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intermediate_source/monarch_distributed_tutorial.rst

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@@ -198,14 +198,14 @@ environment.
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logger.info(f"{self.uid} trainer cleaned up")
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Actor endpoints can be invoked in a variety of patterns. We'll explore a concrete example in `Step 4: Execute the Training Workflow`_,
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but here are some common usages:
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but here is some pseudocode with common usages:
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.. code-block:: python
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try:
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# where mesh0 is 4 nodes * 8 GPUs
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proc_mesh = mesh0.spawn_procs({"gpus": 32})
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trainer_actors = proc_mesh.spawn(...)
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# where mesh0 is made of N nodes, each node having 8 GPUs
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proc_mesh = mesh0.spawn_procs({"gpus": 8})
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trainer_actors = proc_mesh.spawn("trainers", TrainerActor, ...)
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# Call on all ranks
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await trainer_actors.ping_rank.call()

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