advanced patterns When Using Multiprocessing with Shared Memory in Python 3.10
I'm working with an scenario when trying to use the `multiprocessing` module along with shared memory in Python 3.10..... I want to share a large NumPy array across processes to improve performance, but I'm running into unexpected behavior. When I modify the shared array in one process, the changes don't reflect in the other processes. Here is a simplified version of my code: ```python import numpy as np from multiprocessing import Process, shared_memory def modify_array(shm_name, size): shm = shared_memory.SharedMemory(name=shm_name) arr = np.ndarray((size,), dtype=np.float64, buffer=shm.buf) arr[0] = 42.0 # Modify the first element if __name__ == '__main__': size = 10 shm = shared_memory.SharedMemory(create=True, size=size * np.dtype(np.float64).itemsize) arr = np.ndarray((size,), dtype=np.float64, buffer=shm.buf) # Initializing the array arr.fill(0.0) p = Process(target=modify_array, args=(shm.name, size)) p.start() p.join() print(arr) # Expecting [42.0, 0.0, ...] but getting [0.0, 0.0, ...] shm.close() shm.unlink() ``` I've tried ensuring that the size of the shared memory matches the size of the NumPy array. The strange part is that the process runs without any errors, but the changes to the shared array are not reflected in the main process. I am using Python 3.10.1 on a Windows 11 machine. Is there something I'm missing in how the shared memory is set up or accessed? Any help would be appreciated! For context: I'm using Python on macOS. Am I missing something obvious? I'm working with Python in a Docker container on Windows 11. Is there a simpler solution I'm overlooking?