ai-voice-cloning/src/train.py

41 lines
1.4 KiB
Python
Executable File

import torch
import argparse
import os
import sys
sys.path.insert(0, './dlas/codes/')
sys.path.insert(0, './dlas/')
from codes import train as tr
from utils import util, options as option
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
args = parser.parse_args()
opt = option.parse(args.opt, is_train=True)
if args.launcher != 'none':
# export CUDA_VISIBLE_DEVICES for running in distributed mode.
if 'gpu_ids' in opt.keys():
gpu_list = ','.join(str(x) for x in opt['gpu_ids'])
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list
print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
trainer = tr.Trainer()
#### distributed training settings
if args.launcher == 'none': # disabled distributed training
opt['dist'] = False
trainer.rank = -1
if len(opt['gpu_ids']) == 1:
torch.cuda.set_device(opt['gpu_ids'][0])
print('Disabled distributed training.')
else:
opt['dist'] = True
init_dist('nccl')
trainer.world_size = torch.distributed.get_world_size()
trainer.rank = torch.distributed.get_rank()
torch.cuda.set_device(torch.distributed.get_rank())
trainer.init(args.opt, opt, args.launcher)
trainer.do_training()