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<div class="section" id="ndarray-api">
<span id="ndarray-api"></span><h1>NDArray API<a class="headerlink" href="#ndarray-api" title="Permalink to this headline"></a></h1>
<p>The NDArray package (<code class="docutils literal"><span class="pre">mxnet.ndarray</span></code>) contains tensor operations similar to <code class="docutils literal"><span class="pre">numpy.ndarray</span></code>. The syntax is also similar, except for some additional calls for dealing with I/O and multiple devices.</p>
<p>Topics:</p>
<ul class="simple">
<li><a class="reference external" href="#create-ndarray">Create NDArray</a></li>
<li><a class="reference external" href="#ndarray-operations">NDArray Operations</a></li>
<li><a class="reference external" href="http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.NDArray">NDArray API Reference</a></li>
</ul>
<div class="section" id="create-ndarray">
<span id="create-ndarray"></span><h2>Create NDArray<a class="headerlink" href="#create-ndarray" title="Permalink to this headline"></a></h2>
<p>Create <code class="docutils literal"><span class="pre">mxnet.ndarray</span></code> as follows:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">scala</span><span class="o">></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// all-zero array of dimension 100x50</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">a</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">zeros</span><span class="o">(</span><span class="mi">100</span><span class="o">,</span> <span class="mi">50</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// all-one array of dimension 256x32x128x1</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">b</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">ones</span><span class="o">(</span><span class="mi">256</span><span class="o">,</span> <span class="mi">32</span><span class="o">,</span> <span class="mi">128</span><span class="o">,</span> <span class="mi">1</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// initialize array with contents, you can specify dimensions of array using Shape parameter while creating array.</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">c</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">array</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mi">1</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="mi">3</span><span class="o">,</span> <span class="mi">4</span><span class="o">,</span> <span class="mi">5</span><span class="o">,</span> <span class="mi">6</span><span class="o">),</span> <span class="n">shape</span> <span class="k">=</span> <span class="nc">Shape</span><span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="mi">3</span><span class="o">))</span>
</pre></div>
</div>
<p>This is similar to the way you use <code class="docutils literal"><span class="pre">numpy</span></code>.</p>
</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>We provide some basic ndarray operations, like arithmetic and slice operations.</p>
<div class="section" id="arithmetic-operations">
<span id="arithmetic-operations"></span><h3>Arithmetic Operations<a class="headerlink" href="#arithmetic-operations" title="Permalink to this headline"></a></h3>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">scala</span><span class="o">></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">a</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">zeros</span><span class="o">(</span><span class="mi">100</span><span class="o">,</span> <span class="mi">50</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span>
<span class="n">ml</span><span class="o">.</span><span class="n">dmlc</span><span class="o">.</span><span class="n">mxnet</span><span class="o">.</span><span class="nc">Shape</span> <span class="k">=</span> <span class="o">(</span><span class="mi">100</span><span class="o">,</span><span class="mi">50</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">b</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">ones</span><span class="o">(</span><span class="mi">100</span><span class="o">,</span> <span class="mi">50</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// c and d will be calculated in parallel here!</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">c</span> <span class="k">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">d</span> <span class="k">=</span> <span class="n">a</span> <span class="o">-</span> <span class="n">b</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// inplace operation, b's contents will be modified, but c and d won't be affected.</span>
<span class="n">scala</span><span class="o">></span> <span class="n">b</span> <span class="o">+=</span> <span class="n">d</span>
</pre></div>
</div>
</div>
<div class="section" id="multiplication-division-operations">
<span id="multiplication-division-operations"></span><h3>Multiplication/Division Operations<a class="headerlink" href="#multiplication-division-operations" title="Permalink to this headline"></a></h3>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">scala</span><span class="o">></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="c1">//Multiplication</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">ndones</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">ones</span><span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="mi">1</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">ndtwos</span> <span class="k">=</span> <span class="n">ndones</span> <span class="o">*</span> <span class="mi">2</span>
<span class="n">scala</span><span class="o">></span> <span class="n">ndtwos</span><span class="o">.</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">2.0</span><span class="o">,</span> <span class="mf">2.0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="o">(</span><span class="n">ndones</span> <span class="o">*</span> <span class="n">ndones</span><span class="o">).</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="o">(</span><span class="n">ndtwos</span> <span class="o">*</span> <span class="n">ndtwos</span><span class="o">).</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">4.0</span><span class="o">,</span> <span class="mf">4.0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">ndtwos</span> <span class="o">*=</span> <span class="n">ndtwos</span> <span class="c1">// inplace</span>
<span class="n">scala</span><span class="o">></span> <span class="n">ndtwos</span><span class="o">.</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">4.0</span><span class="o">,</span> <span class="mf">4.0</span><span class="o">)</span>
