[python] How should I log while using multiprocessing in Python?

Right now I have a central module in a framework that spawns multiple processes using the Python 2.6 multiprocessing module. Because it uses multiprocessing, there is module-level multiprocessing-aware log, LOG = multiprocessing.get_logger(). Per the docs, this logger has process-shared locks so that you don't garble things up in sys.stderr (or whatever filehandle) by having multiple processes writing to it simultaneously.

The issue I have now is that the other modules in the framework are not multiprocessing-aware. The way I see it, I need to make all dependencies on this central module use multiprocessing-aware logging. That's annoying within the framework, let alone for all clients of the framework. Are there alternatives I'm not thinking of?

If you have deadlocks occurring in a combination of locks, threads and forks in the logging module, that is reported in bug report 6721 (see also related SO question).

There is a small fixup solution posted here.

However, that will just fix any potential deadlocks in logging. That will not fix that things are maybe garbled up. See the other answers presented here.

Below is a class that can be used in Windows environment, requires ActivePython. You can also inherit for other logging handlers (StreamHandler etc.)

class SyncronizedFileHandler(logging.FileHandler):
    MUTEX_NAME = 'logging_mutex'

    def __init__(self , *args , **kwargs):

        self.mutex = win32event.CreateMutex(None , False , self.MUTEX_NAME)
        return super(SyncronizedFileHandler , self ).__init__(*args , **kwargs)

    def emit(self, *args , **kwargs):
        try:
            win32event.WaitForSingleObject(self.mutex , win32event.INFINITE)
            ret = super(SyncronizedFileHandler , self ).emit(*args , **kwargs)
        finally:
            win32event.ReleaseMutex(self.mutex)
        return ret

And here is an example that demonstrates usage:

import logging
import random , time , os , sys , datetime
from string import letters
import win32api , win32event
from multiprocessing import Pool

def f(i):
    time.sleep(random.randint(0,10) * 0.1)
    ch = random.choice(letters)
    logging.info( ch * 30)


def init_logging():
    '''
    initilize the loggers
    '''
    formatter = logging.Formatter("%(levelname)s - %(process)d - %(asctime)s - %(filename)s - %(lineno)d - %(message)s")
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    file_handler = SyncronizedFileHandler(sys.argv[1])
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(formatter)
    logger.addHandler(file_handler)

#must be called in the parent and in every worker process
init_logging() 

if __name__ == '__main__':
    #multiprocessing stuff
    pool = Pool(processes=10)
    imap_result = pool.imap(f , range(30))
    for i , _ in enumerate(imap_result):
        pass

Below is another solution with a focus on simplicity for anyone else (like me) who get here from Google. Logging should be easy! Only for 3.2 or higher.

import multiprocessing
import logging
from logging.handlers import QueueHandler, QueueListener
import time
import random


def f(i):
    time.sleep(random.uniform(.01, .05))
    logging.info('function called with {} in worker thread.'.format(i))
    time.sleep(random.uniform(.01, .05))
    return i


def worker_init(q):
    # all records from worker processes go to qh and then into q
    qh = QueueHandler(q)
    logger = logging.getLogger()
    logger.setLevel(logging.DEBUG)
    logger.addHandler(qh)


def logger_init():
    q = multiprocessing.Queue()
    # this is the handler for all log records
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter("%(levelname)s: %(asctime)s - %(process)s - %(message)s"))

    # ql gets records from the queue and sends them to the handler
    ql = QueueListener(q, handler)
    ql.start()

    logger = logging.getLogger()
    logger.setLevel(logging.DEBUG)
    # add the handler to the logger so records from this process are handled
    logger.addHandler(handler)

    return ql, q


def main():
    q_listener, q = logger_init()

    logging.info('hello from main thread')
    pool = multiprocessing.Pool(4, worker_init, [q])
    for result in pool.map(f, range(10)):
        pass
    pool.close()
    pool.join()
    q_listener.stop()

if __name__ == '__main__':
    main()

All current solutions are too coupled to the logging configuration by using a handler. My solution has the following architecture and features:

• You can use any logging configuration you want
• Logging is done in a daemon thread
• Safe shutdown of the daemon by using a context manager
• Communication to the logging thread is done by multiprocessing.Queue
• In subprocesses, logging.Logger (and already defined instances) are patched to send all records to the queue
• New: format traceback and message before sending to queue to prevent pickling errors

Code with usage example and output can be found at the following Gist: https://gist.github.com/schlamar/7003737

just publish somewhere your instance of the logger. that way, the other modules and clients can use your API to get the logger without having to import multiprocessing.

