




"""simple docstring"""





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 		lowerCAmelCase				(			*lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       str				      =       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__							:       Tuple				      =       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
							_lowercase						   =     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
							_lowercase						   =     [
							    """pip:./""",
							    """transformers""",
							    """datasets""",
							    """evaluate""",
							    """tqdm""",
							    """scipy""",
							    """scikit-learn""",
							    """tensorboard""",
							    """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
							]
							_lowercase						   =     rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

							# Define train args/config, run train function
							_lowercase						   =     argparse.Namespace(cpu=False, mixed_precision="""fp16""")
							_lowercase						   =     {"""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'])








"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


