





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

		# Define train args/config, run train function
		__magic_name__					:				Any	= argparse.Namespace(cpu=False, mixed_precision="""fp16""")
		__magic_name__					:				Optional[int]	= {"""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 transformers import PretrainedConfig






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Optional[int]								=     """custom"""







			def __init__(			self	,      _A=1	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		str							      = attribute
									super().__init__(**_A						)






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Any								=     """custom"""







			def __init__(			self	,      _A=1	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = attribute