One of the alternatives is to write the mutliprocessing logging to a known file and register an atexit handler to join on those processes read it back on stderr; however, you won't get a real-time flow to the output messages on stderr that way.

There is this great package

Package: https://pypi.python.org/pypi/multiprocessing-logging/

code: https://github.com/jruere/multiprocessing-logging

Install:

pip install multiprocessing-logging

Then add:

import multiprocessing_logging

# This enables logs inside process
multiprocessing_logging.install_mp_handler()

For whoever might need this, I wrote a decorator for multiprocessing_logging package that adds the current process name to logs, so it becomes clear who logs what.

It also runs install_mp_handler() so it becomes unuseful to run it before creating a pool.

This allows me to see which worker creates which logs messages.

Here's the blueprint with an example:

import sys
import logging
from functools import wraps
import multiprocessing
import multiprocessing_logging

# Setup basic console logger as 'logger'
logger = logging.getLogger()
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(logging.Formatter(u'%(asctime)s :: %(levelname)s :: %(message)s'))
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)


# Create a decorator for functions that are called via multiprocessing pools
def logs_mp_process_names(fn):
    class MultiProcessLogFilter(logging.Filter):
        def filter(self, record):
            try:
                process_name = multiprocessing.current_process().name
            except BaseException:
                process_name = __name__
            record.msg = f'{process_name} :: {record.msg}'
            return True

    multiprocessing_logging.install_mp_handler()
    f = MultiProcessLogFilter()

    # Wraps is needed here so apply / apply_async know the function name
    @wraps(fn)
    def wrapper(*args, **kwargs):
        logger.removeFilter(f)
        logger.addFilter(f)
        return fn(*args, **kwargs)

    return wrapper


# Create a test function and decorate it
@logs_mp_process_names
def test(argument):
    logger.info(f'test function called via: {argument}')


# You can also redefine undecored functions
def undecorated_function():
    logger.info('I am not decorated')


@logs_mp_process_names
def redecorated(*args, **kwargs):
    return undecorated_function(*args, **kwargs)


# Enjoy
if __name__ == '__main__':
    with multiprocessing.Pool() as mp_pool:
        # Also works with apply_async
        mp_pool.apply(test, ('mp pool',))
        mp_pool.apply(redecorated)
        logger.info('some main logs')
        test('main program')

QueueHandler is native in Python 3.2+, and does exactly this. It is easily replicated in previous versions.

Python docs have two complete examples: Logging to a single file from multiple processes

For those using Python < 3.2, just copy QueueHandler into your own code from: https://gist.github.com/vsajip/591589 or alternatively import logutils.

Each process (including the parent process) puts its logging on the Queue, and then a listener thread or process (one example is provided for each) picks those up and writes them all to a file - no risk of corruption or garbling.

I also like zzzeek's answer but Andre is correct that a queue is required to prevent garbling. I had some luck with the pipe, but did see garbling which is somewhat expected. Implementing it turned out to be harder than I thought, particularly due to running on Windows, where there are some additional restrictions about global variables and stuff (see: How's Python Multiprocessing Implemented on Windows?)

But, I finally got it working. This example probably isn't perfect, so comments and suggestions are welcome. It also does not support setting the formatter or anything other than the root logger. Basically, you have to reinit the logger in each of the pool processes with the queue and set up the other attributes on the logger.

Again, any suggestions on how to make the code better are welcome. I certainly don't know all the Python tricks yet :-)

import multiprocessing, logging, sys, re, os, StringIO, threading, time, Queue

class MultiProcessingLogHandler(logging.Handler):
    def __init__(self, handler, queue, child=False):
        logging.Handler.__init__(self)

        self._handler = handler
        self.queue = queue

        # we only want one of the loggers to be pulling from the queue.
        # If there is a way to do this without needing to be passed this
        # information, that would be great!
        if child == False:
            self.shutdown = False
            self.polltime = 1
            t = threading.Thread(target=self.receive)
            t.daemon = True
            t.start()

    def setFormatter(self, fmt):
        logging.Handler.setFormatter(self, fmt)
        self._handler.setFormatter(fmt)

    def receive(self):
        #print "receive on"
        while (self.shutdown == False) or (self.queue.empty() == False):
            # so we block for a short period of time so that we can
            # check for the shutdown cases.
            try:
                record = self.queue.get(True, self.polltime)
                self._handler.emit(record)
            except Queue.Empty, e:
                pass

