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<!DOCTYPE html>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#rnncell"><span class="hidden-section">RNNCell</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#grucell"><span class="hidden-section">GRUCell</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#linear-layers">Linear layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#linear"><span class="hidden-section">Linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#dropout-layers">Dropout layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout"><span class="hidden-section">Dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout2d"><span class="hidden-section">Dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#sparse-layers">Sparse layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#embedding"><span class="hidden-section">Embedding</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#loss-functions">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#l1loss"><span class="hidden-section">L1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#mseloss"><span class="hidden-section">MSELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#crossentropyloss"><span class="hidden-section">CrossEntropyLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#nllloss"><span class="hidden-section">NLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#nllloss2d"><span class="hidden-section">NLLLoss2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#bceloss"><span class="hidden-section">BCELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#marginrankingloss"><span class="hidden-section">MarginRankingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hingeembeddingloss"><span class="hidden-section">HingeEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabelmarginloss"><span class="hidden-section">MultiLabelMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#smoothl1loss"><span class="hidden-section">SmoothL1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmarginloss"><span class="hidden-section">SoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosineembeddingloss"><span class="hidden-section">CosineEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multimarginloss"><span class="hidden-section">MultiMarginLoss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#vision-layers">Vision layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pixelshuffle"><span class="hidden-section">PixelShuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsamplingnearest2d"><span class="hidden-section">UpsamplingNearest2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsamplingbilinear2d"><span class="hidden-section">UpsamplingBilinear2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#multi-gpu-layers">Multi-GPU layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dataparallel"><span class="hidden-section">DataParallel</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#utilities">Utilities</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#clip-grad-norm"><span class="hidden-section">clip_grad_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#packedsequence"><span class="hidden-section">PackedSequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pack-padded-sequence"><span class="hidden-section">pack_padded_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn.html#torch-nn-functional">torch.nn.functional</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn.html#convolution-functions">Convolution functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id13"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id14"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id15"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose3d"><span class="hidden-section">conv_transpose3d</span></a></li>
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</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#pooling-functions">Pooling functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool1d"><span class="hidden-section">avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool2d"><span class="hidden-section">avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool3d"><span class="hidden-section">avg_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool1d"><span class="hidden-section">max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool2d"><span class="hidden-section">max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool3d"><span class="hidden-section">max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool1d"><span class="hidden-section">max_unpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool2d"><span class="hidden-section">max_unpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool3d"><span class="hidden-section">max_unpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activation-functions">Non-linear activation functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id16"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id17"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id18"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id19"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id20"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#leaky-relu"><span class="hidden-section">leaky_relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id21"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rrelu"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id22"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hardshrink"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id23"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id24"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id25"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id26"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id27"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id28"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id29"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id30"><span class="hidden-section">sigmoid</span></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#normalization-functions">Normalization functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batch-norm"><span class="hidden-section">batch_norm</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#linear-functions">Linear functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id31"><span class="hidden-section">linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#dropout-functions">Dropout functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id32"><span class="hidden-section">dropout</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id33">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#nll-loss"><span class="hidden-section">nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#kl-div"><span class="hidden-section">kl_div</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cross-entropy"><span class="hidden-section">cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#binary-cross-entropy"><span class="hidden-section">binary_cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#smooth-l1-loss"><span class="hidden-section">smooth_l1_loss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#vision-functions">Vision functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pixel-shuffle"><span class="hidden-section">pixel_shuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad"><span class="hidden-section">pad</span></a></li>
</ul>
</li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="nn.html#torch-nn-init">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="optim.html">torch.optim</a><ul>
<li class="toctree-l2"><a class="reference internal" href="optim.html#how-to-use-an-optimizer">How to use an optimizer</a><ul>
<li class="toctree-l3"><a class="reference internal" href="optim.html#constructing-it">Constructing it</a></li>
