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<div class="section" id="module-torch.cuda">
<span id="torch-cuda"></span><h1>torch.cuda<a class="headerlink" href="#module-torch.cuda" title="Permalink to this headline">¶</a></h1>
<p>This package adds support for CUDA tensor types, that implement the same
function as CPU tensors, but they utilize GPUs for computation.</p>
<p>It is lazily initialized, so you can always import it, and use
<a class="reference internal" href="#torch.cuda.is_available" title="torch.cuda.is_available"><code class="xref py py-func docutils literal notranslate"><span class="pre">is_available()</span></code></a> to determine if your system supports CUDA.</p>
<p><a class="reference internal" href="notes/cuda.html#cuda-semantics"><span class="std std-ref">CUDA semantics</span></a> has more details about working with CUDA.</p>
<dl class="function">
<dt id="torch.cuda.current_blas_handle">
<code class="descclassname">torch.cuda.</code><code class="descname">current_blas_handle</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#current_blas_handle"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.current_blas_handle" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns cublasHandle_t pointer to current cuBLAS handle</p>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.current_device">
<code class="descclassname">torch.cuda.</code><code class="descname">current_device</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#current_device"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.current_device" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the index of a currently selected device.</p>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.current_stream">
<code class="descclassname">torch.cuda.</code><code class="descname">current_stream</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#current_stream"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.current_stream" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a currently selected <a class="reference internal" href="#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a>.</p>
</dd></dl>
<dl class="class">
<dt id="torch.cuda.device">
<em class="property">class </em><code class="descclassname">torch.cuda.</code><code class="descname">device</code><span class="sig-paren">(</span><em>device</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#device"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.device" title="Permalink to this definition">¶</a></dt>
<dd><p>Context-manager that changes the selected device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><em>int</em></a>) – device index to select. It’s a no-op if
this argument is a negative integer or <code class="docutils literal notranslate"><span class="pre">None</span></code>.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.device_count">
<code class="descclassname">torch.cuda.</code><code class="descname">device_count</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#device_count"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.device_count" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of GPUs available.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.cuda.device_ctx_manager">
<code class="descclassname">torch.cuda.</code><code class="descname">device_ctx_manager</code><a class="headerlink" href="#torch.cuda.device_ctx_manager" title="Permalink to this definition">¶</a></dt>
<dd><p>alias of <a class="reference internal" href="#torch.cuda.device" title="torch.cuda.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.device</span></code></a></p>
</dd></dl>
<dl class="class">
<dt id="torch.cuda.device_of">
<em class="property">class </em><code class="descclassname">torch.cuda.</code><code class="descname">device_of</code><span class="sig-paren">(</span><em>obj</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#device_of"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.device_of" title="Permalink to this definition">¶</a></dt>
<dd><p>Context-manager that changes the current device to that of given object.</p>
<p>You can use both tensors and storages as arguments. If a given object is
not allocated on a GPU, this is a no-op.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>obj</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><em>Storage</em>) – object allocated on the selected device.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.empty_cache">
<code class="descclassname">torch.cuda.</code><code class="descname">empty_cache</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#empty_cache"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.empty_cache" title="Permalink to this definition">¶</a></dt>
<dd><p>Releases all unoccupied cached memory currently held by the caching
allocator so that those can be used in other GPU application and visible in
<cite>nvidia-smi</cite>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last"><a class="reference internal" href="#torch.cuda.empty_cache" title="torch.cuda.empty_cache"><code class="xref py py-meth docutils literal notranslate"><span class="pre">empty_cache()</span></code></a> doesn’t increase the amount of GPU
memory available for PyTorch. 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>
<dl class="function">
<dt id="torch.cuda.get_device_capability">
<code class="descclassname">torch.cuda.</code><code class="descname">get_device_capability</code><span class="sig-paren">(</span><em>device</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#get_device_capability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.get_device_capability" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets the cuda capability of a device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – device for which to return the
device capability. This function is a no-op if this argument is
a negative integer. Uses the current device, given by
<a class="reference internal" href="#torch.cuda.current_device" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>, if <a class="reference internal" href="#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).</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">the major and minor cuda capability of the device</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a>(<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)">int</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)">int</a>)</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.get_device_name">
<code class="descclassname">torch.cuda.</code><code class="descname">get_device_name</code><span class="sig-paren">(</span><em>device</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#get_device_name"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.get_device_name" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets the name of a device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – device for which to return the
name. This function is a no-op if this argument is a negative
integer. Uses the current device, given by <a class="reference internal" href="#torch.cuda.current_device" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="#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).</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.init">
