
#
# 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		(				*snake_case__:   Optional[int]   ):


				'''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   )


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

__a         =   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
						__a         =   dist.get_rank()
						__a         =   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 sqrt

def   __snake_case    (       _lowerCAmelCase			:			int						)			->    bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5					,					int(sqrt(_lowerCAmelCase						) + 1						)					,					6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def   __snake_case    (       _lowerCAmelCase			:			int = 10001						)			->    int:
   A_       :					Any        =      0
   A_       :					Tuple        =      1
   while count != nth and number < 3:
      number += 1
      if is_prime(_lowerCAmelCase						):
         count += 1
   while count != nth:
      number += 2
      if is_prime(_lowerCAmelCase						):
         count += 1
   return number


if __name__ == "__main__":
 print(F'''{solution() = }''')
