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import logging.config

from pathlib import Path

import torch

import torch.multiprocessing as mp

from dp.preprocess import preprocess

from dp.train import train

from dp.utils.io import read_config

config_file_path = Path('logging.yaml')

config = read_config(config_file_path)

logging.config.dictConfig(config)

if __name__ == '__main__':

train_data = [('en_us', 'young', 'jʌŋ'),

('de', 'benützten', 'bənʏt͡stn̩'),

('de', 'gewürz', 'ɡəvʏʁt͡s')] * 1000

val_data = [('en_us', 'young', 'jʌŋ'),

('de', 'benützten', 'bənʏt͡stn̩')] * 100

config_file = 'dp/configs/forward_config.yaml'

preprocess(config_file=config_file,

train_data=train_data,

val_data=val_data,

deduplicate_train_data=False)

num_gpus = torch.cuda.device_count()

if num_gpus > 1:

mp.spawn(train, nprocs=num_gpus, args=(num_gpus, config_file))

else:

train(rank=0, num_gpus=num_gpus, config_file=config_file)