<code class="descclassname">torch.cuda.</code><code class="descname">init</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#init"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.init" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize PyTorch’s CUDA state. You may need to call
this explicitly if you are interacting with PyTorch via
its C API, as Python bindings for CUDA functionality will not
be until this initialization takes place. Ordinary users
should not need this, as all of PyTorch’s CUDA methods
automatically initialize CUDA state on-demand.</p>
<p>Does nothing if the CUDA state is already initialized.</p>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.is_available">
<code class="descclassname">torch.cuda.</code><code class="descname">is_available</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#is_available"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a bool indicating if CUDA is currently available.</p>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.max_memory_allocated">
<code class="descclassname">torch.cuda.</code><code class="descname">max_memory_allocated</code><span class="sig-paren">(</span><em>device=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#max_memory_allocated"><span class="viewcode-link">[source]</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 usage by tensors in bytes for a given
device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><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" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="#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).</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">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>
<dl class="function">
<dt id="torch.cuda.max_memory_cached">
<code class="descclassname">torch.cuda.</code><code class="descname">max_memory_cached</code><span class="sig-paren">(</span><em>device=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#max_memory_cached"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.max_memory_cached" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the maximum GPU memory managed by the caching allocator in bytes
for a given device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><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" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="#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).</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">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>
<dl class="function">
<dt id="torch.cuda.memory_allocated">
<code class="descclassname">torch.cuda.</code><code class="descname">memory_allocated</code><span class="sig-paren">(</span><em>device=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#memory_allocated"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.memory_allocated" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current GPU memory usage by tensors in bytes for a given
device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><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" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="#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).</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This is likely less than the amount shown in <cite>nvidia-smi</cite> since some
unused memory can be held by the caching allocator and some context
needs to be created on GPU. 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>
<dl class="function">
<dt id="torch.cuda.memory_cached">
<code class="descclassname">torch.cuda.</code><code class="descname">memory_cached</code><span class="sig-paren">(</span><em>device=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#memory_cached"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.memory_cached" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current GPU memory managed by the caching allocator in bytes
for a given device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><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" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="#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).</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">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>
<dl class="function">
<dt id="torch.cuda.set_device">
<code class="descclassname">torch.cuda.</code><code class="descname">set_device</code><span class="sig-paren">(</span><em>device</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#set_device"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.set_device" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the current device.</p>
<p>Usage of this function is discouraged in favor of <a class="reference internal" href="#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">device</span></code></a>. In most
cases it’s better to use <code class="docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> environmental variable.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><em>int</em></a>) – selected device. This function is a no-op
if this argument is negative.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.stream">
<code class="descclassname">torch.cuda.</code><code class="descname">stream</code><span class="sig-paren">(</span><em>stream</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#stream"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.stream" title="Permalink to this definition">¶</a></dt>
<dd><p>Context-manager that selects a given stream.</p>
<p>All CUDA kernels queued within its context will be enqueued on a selected
stream.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>stream</strong> (<a class="reference internal" href="#torch.cuda.Stream" title="torch.cuda.Stream"><em>Stream</em></a>) – selected stream. This manager is a no-op if it’s
<code class="docutils literal notranslate"><span class="pre">None</span></code>.</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Streams are per-device, and this function changes the “current
stream” only for the currently selected device. It is illegal to select
a stream that belongs to a different device.</p>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.synchronize">
<code class="descclassname">torch.cuda.</code><code class="descname">synchronize</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#synchronize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.synchronize" title="Permalink to this definition">¶</a></dt>
<dd><p>Waits for all kernels in all streams on current device to complete.</p>
</dd></dl>
<div class="section" id="random-number-generator">
<h2>Random Number Generator<a class="headerlink" href="#random-number-generator" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.cuda.get_rng_state">
<code class="descclassname">torch.cuda.</code><code class="descname">get_rng_state</code><span class="sig-paren">(</span><em>device=-1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/random.html#get_rng_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.get_rng_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the random number generator state of the current
GPU as a ByteTensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – The device to return the RNG state of.
