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<div class="section" id="torch-cuda-max-memory-allocated">
<h1>torch.cuda.max_memory_allocated<a class="headerlink" href="#torch-cuda-max-memory-allocated" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.cuda.max_memory_allocated">
<code class="sig-prename descclassname"><span class="pre">torch.cuda.</span></code><code class="sig-name descname"><span class="pre">max_memory_allocated</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the maximum GPU memory occupied by tensors in bytes for a given
device.</p>
<p>By default, this returns the peak allocated memory since the beginning of
this program. <a class="reference internal" href="torch.cuda.reset_peak_memory_stats.html#torch.cuda.reset_peak_memory_stats" title="torch.cuda.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to
reset the starting point in tracking this metric. For example, these two
functions can measure the peak allocated memory usage of each iteration in a
training loop.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>device</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><em>torch.device</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – selected device. Returns
statistic for the current device, given by <a class="reference internal" href="torch.cuda.current_device.html#torch.cuda.current_device" title="torch.cuda.current_device"><code class="xref py py-func docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="torch.cuda.device.html#torch.cuda.device" title="torch.cuda.device"><code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code></a> is <code class="docutils literal notranslate"><span class="pre">None</span></code> (default).</p>
</dd>
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<div class="admonition note">
<p class="admonition-title">Note</p>
<p>See <a class="reference internal" href="../notes/cuda.html#cuda-memory-management"><span class="std std-ref">Memory management</span></a> for more details about GPU memory
management.</p>
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