


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def    _UpperCAmelCase	(   *__lowerCamelCase       )		->Dict:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      torch.cuda.device_count()
 print(f'''Device count: {num_processes}'''       )
 with patch_environment(
     world_size=__lowerCamelCase ,     master_addr='''127.0.01''' ,     master_port='''29500''' ,     mixed_precision=args[1].mixed_precision       ):
  snake_case   : Optional[Any]	  =      PrepareForLaunch(__lowerCamelCase ,     distributed_type='''MULTI_GPU'''       )
  torch.multiprocessing.start_processes(__lowerCamelCase ,     args=__lowerCamelCase ,     nprocs=__lowerCamelCase ,     start_method='''spawn'''       )


if __name__ == "__main__":
  # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
  # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

  # on-demand GPU
  # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
  a				:							Tuple					=	rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
  gpu.up_if_not()

  # on-prem GPU
  # gpu = rh.cluster(
  #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
  #       )

  # Set up remote function
  a				:							Dict					=	[
      'pip:./',
      'transformers',
      'datasets',
      'evaluate',
      'tqdm',
      'scipy',
      'scikit-learn',
      'tensorboard',
      'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
  ]
  a				:							List[str]					=	rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

  # Define train args/config, run train function
  a				:							str					=	argparse.Namespace(cpu=False, mixed_precision='fp16')
  a				:							Optional[int]					=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  launch_train_gpu(config, train_args, stream_logs=True)

  # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
  # gpu.install_packages(reqs)
  # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])










from typing import Callable






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Tuple:


 '''simple docstring'''

 def wrapper(__lowerCamelCase       ):
  snake_case   : str	  =      function_with_docstring.__doc__
  return target_function

 return wrapper






