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
When I run something like:
from multiprocessing import Pool
p = Pool(5)
def f(x):
return x*x
p.map(f, [1,2,3])
it works fine. However, putting this as a function of a class:
class calculate(object):
def run(self):
def f(x):
return x*x
p = Pool()
return p.map(f, [1,2,3])
cl = calculate()
print cl.run()
Gives me the following error:
Exception in thread Thread-1:
Traceback (most recent call last):
File "/sw/lib/python2.6/threading.py", line 532, in __bootstrap_inner
self.run()
File "/sw/lib/python2.6/threading.py", line 484, in run
self.__target(*self.__args, **self.__kwargs)
File "/sw/lib/python2.6/multiprocessing/pool.py", line 225, in _handle_tasks
put(task)
PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed
I've seen a post from Alex Martelli dealing with the same kind of problem, but it wasn't explicit enough.
I could not use the code posted so far because code using "multiprocessing.Pool" do not work with lambda expressions and code not using "multiprocessing.Pool" spawn as many processes as there are work items.
I adapted the code s.t. it spawns a predefined amount of workers and only iterates through the input list if there exists an idle worker. I also enabled the "daemon" mode for the workers s.t. ctrl-c works as expected.
import multiprocessing
def fun(f, q_in, q_out):
while True:
i, x = q_in.get()
if i is None:
break
q_out.put((i, f(x)))
def parmap(f, X, nprocs=multiprocessing.cpu_count()):
q_in = multiprocessing.Queue(1)
q_out = multiprocessing.Queue()
proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out))
for _ in range(nprocs)]
for p in proc:
p.daemon = True
p.start()
sent = [q_in.put((i, x)) for i, x in enumerate(X)]
[q_in.put((None, None)) for _ in range(nprocs)]
res = [q_out.get() for _ in range(len(sent))]
[p.join() for p in proc]
return [x for i, x in sorted(res)]
if __name__ == '__main__':
print(parmap(lambda i: i * 2, [1, 2, 3, 4, 6, 7, 8]))
Multiprocessing and pickling is broken and limited unless you jump outside the standard library.
If you use a fork of multiprocessing called pathos.multiprocesssing, you can directly use classes and class methods in multiprocessing's map functions. This is because dill is used instead of pickle or cPickle, and dill can serialize almost anything in python.
pathos.multiprocessing also provides an asynchronous map function… and it can map functions with multiple arguments (e.g. map(math.pow, [1,2,3], [4,5,6]))
See discussions:
What can multiprocessing and dill do together?
and:
http://matthewrocklin.com/blog/work/2013/12/05/Parallelism-and-Serialization
It even handles the code you wrote initially, without modification, and from the interpreter. Why do anything else that's more fragile and specific to a single case?
>>> from pathos.multiprocessing import ProcessingPool as Pool
>>> class calculate(object):
... def run(self):
... def f(x):
... return x*x
... p = Pool()
... return p.map(f, [1,2,3])
...
>>> cl = calculate()
>>> print cl.run()
[1, 4, 9]
Get the code here:
https://github.com/uqfoundation/pathos
And, just to show off a little more of what it can do:
>>> from pathos.multiprocessing import ProcessingPool as Pool
>>>
>>> p = Pool(4)
>>>
>>> def add(x,y):
... return x+y
...
>>> x = [0,1,2,3]
>>> y = [4,5,6,7]
>>>
>>> p.map(add, x, y)
[4, 6, 8, 10]
>>>
>>> class Test(object):
... def plus(self, x, y):
... return x+y
...
