In the example code below, I'd like to get the return value of the function worker. How can I go about doing this? Where is this value stored?
Example Code:
import multiprocessing
def worker(procnum):
'''worker function'''
print str(procnum) + ' represent!'
return procnum
if __name__ == '__main__':
jobs = []
for i in range(5):
p = multiprocessing.Process(target=worker, args=(i,))
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
print jobs
Output:
0 represent!
1 represent!
2 represent!
3 represent!
4 represent!
[<Process(Process-1, stopped)>, <Process(Process-2, stopped)>, <Process(Process-3, stopped)>, <Process(Process-4, stopped)>, <Process(Process-5, stopped)>]
I can't seem to find the relevant attribute in the objects stored in jobs.
Use shared variable to communicate. For example like this:
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()
print(return_dict.values())
I think the approach suggested by #sega_sai is the better one. But it really needs a code example, so here goes:
import multiprocessing
from os import getpid
def worker(procnum):
print('I am number %d in process %d' % (procnum, getpid()))
return getpid()
if __name__ == '__main__':
pool = multiprocessing.Pool(processes = 3)
print(pool.map(worker, range(5)))
Which will print the return values:
I am number 0 in process 19139
I am number 1 in process 19138
I am number 2 in process 19140
I am number 3 in process 19139
I am number 4 in process 19140
[19139, 19138, 19140, 19139, 19140]
If you are familiar with map (the Python 2 built-in) this should not be too challenging. Otherwise have a look at sega_Sai's link.
Note how little code is needed. (Also note how processes are re-used).
For anyone else who is seeking how to get a value from a Process using Queue:
import multiprocessing
ret = {'foo': False}
def worker(queue):
ret = queue.get()
ret['foo'] = True
queue.put(ret)
if __name__ == '__main__':
queue = multiprocessing.Queue()
queue.put(ret)
p = multiprocessing.Process(target=worker, args=(queue,))
p.start()
p.join()
print(queue.get()) # Prints {"foo": True}
Note that in Windows or Jupyter Notebook, with multithreading you have to save this as a file and execute the file. If you do it in a command prompt you will see an error like this:
AttributeError: Can't get attribute 'worker' on <module '__main__' (built-in)>
For some reason, I couldn't find a general example of how to do this with Queue anywhere (even Python's doc examples don't spawn multiple processes), so here's what I got working after like 10 tries:
from multiprocessing import Process, Queue
def add_helper(queue, arg1, arg2): # the func called in child processes
ret = arg1 + arg2
queue.put(ret)
def multi_add(): # spawns child processes
q = Queue()
processes = []
rets = []
for _ in range(0, 100):
p = Process(target=add_helper, args=(q, 1, 2))
processes.append(p)
p.start()
for p in processes:
ret = q.get() # will block
rets.append(ret)
for p in processes:
p.join()
return rets
Queue is a blocking, thread-safe queue that you can use to store the return values from the child processes. So you have to pass the queue to each process. Something less obvious here is that you have to get() from the queue before you join the Processes or else the queue fills up and blocks everything.
Update for those who are object-oriented (tested in Python 3.4):
from multiprocessing import Process, Queue
class Multiprocessor():
def __init__(self):
self.processes = []
self.queue = Queue()
#staticmethod
def _wrapper(func, queue, args, kwargs):
ret = func(*args, **kwargs)
queue.put(ret)
def run(self, func, *args, **kwargs):
args2 = [func, self.queue, args, kwargs]
p = Process(target=self._wrapper, args=args2)
self.processes.append(p)
p.start()
def wait(self):
rets = []
for p in self.processes:
ret = self.queue.get()
rets.append(ret)
for p in self.processes:
p.join()
return rets
# tester
if __name__ == "__main__":
mp = Multiprocessor()
num_proc = 64
for _ in range(num_proc): # queue up multiple tasks running `sum`
mp.run(sum, [1, 2, 3, 4, 5])
ret = mp.wait() # get all results
print(ret)
assert len(ret) == num_proc and all(r == 15 for r in ret)
This example shows how to use a list of multiprocessing.Pipe instances to return strings from an arbitrary number of processes:
import multiprocessing
def worker(procnum, send_end):
'''worker function'''
result = str(procnum) + ' represent!'
