


#
# 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 				UpperCAmelCase_					(							*__snake_case						)     ->			Tuple:







 """simple docstring"""


 with open(__a     ,						'''r'''						) as fh:
  fcntl.flock(__a     ,						fcntl.LOCK_EX						)
  try:
   print(*__a						)
  finally:
   fcntl.flock(__a     ,						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








from math import ceil







def    __lowerCamelCase					(							__a   :int = 1_0_0_1    )		->				int:






		"""simple docstring"""

		A__        =   1

		for i in range(1    ,     int(ceil(n / 2.0    )    )    ):
				A__        =   2 * i + 1
				A__        =   2 * i
				A__        =   total + 4 * odd**2 - 6 * even

		return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															A    :					List[str] =      int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('''Invalid entry - please enter a number''')



