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<!DOCTYPE html>
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<title>Distributed communication package - torch.distributed — PyTorch master documentation</title>
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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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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<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#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>
</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#id16"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id17"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id18"><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-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>
</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#id19"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id20"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id21"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id22"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id23"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id24"><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#id25"><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#id26"><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#id27"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id28"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id29"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id30"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id31"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id32"><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#id33"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id34"><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#id35"><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#id36"><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#id37"><span class="hidden-section">dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id38"><span class="hidden-section">dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#id39">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#id40">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#id42"><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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#affine-grid"><span class="hidden-section">affine_grid</span></a></li>
</ul>
</li>
</ul>
</li>
<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>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="optim.html#algorithms">Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="optim.html#how-to-adjust-learning-rate">How to adjust Learning Rate</a></li>
</ul>
</li>
<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>
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<div class="section" id="module-torch.distributed">
<span id="distributed-communication-package-torch-distributed"></span><h1>Distributed communication package - torch.distributed<a class="headerlink" href="#module-torch.distributed" title="Permalink to this headline">¶</a></h1>
<p>torch.distributed provides an MPI-like interface for exchanging tensor
data accross multi-machine networks. It supports a few different backends
and initialization methods.</p>
<p>Currently torch.distributed supports three backends, each with
different capabilities. The table below shows which functions are available
for use with CPU / CUDA tensors.
MPI supports cuda only iff the implementation used to build PyTorch supports it.</p>
<table border="1" class="docutils">
<colgroup>
<col width="29%" />
<col width="12%" />
<col width="12%" />
<col width="12%" />
<col width="12%" />
<col width="12%" />
<col width="12%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Backend</th>
<th class="head" colspan="2"><code class="docutils literal"><span class="pre">tcp</span></code></th>
<th class="head" colspan="2"><code class="docutils literal"><span class="pre">gloo</span></code></th>
<th class="head" colspan="2"><code class="docutils literal"><span class="pre">mpi</span></code></th>
</tr>
<tr class="row-even"><th class="head">Device</th>
<th class="head">CPU</th>
<th class="head">GPU</th>
<th class="head">CPU</th>
<th class="head">GPU</th>
<th class="head">CPU</th>
<th class="head">GPU</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-odd"><td>send</td>
<td>✓</td>
<td>✘</td>
<td>✘</td>
<td>✘</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-even"><td>recv</td>
<td>✓</td>
<td>✘</td>
<td>✘</td>
<td>✘</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-odd"><td>broadcast</td>
<td>✓</td>
<td>✘</td>
<td>✓</td>
<td>✓</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-even"><td>all_reduce</td>
<td>✓</td>
<td>✘</td>
<td>✓</td>
<td>✓</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-odd"><td>reduce</td>
<td>✓</td>
<td>✘</td>
<td>✘</td>
<td>✘</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-even"><td>all_gather</td>
<td>✓</td>
<td>✘</td>
<td>✘</td>
<td>✘</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-odd"><td>gather</td>
<td>✓</td>
<td>✘</td>
<td>✘</td>
