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<div class="section" id="ndarray-imperative-tensor-operations-on-cpu-gpu">
<span id="ndarray-imperative-tensor-operations-on-cpu-gpu"></span><h1>NDArray - Imperative tensor operations on CPU/GPU<a class="headerlink" href="#ndarray-imperative-tensor-operations-on-cpu-gpu" title="Permalink to this headline"></a></h1>
<p>In <em>MXNet</em>, <code class="docutils literal"><span class="pre">NDArray</span></code> is the core data structure for all mathematical
computations. An <code class="docutils literal"><span class="pre">NDArray</span></code> represents a multidimensional, fixed-size homogenous
array. If you’re familiar with the scientific computing python package
<a class="reference external" href="http://www.numpy.org/">NumPy</a>, you might notice that <code class="docutils literal"><span class="pre">mxnet.ndarray</span></code> is similar
to <code class="docutils literal"><span class="pre">numpy.ndarray</span></code>. Like the corresponding NumPy data structure, MXNet’s
<code class="docutils literal"><span class="pre">NDArray</span></code> enables imperative computation.</p>
<p>So you might wonder, why not just use NumPy? MXNet offers two compelling
advantages. First, MXNet’s <code class="docutils literal"><span class="pre">NDArray</span></code> supports fast execution on a wide range of
hardware configurations, including CPU, GPU, and multi-GPU machines. <em>MXNet</em>
also scales to distributed systems in the cloud. Second, MXNet’s <code class="docutils literal"><span class="pre">NDArray</span></code>
executes code lazily, allowing it to automatically parallelize multiple
operations across the available hardware.</p>
<p>An <code class="docutils literal"><span class="pre">NDArray</span></code> is a multidimensional array of numbers with the same type. We
could represent the coordinates of a point in 3D space, e.g. <code class="docutils literal"><span class="pre">[2,</span> <span class="pre">1,</span> <span class="pre">6]</span></code> as a 1D
array with shape (3). Similarly, we could represent a 2D array. Below, we
present an array with length 2 along the first axis and length 3 along the
second axis.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">0</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="mi">5</span><span class="p">]]</span>
</pre></div>
</div>
<p>Note that here the use of “dimension” is overloaded. When we say a 2D array, we
mean an array with 2 axes, not an array with two components.</p>
<p>Each NDArray supports some important attributes that you’ll often want to query:</p>
<ul class="simple">
<li><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>.</li>
<li><strong>ndarray.dtype</strong>: A <code class="docutils literal"><span class="pre">numpy</span></code> <em>type</em> object describing the type of its
elements.</li>
<li><strong>ndarray.size</strong>: The total number of components in the array - equal to the
product of the components of its <code class="docutils literal"><span class="pre">shape</span></code></li>
<li><strong>ndarray.context</strong>: The device on which this array is stored, e.g. <code class="docutils literal"><span class="pre">cpu()</span></code> or
<code class="docutils literal"><span class="pre">gpu(1)</span></code>.</li>
</ul>
<div class="section" id="prerequisites">
<span id="prerequisites"></span><h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h2>
<p>To complete this tutorial, we need:</p>
<ul>
<li><p class="first">MXNet. See the instructions for your operating system in <a class="reference external" href="/versions/0.12.1/install/index.html">Setup and Installation</a></p>
</li>
<li><p class="first"><a class="reference external" href="http://jupyter.org/">Jupyter</a></p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">jupyter</span>
</pre></div>
</div>
</li>
<li><p class="first">GPUs - A section of this tutorial uses GPUs. If you don’t have GPUs on your
machine, simply set the variable gpu_device (set in the GPUs section of this
tutorial) to mx.cpu().</p>
</li>
</ul>
</div>
<div class="section" id="array-creation">
<span id="array-creation"></span><h2>Array Creation<a class="headerlink" href="#array-creation" title="Permalink to this headline"></a></h2>
<p>There are a few different ways to create an <code class="docutils literal"><span class="pre">NDArray</span></code>.</p>
<ul class="simple">
<li>We can create an NDArray from a regular Python list or tuple by using the <code class="docutils literal"><span class="pre">array</span></code> function:</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
<span class="c1"># create a 1-dimensional array with a python list</span>
<span class="n">a</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="c1"># create a 2-dimensional array with a nested python list</span>
<span class="n">b</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">2</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="p">{</span><span class="s1">'a.shape'</span><span class="p">:</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s1">'b.shape'</span><span class="p">:</span><span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">}</span>
</pre></div>
</div>
<ul class="simple">
<li>We can also create an MXNet NDArray from a <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> object:</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></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">math</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
<span class="c1"># create a 2-dimensional array from a numpy.ndarray object</span>
<span class="n">a</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">c</span><span class="p">)</span>
<span class="p">{</span><span class="s1">'a.shape'</span><span class="p">:</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">}</span>
</pre></div>
</div>
<p>We can specify the element type with the option <code class="docutils literal"><span class="pre">dtype</span></code>, which accepts a numpy
type. By default, <code class="docutils literal"><span class="pre">float32</span></code> is used:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># float32 is used by default</span>
<span class="n">a</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="c1"># create an int32 array</span>
<span class="n">b</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="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="c1"># create a 16-bit float array</span>
<span class="n">c</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="mf">1.2</span><span class="p">,</span> <span class="mf">2.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
