





"""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       __A (     *a_  :Dict)						->						Optional[int]:
				with open(a_				,    '''r''') as fh:
								fcntl.flock(a_				,    fcntl.LOCK_EX)
								try:
												print(*a_)
								finally:
												fcntl.flock(a_				,    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






"""simple docstring"""






def       __A (     a_  :float)						->						float:

				if edge <= 0 or not isinstance(a_				,    a_):
								raise ValueError('''Length must be a positive.''')
				return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)

def       __A (     a_  :float)						->						float:

				if edge <= 0 or not isinstance(a_				,    a_):
								raise ValueError('''Length must be a positive.''')
				return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
		import doctest

		doctest.testmod()