The number of processes running concurrently on your computer is not limited by the number of cores. In fact you probably have hundreds of programs running right now on your computer – each with its own process. To make it work the OS assigns one of your 8 processors to each process or thread only … Read more
Always separate the number of processes from the number of tasks. There’s no reason why the two should be identical, and by making the number of processes a variable, you can experiment to see what works well for your particular problem. No theoretical answer is as good as old-fashioned get-your-hands-dirty benchmarking with real data. Here’s … Read more
Did you try to close pool by using pool.close and then wait for process to finish by pool.join, because if parent process keeps on running and does not wait for child processes they will become zombies
I would suggest that you use a Queue for this. class Worker(Process): def __init__(self, queue): super(Worker, self).__init__() self.queue = queue def run(self): print(‘Worker started’) # do some initialization here print(‘Computing things!’) for data in iter(self.queue.get, None): # Use data Now you can start a pile of these, all getting work from a single queue request_queue … Read more
Subclassing multiprocessing.Process: However I cannot get back the values, how can I use queues in this way? Process needs a Queue() to receive the results… An example of how to subclass multiprocessing.Process follows… from multiprocessing import Process, Queue class Processor(Process): def __init__(self, queue, idx, **kwargs): super(Processor, self).__init__() self.queue = queue self.idx = idx self.kwargs = … Read more
If you change pool.apply_async to pool.apply, you get this exception: Traceback (most recent call last): File “p.py”, line 15, in <module> pool.apply(job, [l, i]) File “/usr/lib/python2.7/multiprocessing/pool.py”, line 244, in apply return self.apply_async(func, args, kwds).get() File “/usr/lib/python2.7/multiprocessing/pool.py”, line 558, in get raise self._value RuntimeError: Lock objects should only be shared between processes through inheritance pool.apply_async is … Read more
Prerequisite In Python (in the following I use 64-bit build of Python 3.6.5) everything is an object. This has its overhead and with getsizeof we can see exactly the size of an object in bytes: >>> import sys >>> sys.getsizeof(42) 28 >>> sys.getsizeof(‘T’) 50 When fork system call used (default on *nix, see multiprocessing.get_start_method()) to … Read more
Use a shared variable to communicate. For example, like this, Example Code: import multiprocessing def worker(procnum, return_dict): “””worker function””” print(str(procnum) + ” represent!”) return_dict[procnum] = procnum if __name__ == “__main__”: manager = multiprocessing.Manager() return_dict = manager.dict() jobs = [] for i in range(5): p = multiprocessing.Process(target=worker, args=(i, return_dict)) jobs.append(p) p.start() for proc in jobs: proc.join() … Read more
I believe the recommended way to start the task is as follows. python manage.py celery worker -P gevent –loglevel=INFO Gevent needs to be patched as early as possible.
Callback is called once with the result ([[0], [0, 1]]) if you use map_async. >>> from multiprocessing import Pool >>> def myfunc(x): … return [i for i in range(x)] … >>> A = [] >>> def mycallback(x): … print(‘mycallback is called with {}’.format(x)) … A.extend(x) … >>> pool=Pool() >>> r = pool.map_async(myfunc, (1,2), callback=mycallback) >>> … Read more