CODEWITHSUNDEEP
pythonmultiprocessing
user1074057
user1074057

Reputation: 1812

python multiprocessing apply_async only uses one process

I have a script that includes opening a file from a list and then doing something to the text within that file. I'm using python multiprocessing and Pool to try to parallelize this operation. A abstraction of the script is below:

import os
from multiprocessing import Pool

results = []
def testFunc(files):
    for file in files:
        print "Working in Process #%d" % (os.getpid())
        #This is just an illustration of some logic. This is not what I'm actually doing.
        for line in file:
            if 'dog' in line:
                results.append(line)

if __name__=="__main__":
    p = Pool(processes=2)
    files = ['/path/to/file1.txt', '/path/to/file2.txt']
    results = p.apply_async(testFunc, args = (files,))
    results2 = results.get()

When I run this the print out of the process id is the same for each iteration. Basically what I'm trying to do is take each element of the input list and fork it out to a separate process, but it seems like one process is doing all of the work.

Upvotes: 19

Views: 30392

Answers (2)

unutbu
unutbu

Reputation: 879361

  • apply_async farms out one task to the pool. You would need to call apply_async many times to exercise more processors.
  • Don't allow both processes to try to write to the same list, results. Since the pool workers are separate processes, the two won't be writing to the same list. One way to work around this is to use an ouput Queue. You could set it up yourself, or use apply_async's callback to setup the Queue for you. apply_async will call the callback once the function completes.
  • You could use map_async instead of apply_async, but then you'd get a list of lists, which you'd then have to flatten.

So, perhaps try instead something like:

import os
import multiprocessing as mp

results = []   

def testFunc(file):
    result = []
    print "Working in Process #%d" % (os.getpid())
    # This is just an illustration of some logic. This is not what I'm
    # actually doing.
    with open(file, 'r') as f:
        for line in f:
            if 'dog' in line:
                result.append(line)
    return result


def collect_results(result):
    results.extend(result)

if __name__ == "__main__":
    p = mp.Pool(processes=2)
    files = ['/path/to/file1.txt', '/path/to/file2.txt']
    for f in files:
        p.apply_async(testFunc, args=(f, ), callback=collect_results)
    p.close()
    p.join()
    print(results)

Upvotes: 32

Odomontois
Odomontois

Reputation: 16308

Maybe in this case you should use map_async:

import os
from multiprocessing import Pool

results = []
def testFunc(file):
    message =  ("Working in Process #%d" % (os.getpid()))
    #This is just an illustration of some logic. This is not what I'm actually doing.
    for line in file:
        if 'dog' in line:
            results.append(line)
    return message

if __name__=="__main__":
    print("saddsf")
    p = Pool(processes=2)
    files = ['/path/to/file1.txt', '/path/to/file2.txt']
    results = p.map_async(testFunc, files)
    print(results.get())

Upvotes: 8

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