def setup_primary_logging()

in mico/utils/multiprocess_logging.py [0:0]

• 13 lines of code
• 3 McCabe index (conditional complexity)

def setup_primary_logging(log_file_path: str) -> Queue:
    """
    Global logging is setup using this method. In a distributed setup, a multiprocessing queue is setup
    which can be used by the workers to write their log messages. This initializers respective handlers
    to pick messages from the queue and handle them to write to corresponding output buffers.

    Parameters
    ----------
    log_file_path : ``str``, required
        File path to write output log
    error_log_file_path : ``str``, required
        File path to write error log

    Returns
    -------
    log_queue : ``torch.multiprocessing.Queue``
        A log queue to which the log handler listens to. This is used by workers
        in a distributed setup to initialize worker specific log handlers(refer ``setup_worker_logging`` method).
        Messages posted in this queue by the workers are picked up and bubbled up to respective log handlers.
    listener : 
        This is the listener for the queue. Needs to call `.stop()` for no error.
    """
    # Multiprocessing queue to which the workers should log their messages
    log_queue = Queue(-1)

    # Handlers for stream/file logging
    output_file_log_handler = logging.FileHandler(filename=str(log_file_path))
    stream_log_handler = logging.StreamHandler()

    formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')

    output_file_log_handler.setFormatter(formatter)
    stream_log_handler.setFormatter(formatter)

    output_file_log_handler.setLevel(logging.INFO)
    stream_log_handler.setLevel(logging.DEBUG)

    # This listener listens to the `log_queue` and pushes the messages to the list of
    # handlers specified.
    listener = QueueListener(log_queue, output_file_log_handler, stream_log_handler,
                             respect_handler_level=True)

    listener.start()

    return log_queue, listener