>>> t = Test()
>>>
>>> p.map(Test.plus, [t]*4, x, y)
[4, 6, 8, 10]
>>>
>>> res = p.amap(t.plus, x, y)
>>> res.get()
[4, 6, 8, 10]
I also was annoyed by restrictions on what sort of functions pool.map could accept. I wrote the following to circumvent this. It appears to work, even for recursive use of parmap.
from multiprocessing import Process, Pipe
from itertools import izip
def spawn(f):
def fun(pipe, x):
pipe.send(f(x))
pipe.close()
return fun
def parmap(f, X):
pipe = [Pipe() for x in X]
proc = [Process(target=spawn(f), args=(c, x)) for x, (p, c) in izip(X, pipe)]
[p.start() for p in proc]
[p.join() for p in proc]
return [p.recv() for (p, c) in pipe]
if __name__ == '__main__':
print parmap(lambda x: x**x, range(1, 5))
There is currently no solution to your problem, as far as I know: the function that you give to map() must be accessible through an import of your module. This is why robert's code works: the function f() can be obtained by importing the following code:
def f(x):
return x*x
class Calculate(object):
def run(self):
p = Pool()
return p.map(f, [1,2,3])
if __name__ == '__main__':
cl = Calculate()
print cl.run()
I actually added a "main" section, because this follows the recommendations for the Windows platform ("Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects").
I also added an uppercase letter in front of Calculate, so as to follow PEP 8. :)
The solution by mrule is correct but has a bug: if the child sends back a large amount of data, it can fill the pipe's buffer, blocking on the child's pipe.send(), while the parent is waiting for the child to exit on pipe.join(). The solution is to read the child's data before join()ing the child. Furthermore the child should close the parent's end of the pipe to prevent a deadlock. The code below fixes that. Also be aware that this parmap creates one process per element in X. A more advanced solution is to use multiprocessing.cpu_count() to divide X into a number of chunks, and then merge the results before returning. I leave that as an exercise to the reader so as not to spoil the conciseness of the nice answer by mrule. ;)
from multiprocessing import Process, Pipe
from itertools import izip
def spawn(f):
def fun(ppipe, cpipe,x):
ppipe.close()
cpipe.send(f(x))
cpipe.close()
return fun
def parmap(f,X):
pipe=[Pipe() for x in X]
proc=[Process(target=spawn(f),args=(p,c,x)) for x,(p,c) in izip(X,pipe)]
[p.start() for p in proc]
ret = [p.recv() for (p,c) in pipe]
[p.join() for p in proc]
return ret
if __name__ == '__main__':
print parmap(lambda x:x**x,range(1,5))
I've also struggled with this. I had functions as data members of a class, as a simplified example:
from multiprocessing import Pool
import itertools
pool = Pool()
class Example(object):
def __init__(self, my_add):
self.f = my_add
def add_lists(self, list1, list2):
# Needed to do something like this (the following line won't work)
return pool.map(self.f,list1,list2)
I needed to use the function self.f in a Pool.map() call from within the same class and self.f did not take a tuple as an argument. Since this function was embedded in a class, it was not clear to me how to write the type of wrapper other answers suggested.
I solved this problem by using a different wrapper that takes a tuple/list, where the first element is the function, and the remaining elements are the arguments to that function, called eval_func_tuple(f_args). Using this, the problematic line can be replaced by return pool.map(eval_func_tuple, itertools.izip(itertools.repeat(self.f), list1, list2)). Here is the full code:
File: util.py
def add(a, b): return a+b
def eval_func_tuple(f_args):
"""Takes a tuple of a function and args, evaluates and returns result"""
return f_args[0](*f_args[1:])
File: main.py
from multiprocessing import Pool
import itertools
import util
pool = Pool()
class Example(object):
def __init__(self, my_add):
self.f = my_add
def add_lists(self, list1, list2):
# The following line will now work
return pool.map(util.eval_func_tuple,
itertools.izip(itertools.repeat(self.f), list1, list2))
if __name__ == '__main__':
myExample = Example(util.add)
list1 = [1, 2, 3]
list2 = [10, 20, 30]
print myExample.add_lists(list1, list2)
Running main.py will give [11, 22, 33]. Feel free to improve this, for example eval_func_tuple could also be modified to take keyword arguments.