<li class="toctree-l3"><a class="reference internal" href="optim.html#per-parameter-options">Per-parameter options</a></li>
<li class="toctree-l3"><a class="reference internal" href="optim.html#taking-an-optimization-step">Taking an optimization step</a><ul>
<li class="toctree-l4"><a class="reference internal" href="optim.html#optimizer-step"><code class="docutils literal"><span class="pre">optimizer.step()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="optim.html#optimizer-step-closure"><code class="docutils literal"><span class="pre">optimizer.step(closure)</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="optim.html#algorithms">Algorithms</a></li>
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<li class="toctree-l1"><a class="reference internal" href="autograd.html">torch.autograd</a><ul>
<li class="toctree-l2"><a class="reference internal" href="autograd.html#variable">Variable</a><ul>
<li class="toctree-l3"><a class="reference internal" href="autograd.html#api-compatibility">API compatibility</a></li>
<li class="toctree-l3"><a class="reference internal" href="autograd.html#in-place-operations-on-variables">In-place operations on Variables</a></li>
<li class="toctree-l3"><a class="reference internal" href="autograd.html#in-place-correctness-checks">In-place correctness checks</a></li>
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<li class="toctree-l2"><a class="reference internal" href="autograd.html#function"><span class="hidden-section">Function</span></a></li>
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<li class="toctree-l1"><a class="reference internal" href="multiprocessing.html">torch.multiprocessing</a><ul>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#strategy-management">Strategy management</a></li>
<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#sharing-cuda-tensors">Sharing CUDA tensors</a></li>
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<li class="toctree-l3"><a class="reference internal" href="multiprocessing.html#file-descriptor-file-descriptor">File descriptor - <code class="docutils literal"><span class="pre">file_descriptor</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="multiprocessing.html#file-system-file-system">File system - <code class="docutils literal"><span class="pre">file_system</span></code></a></li>
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<li class="toctree-l1"><a class="reference internal" href="legacy.html">torch.legacy</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">torch.cuda</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#communication-collectives">Communication collectives</a></li>
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<li class="toctree-l1"><a class="reference internal" href="ffi.html">torch.utils.ffi</a></li>
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<li class="toctree-l1"><a class="reference internal" href="model_zoo.html">torch.utils.model_zoo</a></li>
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<p class="caption"><span class="caption-text">torchvision Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="torchvision/torchvision.html">torchvision</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#mnist">MNIST</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#coco">COCO</a><ul>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#captions">Captions:</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#detection">Detection:</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#lsun">LSUN</a></li>
<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#imagefolder">ImageFolder</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#stl10">STL10</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/transforms.html#transforms-on-torch-tensor">Transforms on torch.*Tensor</a></li>
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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"><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"><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>idx</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>idx</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – device index to select. It’s a no-op if this argument
is negative.</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="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.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.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"><span class="pre">device</span></code></a>. In most
cases it’s better to use <code class="docutils literal"><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 external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><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"><span class="pre">None</span></code>.</td>
</tr>
</tbody>
</table>
</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="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"><span class="pre">tensor</span></code>, placed on devices
corresponding to indices from <code class="docutils literal"><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><em></em>) – an iterable of tensors to add.</li>
<li><strong>destination</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><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"><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/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>]</em><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/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>]</em><em></em><em>, </em><em>optional</em>) – sizes of chunks to be placed on
each device. It should match <code class="docutils literal"><span class="pre">devices</span></code> in length and sum to
<code class="docutils literal"><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/2/library/functions.html#int" title="(in Python v2.7)"><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"><span class="pre">tensor</span></code>, spread accross given
<code class="docutils literal"><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"><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><em></em>) – iterable of tensors to gather.</li>
<li><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><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/2/library/functions.html#int" title="(in Python v2.7)"><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"><span class="pre">destination</span></code> device, that is a result of
concatenating <code class="docutils literal"><span class="pre">tensors</span></code> along <code class="docutils literal"><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>
<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 external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>, </em><em>optional</em>) – a device on which to allocate the Stream.</li>
<li><strong>priority</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><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>
</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>
</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>
</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/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – indicates if the event should measure time
(default: False)</li>
<li><strong>blocking</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – if true, <a class="reference internal" href="#torch.cuda.Event.wait" title="torch.cuda.Event.wait"><code class="xref py py-meth docutils literal"><span class="pre">wait()</span></code></a> will be blocking (default: False)</li>
<li><strong>interprocess</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – if true, the event can be shared between processes
(default: False)</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>
</div>
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