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| |
| <h1 id="basics">Basics</h1> |
| |
| <p>This tutorial provides basic usages of the C++ package through the classical handwritten digits |
| identification database–<a href="http://yann.lecun.com/exdb/mnist/">MNIST</a>.</p> |
| |
| <p>The following contents assume that the working directory is <code class="highlighter-rouge">/path/to/mxnet/cpp-package/example</code>.</p> |
| |
| <h2 id="load-data">Load Data</h2> |
| <p>Before going into codes, we need to fetch MNIST data. You can either use the script <code class="highlighter-rouge">/path/to/mxnet/cpp-package/example/get_data.sh</code>, |
| or download mnist data by yourself from Lecun’s <a href="http://yann.lecun.com/exdb/mnist/">website</a> |
| and decompress them into <code class="highlighter-rouge">data/mnist_data</code> folder.</p> |
| |
| <p>Except linking the MXNet shared library, the C++ package itself is a header-only package, |
| which means all you need to do is to include the header files. Among the header files, |
| <code class="highlighter-rouge">op.h</code> is special since it is generated dynamically. The generation should be done when |
| <a href="/versions/master/api/cpp/">building the C++ package</a>. |
| It is important to note that you need to <strong>copy the shared library</strong> (<code class="highlighter-rouge">libmxnet.so</code> in Linux and MacOS, |
| <code class="highlighter-rouge">libmxnet.dll</code> in Windows) from <code class="highlighter-rouge">/path/to/mxnet/lib</code> to the working directory. |
| We do not recommend you to use pre-built binaries because MXNet is under heavy development, |
| the operator definitions in <code class="highlighter-rouge">op.h</code> may be incompatible with the pre-built version.</p> |
| |
| <p>In order to use functionalities provides by the C++ package, first we include the general |
| header file <code class="highlighter-rouge">MxNetCpp.h</code> and specify the namespaces.</p> |
| |
| <div class="language-c++ highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="cp">#include "mxnet-cpp/MxNetCpp.h" |
| </span> |
| <span class="k">using</span> <span class="k">namespace</span> <span class="n">std</span><span class="p">;</span> |
| <span class="k">using</span> <span class="k">namespace</span> <span class="n">mxnet</span><span class="o">::</span><span class="n">cpp</span><span class="p">;</span> |
| </code></pre></div></div> |
| |
| <p>Next we can use the data iter to load MNIST data (separated to training sets and validation sets). |
| The digits in MNIST are 2-dimension arrays, so we should set <code class="highlighter-rouge">flat</code> to true to flatten the data.</p> |
| |
| <div class="language-c++ highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">auto</span> <span class="n">train_iter</span> <span class="o">=</span> <span class="n">MXDataIter</span><span class="p">(</span><span class="s">"MNISTIter"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"image"</span><span class="p">,</span> <span class="s">"./data/mnist_data/train-images-idx3-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"./data/mnist_data/train-labels-idx1-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"batch_size"</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"flat"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">CreateDataIter</span><span class="p">();</span> |
| <span class="k">auto</span> <span class="n">val_iter</span> <span class="o">=</span> <span class="n">MXDataIter</span><span class="p">(</span><span class="s">"MNISTIter"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"image"</span><span class="p">,</span> <span class="s">"./data/mnist_data/t10k-images-idx3-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"./data/mnist_data/t10k-labels-idx1-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"batch_size"</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"flat"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">CreateDataIter</span><span class="p">();</span> |
| </code></pre></div></div> |
| |
| <p>The data have been successfully loaded. We can now easily construct various models to identify |
| the digits with the help of C++ package.</p> |
| |
| <h2 id="gpu-support">GPU Support</h2> |
| <p>It’s worth noting that changing context from <code class="highlighter-rouge">Context::cpu()</code> to <code class="highlighter-rouge">Context::gpu()</code> is not enough, |
| because the data read by data iter are stored in memory, we cannot assign it directly to the |
| parameters. To bridge this gap, NDArray provides data synchronization functionalities between |
| GPU and CPU. We will illustrate it by making the mlp code run on GPU.</p> |
| |
| <p>In the previous code, data are used like</p> |
| |
| <div class="language-c++ highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">args</span><span class="p">[</span><span class="s">"X"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_batch</span><span class="p">.</span><span class="n">data</span><span class="p">;</span> |
| <span class="n">args</span><span class="p">[</span><span class="s">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_batch</span><span class="p">.</span><span class="n">label</span><span class="p">;</span> |
| </code></pre></div></div> |
| |
| <p>It will be problematic if other parameters are created in the context of GPU. We can use |
| <code class="highlighter-rouge">NDArray::CopyTo</code> to solve this problem.</p> |
| |
| <div class="language-c++ highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">// Data provided by DataIter are stored in memory, should be copied to GPU first.</span> |
| <span class="n">data_batch</span><span class="p">.</span><span class="n">data</span><span class="p">.</span><span class="n">CopyTo</span><span class="p">(</span><span class="o">&</span><span class="n">args</span><span class="p">[</span><span class="s">"X"</span><span class="p">]);</span> |
| <span class="n">data_batch</span><span class="p">.</span><span class="n">label</span><span class="p">.</span><span class="n">CopyTo</span><span class="p">(</span><span class="o">&</span><span class="n">args</span><span class="p">[</span><span class="s">"label"</span><span class="p">]);</span> |
| <span class="c1">// CopyTo is imperative, need to wait for it to complete.</span> |
| <span class="n">NDArray</span><span class="o">::</span><span class="n">WaitAll</span><span class="p">();</span> |
| </code></pre></div></div> |
| |
| <p>By replacing the former code to the latter one, we successfully port the code to GPU. |
| You can find the complete code in <code class="highlighter-rouge">mlp_gpu.cpp</code>. Compilation is similar to the cpu version. |
| Note that the shared library must be built with GPU support enabled.</p> |
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