On another note, in another answers, the function "parmap" can be made more efficient for the case of more Processes than number of CPUs available. I'm copying an edited version below. This is my first post and I wasn't sure if I should directly edit the original answer. I also renamed some variables.
from multiprocessing import Process, Pipe
from itertools import izip
def spawn(f):
def fun(pipe,x):
pipe.send(f(x))
pipe.close()
return fun
def parmap(f,X):
pipe=[Pipe() for x in X]
processes=[Process(target=spawn(f),args=(c,x)) for x,(p,c) in izip(X,pipe)]
numProcesses = len(processes)
processNum = 0
outputList = []
while processNum < numProcesses:
endProcessNum = min(processNum+multiprocessing.cpu_count(), numProcesses)
for proc in processes[processNum:endProcessNum]:
proc.start()
for proc in processes[processNum:endProcessNum]:
proc.join()
for proc,c in pipe[processNum:endProcessNum]:
outputList.append(proc.recv())
processNum = endProcessNum
return outputList
if __name__ == '__main__':
print parmap(lambda x:x**x,range(1,5))
I know that this question was asked 8 years and 10 months ago but I want to present you my solution:
from multiprocessing import Pool
class Test:
def __init__(self):
self.main()
#staticmethod
def methodForMultiprocessing(x):
print(x*x)
def main(self):
if __name__ == "__main__":
p = Pool()
p.map(Test.methodForMultiprocessing, list(range(1, 11)))
p.close()
TestObject = Test()
You just need to make your class function into a static method. But it's also possible with a class method:
from multiprocessing import Pool
class Test:
def __init__(self):
self.main()
#classmethod
def methodForMultiprocessing(cls, x):
print(x*x)
def main(self):
if __name__ == "__main__":
p = Pool()
p.map(Test.methodForMultiprocessing, list(range(1, 11)))
p.close()
TestObject = Test()
Tested in Python 3.7.3
I know this was asked over 6 years ago now, but just wanted to add my solution, as some of the suggestions above seem horribly complicated, but my solution was actually very simple.
All I had to do was wrap the pool.map() call to a helper function. Passing the class object along with args for the method as a tuple, which looked a bit like this.
def run_in_parallel(args):
return args[0].method(args[1])
myclass = MyClass()
method_args = [1,2,3,4,5,6]
args_map = [ (myclass, arg) for arg in method_args ]
pool = Pool()
pool.map(run_in_parallel, args_map)
I took klaus se's and aganders3's answer, and made a documented module that is more readable and holds in one file. You can just add it to your project. It even has an optional progress bar !
"""
The ``processes`` module provides some convenience functions
for using parallel processes in python.
Adapted from http://stackoverflow.com/a/16071616/287297
Example usage:
print prll_map(lambda i: i * 2, [1, 2, 3, 4, 6, 7, 8], 32, verbose=True)
Comments:
"It spawns a predefined amount of workers and only iterates through the input list
if there exists an idle worker. I also enabled the "daemon" mode for the workers so
that KeyboardInterupt works as expected."
Pitfalls: all the stdouts are sent back to the parent stdout, intertwined.
Alternatively, use this fork of multiprocessing:
https://github.com/uqfoundation/multiprocess
"""
# Modules #
import multiprocessing
from tqdm import tqdm
################################################################################
def apply_function(func_to_apply, queue_in, queue_out):
while not queue_in.empty():
num, obj = queue_in.get()
queue_out.put((num, func_to_apply(obj)))
################################################################################
def prll_map(func_to_apply, items, cpus=None, verbose=False):
# Number of processes to use #
if cpus is None: cpus = min(multiprocessing.cpu_count(), 32)
# Create queues #
q_in = multiprocessing.Queue()
q_out = multiprocessing.Queue()
# Process list #
new_proc = lambda t,a: multiprocessing.Process(target=t, args=a)
processes = [new_proc(apply_function, (func_to_apply, q_in, q_out)) for x in range(cpus)]
# Put all the items (objects) in the queue #
sent = [q_in.put((i, x)) for i, x in enumerate(items)]
# Start them all #
for proc in processes:
proc.daemon = True
proc.start()
# Display progress bar or not #
if verbose:
results = [q_out.get() for x in tqdm(range(len(sent)))]
else:
results = [q_out.get() for x in range(len(sent))]
# Wait for them to finish #
for proc in processes: proc.join()
# Return results #
return [x for i, x in sorted(results)]
################################################################################
def test():
def slow_square(x):
import time
time.sleep(2)
return x**2
objs = range(20)
squares = prll_map(slow_square, objs, 4, verbose=True)
print "Result: %s" % squares
EDIT: Added #alexander-mcfarlane suggestion and a test function
Functions defined in classes (even within functions within classes) don't really pickle. However, this works:
def f(x):
return x*x
class calculate(object):
def run(self):
p = Pool()
return p.map(f, [1,2,3])
cl = calculate()
print cl.run()
I modified klaus se's method because while it was working for me with small lists, it would hang when the number of items was ~1000 or greater. Instead of pushing the jobs one at a time with the None stop condition, I load up the input queue all at once and just let the processes munch on it until it's empty.
