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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="torch-backends">
<h1>torch.backends<a class="headerlink" href="#torch-backends" title="Permalink to this headline">¶</a></h1>
<p><cite>torch.backends</cite> controls the behavior of various backends that PyTorch supports.</p>
<p>These backends include:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.mkl</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.mkldnn</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.openmp</span></code></p></li>
</ul>
<div class="section" id="torch-backends-cuda">
<h2>torch.backends.cuda<a class="headerlink" href="#torch-backends-cuda" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.backends.cuda.is_built">
<code class="sig-prename descclassname">torch.backends.cuda.</code><code class="sig-name descname">is_built</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_built"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.backends.cuda.is_built" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns whether PyTorch is built with CUDA support. Note that this
doesn’t necessarily mean CUDA is available; just that if this PyTorch
binary were run a machine with working CUDA drivers and devices, we
would be able to use it.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.backends.cuda.matmul.allow_tf32">
<code class="sig-prename descclassname">torch.backends.cuda.matmul.</code><code class="sig-name descname">allow_tf32</code><a class="headerlink" href="#torch.backends.cuda.matmul.allow_tf32" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls whether TensorFloat-32 tensor cores may be used in matrix
multiplications on Ampere or newer GPUs. See <a class="reference internal" href="notes/cuda.html#tf32-on-ampere"><span class="std std-ref">TensorFloat-32(TF32) on Ampere devices</span></a>.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.backends.cuda.cufft_plan_cache">
<code class="sig-prename descclassname">torch.backends.cuda.</code><code class="sig-name descname">cufft_plan_cache</code><a class="headerlink" href="#torch.backends.cuda.cufft_plan_cache" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">cufft_plan_cache</span></code> caches the cuFFT plans</p>
<dl class="attribute">
<dt id="torch.backends.cuda.size">
<code class="sig-name descname">size</code><a class="headerlink" href="#torch.backends.cuda.size" title="Permalink to this definition">¶</a></dt>
<dd><p>A readonly <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a> that shows the number of plans currently in the cuFFT plan cache.</p>
</dd></dl>
<dl class="attribute">
<dt id="max_size">
<code class="sig-name descname">max_size</code><a class="headerlink" href="#max_size" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a> that controls cache capacity of cuFFT plan.</p>
</dd></dl>
<dl class="method">
<dt id="clear">
<code class="sig-name descname">clear</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#clear" title="Permalink to this definition">¶</a></dt>
<dd><p>Clears the cuFFT plan cache.</p>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="torch-backends-cudnn">
<h2>torch.backends.cudnn<a class="headerlink" href="#torch-backends-cudnn" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.backends.cudnn.version">
<code class="sig-prename descclassname">torch.backends.cudnn.</code><code class="sig-name descname">version</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#version"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.backends.cudnn.version" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the version of cuDNN</p>
</dd></dl>
<dl class="function">
<dt id="torch.backends.cudnn.is_available">
<code class="sig-prename descclassname">torch.backends.cudnn.</code><code class="sig-name descname">is_available</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#is_available"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.backends.cudnn.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a bool indicating if CUDNN is currently available.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.backends.cudnn.enabled">
<code class="sig-prename descclassname">torch.backends.cudnn.</code><code class="sig-name descname">enabled</code><a class="headerlink" href="#torch.backends.cudnn.enabled" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls whether cuDNN is enabled.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.backends.cudnn.allow_tf32">
<code class="sig-prename descclassname">torch.backends.cudnn.</code><code class="sig-name descname">allow_tf32</code><a class="headerlink" href="#torch.backends.cudnn.allow_tf32" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls where TensorFloat-32 tensor cores may be used in cuDNN
convolutions on Ampere or newer GPUs. See <a class="reference internal" href="notes/cuda.html#tf32-on-ampere"><span class="std std-ref">TensorFloat-32(TF32) on Ampere devices</span></a>.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.backends.cudnn.deterministic">
<code class="sig-prename descclassname">torch.backends.cudnn.</code><code class="sig-name descname">deterministic</code><a class="headerlink" href="#torch.backends.cudnn.deterministic" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that, if True, causes cuDNN to only use deterministic convolution algorithms.
See also <a class="reference internal" href="generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled" title="torch.are_deterministic_algorithms_enabled"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.are_deterministic_algorithms_enabled()</span></code></a> and
<a class="reference internal" href="generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a>.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.backends.cudnn.benchmark">
<code class="sig-prename descclassname">torch.backends.cudnn.</code><code class="sig-name descname">benchmark</code><a class="headerlink" href="#torch.backends.cudnn.benchmark" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that, if True, causes cuDNN to benchmark multiple convolution algorithms
and select the fastest.</p>
</dd></dl>
</div>
<div class="section" id="torch-backends-mkl">
<h2>torch.backends.mkl<a class="headerlink" href="#torch-backends-mkl" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.backends.mkl.is_available">
<code class="sig-prename descclassname">torch.backends.mkl.</code><code class="sig-name descname">is_available</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#is_available"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.backends.mkl.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns whether PyTorch is built with MKL support.</p>
</dd></dl>
</div>
<div class="section" id="torch-backends-mkldnn">
<h2>torch.backends.mkldnn<a class="headerlink" href="#torch-backends-mkldnn" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.backends.mkldnn.is_available">
<code class="sig-prename descclassname">torch.backends.mkldnn.</code><code class="sig-name descname">is_available</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#is_available"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.backends.mkldnn.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns whether PyTorch is built with MKL-DNN support.</p>
</dd></dl>
</div>
<div class="section" id="torch-backends-openmp">
<h2>torch.backends.openmp<a class="headerlink" href="#torch-backends-openmp" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.backends.openmp.is_available">
<code class="sig-prename descclassname">torch.backends.openmp.</code><code class="sig-name descname">is_available</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/openmp.html#is_available"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.backends.openmp.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns whether PyTorch is built with OpenMP support.</p>
</dd></dl>
</div>
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