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| <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="http://mxnet.io/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"><em>Module API</em></a> is a flexible high-level interface for training neural networks.</li> |
| <li><a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> performs operations on NDArrays to assemble neural networks from layers.</li> |
| <li><a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> performs parsing and data loading.</li> |
| <li><a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> performs vector/matrix/tensor operations.</li> |
| <li><a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> performs multi-GPU and multi-host distributed training.</li> |
| </ul> |
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