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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<h1>PyTorch documentation<a class="headerlink" href="#pytorch-documentation" title="Permalink to this headline">¶</a></h1>
<p>PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.</p>
<p>Features described in this documentation are classified by release status:</p>
<blockquote>
<div><p><em>Stable:</em> These features will be maintained long-term and there should generally
be no major performance limitations or gaps in documentation.
We also expect to maintain backwards compatibility (although
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<p><em>Beta:</em> Features are tagged as Beta because the API may change based on
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<p><em>Prototype:</em> These features are typically not available as part of
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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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<li class="toctree-l1"><a class="reference internal" href="notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/randomness.html">Reproducibility</a></li>
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
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