    def send(self, s):
        # send just puts it in the queue for the server to retrieve
        self.queue.put(s)

    def _format_record(self, record):
        ei = record.exc_info
        if ei:
            dummy = self.format(record) # just to get traceback text into record.exc_text
            record.exc_info = None  # to avoid Unpickleable error

        return record

    def emit(self, record):
        try:
            s = self._format_record(record)
            self.send(s)
        except (KeyboardInterrupt, SystemExit):
            raise
        except:
            self.handleError(record)

    def close(self):
        time.sleep(self.polltime+1) # give some time for messages to enter the queue.
        self.shutdown = True
        time.sleep(self.polltime+1) # give some time for the server to time out and see the shutdown

    def __del__(self):
        self.close() # hopefully this aids in orderly shutdown when things are going poorly.

def f(x):
    # just a logging command...
    logging.critical('function number: ' + str(x))
    # to make some calls take longer than others, so the output is "jumbled" as real MP programs are.
    time.sleep(x % 3)

def initPool(queue, level):
    """
    This causes the logging module to be initialized with the necessary info
    in pool threads to work correctly.
    """
    logging.getLogger('').addHandler(MultiProcessingLogHandler(logging.StreamHandler(), queue, child=True))
    logging.getLogger('').setLevel(level)

if __name__ == '__main__':
    stream = StringIO.StringIO()
    logQueue = multiprocessing.Queue(100)
    handler= MultiProcessingLogHandler(logging.StreamHandler(stream), logQueue)
    logging.getLogger('').addHandler(handler)
    logging.getLogger('').setLevel(logging.DEBUG)

    logging.debug('starting main')

    # when bulding the pool on a Windows machine we also have to init the logger in all the instances with the queue and the level of logging.
    pool = multiprocessing.Pool(processes=10, initializer=initPool, initargs=[logQueue, logging.getLogger('').getEffectiveLevel()] ) # start worker processes
    pool.map(f, range(0,50))
    pool.close()

    logging.debug('done')
    logging.shutdown()
    print "stream output is:"
    print stream.getvalue()

Simplest idea as mentioned:

• Grab the filename and the process id of the current process.
• Set up a [WatchedFileHandler][1]. The reasons for this handler are discussed in detail here, but in short there are certain worse race conditions with the other logging handlers. This one has the shortest window for the race condition.
  • Choose a path to save the logs to such as /var/log/...

I liked zzzeek's answer. I would just substitute the Pipe for a Queue since if multiple threads/processes use the same pipe end to generate log messages they will get garbled.

As of 2020 it seems there is a simpler way of logging with multiprocessing.

This function will create the logger. You can set the format here and where you want your output to go (file, stdout):

def create_logger():
    import multiprocessing, logging
    logger = multiprocessing.get_logger()
    logger.setLevel(logging.INFO)
    formatter = logging.Formatter(\
        '[%(asctime)s| %(levelname)s| %(processName)s] %(message)s')
    handler = logging.FileHandler('logs/your_file_name.log')
    handler.setFormatter(formatter)

    # this bit will make sure you won't have 
    # duplicated messages in the output
    if not len(logger.handlers): 
        logger.addHandler(handler)
    return logger

In the init you instantiate the logger:

if __name__ == '__main__': 
    from multiprocessing import Pool
    logger = create_logger()
    logger.info('Starting pooling')
    p = Pool()
    # rest of the code

Now, you only need to add this reference in each function where you need logging:

logger = create_logger()

And output messages:

logger.info(f'My message from {something}')

Hope this helps.

Since we can represent multiprocess logging as many publishers and one subscriber (listener), using ZeroMQ to implement PUB-SUB messaging is indeed an option.

Moreover, PyZMQ module, the Python bindings for ZMQ, implements PUBHandler, which is object for publishing logging messages over a zmq.PUB socket.