from multiprocessing import cpu_count, Queue, Process
def apply_func(f, q_in, q_out):
while not q_in.empty():
i, x = q_in.get()
q_out.put((i, f(x)))
# map a function using a pool of processes
def parmap(f, X, nprocs = cpu_count()):
q_in, q_out = Queue(), Queue()
proc = [Process(target=apply_func, args=(f, q_in, q_out)) for _ in range(nprocs)]
sent = [q_in.put((i, x)) for i, x in enumerate(X)]
[p.start() for p in proc]
res = [q_out.get() for _ in sent]
[p.join() for p in proc]
return [x for i,x in sorted(res)]
Edit: unfortunately now I am running into this error on my system: Multiprocessing Queue maxsize limit is 32767, hopefully the workarounds there will help.
You can run your code without any issues if you somehow manually ignore the Pool object from the list of objects in the class because it is not pickleable as the error says. You can do this with the __getstate__ function (look here too) as follow. The Pool object will try to find the __getstate__ and __setstate__ functions and execute them if it finds it when you run map, map_async etc:
class calculate(object):
def __init__(self):
self.p = Pool()
def __getstate__(self):
self_dict = self.__dict__.copy()
del self_dict['p']
return self_dict
def __setstate__(self, state):
self.__dict__.update(state)
def f(self, x):
return x*x
def run(self):
return self.p.map(self.f, [1,2,3])
Then do:
cl = calculate()
cl.run()
will give you the output:
[1, 4, 9]
I've tested the above code in Python 3.x and it works.
Here is my solution, which I think is a bit less hackish than most others here. It is similar to nightowl's answer.
someclasses = [MyClass(), MyClass(), MyClass()]
def method_caller(some_object, some_method='the method'):
return getattr(some_object, some_method)()
othermethod = partial(method_caller, some_method='othermethod')
with Pool(6) as pool:
result = pool.map(othermethod, someclasses)
This may not be a very good solution but in my case, I solve it like this.
from multiprocessing import Pool
def foo1(data):
self = data.get('slf')
lst = data.get('lst')
return sum(lst) + self.foo2()
class Foo(object):
def __init__(self, a, b):
self.a = a
self.b = b
def foo2(self):
return self.a**self.b
def foo(self):
p = Pool(5)
lst = [1, 2, 3]
result = p.map(foo1, (dict(slf=self, lst=lst),))
return result
if __name__ == '__main__':
print(Foo(2, 4).foo())
I had to pass self to my function as I have to access attributes and functions of my class through that function. This is working for me. Corrections and suggestions are always welcome.
Here is a boilerplate I wrote for using multiprocessing Pool in python3, specifically python3.7.7 was used to run the tests. I got my fastest runs using imap_unordered. Just plug in your scenario and try it out. You can use timeit or just time.time() to figure out which works best for you.
