






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

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

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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a    =       {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a    =       [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 a    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


