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<div class="section" id="ndarray-scientific-computing-on-cpu-and-gpu">
<span id="ndarray-scientific-computing-on-cpu-and-gpu"></span><h1>NDArray - Scientific computing on CPU and GPU<a class="headerlink" href="#ndarray-scientific-computing-on-cpu-and-gpu" title="Permalink to this headline"></a></h1>
<p>NDArray is a tensor data structure similar to numpy’s multi-dimensional array.
In addition, it supports asynchronous computation on CPU and GPU.</p>
<p>First, let’s import MXNet:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
</pre></div>
</div>
<div class="section" id="creating-ndarray">
<span id="creating-ndarray"></span><h2>Creating NDArray<a class="headerlink" href="#creating-ndarray" title="Permalink to this headline"></a></h2>
<p>There are many ways to create NDArray.</p>
<p>Construct from (nested) list:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Construct from numpy array:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x_numpy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x_numpy</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Array construction routines:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># create an 2x3 array of ones</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># create an 2x3 array of zeros</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># create an 1d-array of 0 to 5 and reshape to 2x3</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>You can convert an NDArray to numpy array to retrieve its data with <code class="docutils literal"><span class="pre">.asnumpy()</span></code>:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="basic-attributes">
<span id="basic-attributes"></span><h2>Basic attributes<a class="headerlink" href="#basic-attributes" title="Permalink to this headline"></a></h2>
<p>NDArray has some basic attributes that you often want to query:</p>
<p><strong>NDArray.shape</strong>: The dimensions of the array. It is a tuple of integers
indicating the length of the array along each axis. For a matrix with <code class="docutils literal"><span class="pre">n</span></code> rows
and <code class="docutils literal"><span class="pre">m</span></code> columns, its <code class="docutils literal"><span class="pre">shape</span></code> will be <code class="docutils literal"><span class="pre">(n,</span> <span class="pre">m)</span></code>.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.shape:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>NDArray.dtype</strong>: A <code class="docutils literal"><span class="pre">numpy</span></code> <em>type</em> object describing the type of array
elements.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.dtype:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>NDArray.size</strong>: the total number of components in the array - equals to the
product of the components of its <code class="docutils literal"><span class="pre">shape</span></code></p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.size:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>NDArray.context</strong>: The device on which this array is stored, e.g. <code class="docutils literal"><span class="pre">mx.cpu()</span></code>
or <code class="docutils literal"><span class="pre">mx.gpu(1)</span></code>.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.context:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">context</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="ndarray-operations">
<span id="ndarray-operations"></span><h2>NDArray Operations<a class="headerlink" href="#ndarray-operations" title="Permalink to this headline"></a></h2>
<p>NDArray supports a wide range of operations. Simple operations can be called
with python syntax:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p>You can also call operators from the <code class="docutils literal"><span class="pre">mxnet.ndarray</span></code> (or <code class="docutils literal"><span class="pre">mx.nd</span></code> for short) name space:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
</pre></div>
</div>
<p>You can also pass additional flags to operators:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'axis=0:'</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'axis=1:'</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="using-gpu">
<span id="using-gpu"></span><h2>Using GPU<a class="headerlink" href="#using-gpu" title="Permalink to this headline"></a></h2>
<p>Each NDArray lives on a <code class="docutils literal"><span class="pre">Context</span></code>. MXNet supports <code class="docutils literal"><span class="pre">mx.cpu()</span></code> for CPU and <code class="docutils literal"><span class="pre">mx.gpu(0)</span></code>,
<code class="docutils literal"><span class="pre">mx.gpu(1)</span></code>, etc for GPU. You can specify context when creating NDArray:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># creates on CPU (the default).</span>
<span class="c1"># Replace mx.cpu() with mx.gpu(0) if you have a GPU.</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>You can copy arrays between devices with <code class="docutils literal"><span class="pre">.copyto()</span></code>:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Copy x to cpu. Replace with mx.gpu(0) if you have GPU.</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
<span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Copy x to another NDArray, possibly on another Context.</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p>See the <a class="reference internal" href="../basic/ndarray.html"><em>Advanced NDArray tutorial</em></a> for a more detailed
introduction to NDArray API.</p>
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<div class="download-btn"><a download="ndarray.ipynb" href="ndarray.ipynb"><span class="glyphicon glyphicon-download-alt"></span> ndarray.ipynb</a></div></div></div>
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<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">NDArray - Scientific computing on CPU and GPU</a><ul>
<li><a class="reference internal" href="#creating-ndarray">Creating NDArray</a></li>
<li><a class="reference internal" href="#basic-attributes">Basic attributes</a></li>
<li><a class="reference internal" href="#ndarray-operations">NDArray Operations</a></li>
<li><a class="reference internal" href="#using-gpu">Using GPU</a></li>
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