There's a solution on the web, for centralized logging from distributed application using PyZMQ and PUBHandler, which can be easily adopted for working locally with multiple publishing processes.

formatters = {
    logging.DEBUG: logging.Formatter("[%(name)s] %(message)s"),
    logging.INFO: logging.Formatter("[%(name)s] %(message)s"),
    logging.WARN: logging.Formatter("[%(name)s] %(message)s"),
    logging.ERROR: logging.Formatter("[%(name)s] %(message)s"),
    logging.CRITICAL: logging.Formatter("[%(name)s] %(message)s")
}

# This one will be used by publishing processes
class PUBLogger:
    def __init__(self, host, port=config.PUBSUB_LOGGER_PORT):
        self._logger = logging.getLogger(__name__)
        self._logger.setLevel(logging.DEBUG)
        self.ctx = zmq.Context()
        self.pub = self.ctx.socket(zmq.PUB)
        self.pub.connect('tcp://{0}:{1}'.format(socket.gethostbyname(host), port))
        self._handler = PUBHandler(self.pub)
        self._handler.formatters = formatters
        self._logger.addHandler(self._handler)

    @property
    def logger(self):
        return self._logger

# This one will be used by listener process
class SUBLogger:
    def __init__(self, ip, output_dir="", port=config.PUBSUB_LOGGER_PORT):
        self.output_dir = output_dir
        self._logger = logging.getLogger()
        self._logger.setLevel(logging.DEBUG)

        self.ctx = zmq.Context()
        self._sub = self.ctx.socket(zmq.SUB)
        self._sub.bind('tcp://*:{1}'.format(ip, port))
        self._sub.setsockopt(zmq.SUBSCRIBE, "")

        handler = handlers.RotatingFileHandler(os.path.join(output_dir, "client_debug.log"), "w", 100 * 1024 * 1024, 10)
        handler.setLevel(logging.DEBUG)
        formatter = logging.Formatter("%(asctime)s;%(levelname)s - %(message)s")
        handler.setFormatter(formatter)
        self._logger.addHandler(handler)

  @property
  def sub(self):
      return self._sub

  @property
  def logger(self):
      return self._logger

#  And that's the way we actually run things:

# Listener process will forever listen on SUB socket for incoming messages
def run_sub_logger(ip, event):
    sub_logger = SUBLogger(ip)
    while not event.is_set():
        try:
            topic, message = sub_logger.sub.recv_multipart(flags=zmq.NOBLOCK)
            log_msg = getattr(logging, topic.lower())
            log_msg(message)
        except zmq.ZMQError as zmq_error:
            if zmq_error.errno == zmq.EAGAIN:
                pass


# Publisher processes loggers should be initialized as follows:

class Publisher:
    def __init__(self, stop_event, proc_id):
        self.stop_event = stop_event
        self.proc_id = proc_id
        self._logger = pub_logger.PUBLogger('127.0.0.1').logger

     def run(self):
         self._logger.info("{0} - Sending message".format(proc_id))

def run_worker(event, proc_id):
    worker = Publisher(event, proc_id)
    worker.run()

# Starting subscriber process so we won't loose publisher's messages
sub_logger_process = Process(target=run_sub_logger,
                                 args=('127.0.0.1'), stop_event,))
sub_logger_process.start()

#Starting publisher processes
for i in range(MAX_WORKERS_PER_CLIENT):
    processes.append(Process(target=run_worker,
                                 args=(stop_event, i,)))
for p in processes:
    p.start()

Yet another alternative might be the various non-file-based logging handlers in the logging package:

• SocketHandler
• DatagramHandler
• SyslogHandler

(and others)

This way, you could easily have a logging daemon somewhere that you could write to safely and would handle the results correctly. (E.g., a simple socket server that just unpickles the message and emits it to its own rotating file handler.)

The SyslogHandler would take care of this for you, too. Of course, you could use your own instance of syslog, not the system one.

I just now wrote a log handler of my own that just feeds everything to the parent process via a pipe. I've only been testing it for ten minutes but it seems to work pretty well.

(Note: This is hardcoded to RotatingFileHandler, which is my own use case.)

Update: @javier now maintains this approach as a package available on Pypi - see multiprocessing-logging on Pypi, github at https://github.com/jruere/multiprocessing-logging

Update: Implementation!