Default: -1 (i.e., use the current device).</td>
</tr>
</tbody>
</table>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">This function eagerly initializes CUDA.</p>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.set_rng_state">
<code class="descclassname">torch.cuda.</code><code class="descname">set_rng_state</code><span class="sig-paren">(</span><em>new_state</em>, <em>device=-1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/random.html#set_rng_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.set_rng_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the random number generator state of the current GPU.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>new_state</strong> (<a class="reference internal" href="tensors.html#torch.ByteTensor" title="torch.ByteTensor"><em>torch.ByteTensor</em></a>) – The desired state</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.manual_seed">
<code class="descclassname">torch.cuda.</code><code class="descname">manual_seed</code><span class="sig-paren">(</span><em>seed</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/random.html#manual_seed"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – The desired seed.</td>
</tr>
</tbody>
</table>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">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" 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>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.manual_seed_all">
<code class="descclassname">torch.cuda.</code><code class="descname">manual_seed_all</code><span class="sig-paren">(</span><em>seed</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/random.html#manual_seed_all"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.manual_seed_all" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers on all GPUs.
It’s safe to call this function if CUDA is not available; in that
case, it is silently ignored.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – The desired seed.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.seed">
<code class="descclassname">torch.cuda.</code><code class="descname">seed</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/random.html#seed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers to a random number for the current GPU.
It’s safe to call this function if CUDA is not available; in that
case, it is silently ignored.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">If you are working with a multi-GPU model, this function will only initialize
the seed on one GPU. To initialize all GPUs, use <a class="reference internal" href="#torch.cuda.seed_all" title="torch.cuda.seed_all"><code class="xref py py-func docutils literal notranslate"><span class="pre">seed_all()</span></code></a>.</p>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.seed_all">
<code class="descclassname">torch.cuda.</code><code class="descname">seed_all</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/random.html#seed_all"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.seed_all" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers to a random number on all GPUs.
It’s safe to call this function if CUDA is not available; in that
case, it is silently ignored.</p>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.initial_seed">
<code class="descclassname">torch.cuda.</code><code class="descname">initial_seed</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/random.html#initial_seed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.initial_seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current random seed of the current GPU.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">This function eagerly initializes CUDA.</p>
</div>
</dd></dl>
</div>
<div class="section" id="communication-collectives">
<h2>Communication collectives<a class="headerlink" href="#communication-collectives" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.cuda.comm.broadcast">
<code class="descclassname">torch.cuda.comm.</code><code class="descname">broadcast</code><span class="sig-paren">(</span><em>tensor</em>, <em>devices</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/comm.html#broadcast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.comm.broadcast" title="Permalink to this definition">¶</a></dt>
<dd><p>Broadcasts a tensor to a number of GPUs.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – tensor to broadcast.</li>
<li><strong>devices</strong> (<em>Iterable</em>) – an iterable of devices among which to broadcast.
Note that it should be like (src, dst1, dst2, …), the first element
of which is the source device to broadcast from.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A tuple containing copies of the <code class="docutils literal notranslate"><span class="pre">tensor</span></code>, placed on devices
corresponding to indices from <code class="docutils literal notranslate"><span class="pre">devices</span></code>.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.comm.broadcast_coalesced">
<code class="descclassname">torch.cuda.comm.</code><code class="descname">broadcast_coalesced</code><span class="sig-paren">(</span><em>tensors</em>, <em>devices</em>, <em>buffer_size=10485760</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/comm.html#broadcast_coalesced"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.comm.broadcast_coalesced" title="Permalink to this definition">¶</a></dt>
<dd><p>Broadcasts a sequence tensors to the specified GPUs.
Small tensors are first coalesced into a buffer to reduce the number
of synchronizations.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>tensors</strong> (<em>sequence</em>) – tensors to broadcast.</li>
<li><strong>devices</strong> (<em>Iterable</em>) – an iterable of devices among which to broadcast.