<span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</pre></div>
</div>
<p>If we know the size of the desired NDArray, but not the element values, MXNet
offers several functions to create arrays with placeholder content:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># create a 2-dimensional array full of zeros with shape (2,3)</span>
<span class="n">a</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="c1"># create a same shape array full of ones</span>
<span class="n">b</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="c1"># create a same shape array with all elements set to 7</span>
<span class="n">c</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">full</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="mi">7</span><span class="p">)</span>
<span class="c1"># create a same shape whose initial content is random and</span>
<span class="c1"># depends on the state of the memory</span>
<span class="n">d</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">empty</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="printing-arrays">
<span id="printing-arrays"></span><h2>Printing Arrays<a class="headerlink" href="#printing-arrays" title="Permalink to this headline"></a></h2>
<p>When inspecting the contents of an <code class="docutils literal"><span class="pre">NDArray</span></code>, it’s often convenient to first
extract its contents as a <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> using the <code class="docutils literal"><span class="pre">asnumpy</span></code> function. Numpy
uses the following layout:</p>
<ul class="simple">
<li>The last axis is printed from left to right,</li>
<li>The second-to-last is printed from top to bottom,</li>
<li>The rest are also printed from top to bottom, with each slice separated from
the next by an empty line.</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">b</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">18</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">3</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">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="basic-operations">
<span id="basic-operations"></span><h2>Basic Operations<a class="headerlink" href="#basic-operations" title="Permalink to this headline"></a></h2>
<p>When applied to NDArrays, the standard arithmetic operators apply <em>elementwise</em>
calculations. The returned value is a new array whose content contains the
result.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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="n">b</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="c1"># elementwise plus</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="c1"># elementwise minus</span>
<span class="n">d</span> <span class="o">=</span> <span class="o">-</span> <span class="n">c</span>
<span class="c1"># elementwise pow and sin, and then transpose</span>
<span class="n">e</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">sin</span><span class="p">(</span><span class="n">c</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
<span class="c1"># elementwise max</span>
<span class="n">f</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">maximum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
<span class="n">f</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>As in <code class="docutils literal"><span class="pre">NumPy</span></code>, <code class="docutils literal"><span class="pre">*</span></code> represents element-wise multiplication. For matrix-matrix
multiplication, use <code class="docutils literal"><span class="pre">dot</span></code>.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">4</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">2</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">a</span> <span class="o">*</span> <span class="n">a</span>
<span class="n">c</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">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">a</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"b: </span><span class="si">%s</span><span class="s2">, </span><span class="se">\n</span><span class="s2"> c: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="n">c</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()))</span>
</pre></div>
</div>
<p>The assignment operators such as <code class="docutils literal"><span class="pre">+=</span></code> and <code class="docutils literal"><span class="pre">*=</span></code> modify arrays in place, and thus
don’t allocate new memory to create a new array.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">2</span><span class="p">))</span>
<span class="n">b</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="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">b</span> <span class="o">+=</span> <span class="n">a</span>
<span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="indexing-and-slicing">
<span id="indexing-and-slicing"></span><h2>Indexing and Slicing<a class="headerlink" href="#indexing-and-slicing" title="Permalink to this headline"></a></h2>
<p>The slice operator <code class="docutils literal"><span class="pre">[]</span></code> applies on axis 0.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">np</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">3</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">a</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="o">=</span> <span class="mi">1</span>
<span class="n">a</span><span class="p">[:]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>We can also slice a particular axis with the method <code class="docutils literal"><span class="pre">slice_axis</span></code></p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">d</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">slice_axis</span><span class="p">(</span><span class="n">a</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="n">begin</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="shape-manipulation">
<span id="shape-manipulation"></span><h2>Shape Manipulation<a class="headerlink" href="#shape-manipulation" title="Permalink to this headline"></a></h2>
<p>Using <code class="docutils literal"><span class="pre">reshape</span></code>, we can manipulate any arrays shape as long as the size remains
unchanged.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">24</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">a</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="mi">4</span><span class="p">))</span>
<span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>The <code class="docutils literal"><span class="pre">concat</span></code> method stacks multiple arrays along the first axis. Their
