How can I get the return value of a function passed to multiprocessing.Process?

Unleashing Advanced Techniques & Tips

Harnessing multiprocessing.Manager()

• With this technique, you can convert a simple worker process into a team:

from multiprocessing import Process, Manager

def worker(return_dict, arg):
    return_dict[arg] = arg * 2  # "Team, Here's my part"

if __name__ == '__main__':
    with Manager() as manager:
        return_dict = manager.dict()  # Team communication board
        processes = [Process(target=worker, args=(return_dict, i)) for i in range(5)]  # Squad of five workers!
        for p in processes:
            p.start()
        for p in processes:
            p.join()
        print(list(return_dict.values()))  # And the complete work is here!
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Points to ponder:

• Manager dictionaries, a nifty tool for handling shared data structures across processes.
• Fetch values via invoking return_dict.values() after utilizing join().

Making multiprocessing.Pool Your Friend

Pool object lends an assist in making the process of parallelizing tasks and hodling onto their results an absolute breeze:

from multiprocessing import Pool

def worker(arg):
    return arg * 2  # Doubling is my game

if __name__ == '__main__':
    with Pool(5) as p:
        results = p.map(worker, range(5))  # Let 5 'pools' work it out
        print(results)  # And fetch all their outputs right here!
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Benefits:

• Minimal coding required, fewer headaches!
• Highly efficient way of recycling worker processes.

Error-Proofing Your Code

Errors are bound to slither into your worker processes, nip them in the bud with try-except blocks:

def worker(arg, q):
    try:
        # Potential bomb right below
        raise ValueError('Example Error')
    except Exception as e:
        q.put(e)  # Put that error where it can be seen!

if __name__ == '__main__':
    q = Queue()
    p = Process(target=worker, args=(5, q))
    p.start()  # Defusing, er... working starts
    p.join()   # Wait till defusal, er... working ends
    result = q.get()
    if isinstance(result, Exception):  # "Was there an error?"
        print(f"Error Occurred: {result}")  # "Ah! I see you there!"
    else:
        print(f"Result: {result}")  # No error? Now, that's a perfect score!
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Catering to Different Platforms

When working with Windows or Jupyter Notebook, remember these quirks while handling multiprocessing:

• Always use if __name__ == '__main__': to protect against Windows' notorious fork bombs.
• Save your code as a .py file and run it directly instead of running in Jupyter. Might fend off some attribute errors.

Wading Deeper: Alternate Methods & Tricky Edges

Opting for multiprocessing.Pipe

multiprocessing.Pipe fits the bill for situations demanding high speed and exclusive one-to-one communication:

from multiprocessing import Process, Pipe

def worker(conn):
    conn.send('Work done!')  # Leave mail for parent process
    conn.close()  # Job done! Close communication

if __name__ == '__main__':
    parent_conn, child_conn = Pipe()
    p = Process(target=worker, args=(child_conn,))  # Assign task to worker process
    p.start()
    p.join()
    print(parent_conn.recv())  # Read the mail from child process
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Key Insights:

• Swift and resource-friendly.
• Apt for exclusive sender-receiver pairs.

Ensuring Graceful Termination

Proper process termination and resource release is essential to prevent resource leaks:

p.terminate()  # Termination time, buddy.
p.join()  # Ensure peaceful termination
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Crucial Guideline:

• Use terminate with care. The Grim Reaper is not a pretty sight.

Parallel Computing Philosophy

Understand the trade-offs of using multiprocessing, with a keen eye on choosing between shared state and stateless functions.

Food for thought:

• Stateless designs often lead to simplified concurrency.
• Steer clear of shared mutable state to avoid entering the nightmare alley of race conditions.