Note that it should be like (src, dst1, dst2, …), the first element
of which is the source device to broadcast from.</li>
<li><strong>buffer_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – maximum size of the buffer used for coalescing</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A tuple containing copies of the <code class="docutils literal notranslate"><span class="pre">tensor</span></code>, placed on devices
corresponding to indices from <code class="docutils literal notranslate"><span class="pre">devices</span></code>.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.comm.reduce_add">
<code class="descclassname">torch.cuda.comm.</code><code class="descname">reduce_add</code><span class="sig-paren">(</span><em>inputs</em>, <em>destination=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/comm.html#reduce_add"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.comm.reduce_add" title="Permalink to this definition">¶</a></dt>
<dd><p>Sums tensors from multiple GPUs.</p>
<p>All inputs should have matching shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>inputs</strong> (<em>Iterable</em><em>[</em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>]</em>) – an iterable of tensors to add.</li>
<li><strong>destination</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – a device on which the output will be
placed (default: current device).</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A tensor containing an elementwise sum of all inputs, placed on the
<code class="docutils literal notranslate"><span class="pre">destination</span></code> device.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.comm.scatter">
<code class="descclassname">torch.cuda.comm.</code><code class="descname">scatter</code><span class="sig-paren">(</span><em>tensor</em>, <em>devices</em>, <em>chunk_sizes=None</em>, <em>dim=0</em>, <em>streams=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/comm.html#scatter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.comm.scatter" title="Permalink to this definition">¶</a></dt>
<dd><p>Scatters tensor across multiple GPUs.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – tensor to scatter.</li>
<li><strong>devices</strong> (<em>Iterable</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>]</em>) – iterable of ints, specifying among which
devices the tensor should be scattered.</li>
<li><strong>chunk_sizes</strong> (<em>Iterable</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>]</em><em>, </em><em>optional</em>) – sizes of chunks to be placed on
each device. It should match <code class="docutils literal notranslate"><span class="pre">devices</span></code> in length and sum to
<code class="docutils literal notranslate"><span class="pre">tensor.size(dim)</span></code>. If not specified, the tensor will be divided
into equal chunks.</li>
<li><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – A dimension along which to chunk the tensor.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A tuple containing chunks of the <code class="docutils literal notranslate"><span class="pre">tensor</span></code>, spread across given
<code class="docutils literal notranslate"><span class="pre">devices</span></code>.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.cuda.comm.gather">
<code class="descclassname">torch.cuda.comm.</code><code class="descname">gather</code><span class="sig-paren">(</span><em>tensors</em>, <em>dim=0</em>, <em>destination=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/comm.html#gather"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.comm.gather" title="Permalink to this definition">¶</a></dt>
<dd><p>Gathers tensors from multiple GPUs.</p>
<p>Tensor sizes in all dimension different than <code class="docutils literal notranslate"><span class="pre">dim</span></code> have to match.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>tensors</strong> (<em>Iterable</em><em>[</em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>]</em>) – iterable of tensors to gather.</li>
<li><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – a dimension along which the tensors will be concatenated.</li>
<li><strong>destination</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – output device (-1 means CPU, default:
current device)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A tensor located on <code class="docutils literal notranslate"><span class="pre">destination</span></code> device, that is a result of
concatenating <code class="docutils literal notranslate"><span class="pre">tensors</span></code> along <code class="docutils literal notranslate"><span class="pre">dim</span></code>.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="streams-and-events">
<h2>Streams and events<a class="headerlink" href="#streams-and-events" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.cuda.Stream">
<em class="property">class </em><code class="descclassname">torch.cuda.</code><code class="descname">Stream</code><a class="reference internal" href="_modules/torch/cuda/streams.html#Stream"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><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 v3.7)"><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" 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.</li>
<li><strong>priority</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – priority of the stream. Lower numbers
represent higher priorities.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.cuda.Stream.query">
<code class="descname">query</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">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A boolean indicating if all kernels in this stream are completed.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Stream.record_event">
<code class="descname">record_event</code><span class="sig-paren">(</span><em>event=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Stream.record_event"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Stream.record_event" title="Permalink to this definition">¶</a></dt>
<dd><p>Records an event.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>event</strong> (<a class="reference internal" href="#torch.cuda.Event" title="torch.cuda.Event"><em>Event</em></a><em>, </em><em>optional</em>) – event to record. If not given, a new one
will be allocated.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Recorded event.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Stream.synchronize">
<code class="descname">synchronize</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">[source]</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="first admonition-title">Note</p>
<p class="last">This is a wrapper around <code class="docutils literal notranslate"><span class="pre">cudaStreamSynchronize()</span></code>: see
<a class="reference external" href="http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html">CUDA documentation</a> for more info.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Stream.wait_event">
