Using multi-node with LocalExecutor

[LopezGG]

I noticed LocalExecutor has a hard-coded value for nnodes.

NeMo-Run/src/nemo_run/core/execution/local.py

Lines 53 to 54 in b4e2258

	def nnodes(self) -> int:
	return 1

Is there a reason multi-nodes are disabled ? It feeds into torch_run which seems to support multi-nodes

NeMo-Run/src/nemo_run/run/torchx_backend/components/torchrun.py

Lines 104 to 124 in b4e2258

	if max_nnodes == 1:
	# using port 0 makes elastic chose a free random port which is ok
	# for single-node jobs since all workers run under a single agent
	# When nnodes is 0 and max_nnodes is 1, it's still a single node job
	# but pending until the resources become available
	rdzv_endpoint = "localhost:0"
	num_nodes = nnodes_rep
	nproc_per_node = str(nproc_per_node)
	node_rank = "0"
	else:
	# for multi-node, rely on the rank0_env environment variable set by
	# the schedulers (see scheduler implementation for the actual env var this maps to)
	# some schedulers (e.g. aws batch) make the rank0's ip-addr available on all BUT on rank0
	# so default to "localhost" if the env var is not set or is empty
	# rdzv_endpoint bash resolves to something to the effect of
	# ${TORCHX_RANK0_HOST:=localhost}:29500
	# use $$ in the prefix to escape the '$' literal (rather than a string Template substitution argument)
	rdzv_endpoint = torchx_dist._noquote(f"$${ExecutorMacros.HEAD_NODE_IP_VAR}:{rdzv_port}")
	num_nodes = torchx_dist._noquote(f"$${ExecutorMacros.NUM_NODES_VAR}")
	nproc_per_node = str(nproc_per_node)
	node_rank = torchx_dist._noquote(f"$${ExecutorMacros.NODE_RANK_VAR}")

Asking because I am using this with AML where I can usually get multi-node working with torchrun

[hemildesai]

Hey @LopezGG, thanks for the issue. Can you share a sample command you run with torchrun? We can add extra options based on that to support multi-node via LocalExecutor.

Currently LocalExecutor assumes you are on your local workstation which would just be a single node.

[LopezGG]

Thank you for the quick reply @hemildesai . Usually, with AML I use something like

 torchrun  --nproc_per_node=${{inputs.nproc_per_node}} --nnodes=${{inputs.nnodes}}
  --node_rank=$NODE_RANK
  --master_addr=$MASTER_ADDR
  --master_port=$MASTER_PORT  train.py

https://pytorch.org/docs/stable/elastic/run.html

$NODE_RANK, $MASTER_ADDR, and $MASTER_PORT are set automatically by AML or Slurm or can be set manually. Change in your script might look like ( I need to test it though)

Changing num_nodes will feed into

NeMo-Run/src/nemo_run/core/execution/base.py

Lines 183 to 188 in b4e2258

	def nnodes(self) -> int:
	"""
	Helper function called by torchrun component
	to determine --nnodes.
	"""
	raise NotImplementedError

and can be called from