| .. _rpc-index: |
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| Distributed RPC Framework |
| ============================== |
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| 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. |
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| - :ref:`distributed-rpc-framework` |
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| Design Notes |
| ----------- |
| 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. |
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| - :ref:`distributed-autograd-design` |
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| The RRef design note covers the design of the :ref:`rref` (Remote REFerence) protocol used to refer to values on remote workers by the framework. |
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| - :ref:`remote-reference-protocol` |
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| Tutorials |
| --------- |
| The RPC tutorial introduces users to the RPC framework and provides two example applications using :ref:`torch.distributed.rpc<distributed-rpc-framework>` APIs. |
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| - `Getting started with Distributed RPC Framework <https://pytorch.org/tutorials/intermediate/rpc_tutorial.html>`__ |