<span class="c1">//Division</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">ndones</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">ones</span><span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="mi">1</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">ndzeros</span> <span class="k">=</span> <span class="n">ndones</span> <span class="o">-</span> <span class="mi">1</span><span class="n">f</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">ndhalves</span> <span class="k">=</span> <span class="n">ndones</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">scala</span><span class="o">></span> <span class="n">ndhalves</span><span class="o">.</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">0.5</span><span class="o">,</span> <span class="mf">0.5</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="o">(</span><span class="n">ndhalves</span> <span class="o">/</span> <span class="n">ndhalves</span><span class="o">).</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="o">(</span><span class="n">ndones</span> <span class="o">/</span> <span class="n">ndones</span><span class="o">).</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="o">(</span><span class="n">ndzeros</span> <span class="o">/</span> <span class="n">ndones</span><span class="o">).</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">ndhalves</span> <span class="o">/=</span> <span class="n">ndhalves</span>
<span class="n">scala</span><span class="o">></span> <span class="n">ndhalves</span><span class="o">.</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)</span>
</pre></div>
</div>
</div>
<div class="section" id="slice-operations">
<span id="slice-operations"></span><h3>Slice Operations<a class="headerlink" href="#slice-operations" title="Permalink to this headline"></a></h3>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">scala</span><span class="o">></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">a</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">array</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mi">1</span><span class="n">f</span><span class="o">,</span> <span class="mi">2</span><span class="n">f</span><span class="o">,</span> <span class="mi">3</span><span class="n">f</span><span class="o">,</span> <span class="mi">4</span><span class="n">f</span><span class="o">,</span> <span class="mi">5</span><span class="n">f</span><span class="o">,</span> <span class="mi">6</span><span class="n">f</span><span class="o">),</span> <span class="n">shape</span> <span class="k">=</span> <span class="nc">Shape</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="mi">2</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">a1</span> <span class="k">=</span> <span class="n">a</span><span class="o">.</span><span class="n">slice</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">assert</span><span class="o">(</span><span class="n">a1</span><span class="o">.</span><span class="n">shape</span> <span class="o">===</span> <span class="nc">Shape</span><span class="o">(</span><span class="mi">1</span><span class="o">,</span> <span class="mi">2</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="n">assert</span><span class="o">(</span><span class="n">a1</span><span class="o">.</span><span class="n">toArray</span> <span class="o">===</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">3</span><span class="n">f</span><span class="o">,</span> <span class="mi">4</span><span class="n">f</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">a2</span> <span class="k">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">slice</span><span class="o">(</span><span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">assert</span><span class="o">(</span><span class="n">a2</span><span class="o">.</span><span class="n">shape</span> <span class="o">===</span> <span class="nc">Shape</span><span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="mi">2</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="n">assert</span><span class="o">(</span><span class="n">a2</span><span class="o">.</span><span class="n">toArray</span> <span class="o">===</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">3</span><span class="n">f</span><span class="o">,</span> <span class="mi">4</span><span class="n">f</span><span class="o">,</span> <span class="mi">5</span><span class="n">f</span><span class="o">,</span> <span class="mi">6</span><span class="n">f</span><span class="o">))</span>
</pre></div>
</div>
</div>
<div class="section" id="dot-product">
<span id="dot-product"></span><h3>Dot Product<a class="headerlink" href="#dot-product" title="Permalink to this headline"></a></h3>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">scala</span><span class="o">></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">arr1</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">array</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mi">1</span><span class="n">f</span><span class="o">,</span> <span class="mi">2</span><span class="n">f</span><span class="o">),</span> <span class="n">shape</span> <span class="k">=</span> <span class="nc">Shape</span><span class="o">(</span><span class="mi">1</span><span class="o">,</span> <span class="mi">2</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">arr2</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">array</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mi">3</span><span class="n">f</span><span class="o">,</span> <span class="mi">4</span><span class="n">f</span><span class="o">),</span> <span class="n">shape</span> <span class="k">=</span> <span class="nc">Shape</span><span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="mi">1</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">res</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">dot</span><span class="o">(</span><span class="n">arr1</span><span class="o">,</span> <span class="n">arr2</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">res</span><span class="o">.</span><span class="n">shape</span>
<span class="n">ml</span><span class="o">.</span><span class="n">dmlc</span><span class="o">.</span><span class="n">mxnet</span><span class="o">.</span><span class="nc">Shape</span> <span class="k">=</span> <span class="o">(</span><span class="mi">1</span><span class="o">,</span><span class="mi">1</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">res</span><span class="o">.</span><span class="n">toArray</span>
<span class="nc">Array</span><span class="o">[</span><span class="kt">Float</span><span class="o">]</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">11.0</span><span class="o">)</span>
</pre></div>
</div>
</div>
<div class="section" id="save-and-load-ndarray">
<span id="save-and-load-ndarray"></span><h3>Save and Load NDArray<a class="headerlink" href="#save-and-load-ndarray" title="Permalink to this headline"></a></h3>
