Running multiple processes simultaneously is not supported on Windows when using Jupyter Notebook

Question

Running multiple processes simultaneously is not supported on Windows when using Jupyter Notebook

Currently, I'm running into issues with multiprocessing on Windows using Jupyter notebook. Instead of running all my async's in parallel, they are being executed serially one at a time. Can someone offer guidance on what might be going wrong? I need to store the results in a variable for later use. What am I missing?

import multiprocessing as mp
import cylib
Pool = mp.Pool(processes=4)
result1 = Pool.apply_async(cylib.f, [v]) # evaluate asynchronously 
result2 = Pool.apply_async(cylib.f, [x]) # evaluate asynchronously
result3 = Pool.apply_async(cylib.f, [y]) # evaluate asynchronously 
result4 = Pool.apply_async(cylib.f, [z]) # evaluate asynchronously

vr = result1.get(timeout=420) 
xr = result2.get(timeout=420)
yr = result3.get(timeout=420) 
zr = result4.get(timeout=420)

python python-2.7 jupyter-notebook python-multiprocessing

Answer 1

Answer №1

The operations are running in parallel.

Nevertheless, the outcomes are being fetched synchronously, meaning they wait for each result to be ready before moving on to the next one.

vr = result1.get(timeout=420) 
xr = result2.get(timeout=420)
yr = result3.get(timeout=420) 
zr = result4.get(timeout=420)

Take into account this sample code where each operation is polled asynchronously:

from time import sleep
import multiprocessing as mp
pool = mp.Pool(processes=4)

# Assign tasks with longer delay first
tasks = {i: pool.apply_async(sleep, [t]) for i, t in enumerate(reversed(range(3)))}
done = set()


# Continuously check until all operations finish
while len(done) < len(tasks):

    for i, t in tasks.items():

        # Skip completed tasks
        if i in done:
            continue

        result = None

        try:
            result = t.get(timeout=0)
        except mp.TimeoutError:
            pass
        else:
            print("Operation #:{} complete".format(i))
            done.add(i)

You can replicate something similar to the above or utilize the callback option on apply_async to handle tasks automatically as they finalize.

Answer 2

The operations are running in parallel.

Nevertheless, the outcomes are being fetched synchronously, meaning they wait for each result to be ready before moving on to the next one.

vr = result1.get(timeout=420) 
xr = result2.get(timeout=420)
yr = result3.get(timeout=420) 
zr = result4.get(timeout=420)

Take into account this sample code where each operation is polled asynchronously:

from time import sleep
import multiprocessing as mp
pool = mp.Pool(processes=4)

# Assign tasks with longer delay first
tasks = {i: pool.apply_async(sleep, [t]) for i, t in enumerate(reversed(range(3)))}
done = set()


# Continuously check until all operations finish
while len(done) < len(tasks):

    for i, t in tasks.items():

        # Skip completed tasks
        if i in done:
            continue

        result = None

        try:
            result = t.get(timeout=0)
        except mp.TimeoutError:
            pass
        else:
            print("Operation #:{} complete".format(i))
            done.add(i)

You can replicate something similar to the above or utilize the callback option on apply_async to handle tasks automatically as they finalize.

Running multiple processes simultaneously is not supported on Windows when using Jupyter Notebook

Answer №1

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