print result
send_end.send(result)
def main():
jobs = []
pipe_list = []
for i in range(5):
recv_end, send_end = multiprocessing.Pipe(False)
p = multiprocessing.Process(target=worker, args=(i, send_end))
jobs.append(p)
pipe_list.append(recv_end)
p.start()
for proc in jobs:
proc.join()
result_list = [x.recv() for x in pipe_list]
print result_list
if __name__ == '__main__':
main()
Output:
0 represent!
1 represent!
2 represent!
3 represent!
4 represent!
['0 represent!', '1 represent!', '2 represent!', '3 represent!', '4 represent!']
This solution uses fewer resources than a multiprocessing.Queue which uses
a Pipe
at least one Lock
a buffer
a thread
or a multiprocessing.SimpleQueue which uses
a Pipe
at least one Lock
It is very instructive to look at the source for each of these types.
It seems that you should use the multiprocessing.Pool class instead and use the methods .apply() .apply_async(), map()
http://docs.python.org/library/multiprocessing.html?highlight=pool#multiprocessing.pool.AsyncResult
You can use the exit built-in to set the exit code of a process. It can be obtained from the exitcode attribute of the process:
import multiprocessing
def worker(procnum):
print str(procnum) + ' represent!'
exit(procnum)
if __name__ == '__main__':
jobs = []
for i in range(5):
p = multiprocessing.Process(target=worker, args=(i,))
jobs.append(p)
p.start()
result = []
for proc in jobs:
proc.join()
result.append(proc.exitcode)
print result
Output:
0 represent!
1 represent!
2 represent!
3 represent!
4 represent!
[0, 1, 2, 3, 4]
The pebble package has a nice abstraction leveraging multiprocessing.Pipe which makes this quite straightforward:
from pebble import concurrent
#concurrent.process
def function(arg, kwarg=0):
return arg + kwarg
future = function(1, kwarg=1)
print(future.result())
Example from: https://pythonhosted.org/Pebble/#concurrent-decorators
Thought I'd simplify the simplest examples copied from above, working for me on Py3.6. Simplest is multiprocessing.Pool:
import multiprocessing
import time
def worker(x):
time.sleep(1)
return x
pool = multiprocessing.Pool()
print(pool.map(worker, range(10)))
You can set the number of processes in the pool with, e.g., Pool(processes=5). However it defaults to CPU count, so leave it blank for CPU-bound tasks. (I/O-bound tasks often suit threads anyway, as the threads are mostly waiting so can share a CPU core.) Pool also applies chunking optimization.
(Note that the worker method cannot be nested within a method. I initially defined my worker method inside the method that makes the call to pool.map, to keep it all self-contained, but then the processes couldn't import it, and threw "AttributeError: Can't pickle local object outer_method..inner_method". More here. It can be inside a class.)
(Appreciate the original question specified printing 'represent!' rather than time.sleep(), but without it I thought some code was running concurrently when it wasn't.)
Py3's ProcessPoolExecutor is also two lines (.map returns a generator so you need the list()):
from concurrent.futures import ProcessPoolExecutor
with ProcessPoolExecutor() as executor:
print(list(executor.map(worker, range(10))))
With plain Processes:
import multiprocessing
import time
def worker(x, queue):
time.sleep(1)
queue.put(x)
queue = multiprocessing.SimpleQueue()
tasks = range(10)
for task in tasks:
multiprocessing.Process(target=worker, args=(task, queue,)).start()
for _ in tasks:
print(queue.get())
Use SimpleQueue if all you need is put and get. The first loop starts all the processes, before the second makes the blocking queue.get calls. I don't think there's any reason to call p.join() too.
