





#
# 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  __UpperCAmelCase(   *lowercase_   ):
       with open(lowercase_			,     '''r'''   ) as fh:
              fcntl.flock(lowercase_			,     fcntl.LOCK_EX   )
              try:
                     print(*lowercase_   )
              finally:
                     fcntl.flock(lowercase_			,     fcntl.LOCK_UN   )


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

_lowerCamelCase      							=   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
       _lowerCamelCase      							=   dist.get_rank()
       _lowerCamelCase      							=   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








def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       _lowerCamelCase				:  int						=		(boundary[1] - boundary[0]) / steps
       _lowerCamelCase				:  Any						=		boundary[0]
       _lowerCamelCase				:  Tuple						=		boundary[1]
       _lowerCamelCase				:  Optional[Any]						=		make_points(lowercase_			,     lowercase_			,     lowercase_   )
       _lowerCamelCase				:  Dict						=		0.0
       y += (h / 2.0) * f(lowercase_   )
       for i in x_i:
              # print(i)
              y += h * f(lowercase_   )
       y += (h / 2.0) * f(lowercase_   )
       return y




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Dict						=		a + h
       while x < (b - h):
              yield x
              _lowerCamelCase				:  Dict						=		x + h




def  __UpperCAmelCase(   lowercase_   ):  # enter your function here
       _lowerCamelCase				:  List[str]						=		(x - 0) * (x - 0)
       return y




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  List[Any]						=		0.0  # Lower bound of integration
       _lowerCamelCase				:  Union[str, Any]						=		1.0  # Upper bound of integration
       _lowerCamelCase				:  Dict						=		1_0.0  # define number of steps or resolution
       _lowerCamelCase				:  Optional[int]						=		[a, b]  # define boundary of integration
       _lowerCamelCase				:  str						=		method_a(lowercase_			,     lowercase_   )
       print(F"""y = {y}"""   )


if __name__ == "__main__":
       main()