<td>✘</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-even"><td>scatter</td>
<td>✓</td>
<td>✘</td>
<td>✘</td>
<td>✘</td>
<td>✓</td>
<td>?</td>
</tr>
<tr class="row-odd"><td>barrier</td>
<td>✓</td>
<td>✘</td>
<td>✓</td>
<td>✓</td>
<td>✓</td>
<td>?</td>
</tr>
</tbody>
</table>
<div class="section" id="initialization">
<h2>Initialization<a class="headerlink" href="#initialization" title="Permalink to this headline">¶</a></h2>
<p>The package needs to be initialized using the <a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal"><span class="pre">torch.distributed.init_process_group()</span></code></a>
function before calling any other methods.</p>
<dl class="function">
<dt id="torch.distributed.init_process_group">
<code class="descclassname">torch.distributed.</code><code class="descname">init_process_group</code><span class="sig-paren">(</span><em>backend</em>, <em>init_method='env://'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#init_process_group"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.init_process_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the distributed package.</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>backend</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#str" title="(in Python v2.7)"><em>str</em></a>) – Name of the backend to use. Depending on build-time configuration
valid values include: <code class="docutils literal"><span class="pre">tcp</span></code>, <code class="docutils literal"><span class="pre">mpi</span></code> and <code class="docutils literal"><span class="pre">gloo</span></code>.</li>
<li><strong>init_method</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#str" title="(in Python v2.7)"><em>str</em></a><em>, </em><em>optional</em>) – URL specifying how to initialize the package.</li>
<li><strong>world_size</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>) – Number of processes participating in the job.</li>
<li><strong>rank</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>) – Rank of the current process.</li>
<li><strong>group_name</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#str" title="(in Python v2.7)"><em>str</em></a><em>, </em><em>optional</em>) – Group name. See description of init methods.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.get_rank">
<code class="descclassname">torch.distributed.</code><code class="descname">get_rank</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#get_rank"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.get_rank" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the rank of current process.</p>
<p>Rank is a unique identifier assigned to each process withing a distributed
group. They are always consecutive integers ranging from 0 to <code class="docutils literal"><span class="pre">world_size</span></code>.</p>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.get_world_size">
<code class="descclassname">torch.distributed.</code><code class="descname">get_world_size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#get_world_size"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.get_world_size" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of processes in the distributed group.</p>
</dd></dl>
<hr class="docutils" />
<p>Currently three initialization methods are supported:</p>
<div class="section" id="tcp-initialization">
<h3>TCP initialization<a class="headerlink" href="#tcp-initialization" title="Permalink to this headline">¶</a></h3>
<p>Initialization will utilize a network address reachable from all processes.
If the address belongs to one of the machines, initialization requires that all processes
have manually specified ranks.</p>
<p>Alternatively, the address has to be a valid IP multicast address, in which case,
ranks can be assigned automatically. Multicast initialization also supports
a <code class="docutils literal"><span class="pre">group_name</span></code> argument, which allows you to use the same address for multiple jobs,
as long as they use different group names.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="c1"># Use address of one of the machines</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">init_method</span><span class="o">=</span><span class="s1">'tcp://10.1.1.20:23456'</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="c1"># or a multicast address - rank will be assigned automatically if unspecified</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">init_method</span><span class="o">=</span><span class="s1">'tcp://[ff15:1e18:5d4c:4cf0:d02d:b659:53ba:b0a7]:23456'</span><span class="p">,</span>
<span class="n">world_size</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="shared-file-system-initialization">
<h3>Shared file-system initialization<a class="headerlink" href="#shared-file-system-initialization" title="Permalink to this headline">¶</a></h3>
<p>Another initialization method makes use of a file system shared and visible from
all machines in a group. The URL should start with <code class="docutils literal"><span class="pre">file://</span></code> and contain a path
to a non-existent file (in an existing directory) on a shared file system.
