How to share a numpy array between 2 processes on Windows?

[qstdy]

I want to make 2 processes that share a numpy array (one of which writes the array and the other reads it).

It works fine when I make 2 processes with 2 functions like this:

from multiprocessing import Process, Semaphore, shared_memory
import numpy as np
import time

def reader(id, a, shm):
    exst_shm = shared_memory.SharedMemory(name=shm)
    b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf)
        
    time.sleep(2)    
    print('FUNCTION VERSION: ', b[0])   
    

def worker(id, a, shm):
    exst_shm = shared_memory.SharedMemory(name=shm)
    b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf) 
    b[0] += 10 


if __name__ == "__main__":
    a = np.array([0])
    shm = shared_memory.SharedMemory(create=True, size=a.nbytes) 
    c = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)
    
    th1 = Process(target=reader, args=(1, a, shm.name))
    th2 = Process(target=worker, args=(2, a, shm.name))
    
    th1.start()
    th2.start()
    th1.join()
    th2.join()

The code above gives me 10, but when using subclasses it gives me 0 (Windows 10, Python 3.8.7 64bit).

Why do they print out different results? Have I made a mistake or is this a bug?

from multiprocessing import Process, Semaphore, shared_memory
import numpy as np
import time

class Reader(Process):
    def __init__(self, id, a, shm):
        Process.__init__(self)
        
        exst_shm = shared_memory.SharedMemory(name=shm)
        b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf) 
        
        time.sleep(2)    
        print('SUBCLASS VERSION: ', b[0])   
       
class Worker(Process):
    def __init__(self, id, a, shm):
        Process.__init__(self)
            
        exst_shm = shared_memory.SharedMemory(name=shm) 
        b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf) 
        b[0] += 10
        
if __name__ == "__main__":
    a = np.array([0])
    shm = shared_memory.SharedMemory(create=True, size=a.nbytes) 
    c = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)
    
    th1 = Reader(1, a, shm.name)
    th2 = Worker(2, a, shm.name)

    th1.start()
    th2.start()
    th1.join()
    th2.join()