





#
# 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__			(       *lowercase__	):






   '''simple docstring'''






   with open(lowercase__	,     "r"	) as fh:
      fcntl.flock(lowercase__	,     fcntl.LOCK_EX	)
      try:
         print(*lowercase__	)
      finally:
         fcntl.flock(lowercase__	,     fcntl.LOCK_UN	)


__lowercase	: str       =int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
__lowercase	: Any       =torch.device("""cuda""", local_rank)
__lowercase	: Optional[Any]       =socket.gethostname()

__lowercase	: Tuple       =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
     __lowercase	: Optional[Any]       =dist.get_rank()
     __lowercase	: 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









'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    2
     _a           =    []
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__A)
     if n > 1:
          factors.append(__A)
     return factors


if __name__ == "__main__":
  import doctest

  doctest.testmod()



