
'''simple docstring'''





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





def UpperCAmelCase						(					*UpperCAmelCase_	):
    _a							:       Any       					=			torch.cuda.device_count()
    print(F'''Device count: {num_processes}'''	)
    with patch_environment(
        world_size=UpperCAmelCase_      ,  master_addr="""127.0.01"""      ,  master_port="""29500"""      ,  mixed_precision=args[1].mixed_precision	):
        _a							:       Any       					=			PrepareForLaunch(UpperCAmelCase_      ,  distributed_type="""MULTI_GPU"""	)
        torch.multiprocessing.start_processes(UpperCAmelCase_      ,  args=UpperCAmelCase_      ,  nprocs=UpperCAmelCase_      ,  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		:  Optional[int]   			=			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		:  str   			=			[
      '''pip:./''',
      '''transformers''',
      '''datasets''',
      '''evaluate''',
      '''tqdm''',
      '''scipy''',
      '''scikit-learn''',
      '''tensorboard''',
      '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
  ]
  a		:  int   			=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

  # Define train args/config, run train function
  a		:  Dict   			=			argparse.Namespace(cpu=False, mixed_precision='''fp16''')
  a		:  Tuple   			=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
  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'])








'''simple docstring'''





from __future__ import annotations

from enum import Enum




class     UpperCamelCase					(  snake_case__					):
  a    :Optional[int]      					=				2_4
  a    :Dict      					=				2_1
  a    :Dict      					=				1_8
  a    :int      					=				1_5
  a    :List[str]      					=				1_2
  a    :Tuple      					=				9
  a    :Optional[int]      					=				6
  a    :str      					=				3
  a    :Dict      					=				2
  a    :int      					=				1
  a    :List[Any]      					=				-1
  a    :Optional[Any]      					=				-2
  a    :Union[str, Any]      					=				-3
  a    :int      					=				-6
  a    :Union[str, Any]      					=				-9
  a    :Dict      					=				-1_2
  a    :Any      					=				-1_5
  a    :Tuple      					=				-1_8
  a    :Optional[Any]      					=				-2_1
  a    :Union[str, Any]      					=				-2_4






class     UpperCamelCase					(  snake_case__					):
  a    :Optional[Any]      					=				8
  a    :Optional[int]      					=				7
  a    :Optional[int]      					=				6
  a    :List[str]      					=				5
  a    :Union[str, Any]      					=				4
  a    :Union[str, Any]      					=				3
  a    :str      					=				2
  a    :Optional[int]      					=				1






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       int       					=			SIUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			SIUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			BinaryUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Dict       					=			BinaryUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
  import doctest

  doctest.testmod()


