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intermediate_source/rpc_tutorial.rst

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@@ -285,7 +285,11 @@ distinguish their role. Rank 0 is always the agent, and all other ranks are
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observers. As agent as server as master, repeatedly call ``run_episode`` and
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``finish_episode`` until the running reward surpasses the reward threshold
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specified by the environment. All observers just passively waiting for commands
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from the agent.
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from the agent. The code is wrapped by
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`rpc.init_rpc <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.init_rpc>`__ and
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`rpc.shutdown <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.shutdown>`__,
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which initializes and terminates RPC instances respectively. More details are
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available in the API page.
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.. code:: python
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# other ranks are the observer
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rpc.init_rpc(OBSERVER_NAME.format(rank), rank=rank, world_size=world_size)
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# observers passively waiting for instructions from the agent
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# block until all rpcs finish, and shutdown the RPC instance
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rpc.shutdown()
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@@ -334,7 +340,7 @@ In this example, we show how to use RPC as the communication vehicle to pass
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date across workers, and how to use RRef to reference remote objects. It is true
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that you could build the entire structure directly on top of ``ProcessGroup``
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``send`` and ``recv`` APIs or use other communication/RPC libraries. However,
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by using `torch.dstributed.rpc`, you can get the native support plus
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by using `torch.distributed.rpc`, you can get the native support plus
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continuously optimized performance under the hood.
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Next, we will show how to combine RPC and RRef with distributed autograd and
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func()
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# block until all rpcs finish
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# block until all rpcs finish, and shutdown the RPC instance
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rpc.shutdown()
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mp.set_start_method('spawn')

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