shapes must be the same along the other axes.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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="n">b</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="o">*</span><span class="mi">2</span>
<span class="n">c</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">concat</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
<span class="n">c</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="reduce">
<span id="reduce"></span><h2>Reduce<a class="headerlink" href="#reduce" title="Permalink to this headline"></a></h2>
<p>Some functions, like <code class="docutils literal"><span class="pre">sum</span></code> and <code class="docutils literal"><span class="pre">mean</span></code> reduce arrays to scalars.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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="n">b</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">a</span><span class="p">)</span>
<span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>We can also reduce an array along a particular axis:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">c</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_axis</span><span class="p">(</span><span class="n">a</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="n">c</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="broadcast">
<span id="broadcast"></span><h2>Broadcast<a class="headerlink" href="#broadcast" title="Permalink to this headline"></a></h2>
<p>We can also broadcast an array. Broadcasting operations, duplicate an array’s
value along an axis with length 1. The following code broadcasts along axis 1:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">np</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">6</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span> <span class="c1">#</span>
<span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>It’s possible to simultaneously broadcast along multiple axes. In the following example, we broadcast along axes 1 and 2:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">c</span> <span class="o">=</span> <span class="n">a</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">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">broadcast_to</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="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="n">d</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>Broadcasting can be applied automatically when executing some operations,
e.g. <code class="docutils literal"><span class="pre">*</span></code> and <code class="docutils literal"><span class="pre">+</span></code> on arrays of different shapes.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">3</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">b</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">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">c</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="copies">
<span id="copies"></span><h2>Copies<a class="headerlink" href="#copies" title="Permalink to this headline"></a></h2>
<p>When assigning an NDArray to another Python variable, we copy a reference to the
<em>same</em> NDArray. However, we often need to make a copy of the data, so that we
can manipulate the new array without overwriting the original values.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">2</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">a</span>
<span class="n">b</span> <span class="ow">is</span> <span class="n">a</span> <span class="c1"># will be True</span>
</pre></div>
</div>
<p>The <code class="docutils literal"><span class="pre">copy</span></code> method makes a deep copy of the array and its data:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">b</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">b</span> <span class="ow">is</span> <span class="n">a</span> <span class="c1"># will be False</span>
</pre></div>
</div>
<p>The above code allocates a new NDArray and then assigns to <em>b</em>. When we do not
want to allocate additional memory, we can use the <code class="docutils literal"><span class="pre">copyto</span></code> method or the slice
operator <code class="docutils literal"><span class="pre">[]</span></code> instead.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">b</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="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">b</span>
<span class="n">c</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">a</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">b</span>
<span class="n">a</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="p">(</span><span class="n">c</span> <span class="ow">is</span> <span class="n">b</span><span class="p">,</span> <span class="n">d</span> <span class="ow">is</span> <span class="n">b</span><span class="p">)</span> <span class="c1"># Both will be True</span>
</pre></div>
</div>
</div>
<div class="section" id="advanced-topics">
<span id="advanced-topics"></span><h2>Advanced Topics<a class="headerlink" href="#advanced-topics" title="Permalink to this headline"></a></h2>
<p>MXNet’s NDArray offers some advanced features that differentiate it from the
offerings you’ll find in most other libraries.</p>
<div class="section" id="gpu-support">
<span id="gpu-support"></span><h3>GPU Support<a class="headerlink" href="#gpu-support" title="Permalink to this headline"></a></h3>
<p>By default, NDArray operators are executed on CPU. But with MXNet, it’s easy to
switch to another computation resource, such as GPU, when available. Each
NDArray’s device information is stored in <code class="docutils literal"><span class="pre">ndarray.context</span></code>. When MXNet is
compiled with flag <code class="docutils literal"><span class="pre">USE_CUDA=1</span></code> and the machine has at least one NVIDIA GPU, we
can cause all computations to run on GPU 0 by using context <code class="docutils literal"><span class="pre">mx.gpu(0)</span></code>, or
simply <code class="docutils literal"><span class="pre">mx.gpu()</span></code>. When we have access to two or more GPUs, the 2nd GPU is
represented by <code class="docutils literal"><span class="pre">mx.gpu(1)</span></code>, etc.</p>
<p><strong>Note</strong> In order to execute the following section on a cpu set gpu_device to mx.cpu().</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gpu_device</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()</span> <span class="c1"># Change this to mx.cpu() in absence of GPUs.</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">():</span>
