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<div class="section" id="torch-cuda-memory-stats">
<h1>torch.cuda.memory_stats<a class="headerlink" href="#torch-cuda-memory-stats" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.cuda.memory_stats">
<code class="sig-prename descclassname"><span class="pre">torch.cuda.</span></code><code class="sig-name descname"><span class="pre">memory_stats</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#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_stats" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a dictionary of CUDA memory allocator statistics for a
given device.</p>
<p>The return value of this function is a dictionary of statistics, each of
which is a non-negative integer.</p>
<p>Core statistics:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
number of allocation requests received by the memory allocator.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
amount of allocated memory.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"segment.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
number of reserved segments from <code class="docutils literal notranslate"><span class="pre">cudaMalloc()</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
amount of reserved memory.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"active.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
number of active memory blocks.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
amount of active memory.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"inactive_split.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
number of inactive, non-releasable memory blocks.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"inactive_split_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"</span></code>:
amount of inactive, non-releasable memory.</p></li>
</ul>
<p>For these core statistics, values are broken down as follows.</p>
<p>Pool type:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">all</span></code>: combined statistics across all memory pools.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">large_pool</span></code>: statistics for the large allocation pool
(as of October 2019, for size >= 1MB allocations).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">small_pool</span></code>: statistics for the small allocation pool
(as of October 2019, for size < 1MB allocations).</p></li>
</ul>
<p>Metric type:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">current</span></code>: current value of this metric.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">peak</span></code>: maximum value of this metric.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">allocated</span></code>: historical total increase in this metric.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">freed</span></code>: historical total decrease in this metric.</p></li>
</ul>
<p>In addition to the core statistics, we also provide some simple event
counters:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"num_alloc_retries"</span></code>: number of failed <code class="docutils literal notranslate"><span class="pre">cudaMalloc</span></code> calls that
result in a cache flush and retry.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"num_ooms"</span></code>: number of out-of-memory errors thrown.</p></li>
</ul>
<p>The caching allocator can be configured via ENV to not split blocks larger than a
defined size (see Memory Management section of the Cuda Semantics documentation).
This helps avoid memory framentation but may have a performance
penalty. Additional outputs to assist with tuning and evaluating impact:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"max_split_size"</span></code>: blocks above this size will not be split.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"oversize_allocations.{current,peak,allocated,freed}"</span></code>:
number of over-size allocation requests received by the memory allocator.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"oversize_segments.{current,peak,allocated,freed}"</span></code>:
number of over-size reserved segments from <code class="docutils literal notranslate"><span class="pre">cudaMalloc()</span></code>.</p></li>
</ul>
<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
statistics 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>
</dl>
<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>
</div>
</dd></dl>
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