Datasets:
jblitzar
/
github-python-corpus

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print(metann_params)
print(results)
print(walltimes)
print('\n* Saving to file {}'.format(filename))
with open(filename, 'wb') as f:
pickle.dump([algorithm_params, metann_params, results, walltimes, run_data], f)
f.close()
def main(args):
# make save directory
save_dir = args.save_dir
if not os.path.exists(save_dir):
os.mkdir(save_dir)
algo_params = args.algo_params
save_path = save_dir + '/' + algo_params + '/'
if not os.path.exists(save_path):
os.mkdir(save_path)
# set up logging
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(save_dir, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
logging.info(args)
run_experiments(args, save_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Args for BANANAS experiments')
parser.add_argument('--trials', type=int, default=500, help='Number of trials')
parser.add_argument('--search_space', type=str, default='nasbench', \
help='nasbench or darts')
parser.add_argument('--algo_params', type=str, default='main_experiments', help='which parameters to use')
parser.add_argument('--output_filename', type=str, default='round', help='name of output files')
parser.add_argument('--save_dir', type=str, default='results_output', help='name of save directory')
parser.add_argument('--save_specs', type=bool, default=False, help='save the architecture specs')
args = parser.parse_args()
main(args)
# <FILESEP>
import glob
import logging
import os
import shutil
import sys
import numpy as np
import random
import time
from absl import app
import gin
from internal import configs
from internal import datasets
from internal import image
from internal import models
from internal import train_utils
from internal import utils
from internal import vis
from internal import checkpoints
import torch
import accelerate
import tensorboardX
from tqdm import tqdm
from tqdm.contrib.logging import logging_redirect_tqdm
from torch.utils._pytree import tree_map
TIME_PRECISION = 1000 # Internally represent integer times in milliseconds.
def main(unused_argv):
config = configs.load_config()
config.exp_path = os.path.join("exp", config.exp_name)
config.checkpoint_dir = os.path.join(config.exp_path, 'checkpoints')
config.test_vis_dir = os.path.join(config.exp_path, 'test_vis')
utils.makedirs(config.exp_path)
utils.makedirs(config.test_vis_dir)
with utils.open_file(os.path.join(config.exp_path, 'config.gin'), 'w') as f:
f.write(gin.config_str())
# accelerator for DDP
accelerator = accelerate.Accelerator()
# setup logger
logging.basicConfig(
format="%(asctime)s: %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
force=True,
handlers=[logging.StreamHandler(sys.stdout),
logging.FileHandler(os.path.join(config.exp_path, 'log_train.txt'))],
level=logging.INFO,