Python-在共享内存中使用numpy数组进行多处理

  • Z时代
  • 2024-01-10
  • 分类：问答

我想在共享内存中使用一个numpy数组，以便与多处理模块一起使用。困难是像numpy数组一样使用它，而不仅仅是ctypes数组。

from multiprocessing import Process, Array
import scipy
def f(a):
    a[0] = -a[0]
if __name__ == '__main__':
    # Create the array
    N = int(10)
    unshared_arr = scipy.rand(N)
    arr = Array('d', unshared_arr)
    print "Originally, the first two elements of arr = %s"%(arr[:2])
    # Create, start, and finish the child processes
    p = Process(target=f, args=(arr,))
    p.start()
    p.join()
    # Printing out the changed values
    print "Now, the first two elements of arr = %s"%arr[:2]

这将产生如下输出：

Originally, the first two elements of arr = [0.3518653236697369, 0.517794725524976]
Now, the first two elements of arr = [-0.3518653236697369, 0.517794725524976]

可以ctypes方式访问该数组，例如arr[i]说得通。但是，它不是一个numpy数组，因此我无法执行-1*arr，或arr.sum()。我想一个解决方案是将ctypes数组转换为numpy数组。但是（除了无法完成这项工作外），我不相信会再共享它。

对于必须解决的常见问题，似乎将有一个标准解决方案。

回答：

添加到@unutbu（不再可用）和@Henry Gomersall的答案中。你可以shared_arr.get_lock()在需要时使用来同步访问：

shared_arr = mp.Array(ctypes.c_double, N)
# ...
def f(i): # could be anything numpy accepts as an index such another numpy array
    with shared_arr.get_lock(): # synchronize access
        arr = np.frombuffer(shared_arr.get_obj()) # no data copying
        arr[i] = -arr[i]

例

import ctypes
import logging
import multiprocessing as mp
from contextlib import closing
import numpy as np
info = mp.get_logger().info
def main():
    logger = mp.log_to_stderr()
    logger.setLevel(logging.INFO)
    # create shared array
    N, M = 100, 11
    shared_arr = mp.Array(ctypes.c_double, N)
    arr = tonumpyarray(shared_arr)
    # fill with random values
    arr[:] = np.random.uniform(size=N)
    arr_orig = arr.copy()
    # write to arr from different processes
    with closing(mp.Pool(initializer=init, initargs=(shared_arr,))) as p:
        # many processes access the same slice
        stop_f = N // 10
        p.map_async(f, [slice(stop_f)]*M)
        # many processes access different slices of the same array
        assert M % 2 # odd
        step = N // 10
        p.map_async(g, [slice(i, i + step) for i in range(stop_f, N, step)])
    p.join()
    assert np.allclose(((-1)**M)*tonumpyarray(shared_arr), arr_orig)
def init(shared_arr_):
    global shared_arr
    shared_arr = shared_arr_ # must be inherited, not passed as an argument
def tonumpyarray(mp_arr):
    return np.frombuffer(mp_arr.get_obj())
def f(i):
    """synchronized."""
    with shared_arr.get_lock(): # synchronize access
        g(i)
def g(i):
    """no synchronization."""
    info("start %s" % (i,))
    arr = tonumpyarray(shared_arr)
    arr[i] = -1 * arr[i]
    info("end   %s" % (i,))
if __name__ == '__main__':
    mp.freeze_support()
    main()

如果不需要同步访问或创建自己的锁，则mp.Array()没有必要。mp.sharedctypes.RawArray在这种情况下，你可以使用。

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