How to retrieve values from a function run in parallel processes?

The issue in your example is that modifications to standard mutable structures within Manager.dict will not be propagated. I'm first showing you how to fix it with manager, just to show you better options afterwards.

multiprocessing.Manager is a bit heavy since it uses a separate Process just for the Manager and working on a shared object needs using locks for data consistency. If you run this on one machine, there are better options with multiprocessing.Pool, in case you don't have to run customized Process classes and if you have to, multiprocessing.Process together with multiprocessing.Queue would be the common way of doing it.

The quoting parts are from the multiprocessing docs.

Manager

If standard (non-proxy) list or dict objects are contained in a referent, modifications to those mutable values will not be propagated through the manager because the proxy has no way of knowing when the values contained within are modified. However, storing a value in a container proxy (which triggers a setitem on the proxy object) does propagate through the manager and so to effectively modify such an item, one could re-assign the modified value to the container proxy...

In your case this would look like:

def worker(xrange, return_dict, lock):    """worker function"""    for x in range(xrange[0], xrange[1]):        y = ((x+5)**2+x-40)        if y <= 0xf+1:            print('Condition met at: ', y, x)            with lock:                list_x = return_dict['x']                list_y = return_dict['y']                list_x.append(x)                list_y.append(y)                return_dict['x'] = list_x                return_dict['y'] = list_y

The lock here would be a manager.Lock instance you have to pass along as argument since the whole (now) locked operation is not by itself atomic. (Hereis an easier example with Manager using Lock)

This approach is perhaps less convenient than employing nested Proxy Objects for most use cases but also demonstrates a level of control over the synchronization.

Since Python 3.6 proxy objects are nestable:

Changed in version 3.6: Shared objects are capable of being nested. For example, a shared container object such as a shared list can contain other shared objects which will all be managed and synchronized by the SyncManager.

Since Python 3.6 you can fill your manager.dict before starting multiprocessing with manager.list as values and then append directly in the worker without having to reassign.

return_dict['x'] = manager.list()return_dict['y'] = manager.list()

EDIT:

Here is the full example with Manager:

import timeimport multiprocessing as mpfrom multiprocessing import Manager, Processfrom contextlib import contextmanager# mp_util.py from first link in code-snippet for "Pool"# section belowfrom mp_utils import calc_batch_sizes, build_batch_ranges# def context_timer ... see code snippet in "Pool" section belowdef worker(batch_range, return_dict, lock):    """worker function"""    for x in batch_range:        y = ((x+5)**2+x-40)        if y <= 0xf+1:            print('Condition met at: ', y, x)            with lock:                return_dict['x'].append(x)                return_dict['y'].append(y)if __name__ == '__main__':    N_WORKERS = mp.cpu_count()    X_MAX = 100000000    batch_sizes = calc_batch_sizes(X_MAX, n_workers=N_WORKERS)    batch_ranges = build_batch_ranges(batch_sizes)    print(batch_ranges)    with Manager() as manager:        lock = manager.Lock()        return_dict = manager.dict()        return_dict['x'] = manager.list()        return_dict['y'] = manager.list()        tasks = [(batch_range, return_dict, lock)                 for batch_range in batch_ranges]        with context_timer():            pool = [Process(target=worker, args=args)                    for args in tasks]            for p in pool:                p.start()            for p in pool:                p.join()        # Create standard container with data from manager before exiting        # the manager.        result = {k: list(v) for k, v in return_dict.items()}    print(result)

Pool

Most often a multiprocessing.Pool will just do it. You have an additional challenge in your example since you want to distribute iteration over a range.Your chunker function doesn't manage to divide the range even so every process has about the same work to do:

chunker((0, 21), 4)# Out: [[0, 4], [5, 9], [10, 14], [15, 21]]  # 4, 4, 4, 6!

For the code below please grab the code snippet for mp_utils.py from my answer here, it provides two functions to chunk ranges as even as possible.

With multiprocessing.Pool your worker function just has to return the result and Pool will take care of transporting the result back over internal queues back to the parent process. The result will be a list, so you will have to rearange your result again in a way you want it to have. Your example could then look like this:

import timeimport multiprocessing as mpfrom multiprocessing import Poolfrom contextlib import contextmanagerfrom itertools import chainfrom mp_utils import calc_batch_sizes, build_batch_ranges@contextmanagerdef context_timer():    start_time = time.perf_counter()    yield    end_time = time.perf_counter()    total_time   = end_time-start_time    print(f'\nEach iteration took: {total_time / X_MAX:.4f} s')    print(f'Total time:          {total_time:.4f} s\n')def worker(batch_range):    """worker function"""    result = []    for x in batch_range:        y = ((x+5)**2+x-40)        if y <= 0xf+1:            print('Condition met at: ', y, x)            result.append((x, y))    return resultif __name__ == '__main__':    N_WORKERS = mp.cpu_count()    X_MAX = 100000000    batch_sizes = calc_batch_sizes(X_MAX, n_workers=N_WORKERS)    batch_ranges = build_batch_ranges(batch_sizes)    print(batch_ranges)    with context_timer():        with Pool(N_WORKERS) as pool:            results = pool.map(worker, iterable=batch_ranges)    print(f'results: {results}')    x, y = zip(*chain.from_iterable(results))  # filter and sort results    print(f'results sorted: x: {x}, y: {y}')

Example Output:

[range(0, 12500000), range(12500000, 25000000), range(25000000, 37500000), range(37500000, 50000000), range(50000000, 62500000), range(62500000, 75000000), range(75000000, 87500000), range(87500000, 100000000)]Condition met at:  -15 0Condition met at:  -3 1Condition met at:  11 2Each iteration took: 0.0000 sTotal time:          8.2408 sresults: [[(0, -15), (1, -3), (2, 11)], [], [], [], [], [], [], []]results sorted: x: (0, 1, 2), y: (-15, -3, 11)Process finished with exit code 0

If you had multiple arguments for your worker you would build a "tasks"-list with argument-tuples and exchange pool.map(...) with pool.starmap(...iterable=tasks). See docs for further details on that.

Process & Queue

If you can't use multiprocessing.Pool for some reason, you have to take care of inter-process communication (IPC) yourself, by passing a multiprocessing.Queue as argument to your worker-functions in the child-processes and letting them enqueue their results to be send back to the parent.

You will also have to build your Pool-like structure so you can iterate over it to start and join the processes and you have to get() the results back from the queue. More about Queue.get usage I've written up here.

A solution with this approach could look like this:

def worker(result_queue, batch_range):    """worker function"""    result = []    for x in batch_range:        y = ((x+5)**2+x-40)        if y <= 0xf+1:            print('Condition met at: ', y, x)            result.append((x, y))    result_queue.put(result)  # <--if __name__ == '__main__':    N_WORKERS = mp.cpu_count()    X_MAX = 100000000    result_queue = mp.Queue()  # <--    batch_sizes = calc_batch_sizes(X_MAX, n_workers=N_WORKERS)    batch_ranges = build_batch_ranges(batch_sizes)    print(batch_ranges)    with context_timer():        pool = [Process(target=worker, args=(result_queue, batch_range))                for batch_range in batch_ranges]        for p in pool:            p.start()        results = [result_queue.get() for _ in batch_ranges]        for p in pool:            p.join()    print(f'results: {results}')    x, y = zip(*chain.from_iterable(results))  # filter and sort results    print(f'results sorted: x: {x}, y: {y}')