run.py

import argparse
import os
import time
import numpy as np

import torch
import torch.distributed as dist
import torch.multiprocessing as mp

from train import training_process

os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in list(range(torch.cuda.device_count())))

def setup(rank, world_size, port):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = port

    # initialize the process group
    torch.cuda.set_device(rank)
    dist.init_process_group("nccl", rank=rank, world_size=world_size)


def cleanup():
    dist.destroy_process_group()


def train(rank, world_size, opt):
    # torch.manual_seed(0)
    setup(rank, world_size, opt.port)
    device = torch.device(rank)
    training_process(rank, world_size, opt, device)
    cleanup()

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--n_epochs", type=int, default=600, help="number of epochs of training")
    parser.add_argument("--sample_interval", type=int, default=500, help="interval between image sampling")
    parser.add_argument('--output_dir', type=str, default='')
    parser.add_argument('--load_dir', type=str, default='')
    parser.add_argument('--config', type=str, default='')
    parser.add_argument('--port', type=str, default='12355')
    parser.add_argument('--set_step', type=int, default=None)
    parser.add_argument('--model_save_interval', type=int, default=5000)
    parser.add_argument('--patch_split', type=int, default=None)

    opt = parser.parse_args()
    os.makedirs(opt.output_dir, exist_ok=True)

    print(opt)
    num_gpus = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))
    mp.spawn(train, args=(num_gpus, opt), nprocs=num_gpus, join=True)