Python: Executing multiple functions simultaneously

You are doing it correctly. :)

Try running this silly piece of code:

from multiprocessing import Process
import sys

rocket = 0

def func1():
    global rocket
    print 'start func1'
    while rocket < sys.maxint:
        rocket += 1
    print 'end func1'

def func2():
    global rocket
    print 'start func2'
    while rocket < sys.maxint:
        rocket += 1
    print 'end func2'

if __name__=='__main__':
    p1 = Process(target = func1)
    p1.start()
    p2 = Process(target = func2)
    p2.start()

You will see it print 'start func1' and then 'start func2' and then after a (very) long time you will finally see the functions end. But they will indeed execute simultaneously.

Because processes take a while to start up, you may even see 'start func2' before 'start func1'.

This is just what i needed. I know it wasn't asked but i modified shashank's code to suit Python 3 for anyone else looking :)

from multiprocessing import Process
import sys

rocket = 0

def func1():
    global rocket
    print ('start func1')
    while rocket < sys.maxsize:
        rocket += 1
    print ('end func1')

def func2():
    global rocket
    print ('start func2')
    while rocket < sys.maxsize:
        rocket += 1
    print ('end func2')

if __name__=='__main__':
    p1 = Process(target=func1)
    p1.start()
    p2 = Process(target=func2)
    p2.start()

Substitute sys.maxsize for an number then print(rocket)and you can see it count up one at a time. Get to a number and stop

This can be done elegantly with Ray, a system that allows you to easily parallelize and distribute your Python code.

To parallelize your example, you'd need to define your functions with the @ray.remote decorator, and then invoke them with .remote.

import ray

ray.init()

# Define functions you want to execute in parallel using 
# the ray.remote decorator.
@ray.remote
def func1():
    #does something

@ray.remote
def func2():
    #does something

# Execute func1 and func2 in parallel.
ray.get([func1.remote(), func2.remote()])

If func1() and func2() return results, you need to rewrite the code as follows:

ret_id1 = func1.remote()
ret_id2 = func1.remote()
ret1, ret2 = ray.get([ret_id1, ret_id2])

There are a number of advantages of using Ray over the multiprocessing module. In particular, the same code will run on a single machine as well as on a cluster of machines. For more advantages of Ray see this related post.