<code class="descname">wait_event</code><span class="sig-paren">(</span><em>event</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Stream.wait_event"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>event</strong> (<a class="reference internal" href="#torch.cuda.Event" title="torch.cuda.Event"><em>Event</em></a>) – an event to wait for.</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first 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="http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html">CUDA
documentation</a> for more info.</p>
<p class="last">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="method">
<dt id="torch.cuda.Stream.wait_stream">
<code class="descname">wait_stream</code><span class="sig-paren">(</span><em>stream</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Stream.wait_stream"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>stream</strong> (<a class="reference internal" href="#torch.cuda.Stream" title="torch.cuda.Stream"><em>Stream</em></a>) – a stream to synchronize.</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This function returns without waiting for currently enqueued
kernels in <a class="reference internal" href="#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>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.cuda.Event">
<em class="property">class </em><code class="descclassname">torch.cuda.</code><code class="descname">Event</code><span class="sig-paren">(</span><em>enable_timing=False</em>, <em>blocking=False</em>, <em>interprocess=False</em>, <em>_handle=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Event"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Event" title="Permalink to this definition">¶</a></dt>
<dd><p>Wrapper around CUDA event.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>enable_timing</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – indicates if the event should measure time
(default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</li>
<li><strong>blocking</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – if <code class="docutils literal notranslate"><span class="pre">True</span></code>, <a class="reference internal" href="#torch.cuda.Event.wait" title="torch.cuda.Event.wait"><code class="xref py py-meth docutils literal notranslate"><span class="pre">wait()</span></code></a> will be blocking (default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</li>
<li><strong>interprocess</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – if <code class="docutils literal notranslate"><span class="pre">True</span></code>, the event can be shared between processes
(default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.cuda.Event.elapsed_time">
<code class="descname">elapsed_time</code><span class="sig-paren">(</span><em>end_event</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Event.elapsed_time"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the time elapsed before the event was recorded.</p>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Event.ipc_handle">
<code class="descname">ipc_handle</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Event.ipc_handle"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Event.ipc_handle" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an IPC handle of this event.</p>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Event.query">
<code class="descname">query</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Event.query"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Event.query" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks if the event has been recorded.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A boolean indicating if the event has been recorded.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Event.record">
<code class="descname">record</code><span class="sig-paren">(</span><em>stream=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Event.record"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Event.record" title="Permalink to this definition">¶</a></dt>
<dd><p>Records the event in a given stream.</p>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Event.synchronize">
<code class="descname">synchronize</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Event.synchronize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Event.synchronize" title="Permalink to this definition">¶</a></dt>
<dd><p>Synchronizes with the event.</p>
</dd></dl>
<dl class="method">
<dt id="torch.cuda.Event.wait">
<code class="descname">wait</code><span class="sig-paren">(</span><em>stream=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/streams.html#Event.wait"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.cuda.Event.wait" title="Permalink to this definition">¶</a></dt>
<dd><p>Makes a given stream wait for the event.</p>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="memory-management">
<h2>Memory management<a class="headerlink" href="#memory-management" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt>
<code class="descclassname">torch.cuda.</code><code class="descname">empty_cache</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#empty_cache"><span class="viewcode-link">[source]</span></a></dt>
<dd><p>Releases all unoccupied cached memory currently held by the caching
allocator so that those can be used in other GPU application and visible in
<cite>nvidia-smi</cite>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last"><a class="reference internal" href="#torch.cuda.empty_cache" title="torch.cuda.empty_cache"><code class="xref py py-meth docutils literal notranslate"><span class="pre">empty_cache()</span></code></a> doesn’t increase the amount of GPU
memory available for PyTorch. 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>
<dl class="function">
<dt>
<code class="descclassname">torch.cuda.</code><code class="descname">memory_allocated</code><span class="sig-paren">(</span><em>device=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#memory_allocated"><span class="viewcode-link">[source]</span></a></dt>
<dd><p>Returns the current GPU memory usage by tensors in bytes for a given
device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><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" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="#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).</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This is likely less than the amount shown in <cite>nvidia-smi</cite> since some
unused memory can be held by the caching allocator and some context
needs to be created on GPU. 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>
<dl class="function">
<dt>
<code class="descclassname">torch.cuda.</code><code class="descname">max_memory_allocated</code><span class="sig-paren">(</span><em>device=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda.html#max_memory_allocated"><span class="viewcode-link">[source]</span></a></dt>
<dd><p>Returns the maximum GPU memory usage by tensors in bytes for a given
device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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 v3.7)"><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" title="torch.cuda.current_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">current_device()</span></code></a>,
if <a class="reference internal" href="#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).</td>
</tr>
</tbody>
</table>
<div class="admonition note">