<span class="n">a</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">100</span><span class="p">,</span><span class="mi">100</span><span class="p">))</span>
<span class="n">b</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">100</span><span class="p">,</span><span class="mi">100</span><span class="p">))</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="k">print</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
<span class="c1"># in default mx.cpu() is used</span>
<span class="n">f</span><span class="p">()</span>
<span class="c1"># change the default context to the first GPU</span>
<span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="n">gpu_device</span><span class="p">):</span>
<span class="n">f</span><span class="p">()</span>
</pre></div>
</div>
<p>We can also explicitly specify the context when creating an array:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">gpu_device</span><span class="p">)</span>
<span class="n">a</span>
</pre></div>
</div>
<p>Currently, MXNet requires two arrays to sit on the same device for
computation. There are several methods for copying data between devices.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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">100</span><span class="p">,</span><span class="mi">100</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="n">b</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">100</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span> <span class="n">gpu_device</span><span class="p">)</span>
<span class="n">c</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">100</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span> <span class="n">gpu_device</span><span class="p">)</span>
<span class="n">a</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># copy from CPU to GPU</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">b</span> <span class="o">+</span> <span class="n">c</span>
<span class="n">e</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> <span class="o">+</span> <span class="n">c</span> <span class="c1"># same to above</span>
<span class="p">{</span><span class="s1">'d'</span><span class="p">:</span><span class="n">d</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">:</span><span class="n">e</span><span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="serialize-from-to-distributed-filesystems">
<span id="serialize-from-to-distributed-filesystems"></span><h3>Serialize From/To (Distributed) Filesystems<a class="headerlink" href="#serialize-from-to-distributed-filesystems" title="Permalink to this headline"></a></h3>
<p>MXNet offers two simple ways to save (load) data to (from) disk. The first way
is to use <code class="docutils literal"><span class="pre">pickle</span></code>, as you might with any other Python objects. <code class="docutils literal"><span class="pre">NDArray</span></code> is
pickle-compatible.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span> <span class="kn">as</span> <span class="nn">pkl</span>
<span class="n">a</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="c1"># pack and then dump into disk</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pkl</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">pkl</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'tmp.pickle'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">))</span>
<span class="c1"># load from disk and then unpack</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pkl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">'tmp.pickle'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">pkl</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>The second way is to directly dump to disk in binary format by using the <code class="docutils literal"><span class="pre">save</span></code>
and <code class="docutils literal"><span class="pre">load</span></code> methods. We can save/load a single NDArray, or a list of NDArrays:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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="n">b</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">5</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"temp.ndarray"</span><span class="p">,</span> <span class="p">[</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">])</span>
<span class="n">c</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">load</span><span class="p">(</span><span class="s2">"temp.ndarray"</span><span class="p">)</span>
<span class="n">c</span>
</pre></div>
</div>
<p>It’s also possible to save or load a dict of NDArrays in this fashion:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span><span class="n">a</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">:</span><span class="n">b</span><span class="p">}</span>
<span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"temp.ndarray"</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
<span class="n">c</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">load</span><span class="p">(</span><span class="s2">"temp.ndarray"</span><span class="p">)</span>
<span class="n">c</span>
</pre></div>
</div>
<p>The <code class="docutils literal"><span class="pre">load</span></code> and <code class="docutils literal"><span class="pre">save</span></code> methods are preferable to pickle in two respects</p>
<ol class="simple">
<li>When using these methods, you can save data from within the Python interface
and then use it later from another language’s binding. For example, if we save
the data in Python:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"temp.ndarray"</span><span class="p">,</span> <span class="p">[</span><span class="n">a</span><span class="p">,])</span>
</pre></div>
</div>
<p>we can later load it from R:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">a</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">load</span><span class="p">(</span><span class="s2">"temp.ndarray"</span><span class="p">)</span>
<span class="k">as</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">[[</span><span class="mi">1</span><span class="p">]])</span>
<span class="c1">## [,1] [,2] [,3]</span>
<span class="c1">## [1,] 1 1 1</span>
<span class="c1">## [2,] 1 1 1</span>
</pre></div>
</div>
<ol class="simple">
<li>When a distributed filesystem such as Amazon S3 or Hadoop HDFS is set up, we
can directly save to and load from it.</li>
</ol>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'s3://mybucket/mydata.ndarray'</span><span class="p">,</span> <span class="p">[</span><span class="n">a</span><span class="p">,])</span> <span class="c1"># if compiled with USE_S3=1</span>