This now uses a queue for correct handling of concurrency, and also recovers from errors correctly. I've now been using this in production for several months, and the current version below works without issue.

from logging.handlers import RotatingFileHandler
import multiprocessing, threading, logging, sys, traceback

class MultiProcessingLog(logging.Handler):
    def __init__(self, name, mode, maxsize, rotate):
        logging.Handler.__init__(self)

        self._handler = RotatingFileHandler(name, mode, maxsize, rotate)
        self.queue = multiprocessing.Queue(-1)

        t = threading.Thread(target=self.receive)
        t.daemon = True
        t.start()

    def setFormatter(self, fmt):
        logging.Handler.setFormatter(self, fmt)
        self._handler.setFormatter(fmt)

    def receive(self):
        while True:
            try:
                record = self.queue.get()
                self._handler.emit(record)
            except (KeyboardInterrupt, SystemExit):
                raise
            except EOFError:
                break
            except:
                traceback.print_exc(file=sys.stderr)

    def send(self, s):
        self.queue.put_nowait(s)

    def _format_record(self, record):
        # ensure that exc_info and args
        # have been stringified.  Removes any chance of
        # unpickleable things inside and possibly reduces
        # message size sent over the pipe
        if record.args:
            record.msg = record.msg % record.args
            record.args = None
        if record.exc_info:
            dummy = self.format(record)
            record.exc_info = None

        return record

    def emit(self, record):
        try:
            s = self._format_record(record)
            self.send(s)
        except (KeyboardInterrupt, SystemExit):
            raise
        except:
            self.handleError(record)

    def close(self):
        self._handler.close()
        logging.Handler.close(self)

I have a solution that's similar to ironhacker's except that I use logging.exception in some of my code and found that I needed to format the exception before passing it back over the Queue since tracebacks aren't pickle'able:

class QueueHandler(logging.Handler):
    def __init__(self, queue):
        logging.Handler.__init__(self)
        self.queue = queue
    def emit(self, record):
        if record.exc_info:
            # can't pass exc_info across processes so just format now
            record.exc_text = self.formatException(record.exc_info)
            record.exc_info = None
        self.queue.put(record)
    def formatException(self, ei):
        sio = cStringIO.StringIO()
        traceback.print_exception(ei[0], ei[1], ei[2], None, sio)
        s = sio.getvalue()
        sio.close()
        if s[-1] == "\n":
            s = s[:-1]
        return s

A variant of the others that keeps the logging and queue thread separate.

"""sample code for logging in subprocesses using multiprocessing

* Little handler magic - The main process uses loggers and handlers as normal.
* Only a simple handler is needed in the subprocess that feeds the queue.
* Original logger name from subprocess is preserved when logged in main
  process.
* As in the other implementations, a thread reads the queue and calls the
  handlers. Except in this implementation, the thread is defined outside of a
  handler, which makes the logger definitions simpler.
* Works with multiple handlers.  If the logger in the main process defines
  multiple handlers, they will all be fed records generated by the
  subprocesses loggers.

tested with Python 2.5 and 2.6 on Linux and Windows

"""

import os
import sys
import time
import traceback
import multiprocessing, threading, logging, sys

DEFAULT_LEVEL = logging.DEBUG

formatter = logging.Formatter("%(levelname)s: %(asctime)s - %(name)s - %(process)s - %(message)s")

class SubProcessLogHandler(logging.Handler):
    """handler used by subprocesses

    It simply puts items on a Queue for the main process to log.

    """

    def __init__(self, queue):
        logging.Handler.__init__(self)
        self.queue = queue

    def emit(self, record):
        self.queue.put(record)

class LogQueueReader(threading.Thread):
    """thread to write subprocesses log records to main process log

    This thread reads the records written by subprocesses and writes them to
    the handlers defined in the main process's handlers.

    """

    def __init__(self, queue):
        threading.Thread.__init__(self)
        self.queue = queue
        self.daemon = True

    def run(self):
        """read from the queue and write to the log handlers

        The logging documentation says logging is thread safe, so there
        shouldn't be contention between normal logging (from the main
        process) and this thread.

        Note that we're using the name of the original logger.

        """
        # Thanks Mike for the error checking code.
        while True:
            try:
                record = self.queue.get()
                # get the logger for this record
                logger = logging.getLogger(record.name)
                logger.callHandlers(record)
            except (KeyboardInterrupt, SystemExit):
                raise
            except EOFError:
                break
            except:
                traceback.print_exc(file=sys.stderr)

class LoggingProcess(multiprocessing.Process):

    def __init__(self, queue):
        multiprocessing.Process.__init__(self)
        self.queue = queue

    def _setupLogger(self):
        # create the logger to use.
        logger = logging.getLogger('test.subprocess')
        # The only handler desired is the SubProcessLogHandler.  If any others
        # exist, remove them. In this case, on Unix and Linux the StreamHandler
        # will be inherited.

