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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="nd">@torch.jit.script</span>
<span class="k">def</span> <span class="nf">RNN</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">W_h</span><span class="p">,</span> <span class="n">U_h</span><span class="p">,</span> <span class="n">W_y</span><span class="p">,</span> <span class="n">b_h</span><span class="p">,</span> <span class="n">b_y</span><span class="p">):</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
<span class="n">h</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="err">@</span> <span class="n">W_h</span> <span class="o">+</span> <span class="n">h</span> <span class="err">@</span> <span class="n">U_h</span> <span class="o">+</span> <span class="n">b_h</span><span class="p">)</span>
<span class="n">y</span> <span class="o">+=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">h</span> <span class="err">@</span> <span class="n">W_y</span> <span class="o">+</span> <span class="n">b_y</span><span class="p">)]</span>
<span class="k">if</span> <span class="n">t</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"stats: "</span><span class="p">,</span> <span class="n">h</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">h</span><span class="o">.</span><span class="n">var</span><span class="p">())</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">h</span>
</code></pre></div></div>
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<h3>Hybrid Front-End</h3>
<p>A new hybrid front-end provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.</p>
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<h3>Distributed Training</h3>
<p>Optimize performance in both research and production by taking advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++.</p>
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="n">dist</span>
<span class="kn">from</span> <span class="nn">torch.nn.parallel</span> <span class="kn">import</span> <span class="n">DistributedDataParallel</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="s">'gloo'</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">DistributedDataParallel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</code></pre></div></div>
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</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="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">add</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="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
<span class="k">print</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="n">b</span><span class="p">)</span>
</code></pre></div></div>
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<h3>Python-First</h3>
<p>PyTorch is not a Python binding into a monolithic C++ framework. It’s built to be deeply integrated into Python so it can be used with popular libraries and packages such as Cython and Numba.</p>
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<h3>Tools & Libraries</h3>
<p>An active community of researchers and developers have built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas from computer vision to reinforcement learning.</p>
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="n">models</span>
<span class="n">resnet18</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">alexnet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">squeezenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">squeezenet1_0</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">vgg16</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">densenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet161</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">inception</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">inception_v3</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</code></pre></div></div>
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="n">dummy_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</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="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dummy_input</span><span class="p">,</span> <span class="s">"alexnet.onnx"</span><span class="p">)</span>
</code></pre></div></div>
</div>
</div>
</div>
<div class="col-md-6">
<div class="feature-content">
<h3>Native ONNX Support</h3>
<p>Export models in the standard ONNX (Open Neural Network Exchange) format for direct access to ONNX-compatible platforms, runtimes, visualizers, and more.</p>
</div>
</div>
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<div class="feature-content">
<h3>C++ Front-End</h3>
<p>The C++ frontend is a pure C++ interface to PyTorch that follows the design and architecture of the established Python frontend. It is intended to enable research in high performance, low latency and bare metal C++ applications.</p>
</div>
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<div class="feature-content-holder">
<div class="language-cpp highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="cp">#include <torch/torch.h>
</span>
<span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Linear</span> <span class="n">model</span><span class="p">(</span><span class="n">num_features</span><span class="p">,</span> <span class="mi">1</span><span class="p">);</span>
<span class="n">torch</span><span class="o">::</span><span class="n">optim</span><span class="o">::</span><span class="n">SGD</span> <span class="n">optimizer</span><span class="p">(</span><span class="n">model</span><span class="o">-></span><span class="n">parameters</span><span class="p">());</span>
<span class="k">auto</span> <span class="n">data_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">data_loader</span><span class="p">(</span><span class="n">dataset</span><span class="p">);</span>
<span class="k">for</span> <span class="p">(</span><span class="kt">size_t</span> <span class="n">epoch</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">epoch</span> <span class="o"><</span> <span class="mi">10</span><span class="p">;</span> <span class="o">++</span><span class="n">epoch</span><span class="p">)</span> <span class="p">{</span>
<span class="k">for</span> <span class="p">(</span><span class="k">auto</span> <span class="n">batch</span> <span class="o">:</span> <span class="n">data_loader</span><span class="p">)</span> <span class="p">{</span>
<span class="k">auto</span> <span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">-></span><span class="n">forward</span><span class="p">(</span><span class="n">batch</span><span class="p">.</span><span class="n">data</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_function</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">batch</span><span class="p">.</span><span class="n">target</span><span class="p">);</span>
<span class="n">loss</span><span class="p">.</span><span class="n">backward</span><span class="p">();</span>
<span class="n">optimizer</span><span class="p">.</span><span class="n">step</span><span class="p">();</span>
<span class="p">}</span>
<span class="p">}</span>
</code></pre></div></div>
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<div class="language-sh highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="nb">export </span><span class="nv">IMAGE_FAMILY</span><span class="o">=</span><span class="s2">"pytorch-latest-cpu"</span>
<span class="nb">export </span><span class="nv">ZONE</span><span class="o">=</span><span class="s2">"us-west1-b"</span>
<span class="nb">export </span><span class="nv">INSTANCE_NAME</span><span class="o">=</span><span class="s2">"my-instance"</span>
gcloud compute instances create <span class="nv">$INSTANCE_NAME</span> <span class="se">\</span>
<span class="nt">--zone</span><span class="o">=</span><span class="nv">$ZONE</span> <span class="se">\</span>
<span class="nt">--image-family</span><span class="o">=</span><span class="nv">$IMAGE_FAMILY</span> <span class="se">\</span>
<span class="nt">--image-project</span><span class="o">=</span>deeplearning-platform-release
</code></pre></div></div>
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