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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<div class="section" id="onnx-backend-for-torchdynamo">
<h1>ONNX Backend for TorchDynamo<a class="headerlink" href="#onnx-backend-for-torchdynamo" title="Permalink to this heading">¶</a></h1>
<p>For a quick overview of <code class="docutils literal notranslate"><span class="pre">torch.compiler</span></code>, see <a class="reference internal" href="torch.compiler.html#torch-compiler-overview"><span class="std std-ref">torch.compiler</span></a>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The ONNX backend for torch.compile is a rapidly evolving beta technology.</p>
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<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.is_onnxrt_backend_supported">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_onnxrt_backend_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.is_onnxrt_backend_supported" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if ONNX Runtime dependencies are installed and usable
to support TorchDynamo backend integration; <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">>>></span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">is_onnxrt_backend_supported</span><span class="p">():</span>
<span class="o">...</span> <span class="nd">@torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="s2">"onnxrt"</span><span class="p">)</span>
<span class="o">...</span> <span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="o">...</span> <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">x</span>
<span class="o">...</span> <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">)))</span>
<span class="o">...</span> <span class="k">else</span><span class="p">:</span>
<span class="o">...</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"pip install onnx onnxscript-preview onnxruntime"</span><span class="p">)</span>
<span class="o">...</span>
</pre></div>
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<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
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