This initialization method also supports a <code class="docutils literal"><span class="pre">group_name</span></code> argument, which allows you to
use the same shared file path for multiple jobs, as long as they use different
group names.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">This method assumes that the file system supports locking using <code class="docutils literal"><span class="pre">fcntl</span></code> - most
local systems and NFS support it.</p>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="c1"># Rank will be assigned automatically if unspecified</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">init_method</span><span class="o">=</span><span class="s1">'file:///mnt/nfs/sharedfile'</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">group_name</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">group</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="environment-variable-initialization">
<h3>Environment variable initialization<a class="headerlink" href="#environment-variable-initialization" title="Permalink to this headline">¶</a></h3>
<p>This method will read the configuration from environment variables, allowing
one to fully customize how the information is obtained. The variables to be set
are:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">MASTER_PORT</span></code> - required; has to be a free port on machine with rank 0</li>
<li><code class="docutils literal"><span class="pre">MASTER_ADDR</span></code> - required (except for rank 0); address of rank 0 node</li>
<li><code class="docutils literal"><span class="pre">WORLD_SIZE</span></code> - required; can be set either here, or in a call to init function</li>
<li><code class="docutils literal"><span class="pre">RANK</span></code> - required; can be set either here, or in a call to init function</li>
</ul>
<p>The machine with rank 0 will be used to set up all connections.</p>
<p>This is the default method, meaning that <code class="docutils literal"><span class="pre">init_method</span></code> does not have to be specified (or
can be <code class="docutils literal"><span class="pre">env://</span></code>).</p>
</div>
</div>
<div class="section" id="groups">
<h2>Groups<a class="headerlink" href="#groups" title="Permalink to this headline">¶</a></h2>
<p>By default collectives operate on the default group (also called the world) and
require all processes to enter the distributed function call. However, some workloads can benefit
from more fine-grained communication. This is where distributed groups come
into play. <a class="reference internal" href="#torch.distributed.new_group" title="torch.distributed.new_group"><code class="xref py py-func docutils literal"><span class="pre">new_group()</span></code></a> function can be
used to create new groups, with arbitrary subsets of all processes. It returns
an opaque group handle that can be given as a <code class="docutils literal"><span class="pre">group</span></code> argument to all collectives
(collectives are distributed functions to exchange information in certain well-known programming patterns).</p>
<dl class="function">
<dt id="torch.distributed.new_group">
<code class="descclassname">torch.distributed.</code><code class="descname">new_group</code><span class="sig-paren">(</span><em>ranks=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#new_group"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.new_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new distributed group.</p>
<p>This function requires that all processes in the main group (i.e. all
processes that are part of the distributed job) enter this function, even
if they are not going to be members of the group. Additionally, groups
should be created in the same order in all processes.</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>ranks</strong> (<em>list</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>) – List of ranks of group members.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">A handle of distributed group that can be given to collective calls.</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="point-to-point-communication">
<h2>Point-to-point communication<a class="headerlink" href="#point-to-point-communication" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.distributed.send">
<code class="descclassname">torch.distributed.</code><code class="descname">send</code><span class="sig-paren">(</span><em>tensor</em>, <em>dst</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#send"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.send" title="Permalink to this definition">¶</a></dt>
<dd><p>Sends a tensor synchronously.</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>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to send.</li>
<li><strong>dst</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Destination rank.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.recv">
<code class="descclassname">torch.distributed.</code><code class="descname">recv</code><span class="sig-paren">(</span><em>tensor</em>, <em>src=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#recv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.recv" title="Permalink to this definition">¶</a></dt>
<dd><p>Receives a tensor synchronously.</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>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to fill with received data.</li>
<li><strong>src</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Source rank.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<p><a class="reference internal" href="#torch.distributed.isend" title="torch.distributed.isend"><code class="xref py py-func docutils literal"><span class="pre">isend()</span></code></a> and <a class="reference internal" href="#torch.distributed.irecv" title="torch.distributed.irecv"><code class="xref py py-func docutils literal"><span class="pre">irecv()</span></code></a>
return distributed request objects when used. In general, the type of this object is unspecified
as they should never be created manually, but they are guaranteed to support two methods:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">is_completed()</span></code> - returns True if the operation has finished</li>
<li><code class="docutils literal"><span class="pre">wait()</span></code> - will block the process until the operation is finished.
