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Table of Contents

[ 1.6.0 ▼ ](http://pytorch.org/docs/versions.html)

Notes

  * [ Automatic Mixed Precision examples ](notes/amp_examples.html)
  * [ Autograd mechanics ](notes/autograd.html)
  * [ Broadcasting semantics ](notes/broadcasting.html)
  * [ CPU threading and TorchScript inference ](notes/cpu_threading_torchscript_inference.html)
  * [ CUDA semantics ](notes/cuda.html)
  * [ Distributed Data Parallel ](notes/ddp.html)
  * [ Extending PyTorch ](notes/extending.html)
  * [ Frequently Asked Questions ](notes/faq.html)
  * [ Features for large-scale deployments ](notes/large_scale_deployments.html)
  * [ Multiprocessing best practices ](notes/multiprocessing.html)
  * [ Reproducibility ](notes/randomness.html)
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Python API

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  * [ torch.distributions ](distributions.html)
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  * [ Complex Numbers ](complex_numbers.html)
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  * Distributed RPC Framework 
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  * [ torch.Storage ](storage.html)
  * [ torch.utils.bottleneck ](bottleneck.html)
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Community

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  * Distributed RPC Framework 
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Shortcuts

#  Distributed RPC Framework  ¶

The distributed RPC framework provides mechanisms for multi-machine model
training through a set of primitives to allow for remote communication, and a
higher-level API to automatically differentiate models split across several
machines.

War