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multiprocessing.Pool.map_async doesn't seem to... do anything at all?


multiprocessing.Pool.map_async doesn't seem to... do anything at all?

By : EEE
Date : November 23 2020, 03:01 PM
To fix the issue you can do Let's see what happen:
You are using a context manager to automatically "close" the Pool, but, what is important, if you check Pool.__exit__'s source code, you will find:
code :
def __exit__(self, exc_type, exc_val, exc_tb):
    self.terminate()
with multiprocessing.Pool() as p:
    p.map_async(printme, l, callback=print_result)
    p.close()
    p.join()
p = multiprocessing.Pool()
p.map_async(printme, l, callback=print_result)
p.close()
p.join()


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Python: Non-Responsive multiprocessing.pool.map_async() function

Python: Non-Responsive multiprocessing.pool.map_async() function


By : SiD
Date : March 29 2020, 07:55 AM
I wish this help you You have several problems here - I'm guessing you never used multiprocessing before.
One of your problems is that you fire off an async operation but never wait for it to end. If you did wait for it to end, you'd get more info. For example, add:
code :
result = SeqFile.run.get()
execfile("/")
print "I'm A!"
if __name__ == "__main__":
    import multiprocessing as mp

    files = ["a.py", "b.py", "c.py"]
    pool = mp.Pool(2)
    pool.imap_unordered(execfile, files)
    pool.close()
    pool.join()
I'm A!
I'm B!
I'm C!
async = pool.map_async(execfile, files)
async.get()
asyncs = [pool.apply_async(execfile, (fn,)) for fn in files]
for a in asyncs:
    a.get()
multiprocessing.Pool: What's the difference between map_async and imap?

multiprocessing.Pool: What's the difference between map_async and imap?


By : user2617700
Date : March 29 2020, 07:55 AM
I hope this helps you . There are two key differences between imap/imap_unordered and map/map_async:
The way they consume the iterable you pass to them. The way they return the result back to you.
code :
import multiprocessing
import time

def func(x):
    time.sleep(x)
    return x + 2

if __name__ == "__main__":    
    p = multiprocessing.Pool()
    start = time.time()
    for x in p.imap(func, [1,5,3]):
        print("{} (Time elapsed: {}s)".format(x, int(time.time() - start)))
3 (Time elapsed: 1s)
7 (Time elapsed: 5s)
5 (Time elapsed: 5s)
3 (Time elapsed: 1s)
5 (Time elapsed: 3s)
7 (Time elapsed: 5s)
3 (Time elapsed: 5s)
7 (Time elapsed: 5s)
5 (Time elapsed: 5s)
unexpected behaviour of multiprocessing Pool map_async

unexpected behaviour of multiprocessing Pool map_async


By : Viêt Hoà
Date : March 29 2020, 07:55 AM
wish of those help This is happening because when you use the multiprocessing.Pool as a context manager, pool.terminate() is called when you leave the with block, which immediately exits all workers, without waiting for in-progress tasks to finish.
python multiprocessing Pool with map_async

python multiprocessing Pool with map_async


By : sfrank17
Date : March 29 2020, 07:55 AM
wish of those help "test" is interpreted as map_async's chunksize keyword argument (see the docs).
Your code should probably be (here copy-pasted from my IPython session) :
Handle multiprocessing.TimeoutError in multiprocessing pool.map_async()

Handle multiprocessing.TimeoutError in multiprocessing pool.map_async()


By : Don Júan
Date : March 29 2020, 07:55 AM
To fix the issue you can do As far as I know, you can't, or at least not with map_async. map_async is a convenience method there to solve a particular problem for a particular use case, and that doesn't match up with what you've got because you want more fine control.
However, you can still do it, you just need to use the more fine-grained methods in the multiprocessing module. In particular, you can add jobs to your pool on the fly by using apply_async, which gives you much more control over how to handle success and failure of individual tasks.
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