如何在Python中从SharedMemoryManager访问共享内存

如何解决如何在Python中从SharedMemoryManager访问共享内存

我想做的是使用Python 3.8中的新SharedMemoryManager。
我已经仅使用shared_memory.SharedMemory模块成功共享了内存
服务器

import numpy as np
from multiprocessing import shared_memory
img = np.ones((700,700,3),np.int8)
shm = shared_memory.SharedMemory(name='uniquememoryname',create=True,size=img.nbytes)
b = np.ndarray(img.shape,dtype=img.dtype,buffer=shm.buf)
b[:] = img[:]  # copy the original data into shared memory
print('server started - run client Now')
time.sleep(20)
shm.close()
shm.unlink()

客户

import numpy as np
import cv2
from multiprocessing import shared_memory
existing_shm = shared_memory.SharedMemory(name='uniquememoryname')
img = np.ndarray((700,dtype=np.uint8,buffer=existing_shm.buf)
cv2.imshow('img',img)
cv2.waitKey()
existing_shm.close()

但是我现在想做的是能够使用管理器创建内存并访问它们。
到目前为止，我所拥有的：
服务器

# In the first Python interactive shell
if __name__ == '__main__':
    import time
    import numpy as np
    from multiprocessing.managers import SharedMemoryManager
    from multiprocessing import shared_memory

    img = np.zeros((700,dtype=np.uint8)

    with SharedMemoryManager(address=('127.0.0.1',50000),authkey=b'abc') as smm:
        shm = smm.SharedMemory(size=img.nbytes)

        # Now create a NumPy array backed by shared memory
        b = np.ndarray(img.shape,buffer=shm.buf)
        b[:] = img[:]  # copy the original data into shared memory

        print('server started - run client Now')

        time.sleep(20)


    print('server end')

客户

from multiprocessing.managers import SharedMemoryManager
# Attach to the existing shared memory block.
if __name__ == '__main__':
    smm = SharedMemoryManager(address=('127.0.0.1',authkey=b'abc')
    smm.connect()
    #what do i do Now to acess the img from server

那我现在需要在CLIENT中做什么才能从服务器访问b？

解决方法

据我所知，没有办法获得当前管理的内存列表。您需要一种在服务器和客户端之间进行通信并传递名称的方法。从管理器中获取 SharedMemory 对象后，您就可以获取使用 shm.name 分配的名称。多处理队列可能是最简单的，名称只是一个字符串，因此如果您需要更复杂的东西，可以很容易地与任意数量的通信库一起发送。

服务器

# In the first Python interactive shell
if __name__ == '__main__':
    import time
    import numpy as np
    from multiprocessing.managers import SharedMemoryManager
    from multiprocessing import shared_memory

    img = np.zeros((700,700,3),dtype=np.uint8)

    with SharedMemoryManager(address=('127.0.0.1',50000),authkey=b'abc') as smm:
        shm = smm.SharedMemory(size=img.nbytes)
        name = shm.name
        # Here you need a way to communicate with the client to send the name

        # Now create a NumPy array backed by shared memory
        b = np.ndarray(img.shape,dtype=img.dtype,buffer=shm.buf)
        b[:] = img[:]  # Copy the original data into shared memory

        print('server started - run client now')

        time.sleep(20)


    print('server end')

客户

from multiprocessing.managers import SharedMemoryManager
from multiprocessing.shared_memory import SharedMemory
# Attach to the existing shared memory block.
if __name__ == '__main__':
    smm = SharedMemoryManager(address=('127.0.0.1',authkey=b'abc')
    smm.connect()
    # Here you need a way to access the unique name
    shm = SharedMemory(name=name)