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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>
<li class="toctree-l2"><a class="reference internal" href="notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#replicationpad3d"><span class="hidden-section">ReplicationPad3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#zeropad2d"><span class="hidden-section">ZeroPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#constantpad2d"><span class="hidden-section">ConstantPad2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activations">Non-linear Activations</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#relu"><span class="hidden-section">ReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#relu6"><span class="hidden-section">ReLU6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#elu"><span class="hidden-section">ELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#selu"><span class="hidden-section">SELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#prelu"><span class="hidden-section">PReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#leakyrelu"><span class="hidden-section">LeakyReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#threshold"><span class="hidden-section">Threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hardtanh"><span class="hidden-section">Hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#sigmoid"><span class="hidden-section">Sigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#tanh"><span class="hidden-section">Tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#logsigmoid"><span class="hidden-section">LogSigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softplus"><span class="hidden-section">Softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softshrink"><span class="hidden-section">Softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softsign"><span class="hidden-section">Softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#tanhshrink"><span class="hidden-section">Tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmin"><span class="hidden-section">Softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmax"><span class="hidden-section">Softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmax2d"><span class="hidden-section">Softmax2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#logsoftmax"><span class="hidden-section">LogSoftmax</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#normalization-layers">Normalization layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batchnorm1d"><span class="hidden-section">BatchNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batchnorm2d"><span class="hidden-section">BatchNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#batchnorm3d"><span class="hidden-section">BatchNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm1d"><span class="hidden-section">InstanceNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm2d"><span class="hidden-section">InstanceNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm3d"><span class="hidden-section">InstanceNorm3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#recurrent-layers">Recurrent layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rnn"><span class="hidden-section">RNN</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lstm"><span class="hidden-section">LSTM</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#gru"><span class="hidden-section">GRU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rnncell"><span class="hidden-section">RNNCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lstmcell"><span class="hidden-section">LSTMCell</span></a></li>
<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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#bilinear"><span class="hidden-section">Bilinear</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#alphadropout"><span class="hidden-section">AlphaDropout</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#embeddingbag"><span class="hidden-section">EmbeddingBag</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#distance-functions">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosinesimilarity"><span class="hidden-section">CosineSimilarity</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pairwisedistance"><span class="hidden-section">PairwiseDistance</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#poissonnllloss"><span class="hidden-section">PoissonNLLLoss</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#bcewithlogitsloss"><span class="hidden-section">BCEWithLogitsLoss</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#tripletmarginloss"><span class="hidden-section">TripletMarginLoss</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#upsample"><span class="hidden-section">Upsample</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#dataparallel-layers-multi-gpu-distributed">DataParallel layers (multi-GPU, distributed)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dataparallel"><span class="hidden-section">DataParallel</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#distributeddataparallel"><span class="hidden-section">DistributedDataParallel</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#weight-norm"><span class="hidden-section">weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#remove-weight-norm"><span class="hidden-section">remove_weight_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#id14"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id15"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id16"><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>
</ul>
</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool3d"><span class="hidden-section">adaptive_max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool3d"><span class="hidden-section">adaptive_avg_pool3d</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#id17"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id18"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id19"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id20"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id21"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id22"><span class="hidden-section">selu</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#id23"><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#glu"><span class="hidden-section">glu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id24"><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#id25"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id26"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id27"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id28"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id29"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id30"><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#id31"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id32"><span class="hidden-section">sigmoid</span></a></li>
</ul>
</li>
<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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#normalize"><span class="hidden-section">normalize</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#id33"><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#id34"><span class="hidden-section">dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#alpha-dropout"><span class="hidden-section">alpha_dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id35"><span class="hidden-section">dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id36"><span class="hidden-section">dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id37">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pairwise-distance"><span class="hidden-section">pairwise_distance</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosine-similarity"><span class="hidden-section">cosine_similarity</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id38">Loss functions</a><ul>
<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#poisson-nll-loss"><span class="hidden-section">poisson_nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosine-embedding-loss"><span class="hidden-section">cosine_embedding_loss</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#hinge-embedding-loss"><span class="hidden-section">hinge_embedding_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#l1-loss"><span class="hidden-section">l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#mse-loss"><span class="hidden-section">mse_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#margin-ranking-loss"><span class="hidden-section">margin_ranking_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabel-margin-loss"><span class="hidden-section">multilabel_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabel-soft-margin-loss"><span class="hidden-section">multilabel_soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multi-margin-loss"><span class="hidden-section">multi_margin_loss</span></a></li>
<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#binary-cross-entropy-with-logits"><span class="hidden-section">binary_cross_entropy_with_logits</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#soft-margin-loss"><span class="hidden-section">soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#triplet-margin-loss"><span class="hidden-section">triplet_margin_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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id40"><span class="hidden-section">upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsample-nearest"><span class="hidden-section">upsample_nearest</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsample-bilinear"><span class="hidden-section">upsample_bilinear</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#grid-sample"><span class="hidden-section">grid_sample</span></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="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"><span class="pre">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>
</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 external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – device for which to return the name. This function is a
no-op if this argument is negative.</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/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a>(<a class="reference internal" href="storage.html#torch.FloatStorage.int" title="torch.FloatStorage.int">int</a>, <a class="reference internal" href="storage.html#torch.FloatStorage.int" title="torch.FloatStorage.int">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 external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – device for which to return the name. This function is a
no-op if this argument is negative.</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.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>
<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/2/library/functions.html#int" title="(in Python v2.7)"><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/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#long" title="(in Python v2.7)"><em>long</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"><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/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#long" title="(in Python v2.7)"><em>long</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"><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"><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 across 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>
<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 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">