If you are using Python 3, you can use concurrent.futures.ProcessPoolExecutor as a convenient abstraction:
from concurrent.futures import ProcessPoolExecutor
def worker(procnum):
'''worker function'''
print(str(procnum) + ' represent!')
return procnum
if __name__ == '__main__':
with ProcessPoolExecutor() as executor:
print(list(executor.map(worker, range(5))))
Output:
0 represent!
1 represent!
2 represent!
3 represent!
4 represent!
[0, 1, 2, 3, 4]
A simple solution:
import multiprocessing
output=[]
data = range(0,10)
def f(x):
return x**2
def handler():
p = multiprocessing.Pool(64)
r=p.map(f, data)
return r
if __name__ == '__main__':
output.append(handler())
print(output[0])
Output:
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
You can use ProcessPoolExecutor to get a return value from a function as shown below:
from concurrent.futures import ProcessPoolExecutor
def test(num1, num2):
return num1 + num2
with ProcessPoolExecutor() as executor:
feature = executor.submit(test, 2, 3)
print(feature.result()) # 5
I modified vartec's answer a bit since I needed to get the error codes from the function. (Thanks vertec!!! its an awesome trick)
This can also be done with a manager.list but I think is better to have it in a dict and store a list within it. That way, way we keep the function and the results since we can't be sure of the order in which the list will be populated.
from multiprocessing import Process
import time
import datetime
import multiprocessing
def func1(fn, m_list):
print 'func1: starting'
time.sleep(1)
m_list[fn] = "this is the first function"
print 'func1: finishing'
# return "func1" # no need for return since Multiprocess doesnt return it =(
def func2(fn, m_list):
print 'func2: starting'
time.sleep(3)
m_list[fn] = "this is function 2"
print 'func2: finishing'
# return "func2"
def func3(fn, m_list):
print 'func3: starting'
time.sleep(9)
# if fail wont join the rest because it never populate the dict
# or do a try/except to get something in return.
raise ValueError("failed here")
# if we want to get the error in the manager dict we can catch the error
try:
raise ValueError("failed here")
m_list[fn] = "this is third"
except:
m_list[fn] = "this is third and it fail horrible"
# print 'func3: finishing'
# return "func3"
def runInParallel(*fns): # * is to accept any input in list
start_time = datetime.datetime.now()
proc = []
manager = multiprocessing.Manager()
m_list = manager.dict()
for fn in fns:
# print fn
# print dir(fn)
p = Process(target=fn, name=fn.func_name, args=(fn, m_list))
p.start()
proc.append(p)
for p in proc:
p.join() # 5 is the time out
print datetime.datetime.now() - start_time
return m_list, proc
if __name__ == '__main__':
manager, proc = runInParallel(func1, func2, func3)
# print dir(proc[0])
# print proc[0]._name
# print proc[0].name
# print proc[0].exitcode
# here you can check what did fail
for i in proc:
print i.name, i.exitcode # name was set up in the Process line 53
# here will only show the function that worked and where able to populate the
# manager dict
for i, j in manager.items():
print dir(i) # things you can do to the function
print i, j
I'm trying to use a queue with the multiprocessing library in Python. After executing the code below (the print statements work), but the processes do not quit after I call join on the Queue and there are still alive. How can I terminate the remaining processes?
Thanks!
def MultiprocessTest(self):
print "Starting multiprocess."
print "Number of CPUs",multiprocessing.cpu_count()
num_procs = 4
def do_work(message):
print "work",message ,"completed"
def worker():
while True:
item = q.get()
do_work(item)
q.task_done()
q = multiprocessing.JoinableQueue()
for i in range(num_procs):
p = multiprocessing.Process(target=worker)
p.daemon = True
p.start()
source = ['hi','there','how','are','you','doing']
for item in source:
q.put(item)
print "q close"
q.join()
#q.close()
print "Finished everything...."
