NVIDIA Modulus Launch v0.1.0
Launch v0.1.0

deeplearning/modulus/modulus-launch-v010/_modules/modulus/launch/logging/console.html

Source code for modulus.launch.logging.console

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import logging
import os
from termcolor import colored


[docs]class PythonLogger: """Simple console logger for DL training This is a WIP """ def __init__(self, name: str = "launch"): self.logger = logging.getLogger(name) self.logger.handlers.clear() formatter = logging.Formatter( "[%(asctime)s - %(name)s - %(levelname)s] %(message)s", datefmt="%H:%M:%S" ) streamhandler = logging.StreamHandler() streamhandler.setFormatter(formatter) streamhandler.setLevel(logging.INFO) self.logger.addHandler(streamhandler) # Not sure if this works self.logger.setLevel(logging.DEBUG) self.logger.propagate = False # Prevent parent logging
[docs] def file_logging(self, file_name: str = "launch.log"): """Log to file""" if os.path.exists(file_name): os.remove(file_name) filehandler = logging.FileHandler(file_name) filehandler.setFormatter(formatter) filehandler.setLevel(logging.DEBUG) self.logger.addHandler(filehandler)
[docs] def log(self, message: str): """Log message""" self.logger.info(message)
[docs] def info(self, message: str): """Log info""" self.logger.info(colored(message, "light_blue"))
[docs] def success(self, message: str): """Log success""" self.logger.info(colored(message, "light_green"))
[docs] def warning(self, message: str): """Log warning""" self.logger.warning(colored(message, "light_yellow"))
[docs] def error(self, message: str): """Log error""" self.logger.error(colored(message, "light_red"))
[docs]class RankZeroLoggingWrapper: """Wrapper class to only log from rank 0 process in distributed training.""" def __init__(self, obj, dist): self.obj = obj self.dist = dist def __getattr__(self, name): attr = getattr(self.obj, name) if callable(attr): def wrapper(*args, **kwargs): if self.dist.rank == 0: return attr(*args, **kwargs) else: return None return wrapper else: return attr
© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.