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May 10, 2021
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Update rpc_ddp_tutorial.rst #1516

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Update rpc_ddp_tutorial.rst
brianjo May 10, 2021
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Delete advanced_source/rpc_ddp_tutorial directory
brianjo May 10, 2021
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Create main.py
brianjo May 10, 2021
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Update rpc_ddp_tutorial.rst
brianjo May 10, 2021
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0 advanced_source/rpc_ddp_tutorial/main.py → advanced_source/rpc_ddp/main.py
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9 changes: 5 additions & 4 deletions advanced_source/rpc_ddp_tutorial.rst
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Expand Up @@ -90,7 +90,7 @@ The parameter server just initializes the RPC framework and waits for RPCs from
the trainers and master.


.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
.. literalinclude:: ../advanced_source/rpc_ddp/main.py
:language: py
:start-after: BEGIN run_worker
:end-before: END run_worker
Expand All @@ -107,7 +107,7 @@ embedding lookup on the parameter server using RemoteModule's ``forward``
and passes its output onto the FC layer.


.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
.. literalinclude:: ../advanced_source/rpc_ddp/main.py
:language: py
:start-after: BEGIN hybrid_model
:end-before: END hybrid_model
Expand All @@ -134,7 +134,7 @@ which is not supported by ``RemoteModule``.
Finally, we create our DistributedOptimizer using all the RRefs and define a
CrossEntropyLoss function.

.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
.. literalinclude:: ../advanced_source/rpc_ddp/main.py
:language: py
:start-after: BEGIN setup_trainer
:end-before: END setup_trainer
Expand All @@ -151,10 +151,11 @@ batch:
4) Use Distributed Autograd to execute a distributed backward pass using the loss.
5) Finally, run a Distributed Optimizer step to optimize all the parameters.

.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
.. literalinclude:: ../advanced_source/rpc_ddp/main.py
:language: py
:start-after: BEGIN run_trainer
:end-before: END run_trainer
.. code:: python

Source code for the entire example can be found `here <https://github.com/pytorch/examples/tree/master/distributed/rpc/ddp_rpc>`__.