How to run functions in parallel?

Question

I researched first and couldn\'t find an answer to my question. I am trying to run multiple functions in parallel in Python.

I have something like this:

Answer 1

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()

dir1 = 'C:\\folder1'
dir2 = 'C:\\folder2'
filename = 'test.txt'
addFiles = [25, 5, 15, 35, 45, 25, 5, 15, 35, 45]

# Define the functions. 
# You need to pass every global variable used by the function as an argument.
# This is needed because each remote function runs in a different process,
# and thus it does not have access to the global variables defined in 
# the current process.
@ray.remote
def func1(filename, addFiles, dir):
    # func1() code here...

@ray.remote
def func2(filename, addFiles, dir):
    # func2() code here...

# Start two tasks in the background and wait for them to finish.
ray.get([func1.remote(filename, addFiles, dir1), func2.remote(filename, addFiles, dir2)]) 

If you pass the same argument to both functions and the argument is large, a more efficient way to do this is using ray.put(). This avoids the large argument to be serialized twice and to create two memory copies of it:

largeData_id = ray.put(largeData)

ray.get([func1(largeData_id), func2(largeData_id)])

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

ret_id1 = func1.remote(filename, addFiles, dir1)
ret_id2 = func2.remote(filename, addFiles, dir2)
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.