from math import pi




def      lowerCAmelCase_				(  __A							)  ->       float:







    '''simple docstring'''


    return degree / (180 / pi)


if __name__ == "__main__":
      from doctest import testmod

      testmod()




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def      lowerCAmelCase_				(  *__A							)  ->       Tuple:







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

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



