def main(): argparser = argparse.ArgumentParser() argparser.add_argument('--config_file', default='../configs/default.cfg') args, extra_args = argparser.parse_known_args() config = Configurable(args.config_file, extra_args, logger) tokenizer, train_loader, dev_loader, test_loader, num_train_steps, label_list = load_data( config) model, optimizer, device, n_gpu = load_model(config, num_train_steps, label_list) train(tokenizer, model, optimizer, train_loader, dev_loader, test_loader, config, device, n_gpu, label_list, num_train_steps)
from parser import Parser from config import Configurable import torch import numpy as np import os if __name__ == '__main__': default_seed = int(time.time()) argparser = argparse.ArgumentParser() argparser.add_argument( '--exp_des', default='description-of-this-experiment-no-whitespace') argparser.add_argument('--config_file', default='config.txt') argparser.add_argument('--random_seed', type=int, default=default_seed) # argparser.add_argument('--thread', default=4, type=int, help='thread num') args, extra_args = argparser.parse_known_args() conf = Configurable(args.config_file, extra_args) # cudaNo = conf.cudaNo # os.environ["CUDA_VISIBLE_DEVICES"] = cudaNo all_seeds = [args.random_seed] random.seed(all_seeds[0]) for i in range(3): all_seeds.append(random.randint(1, 987654321)) np.random.seed(all_seeds[1]) torch.cuda.manual_seed(all_seeds[2]) torch.manual_seed(all_seeds[3]) print('random_seeds = ', all_seeds, flush=True) torch.set_num_threads( 4) # run with CPU, then use multi-thread? What does this mean?