<span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'hdfs///users/myname/mydata.bin'</span><span class="p">,</span> <span class="p">[</span><span class="n">a</span><span class="p">,])</span> <span class="c1"># if compiled with USE_HDFS=1</span>
</pre></div>
</div>
</div>
<div class="section" id="lazy-evaluation-and-automatic-parallelization">
<span id="lazy-evaluation-and-automatic-parallelization"></span><h3>Lazy Evaluation and Automatic Parallelization<a class="headerlink" href="#lazy-evaluation-and-automatic-parallelization" title="Permalink to this headline"></a></h3>
<p>MXNet uses lazy evaluation to achieve superior performance. When we run <code class="docutils literal"><span class="pre">a=b+1</span></code>
in Python, the Python thread just pushes this operation into the backend engine
and then returns. There are two benefits to this approach:</p>
<ol class="simple">
<li>The main Python thread can continue to execute other computations once the
previous one is pushed. It is useful for frontend languages with heavy
overheads.</li>
<li>It is easier for the backend engine to explore further optimization, such as
auto parallelization.</li>
</ol>
<p>The backend engine can resolve data dependencies and schedule the computations
correctly. It is transparent to frontend users. We can explicitly call the
method <code class="docutils literal"><span class="pre">wait_to_read</span></code> on the result array to wait until the computation
finishes. Operations that copy data from an array to other packages, such as
<code class="docutils literal"><span class="pre">asnumpy</span></code>, will implicitly call <code class="docutils literal"><span class="pre">wait_to_read</span></code>.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="k">def</span> <span class="nf">do</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
<span class="sd">"""push computation into the backend engine"""</span>
<span class="k">return</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
<span class="k">def</span> <span class="nf">wait</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="sd">"""wait until all results are available"""</span>
<span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">x</span><span class="p">:</span>
<span class="n">y</span><span class="o">.</span><span class="n">wait_to_read</span><span class="p">()</span>
<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">a</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">1000</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">do</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'time for all computations are pushed into the backend engine:</span><span class="se">\n</span><span class="s1"> </span><span class="si">%f</span><span class="s1"> sec'</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">))</span>
<span class="n">wait</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'time for all computations are finished:</span><span class="se">\n</span><span class="s1"> </span><span class="si">%f</span><span class="s1"> sec'</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">))</span>
</pre></div>
</div>
<p>Besides analyzing data read and write dependencies, the backend engine is able
to schedule computations with no dependency in parallel. For example, in the
following code:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</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="n">b</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="mi">2</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="n">c</span>
</pre></div>
</div>
<p>the second and third lines can be executed in parallel. The following example
first runs on CPU and then on GPU:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">a</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">1000</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
<span class="n">b</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">6000</span><span class="p">,</span><span class="mi">6000</span><span class="p">),</span> <span class="n">gpu_device</span><span class="p">)</span>
<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">do</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="n">wait</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'Time to finish the CPU workload: </span><span class="si">%f</span><span class="s1"> sec'</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">))</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">do</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="n">wait</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'Time to finish both CPU/GPU workloads: </span><span class="si">%f</span><span class="s1"> sec'</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">))</span>
</pre></div>
</div>
<p>Now we issue all workloads at the same time. The backend engine will try to
parallel the CPU and GPU computations.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">do</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">do</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="n">wait</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
<span class="n">wait</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'Both as finished in: </span><span class="si">%f</span><span class="s1"> sec'</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">))</span>
</pre></div>
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<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">NDArray - Imperative tensor operations on CPU/GPU</a><ul>
<li><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
<li><a class="reference internal" href="#array-creation">Array Creation</a></li>
<li><a class="reference internal" href="#printing-arrays">Printing Arrays</a></li>
<li><a class="reference internal" href="#basic-operations">Basic Operations</a></li>
<li><a class="reference internal" href="#indexing-and-slicing">Indexing and Slicing</a></li>
<li><a class="reference internal" href="#shape-manipulation">Shape Manipulation</a></li>
<li><a class="reference internal" href="#reduce">Reduce</a></li>
<li><a class="reference internal" href="#broadcast">Broadcast</a></li>
<li><a class="reference internal" href="#copies">Copies</a></li>
<li><a class="reference internal" href="#advanced-topics">Advanced Topics</a><ul>
<li><a class="reference internal" href="#gpu-support">GPU Support</a></li>
<li><a class="reference internal" href="#serialize-from-to-distributed-filesystems">Serialize From/To (Distributed) Filesystems</a></li>
<li><a class="reference internal" href="#lazy-evaluation-and-automatic-parallelization">Lazy Evaluation and Automatic Parallelization</a></li>
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