import multiprocessing
import time
NUMBER_OF_PROCESSES = multiprocessing.cpu_count()
MP_FUNCTION = 'starmap' # 'imap_unordered' or 'starmap' or 'apply_async'
def process_chunk(a_chunk):
print(f"processig mp chunk {a_chunk}")
return a_chunk
map_jobs = [1, 2, 3, 4]
result_sum = 0
s = time.time()
if MP_FUNCTION == 'imap_unordered':
pool = multiprocessing.Pool(processes=NUMBER_OF_PROCESSES)
for i in pool.imap_unordered(process_chunk, map_jobs):
result_sum += i
elif MP_FUNCTION == 'starmap':
pool = multiprocessing.Pool(processes=NUMBER_OF_PROCESSES)
try:
map_jobs = [(i, ) for i in map_jobs]
result_sum = pool.starmap(process_chunk, map_jobs)
result_sum = sum(result_sum)
finally:
pool.close()
pool.join()
elif MP_FUNCTION == 'apply_async':
with multiprocessing.Pool(processes=NUMBER_OF_PROCESSES) as pool:
result_sum = [pool.apply_async(process_chunk, [i, ]).get() for i in map_jobs]
result_sum = sum(result_sum)
print(f"result_sum is {result_sum}, took {time.time() - s}s")
In the above scenario imap_unordered actually seems to perform the worst for me. Try out your case and benchmark it on the machine you plan to run it on. Also read up on Process Pools. Cheers!
I'm not sure if this approach has been taken but a work around i'm using is:
from multiprocessing import Pool
t = None
def run(n):
return t.f(n)
class Test(object):
def __init__(self, number):
self.number = number
def f(self, x):
print x * self.number
def pool(self):
pool = Pool(2)
pool.map(run, range(10))
if __name__ == '__main__':
t = Test(9)
t.pool()
pool = Pool(2)
pool.map(run, range(10))
Output should be:
0
9
18
27
36
45
54
63
72
81
0
9
18
27
36
45
54
63
72
81
class Calculate(object):
# Your instance method to be executed
def f(self, x, y):
return x*y
if __name__ == '__main__':
inp_list = [1,2,3]
y = 2
cal_obj = Calculate()
pool = Pool(2)
results = pool.map(lambda x: cal_obj.f(x, y), inp_list)
There is a possibility that you would want to apply this function for each different instance of the class. Then here is the solution for that also
class Calculate(object):
# Your instance method to be executed
def __init__(self, x):
self.x = x
def f(self, y):
return self.x*y
if __name__ == '__main__':
inp_list = [Calculate(i) for i in range(3)]
y = 2
pool = Pool(2)
results = pool.map(lambda x: x.f(y), inp_list)
From http://www.rueckstiess.net/research/snippets/show/ca1d7d90 and http://qingkaikong.blogspot.com/2016/12/python-parallel-method-in-class.html
We can make an external function and seed it with the class self object:
from joblib import Parallel, delayed
def unwrap_self(arg, **kwarg):
return square_class.square_int(*arg, **kwarg)
class square_class:
def square_int(self, i):
return i * i
def run(self, num):
results = []
results = Parallel(n_jobs= -1, backend="threading")\
(delayed(unwrap_self)(i) for i in zip([self]*len(num), num))
print(results)
OR without joblib:
from multiprocessing import Pool
import time
def unwrap_self_f(arg, **kwarg):
return C.f(*arg, **kwarg)
class C:
def f(self, name):
print 'hello %s,'%name
time.sleep(5)
print 'nice to meet you.'
def run(self):
pool = Pool(processes=2)
names = ('frank', 'justin', 'osi', 'thomas')
pool.map(unwrap_self_f, zip([self]*len(names), names))
if __name__ == '__main__':
c = C()
c.run()
To implement multiprocessing in aws lambda we have two ways.
Note : Threadpool doesn't work in aws lambda
use the example solution which is provided by aws team
please use this link https://aws.amazon.com/blogs/compute/parallel-processing-in-python-with-aws-lambda/
use this package https://pypi.org/project/lambda-multiprocessing/
i have implemented my lambda function with both the solution and both is working fine can't share my code here but this 2 links will help you for sure.
i find 2 nd way more easy to implement.
There are also some libraries to make this easier, for example autothread (only for Python 3.6 and up):
import autothread
class calculate(object):
def run(self):
#autothread.multiprocessed()
def f(x: int):
return x*x
return f([1,2,3])
cl = calculate()
print(cl.run())
You can also take a look at lox.