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<div class="section" id="stream">
<h1>Stream<a class="headerlink" href="#stream" title="Permalink to this headline">¶</a></h1>
<dl class="py class">
<dt id="torch.cuda.Stream">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.cuda.</span></code><code class="sig-name descname"><span class="pre">Stream</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>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream" title="Permalink to this definition">¶</a></dt>
<dd><p>Wrapper around a CUDA stream.</p>
<p>A CUDA stream is a linear sequence of execution that belongs to a specific
device, independent from other streams. See <a class="reference internal" href="../notes/cuda.html#cuda-semantics"><span class="std std-ref">CUDA semantics</span></a> for
details.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><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>) – a device on which to allocate
the stream. 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) or a negative
integer, this will use the current device.</p></li>
<li><p><strong>priority</strong> (<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>) – priority of the stream. Can be either
-1 (high priority) or 0 (low priority). By default, streams have
priority 0.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although CUDA versions >= 11 support more than two levels of
priorities, in PyTorch, we only support two levels of priorities.</p>
</div>
<dl class="py method">
<dt id="torch.cuda.Stream.query">
<code class="sig-name descname"><span class="pre">query</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.query" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks if all the work submitted has been completed.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A boolean indicating if all kernels in this stream are completed.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="torch.cuda.Stream.record_event">
<code class="sig-name descname"><span class="pre">record_event</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</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/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.record_event" title="Permalink to this definition">¶</a></dt>
<dd><p>Records an event.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>event</strong> (<a class="reference internal" href="torch.cuda.Event.html#torch.cuda.Event" title="torch.cuda.Event"><em>torch.cuda.Event</em></a><em>, </em><em>optional</em>) – event to record. If not given, a new one
will be allocated.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Recorded event.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="torch.cuda.Stream.synchronize">
<code class="sig-name descname"><span class="pre">synchronize</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
<dd><p>Wait for all the kernels in this stream to complete.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This is a wrapper around <code class="docutils literal notranslate"><span class="pre">cudaStreamSynchronize()</span></code>: see
<a class="reference external" href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html">CUDA Stream documentation</a> for more info.</p>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.cuda.Stream.wait_event">
<code class="sig-name descname"><span class="pre">wait_event</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
<dd><p>Makes all future work submitted to the stream wait for an event.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>event</strong> (<a class="reference internal" href="torch.cuda.Event.html#torch.cuda.Event" title="torch.cuda.Event"><em>torch.cuda.Event</em></a>) – an event to wait for.</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This is a wrapper around <code class="docutils literal notranslate"><span class="pre">cudaStreamWaitEvent()</span></code>: see
<a class="reference external" href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html">CUDA Stream documentation</a> for more info.</p>
<p>This function returns without waiting for <code class="xref py py-attr docutils literal notranslate"><span class="pre">event</span></code>: only future
operations are affected.</p>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.cuda.Stream.wait_stream">
<code class="sig-name descname"><span class="pre">wait_stream</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
<dd><p>Synchronizes with another stream.</p>
<p>All future work submitted to this stream will wait until all kernels
submitted to a given stream at the time of call complete.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>stream</strong> (<a class="reference internal" href="#torch.cuda.Stream" title="torch.cuda.Stream"><em>Stream</em></a>) – a stream to synchronize.</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function returns without waiting for currently enqueued
kernels in <a class="reference internal" href="torch.cuda.stream.html#torch.cuda.stream" title="torch.cuda.stream"><code class="xref py py-attr docutils literal notranslate"><span class="pre">stream</span></code></a>: only future operations are affected.</p>
</div>
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