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<div class="section" id="torch-cuda-manual-seed">
<h1>torch.cuda.manual_seed<a class="headerlink" href="#torch-cuda-manual-seed" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.cuda.manual_seed">
<code class="sig-prename descclassname"><span class="pre">torch.cuda.</span></code><code class="sig-name descname"><span class="pre">manual_seed</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers for the current GPU.
It’s safe to call this function if CUDA is not available; in that
case, it is silently ignored.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – The desired seed.</p>
</dd>
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<p class="admonition-title">Warning</p>
<p>If you are working with a multi-GPU model, this function is insufficient
to get determinism. To seed all GPUs, use <a class="reference internal" href="torch.cuda.manual_seed_all.html#torch.cuda.manual_seed_all" title="torch.cuda.manual_seed_all"><code class="xref py py-func docutils literal notranslate"><span class="pre">manual_seed_all()</span></code></a>.</p>
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