<code class="docutils literal"><span class="pre">is_completed()</span></code> is guaranteed to return True once it returns.</li>
</ul>
<dl class="function">
<dt id="torch.distributed.isend">
<code class="descclassname">torch.distributed.</code><code class="descname">isend</code><span class="sig-paren">(</span><em>tensor</em>, <em>dst</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#isend"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.isend" title="Permalink to this definition">¶</a></dt>
<dd><p>Sends a tensor asynchronously.</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 send.</li>
<li><strong>dst</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Destination rank.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A distributed request object.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.irecv">
<code class="descclassname">torch.distributed.</code><code class="descname">irecv</code><span class="sig-paren">(</span><em>tensor</em>, <em>src</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#irecv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.irecv" title="Permalink to this definition">¶</a></dt>
<dd><p>Receives a tensor asynchronously.</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 fill with received data.</li>
<li><strong>src</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Source rank.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A distributed request object.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="collective-functions">
<h2>Collective functions<a class="headerlink" href="#collective-functions" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.distributed.broadcast">
<code class="descclassname">torch.distributed.</code><code class="descname">broadcast</code><span class="sig-paren">(</span><em>tensor</em>, <em>src</em>, <em>group=<object object></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#broadcast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.broadcast" title="Permalink to this definition">¶</a></dt>
<dd><p>Broadcasts the tensor to the whole group.</p>
<p><code class="docutils literal"><span class="pre">tensor</span></code> must have the same number of elements in all processes
participating in the collective.</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>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Data to be sent if <code class="docutils literal"><span class="pre">src</span></code> is the rank of current
process, and tensor to be used to save received data otherwise.</li>
<li><strong>src</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Source rank.</li>
<li><strong>group</strong> (<em>optional</em>) – Group of the collective.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.all_reduce">
<code class="descclassname">torch.distributed.</code><code class="descname">all_reduce</code><span class="sig-paren">(</span><em>tensor</em>, <em>op=<object object></em>, <em>group=<object object></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#all_reduce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.all_reduce" title="Permalink to this definition">¶</a></dt>
<dd><p>Reduces the tensor data accross all machines in such a way that all get
the final result.</p>
<p>After the call <code class="docutils literal"><span class="pre">tensor</span></code> is going to be bitwise identical in all processes.</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>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Input and output of the collective. The function
operates in-place.</li>
<li><strong>op</strong> (<em>optional</em>) – One of the values from <code class="docutils literal"><span class="pre">torch.distributed.reduce_op</span></code>
enum. Specifies an operation used for element-wise reductions.</li>
<li><strong>group</strong> (<em>optional</em>) – Group of the collective.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.reduce">
<code class="descclassname">torch.distributed.</code><code class="descname">reduce</code><span class="sig-paren">(</span><em>tensor</em>, <em>dst</em>, <em>op=<object object></em>, <em>group=<object object></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#reduce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.reduce" title="Permalink to this definition">¶</a></dt>
<dd><p>Reduces the tensor data accross all machines.</p>
<p>Only the process with rank <code class="docutils literal"><span class="pre">dst</span></code> is going to receive the final result.</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>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Input and output of the collective. The function
operates in-place.</li>
<li><strong>op</strong> (<em>optional</em>) – One of the values from <code class="docutils literal"><span class="pre">torch.distributed.reduce_op</span></code>
enum. Specifies an operation used for element-wise reductions.</li>
<li><strong>group</strong> (<em>optional</em>) – Group of the collective.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.all_gather">
<code class="descclassname">torch.distributed.</code><code class="descname">all_gather</code><span class="sig-paren">(</span><em>tensor_list</em>, <em>tensor</em>, <em>group=<object object></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#all_gather"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.all_gather" title="Permalink to this definition">¶</a></dt>
<dd><p>Gathers tensors from the whole group in a list.</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>tensor_list</strong> (<em>list</em><em>[</em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>]</em>) – Output list. It should contain
correctly-sized tensors to be used for output of the collective.</li>
<li><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to be broadcast from current process.</li>
<li><strong>group</strong> (<em>optional</em>) – Group of the collective.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.gather">
<code class="descclassname">torch.distributed.</code><code class="descname">gather</code><span class="sig-paren">(</span><em>tensor</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#gather"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.gather" title="Permalink to this definition">¶</a></dt>