






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist





def 					lowercase_ (    *__snake_case     :							Tuple      )     -> str:





				'''simple docstring'''


				with open(__snake_case	,	"r"      ) as fh:
								fcntl.flock(__snake_case	,	fcntl.LOCK_EX      )
								try:
												print(*__snake_case      )
								finally:
												fcntl.flock(__snake_case	,	fcntl.LOCK_UN      )


__UpperCAmelCase							:   Optional[int]							=  int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__UpperCAmelCase							:   Union[str, Any]							=  torch.device("cuda", local_rank)
__UpperCAmelCase							:   str							=  socket.gethostname()

__UpperCAmelCase							:   Optional[int]							=  F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group("nccl")
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	__UpperCAmelCase							:   str							=  dist.get_rank()
	__UpperCAmelCase							:   Optional[Any]							=  dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise







def 					lowercase_ (    __snake_case     :							str      )     -> list:





				'''simple docstring'''


				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(__snake_case      )      )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
	__import__("doctest").testmod()