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?
