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| |
| <h1 id="mxnet---perl-api">MXNet - Perl API</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 href="https://metacpan.org/release/AI-MXNet">excellent metacpan doc interface</a> and to very detailed |
| <a href="/versions/master/api/python">MXNet Python API Documentation</a>.</p> |
| |
| <p>AI::MXNet supports new imperative PyTorch like Gluon MXNet interface. Please get acquainted with this new interface |
| at <a href="https://www.d2l.ai/">Dive into Deep Learning</a>.</p> |
| |
| <p>For specific Perl Gluon usage please refer to Perl examples and tests directories on github, but be assured that the Python and Perl usage |
| are extremely close in order to make the use of the Python Gluon docs and examples as easy as possible.</p> |
| |
| <p>AI::MXNet is seamlessly glued with <a href="https://metacpan.org/release/PDL">PDL</a>, 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="language-perl highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">pdl</span><span class="o">></span> <span class="k">use</span> <span class="nn">AI::</span><span class="nv">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="nv">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$arr</span> <span class="o">=</span> <span class="nv">mx</span><span class="o">-></span><span class="nv">nd</span><span class="o">-></span><span class="nv">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> |
| <span class="o"><</span><span class="nn">AI::MXNet::</span><span class="nv">NDArray</span> <span class="mi">2</span><span class="nv">x3</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| |
| <span class="nv">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nn">Data::Dumper::</span><span class="nv">Dumper</span><span class="p">(</span><span class="nv">$arr</span><span class="o">-></span><span class="nv">shape</span><span class="p">)</span> |
| <span class="nv">$VAR1</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="mi">2</span><span class="p">,</span> |
| <span class="mi">3</span> |
| <span class="p">];</span> |
| |
| <span class="nv">pdl</span><span class="o">></span> <span class="k">print</span> <span class="p">((</span><span class="nv">$arr</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span><span class="o">-></span><span class="nv">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="nv">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$arr</span> <span class="o">=</span> <span class="nv">mx</span><span class="o">-></span><span class="nv">nd</span><span class="o">-></span><span class="nv">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span> <span class="c1">## init the NDArray from Perl array ref given in PDL::pdl constructor format</span> |
| <span class="o"><</span><span class="nn">AI::MXNet::</span><span class="nv">NDArray</span> <span class="mi">2</span><span class="nv">x2</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| <span class="nv">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$arr</span><span class="o">-></span><span class="nv">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="nv">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">mx</span><span class="o">-></span><span class="nv">nd</span><span class="o">-></span><span class="nv">array</span><span class="p">(</span><span class="nv">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="nv">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> |
| </code></pre></div></div> |
| |
| <p>Export/import to/from sparse MXNet tensors are supported via <a href="https://metacpan.org/release/PDL-CCS">PDL::CCS</a>. |
| Please check out the examples directory for the examples on how to use the sparse matrices.</p> |
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