


'''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	(					*__A   )							->		Optional[int]:


						'''simple docstring'''



						UpperCamelCase__  =    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   ):
												UpperCamelCase__  =    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__  : Union[str, Any]				=	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__  : Optional[int]				=	[
		    'pip:./',
		    'transformers',
		    'datasets',
		    'evaluate',
		    'tqdm',
		    'scipy',
		    'scikit-learn',
		    'tensorboard',
		    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
		]
		a__  : Any				=	rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

		# Define train args/config, run train function
		a__  : Optional[Any]				=	argparse.Namespace(cpu=False, mixed_precision='fp16')
		a__  : Any				=	{'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'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__  : Any				=	{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : int				=	[
      'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TvltModel',
      'TvltForPreTraining',
      'TvltForAudioVisualClassification',
      'TvltPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : List[Any]				=	['TvltImageProcessor']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

  a__  : Any				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





