p>
	<p><strong>参数：</strong></p>
	<ul class="simple">
	<li><strong>device_ids(list)</strong> - 一个列表的显卡ID</li>
	</ul>
	<p><strong>返回值：</strong> 一个列表CudaGPU设备</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="singa-device-get-default-device">
	<h3>singa.device.get_default_device()<a class="headerlink" href="#singa-device-get-default-device" title="Permalink to this headline">¶</a></h3>
	<p>获取默认的CppCPU设备。</p>
	<hr class="docutils" />
	<p>下面的代码展示了创建设备的例子：</p>
	<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">device</span>
	<span class="n">cuda</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu_on</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># use GPU card of ID 0</span>
	<span class="n">host</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">get_default_device</span><span class="p">()</span> <span class="c1"># get the default host device (a CppCPU)</span>
	<span class="n">ary1</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># create 2 devices, starting from ID 0</span>
	<span class="n">ary2</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span> <span class="c1"># create 2 devices on ID 0 and 2</span>
	</pre></div>
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