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class="content"> <div class="page-tracker"></div> <div class="section" id="mxnet-perl-api"> <span id="mxnet-perl-api"></span><h1>MXNet - Perl API<a class="headerlink" href="#mxnet-perl-api" title="Permalink to this headline">¶</a></h1> <p>MXNet supports the Perl programming language. The MXNet Perl package brings flexible and efficient GPU computing and state-of-art deep learning to Perl. It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl. It also lets you construct and customize the state-of-art deep learning models in Perl, and apply them to tasks, such as image classification and data science challenges.</p> <p>One important thing to internalize is that Perl interface is written to be as close as possible to the Python’s API, so most if not all of Python’s documentation and examples should just work in Perl after making few changes in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; and in 99% of cases that’s all that is needed there. In addition please refer to <a class="reference external" href="https://metacpan.org/release/AI-MXNet">excellent metacpan doc interface</a> and to very detailed <a class="reference external" href="/versions/0.11.0/api/python/index.html">MXNet Python API Documentation</a>.</p> <p>AI::MXNet is seamlessly glued with PDL, the C++ level state can be easily initialized from PDL and the results can be transferred to PDL objects in order to allow you to use all the glory and power of the PDL!</p> <p>Here is how you can perform tensor or matrix computation in Perl with AI::MXNet and PDL:</p> <div class="highlight-perl"><div class="highlight"><pre><span></span><span class="n">pdl</span><span class="o">></span> <span class="k">use</span> <span class="nn">AI::MXNet</span> <span class="sx">qw(mx)</span><span class="p">;</span> <span class="c1"># creates 'mx' module on the fly with the interface close to the Python's API</span> <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$arr</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">2</span><span class="n">x3</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nn">Data::Dumper::</span><span class="n">Dumper</span><span class="p">(</span><span class="nv">$arr</span><span class="o">-></span><span class="n">shape</span><span class="p">)</span> <span class="nv">$VAR1</span> <span class="o">=</span> <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span> <span class="p">];</span> <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="p">((</span><span class="nv">$arr</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span><span class="o">-></span><span class="n">aspdl</span><span class="p">)</span> <span class="c1">## converts AI::MXNet::NDArray object to PDL object</span> <span class="p">[</span> <span class="p">[</span><span class="mi">2</span> <span class="mi">2</span> <span class="mi">2</span><span class="p">]</span> <span class="p">[</span><span class="mi">2</span> <span class="mi">2</span> <span class="mi">2</span><span class="p">]</span> <span class="p">]</span> <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$arr</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span> <span class="c1">## init the NDArray from Perl array ref given in PDL::pdl constructor format</span> <span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">2</span><span class="n">x2</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$arr</span><span class="o">-></span><span class="n">aspdl</span> <span class="p">[</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span> <span class="p">[</span><span class="mi">3</span> <span class="mi">4</span><span class="p">]</span> <span class="p">]</span> <span class="c1">## init the NDArray from PDL but be aware that PDL methods expect the dimensions order in column major format</span> <span class="c1">## AI::MXNet::NDArray is row major</span> <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">array</span><span class="p">(</span><span class="n">sequence</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="n">aspdl</span> <span class="c1">## 3 rows, 2 columns</span> <span class="p">[</span> <span class="p">[</span><span class="mi">0</span> <span class="mi">1</span><span class="p">]</span> <span class="p">[</span><span class="mi">2</span> <span class="mi">3</span><span class="p">]</span> <span class="p">[</span><span class="mi">4</span> <span class="mi">5</span><span class="p">]</span> <span class="p">]</span> </pre></div> </div> <div class="section" id="perl-api-reference"> <span id="perl-api-reference"></span><h2>Perl API Reference<a class="headerlink" href="#perl-api-reference" title="Permalink to this headline">¶</a></h2> <ul class="simple"> <li><a class="reference internal" href="module.html"><span class="doc">Module API</span></a> is a flexible high-level interface for training neural networks.</li> <li><a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> performs operations on NDArrays to assemble neural networks from layers.</li> <li><a class="reference internal" href="io.html"><span class="doc">IO Data Loading API</span></a> performs parsing and data loading.</li> <li><a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> performs vector/matrix/tensor operations.</li> <li><a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> performs multi-GPU and multi-host distributed training.</li> </ul> </div> </div> </div> </div> <div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> <h3><a href="../../index.html">Table Of Contents</a></h3> <ul> <li><a class="reference internal" href="#">MXNet - Perl API</a><ul> <li><a class="reference internal" href="#perl-api-reference">Perl API Reference</a></li> </ul> </li> </ul> </div> </div> </div><div class="footer"> <div class="section-disclaimer"> <div class="container"> <div> <img height="60" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png"/> <p> Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. </p> <p> "Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation." </p> </div> </div> </div> </div>  </div> <script crossorigin="anonymous" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script> <script src="../../_static/js/sidebar.js" type="text/javascript"></script> <script src="../../_static/js/search.js" type="text/javascript"></script> <script src="../../_static/js/navbar.js" type="text/javascript"></script> <script src="../../_static/js/clipboard.min.js" type="text/javascript"></script> <script src="../../_static/js/copycode.js" type="text/javascript"></script> <script src="../../_static/js/page.js" type="text/javascript"></script> <script src="../../_static/js/docversion.js" type="text/javascript"></script> <script type="text/javascript"> $('body').ready(function () { $('body').css('visibility', 'visible'); }); </script> </body> </html>

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