<p>You can use MXNet functions to save and load a list or dictionary of NDArrays from file systems, as follows:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">scala</span><span class="o">></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">a</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">zeros</span><span class="o">(</span><span class="mi">100</span><span class="o">,</span> <span class="mi">200</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">b</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">zeros</span><span class="o">(</span><span class="mi">100</span><span class="o">,</span> <span class="mi">200</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// save list of NDArrays</span>
<span class="n">scala</span><span class="o">></span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">save</span><span class="o">(</span><span class="s">"/path/to/array/file"</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="n">a</span><span class="o">,</span> <span class="n">b</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// save dictionary of NDArrays to AWS S3</span>
<span class="n">scala</span><span class="o">></span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">save</span><span class="o">(</span><span class="s">"s3://path/to/s3/array"</span><span class="o">,</span> <span class="nc">Map</span><span class="o">(</span><span class="s">"A"</span> <span class="o">-></span> <span class="n">a</span><span class="o">,</span> <span class="s">"B"</span> <span class="o">-></span> <span class="n">b</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="c1">// save list of NDArrays to hdfs.</span>
<span class="n">scala</span><span class="o">></span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">save</span><span class="o">(</span><span class="s">"hdfs://path/to/hdfs/array"</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="n">a</span><span class="o">,</span> <span class="n">b</span><span class="o">))</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">from_file</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="s">"/path/to/array/file"</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">from_s3</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="s">"s3://path/to/s3/array"</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">from_hdfs</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="s">"hdfs://path/to/hdfs/array"</span><span class="o">)</span>
</pre></div>
</div>
<p>The good thing about using the <code class="docutils literal"><span class="pre">save</span></code> and <code class="docutils literal"><span class="pre">load</span></code> interface is that you can use the format across all <code class="docutils literal"><span class="pre">mxnet</span></code> language bindings. They also already support Amazon S3 and HDFS.</p>
</div>
<div class="section" id="multi-device-support">
<span id="multi-device-support"></span><h3>Multi-Device Support<a class="headerlink" href="#multi-device-support" title="Permalink to this headline"></a></h3>
<p>Device information is stored in the <code class="docutils literal"><span class="pre">mxnet.Context</span></code> structure. When creating NDArray in MXNet, you can use the context argument (the default is the CPU context) to create arrays on specific devices as follows:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">scala</span><span class="o">></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">cpu_a</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">zeros</span><span class="o">(</span><span class="mi">100</span><span class="o">,</span> <span class="mi">200</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">cpu_a</span><span class="o">.</span><span class="n">context</span>
<span class="n">ml</span><span class="o">.</span><span class="n">dmlc</span><span class="o">.</span><span class="n">mxnet</span><span class="o">.</span><span class="nc">Context</span> <span class="k">=</span> <span class="n">cpu</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">ctx</span> <span class="k">=</span> <span class="nc">Context</span><span class="o">.</span><span class="n">gpu</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="k">val</span> <span class="n">gpu_b</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">zeros</span><span class="o">(</span><span class="nc">Shape</span><span class="o">(</span><span class="mi">100</span><span class="o">,</span> <span class="mi">200</span><span class="o">),</span> <span class="n">ctx</span><span class="o">)</span>
<span class="n">scala</span><span class="o">></span> <span class="n">gpu_b</span><span class="o">.</span><span class="n">context</span>
<span class="n">ml</span><span class="o">.</span><span class="n">dmlc</span><span class="o">.</span><span class="n">mxnet</span><span class="o">.</span><span class="nc">Context</span> <span class="k">=</span> <span class="n">gpu</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span>
</pre></div>
</div>
<p>Currently, we <em>do not</em> allow operations among arrays from different contexts. To manually enable this, use the <code class="docutils literal"><span class="pre">copyto</span></code> member function to copy the content to different devices, and continue computation:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> scala> import ml.dmlc.mxnet._
scala> val x = NDArray.zeros(100, 200)
scala> val ctx = Context.gpu(0)
scala> val y = NDArray.zeros(Shape(100, 200), ctx)
scala> val z = x + y
mxnet.base.MXNetError: [13:29:12] src/ndarray/ndarray.cc:33:
Check failed: lhs.ctx() == rhs.ctx() operands context mismatch
scala> val cpu_y = NDArray.zeros(100, 200)
scala> y.copyto(cpu_y)
scala> val z = x + cpu_y
</pre></div>
</div>
</div>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li>See <a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training.</li>
</ul>
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<ul>
<li><a class="reference internal" href="#">NDArray API</a><ul>
<li><a class="reference internal" href="#create-ndarray">Create NDArray</a></li>
<li><a class="reference internal" href="#ndarray-operations">NDArray Operations</a><ul>
<li><a class="reference internal" href="#arithmetic-operations">Arithmetic Operations</a></li>
<li><a class="reference internal" href="#multiplication-division-operations">Multiplication/Division Operations</a></li>
<li><a class="reference internal" href="#slice-operations">Slice Operations</a></li>
<li><a class="reference internal" href="#dot-product">Dot Product</a></li>
<li><a class="reference internal" href="#save-and-load-ndarray">Save and Load NDArray</a></li>
<li><a class="reference internal" href="#multi-device-support">Multi-Device Support</a></li>
</ul>
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<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
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