| |
| import os |
| import random |
| import logging |
| import numpy as np |
| import torch |
| from datetime import timezone, datetime, timedelta |
| from zoneinfo import ZoneInfo |
| from contextlib import contextmanager |
| import os |
|
|
| UTC = timezone.utc |
|
|
| def permanent_seed(seed: int) -> None: |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
| if torch.cuda.is_available(): |
| torch.cuda.manual_seed_all(seed) |
|
|
|
|
| @contextmanager |
| def set_seed(seed): |
| random_state = random.getstate() |
| np_random_state = np.random.get_state() |
|
|
| try: |
| random.seed(seed) |
| np.random.seed(seed) |
| yield |
| finally: |
| random.setstate(random_state) |
| np.random.set_state(np_random_state) |
|
|
|
|
| def setup_logging(output_dir): |
| os.makedirs(output_dir, exist_ok=True) |
| log_file = os.path.join(output_dir, 'training.log') |
| logging.basicConfig( |
| level=logging.INFO, |
| format='%(asctime)s - %(levelname)s - %(message)s', |
| datefmt='%Y-%m-%d %H:%M:%S', |
| handlers=[logging.FileHandler(log_file), logging.StreamHandler()] |
| ) |
| logging.Formatter.converter = lambda *args: (datetime.now(UTC) - timedelta(hours=2)).timetuple() |
| return logging.getLogger() |
|
|
|
|
| @contextmanager |
| def NoLoggerWarnings(): |
| from gym import logger |
| logger.set_level(logger.ERROR) |
| try: |
| yield |
| finally: |
| logger.set_level(logger.INFO) |
|
|
|
|