#
# 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  snake_case  (       *snake_case__			:Optional[Any])						->   Optional[Any]:
       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)


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

_SCREAMING_SNAKE_CASE										=						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
    _SCREAMING_SNAKE_CASE										=						dist.get_rank()
    _SCREAMING_SNAKE_CASE										=						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 isqrt






def  snake_case  (       snake_case__			:int)						->   bool:
       return all(number % divisor != 0 for divisor in range(2   ,	isqrt(snake_case__) + 1))






def  snake_case  (       snake_case__			:int = 10**6)						->   int:
       _A        =  0
       _A        =  1
       _A        =  7
       while prime_candidate < max_prime:
              primes_count += is_prime(snake_case__)

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


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




