


"""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    _snake_case  (  *_snake_case :	Dict	):
      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	)


snake_case__						:   str			 = int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
snake_case__						:   int			 = torch.device('''cuda''', local_rank)
snake_case__						:   Union[str, Any]			 = socket.gethostname()

snake_case__						:   str			 = 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
 snake_case__						:   List[Any]			 = dist.get_rank()
 snake_case__						:   List[str]			 = 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 numpy import exp, pi, sqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				float = 0.0      ,    UpperCAmelCase_    :				float = 1.0							)     ->		int:
     return 1 / sqrt(2 * pi * sigma**2							) * exp(-((x - mu) ** 2) / (2 * sigma**2)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