        for handler in logger.handlers:
            # just a check for my sanity
            assert not isinstance(handler, SubProcessLogHandler)
            logger.removeHandler(handler)
        # add the handler
        handler = SubProcessLogHandler(self.queue)
        handler.setFormatter(formatter)
        logger.addHandler(handler)

        # On Windows, the level will not be inherited.  Also, we could just
        # set the level to log everything here and filter it in the main
        # process handlers.  For now, just set it from the global default.
        logger.setLevel(DEFAULT_LEVEL)
        self.logger = logger

    def run(self):
        self._setupLogger()
        logger = self.logger
        # and here goes the logging
        p = multiprocessing.current_process()
        logger.info('hello from process %s with pid %s' % (p.name, p.pid))


if __name__ == '__main__':
    # queue used by the subprocess loggers
    queue = multiprocessing.Queue()
    # Just a normal logger
    logger = logging.getLogger('test')
    handler = logging.StreamHandler()
    handler.setFormatter(formatter)
    logger.addHandler(handler)
    logger.setLevel(DEFAULT_LEVEL)
    logger.info('hello from the main process')
    # This thread will read from the subprocesses and write to the main log's
    # handlers.
    log_queue_reader = LogQueueReader(queue)
    log_queue_reader.start()
    # create the processes.
    for i in range(10):
        p = LoggingProcess(queue)
        p.start()
    # The way I read the multiprocessing warning about Queue, joining a
    # process before it has finished feeding the Queue can cause a deadlock.
    # Also, Queue.empty() is not realiable, so just make sure all processes
    # are finished.
    # active_children joins subprocesses when they're finished.
    while multiprocessing.active_children():
        time.sleep(.1)

Here's my simple hack/workaround... not the most comprehensive, but easily modifiable and simpler to read and understand I think than any other answers I found before writing this:

import logging
import multiprocessing

class FakeLogger(object):
    def __init__(self, q):
        self.q = q
    def info(self, item):
        self.q.put('INFO - {}'.format(item))
    def debug(self, item):
        self.q.put('DEBUG - {}'.format(item))
    def critical(self, item):
        self.q.put('CRITICAL - {}'.format(item))
    def warning(self, item):
        self.q.put('WARNING - {}'.format(item))

def some_other_func_that_gets_logger_and_logs(num):
    # notice the name get's discarded
    # of course you can easily add this to your FakeLogger class
    local_logger = logging.getLogger('local')
    local_logger.info('Hey I am logging this: {} and working on it to make this {}!'.format(num, num*2))
    local_logger.debug('hmm, something may need debugging here')
    return num*2

def func_to_parallelize(data_chunk):
    # unpack our args
    the_num, logger_q = data_chunk
    # since we're now in a new process, let's monkeypatch the logging module
    logging.getLogger = lambda name=None: FakeLogger(logger_q)
    # now do the actual work that happens to log stuff too
    new_num = some_other_func_that_gets_logger_and_logs(the_num)
    return (the_num, new_num)

if __name__ == '__main__':
    multiprocessing.freeze_support()
    m = multiprocessing.Manager()
    logger_q = m.Queue()
    # we have to pass our data to be parallel-processed
    # we also need to pass the Queue object so we can retrieve the logs
    parallelable_data = [(1, logger_q), (2, logger_q)]
    # set up a pool of processes so we can take advantage of multiple CPU cores
    pool_size = multiprocessing.cpu_count() * 2
    pool = multiprocessing.Pool(processes=pool_size, maxtasksperchild=4)
    worker_output = pool.map(func_to_parallelize, parallelable_data)
    pool.close() # no more tasks
    pool.join()  # wrap up current tasks
    # get the contents of our FakeLogger object
    while not logger_q.empty():
        print logger_q.get()
    print 'worker output contained: {}'.format(worker_output)

How about delegating all the logging to another process that reads all log entries from a Queue?

LOG_QUEUE = multiprocessing.JoinableQueue()

class CentralLogger(multiprocessing.Process):
    def __init__(self, queue):
        multiprocessing.Process.__init__(self)
        self.queue = queue
        self.log = logger.getLogger('some_config')
        self.log.info("Started Central Logging process")

    def run(self):
        while True:
            log_level, message = self.queue.get()
            if log_level is None:
                self.log.info("Shutting down Central Logging process")
                break
            else:
                self.log.log(log_level, message)

central_logger_process = CentralLogger(LOG_QUEUE)
central_logger_process.start()

Simply share LOG_QUEUE via any of the multiprocess mechanisms or even inheritance and it all works out fine!