#
# 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 		__lowerCamelCase							(*UpperCAmelCase__       :							int					):
					with open(UpperCAmelCase__ ,	"r"					) as fh:
										fcntl.flock(UpperCAmelCase__ ,	fcntl.LOCK_EX					)
										try:
															print(*UpperCAmelCase__					)
										finally:
															fcntl.flock(UpperCAmelCase__ ,	fcntl.LOCK_UN					)


_lowerCamelCase   :				int     			=						int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
_lowerCamelCase   :				Optional[int]     			=						torch.device('''cuda''', local_rank)
_lowerCamelCase   :				Dict     			=						socket.gethostname()

_lowerCamelCase   :				List[Any]     			=						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
							_lowerCamelCase   :				Tuple     			=						dist.get_rank()
							_lowerCamelCase   :				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 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




