How Can I Retrieve Return Values from multiprocessing.Process Instances in Python?

Extracting Return Values from Functions in Multiprocessing.Process Instances

The ability to retrieve return values from functions passed to multiprocessing.Process can be a useful feature, particularly when asynchronous task execution is required. Unfortunately, the values are not immediately accessible from the Process object, necessitating an alternative approach.

Understanding Value Storage

Unlike traditional Python functions, those passed to multiprocessing.Process do not have a return value attribute. Instead, the value is stored in a separate location, specifically in a shared memory object. This is because the processes created using multiprocessing run in separate memory spaces, preventing direct access to variables in the main process.

Using Shared Variables for Communication

To access the return value, we need to establish a form of communication between the processes. One effective method is to utilize shared variables. These are objects that allow multiple processes to share and access data simultaneously. In our case, we create a manager object and a shared dictionary using multiprocessing.Manager(). The dictionary acts as the shared variable.

Accessing the Return Value

Within the worker function, we populate the shared dictionary with the desired return value. The main process, after waiting for all tasks to complete, can access and retrieve these values from the shared dictionary. By employing this strategy, we effectively extract the return values without compromising the multiprocessing approach.

Example Implementation

The following example showcases the implementation of shared variables to retrieve return values:

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())

Output:

0 represent!
1 represent!
3 represent!
2 represent!
4 represent!
[0, 1, 3, 2, 4]

This approach enables us to retrieve the return values of the worker function and demonstrate the seamless communication between processes in the multiprocessing framework.

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