multiprocessing – Page 13

Python Multiprocessing: Handling Child Errors in Parent

February 5, 2023 by Tarik

Why not to let the Process to take care of its own exceptions, like this: from __future__ import print_function import multiprocessing as mp import traceback class Process(mp.Process): def __init__(self, *args, **kwargs): mp.Process.__init__(self, *args, **kwargs) self._pconn, self._cconn = mp.Pipe() self._exception = None def run(self): try: mp.Process.run(self) self._cconn.send(None) except Exception as e: tb = traceback.format_exc() self._cconn.send((e, tb)) … Read more

how to use initializer to set up my multiprocess pool?

February 3, 2023 by Tarik

The initialize function is called thus: def worker(…): … if initializer is not None: initializer(*args) so there is no return value saved anywhere. You might think this dooms you, but no! Each worker is in a separate process. Thus, you can use an ordinary global variable. This is not exactly pretty, but it works: cursor … Read more

multiprocessing in python – sharing large object (e.g. pandas dataframe) between multiple processes

January 31, 2023 by Tarik

The first argument to Value is typecode_or_type. That is defined as: typecode_or_type determines the type of the returned object: it is either a ctypes type or a one character typecode of the kind used by the array module. *args is passed on to the constructor for the type. Emphasis mine. So, you simply cannot put … Read more

Exception thrown in multiprocessing Pool not detected

January 29, 2023 by Tarik

Maybe I’m missing something, but isn’t that what the get method of the Result object returns? See Process Pools. class multiprocessing.pool.AsyncResult The class of the result returned by Pool.apply_async() and Pool.map_async().get([timeout]) Return the result when it arrives. If timeout is not None and the result does not arrive within timeout seconds then multiprocessing.TimeoutError is raised. … Read more

Passing multiple parameters to pool.map() function in Python [duplicate]

January 29, 2023 by Tarik

You can use functools.partial for this (as you suspected): from functools import partial def target(lock, iterable_item): for item in iterable_item: # Do cool stuff if (… some condition here …): lock.acquire() # Write to stdout or logfile, etc. lock.release() def main(): iterable = [1, 2, 3, 4, 5] pool = multiprocessing.Pool() l = multiprocessing.Lock() func … Read more

Solving embarassingly parallel problems using Python multiprocessing

January 27, 2023 by Tarik

My solution has an extra bell and whistle to make sure that the order of the output has the same as the order of the input. I use multiprocessing.queue’s to send data between processes, sending stop messages so each process knows to quit checking the queues. I think the comments in the source should make … Read more

Processing single file from multiple processes

January 27, 2023 by Tarik

What you are looking for is a Producer/Consumer pattern Basic threading example Here is a basic example using the threading module (instead of multiprocessing) import threading import Queue import sys def do_work(in_queue, out_queue): while True: item = in_queue.get() # process result = item out_queue.put(result) in_queue.task_done() if __name__ == “__main__”: work = Queue.Queue() results = Queue.Queue() … Read more

Efficiently applying a function to a grouped pandas DataFrame in parallel

January 24, 2023 by Tarik

From the comments above, it seems that this is planned for pandas some time (there’s also an interesting-looking rosetta project which I just noticed). However, until every parallel functionality is incorporated into pandas, I noticed that it’s very easy to write efficient & non-memory-copying parallel augmentations to pandas directly using cython + OpenMP and C++. … Read more

Python multiprocessing.Pool: AttributeError

January 20, 2023 by Tarik

Error 1: AttributeError: Can’t pickle local object ‘SomeClass.some_method..single’ You solved this error yourself by moving the nested target-function single() out to the top-level. Background: Pool needs to pickle (serialize) everything it sends to its worker-processes (IPC). Pickling actually only saves the name of a function and unpickling requires re-importing the function by name. For that … Read more

Celery parallel distributed task with multiprocessing

January 20, 2023 by Tarik

Your goals are: Distribute your work to many machines (distributed computing/distributed parallel processing) Distribute the work on a given machine across all CPUs (multiprocessing/threading) Celery can do both of these for you fairly easily. The first thing to understand is that each celery worker is configured by default to run as many tasks as there … Read more