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