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<p>The Device abstract represents any hardware device with memory and compuation
units. All <a href="/docs/tensor">Tensor operations</a> are scheduled by the resident device
for execution. Tensor memory is also managed by the device's memory manager.
Therefore, optimization of memory and execution are implemented in the Device
class.</p>
<h2><a class="anchor" aria-hidden="true" id="specific-devices"></a><a href="#specific-devices" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Specific devices</h2>
<p>Currently, SINGA has three Device implmentations,</p>
<ol>
<li>CudaGPU for an Nvidia GPU card which runs Cuda code</li>
<li>CppCPU for a CPU which runs Cpp code</li>
<li>OpenclGPU for a GPU card which runs OpenCL code</li>
</ol>
<h2><a class="anchor" aria-hidden="true" id="python-api"></a><a href="#python-api" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Python API</h2>
<p>The following code provides examples of creating devices:</p>
<pre><code class="hljs css language-python"><span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> device
cuda = device.create_cuda_gpu_on(<span class="hljs-number">0</span>)  <span class="hljs-comment"># use GPU card of ID 0</span>
host = device.get_default_device()  <span class="hljs-comment"># get the default host device (a CppCPU)</span>
ary1 = device.create_cuda_gpus(<span class="hljs-number">2</span>)  <span class="hljs-comment"># create 2 devices, starting from ID 0</span>
ary2 = device.create_cuda_gpus([<span class="hljs-number">0</span>,<span class="hljs-number">2</span>])  <span class="hljs-comment"># create 2 devices on ID 0 and 2</span>
</code></pre>
<h2><a class="anchor" aria-hidden="true" id="cpp-api"></a><a href="#cpp-api" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>CPP API</h2>
<p><em>work in progress</em></p>
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