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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<div class="section" id="c">
<h1>C++<a class="headerlink" href="#c" title="Permalink to this headline">¶</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If you are looking for the PyTorch C++ API docs, directly go <a class="reference external" href="https://pytorch.org/cppdocs/">here</a>.</p>
</div>
<p>PyTorch provides several features for working with C++, and it’s best to choose from them based on your needs. At a high level, the following support is available:</p>
<div class="section" id="torchscript-c-api">
<h2>TorchScript C++ API<a class="headerlink" href="#torchscript-c-api" title="Permalink to this headline">¶</a></h2>
<p><a class="reference external" href="https://pytorch.org/docs/stable/jit.html">TorchScript</a> allows PyTorch models defined in Python to be serialized and then loaded and run in C++ capturing the model code via compilation or tracing its execution. You can learn more in the <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_export.html">Loading a TorchScript Model in C++ tutorial</a>. This means you can define your models in Python as much as possible, but subsequently export them via TorchScript for doing no-Python execution in production or embedded environments. The TorchScript C++ API is used to interact with these models and the TorchScript execution engine, including:</p>
<ul class="simple">
<li><p>Loading serialized TorchScript models saved from Python</p></li>
<li><p>Doing simple model modifications if needed (e.g. pulling out submodules)</p></li>
<li><p>Constructing the input and doing preprocessing using C++ Tensor API</p></li>
</ul>
</div>
<div class="section" id="extending-pytorch-and-torchscript-with-c-extensions">
<h2>Extending PyTorch and TorchScript with C++ Extensions<a class="headerlink" href="#extending-pytorch-and-torchscript-with-c-extensions" title="Permalink to this headline">¶</a></h2>
<p>TorchScript can be augmented with user-supplied code through custom operators and custom classes.
Once registered with TorchScript, these operators and classes can be invoked in TorchScript code run from
Python or from C++ as part of a serialized TorchScript model. The <a class="reference external" href="https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a> tutorial walks through interfacing TorchScript with OpenCV. In addition to wrapping a function call with a custom operator, C++ classes and structs can be bound into TorchScript through a pybind11-like interface which is explained in the <a class="reference external" href="https://pytorch.org/tutorials/advanced/torch_script_custom_classes.html">Extending TorchScript with Custom C++ Classes</a> tutorial.</p>
</div>
<div class="section" id="tensor-and-autograd-in-c">
<h2>Tensor and Autograd in C++<a class="headerlink" href="#tensor-and-autograd-in-c" title="Permalink to this headline">¶</a></h2>
<p>Most of the tensor and autograd operations in PyTorch Python API are also available in the C++ API. These include:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor</span></code> methods such as <code class="docutils literal notranslate"><span class="pre">add</span></code> / <code class="docutils literal notranslate"><span class="pre">reshape</span></code> / <code class="docutils literal notranslate"><span class="pre">clone</span></code>. For the full list of methods available, please see: <a class="reference external" href="https://pytorch.org/cppdocs/api/classat_1_1_tensor.html">https://pytorch.org/cppdocs/api/classat_1_1_tensor.html</a></p></li>
<li><p>C++ tensor indexing API that looks and behaves the same as the Python API. For details on its usage, please see: <a class="reference external" href="https://pytorch.org/cppdocs/notes/tensor_indexing.html">https://pytorch.org/cppdocs/notes/tensor_indexing.html</a></p></li>
<li><p>The tensor autograd APIs and the <code class="docutils literal notranslate"><span class="pre">torch::autograd</span></code> package that are crucial for building dynamic neural networks in C++ frontend. For more details, please see: <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_autograd.html">https://pytorch.org/tutorials/advanced/cpp_autograd.html</a></p></li>
</ul>
</div>
<div class="section" id="authoring-models-in-c">
<h2>Authoring Models in C++<a class="headerlink" href="#authoring-models-in-c" title="Permalink to this headline">¶</a></h2>
<p>The “author in TorchScript, infer in C++” workflow requires model authoring to be done in TorchScript.
However, there might be cases where the model has to be authored in C++ (e.g. in workflows where a Python
component is undesirable). To serve such use cases, we provide the full capability of authoring and training a neural net model purely in C++, with familiar components such as <code class="docutils literal notranslate"><span class="pre">torch::nn</span></code> / <code class="docutils literal notranslate"><span class="pre">torch::nn::functional</span></code> / <code class="docutils literal notranslate"><span class="pre">torch::optim</span></code> that closely resemble the Python API.</p>
<ul class="simple">
<li><p>For an overview of the PyTorch C++ model authoring and training API, please see: <a class="reference external" href="https://pytorch.org/cppdocs/frontend.html">https://pytorch.org/cppdocs/frontend.html</a></p></li>
<li><p>For a detailed tutorial on how to use the API, please see: <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_frontend.html">https://pytorch.org/tutorials/advanced/cpp_frontend.html</a></p></li>
<li><p>Docs for components such as <code class="docutils literal notranslate"><span class="pre">torch::nn</span></code> / <code class="docutils literal notranslate"><span class="pre">torch::nn::functional</span></code> / <code class="docutils literal notranslate"><span class="pre">torch::optim</span></code> can be found at: <a class="reference external" href="https://pytorch.org/cppdocs/api/library_root.html">https://pytorch.org/cppdocs/api/library_root.html</a></p></li>
</ul>
</div>
<div class="section" id="packaging-for-c">
<h2>Packaging for C++<a class="headerlink" href="#packaging-for-c" title="Permalink to this headline">¶</a></h2>
<p>For guidance on how to install and link with libtorch (the library that contains all of the above C++ APIs), please see: <a class="reference external" href="https://pytorch.org/cppdocs/installing.html">https://pytorch.org/cppdocs/installing.html</a>. Note that on Linux there are two types of libtorch binaries provided: one compiled with GCC pre-cxx11 ABI and the other with GCC cxx11 ABI, and you should make the selection based on the GCC ABI your system is using.</p>
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<li><a class="reference internal" href="#">C++</a><ul>
<li><a class="reference internal" href="#torchscript-c-api">TorchScript C++ API</a></li>
<li><a class="reference internal" href="#extending-pytorch-and-torchscript-with-c-extensions">Extending PyTorch and TorchScript with C++ Extensions</a></li>
<li><a class="reference internal" href="#tensor-and-autograd-in-c">Tensor and Autograd in C++</a></li>
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