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<spanid="rpc-index"></span><h1>Distributed RPC Framework<aclass="headerlink" href="#distributed-rpc-framework" title="Permalink to this headline">¶</a></h1>
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<p>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.</p>
<h2>Design Notes<aclass="headerlink" href="#design-notes" title="Permalink to this headline">¶</a></h2>
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<p>The distributed autograd design note covers the design of the RPC-based distributed autograd framework that is useful for applications such as model parallel training.</p>
<p>The RRef design note covers the design of the <aclass="reference internal" href="rpc.html#rref"><spanclass="std std-ref">RRef</span></a> (Remote REFerence) protocol used to refer to values on remote workers by the framework.</p>
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<p>The RRef design note covers the design of the <aclass="reference internal" href="../rpc.html#rref"><spanclass="std std-ref">RRef</span></a> (Remote REFerence) protocol used to refer to values on remote workers by the framework.</p>
<h2>Tutorials<aclass="headerlink" href="#tutorials" title="Permalink to this headline">¶</a></h2>
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<p>The RPC tutorial introduces users to the RPC framework and provides two example applications using <aclass="reference internal" href="rpc.html#distributed-rpc-framework"><spanclass="std std-ref">torch.distributed.rpc</span></a> APIs.</p>
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<p>The RPC tutorial introduces users to the RPC framework and provides two example applications using <aclass="reference internal" href="../rpc.html#distributed-rpc-framework"><spanclass="std std-ref">torch.distributed.rpc</span></a> APIs.</p>
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<ulclass="simple">
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<li><p><aclass="reference external" href="https://pytorch.org/tutorials/intermediate/rpc_tutorial.html">Getting started with Distributed RPC Framework</a></p></li>
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