print "num active children:",multiprocessing.active_children()
try this:
import multiprocessing
num_procs = 4
def do_work(message):
print "work",message ,"completed"
def worker():
for item in iter( q.get, None ):
do_work(item)
q.task_done()
q.task_done()
q = multiprocessing.JoinableQueue()
procs = []
for i in range(num_procs):
procs.append( multiprocessing.Process(target=worker) )
procs[-1].daemon = True
procs[-1].start()
source = ['hi','there','how','are','you','doing']
for item in source:
q.put(item)
q.join()
for p in procs:
q.put( None )
q.join()
for p in procs:
p.join()
print "Finished everything...."
print "num active children:", multiprocessing.active_children()
Your workers need a sentinel to terminate, or they will just sit on the blocking reads. Note that using sleep on the Q instead of join on the P lets you display status information etc.
My preferred template is:
def worker(q,nameStr):
print 'Worker %s started' %nameStr
while True:
item = q.get()
if item is None: # detect sentinel
break
print '%s processed %s' % (nameStr,item) # do something useful
q.task_done()
print 'Worker %s Finished' % nameStr
q.task_done()
q = multiprocessing.JoinableQueue()
procs = []
for i in range(num_procs):
nameStr = 'Worker_'+str(i)
p = multiprocessing.Process(target=worker, args=(q,nameStr))
p.daemon = True
p.start()
procs.append(p)
source = ['hi','there','how','are','you','doing']
for item in source:
q.put(item)
for i in range(num_procs):
q.put(None) # send termination sentinel, one for each process
while not q.empty(): # wait for processing to finish
sleep(1) # manage timeouts and status updates etc.
Here is a sentinel-free method for the relatively simple case where you put a number of tasks on a JoinableQueue, then launch worker processes that consume the tasks and exit once they read the queue "dry". The trick is to use JoinableQueue.get_nowait() instead of get(). get_nowait(), as the name implies, tries to get a value from the queue in a non-blocking manner and if there's nothing to be gotten then a queue.Empty exception is raised. The worker handles this exception by exiting.
Rudimentary code to illustrate the principle:
import multiprocessing as mp
from queue import Empty
def worker(q):
while True:
try:
work = q.get_nowait()
# ... do something with `work`
q.task_done()
except Empty:
break # completely done
# main
worknum = 4
jq = mp.JoinableQueue()
# fill up the task queue
# let's assume `tasks` contains some sort of data
# that your workers know how to process
for task in tasks:
jq.put(task)
procs = [ mp.Process(target=worker, args=(jq,)) for _ in range(worknum) ]
for p in procs:
p.start()
for p in procs:
p.join()
The advantage is that you do not need to put the "poison pills" on the queue so the code is a bit shorter.
IMPORTANT : in more complex situations where producers and consumers use the same queue in an "interleaved" manner and the workers may have to wait for new tasks to come along, the "poison pill" approach should be used. My suggestion above is for simple cases where the workers "know" that if the task queue is empty, then there's no point hanging around any more.
You have to clear the queue before joining the process, but q.empty() is unreliable.
The best way to clear the queue is to count the number of successful gets or loop until you receive a sentinel value, just like a socket with a reliable network.
The code below may not be very relevant but I post it for your comments/feedbacks so we can learn together. Thank you!
import multiprocessing
def boss(q,nameStr):
source = range(1024)
for item in source:
q.put(nameStr+' '+str(item))
q.put(None) # send termination sentinel, one for each process
def worker(q,nameStr):
while True:
item = q.get()
if item is None: # detect sentinel
break
print '%s processed %s' % (nameStr,item) # do something useful
q = multiprocessing.Queue()
procs = []
num_procs = 4
for i in range(num_procs):
nameStr = 'ID_'+str(i)
p = multiprocessing.Process(target=worker, args=(q,nameStr))
procs.append(p)
p = multiprocessing.Process(target=boss, args=(q,nameStr))
procs.append(p)
for j in procs:
j.start()
for j in procs:
j.join()