



'''simple docstring'''





#
# 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 			a_	(				*_lowerCAmelCase			) -> List[str]:
  with open(_lowerCAmelCase			,'r'			) as fh:
    fcntl.flock(_lowerCAmelCase			,fcntl.LOCK_EX			)
    try:
      print(*_lowerCAmelCase			)
    finally:
      fcntl.flock(_lowerCAmelCase			,fcntl.LOCK_UN			)


_UpperCamelCase											=		int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
_UpperCamelCase											=		torch.device('cuda', local_rank)
_UpperCamelCase											=		socket.gethostname()

_UpperCamelCase											=		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											=		dist.get_rank()
   _UpperCamelCase											=		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











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


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






