使用python执行并行进程时返回值

Question

我在python中使用了多处理，并且成功地使用了队列，但是在进程仍在执行时，我需要（从main）监视一些变量。你知道吗

我知道使用全局变量不是一个好的做法，但即使这种方法也不起作用。你知道吗

有人能给我指出正确的方向吗？你知道吗

提前谢谢

GCCruz公司

附录： 我贴了一个我想做的简单例子：

import multiprocessing
import time

def sampleprocess(array, count):
    '''process with heavy image processing in a loop'''
    for i in range(count)
        # Do processing on this array that outputs a given variable
        sample_variable= count*10 # I would like to monitor this variable  

if __name__ == '__main__':

    p = multiprocessing.Process(target=sampleprocess, args=(array,1000,))
    p.start()

    # continuously monitor the dummy variable that is being computed on the process
    while sample_variable < 1000
        time.sleep(0.1)
        print ' Still less than 1000'

Answer 1

一种选择是对共享数据对象使用多处理值和数组。 https://docs.python.org/2/library/multiprocessing.html#multiprocessing-managers

示例代码中的工作示例。如果你不止一个 进程同步写入将需要一个锁。你知道吗

---

从多进程导入进程、值、数组、锁 导入时间

def sampleprocess（s，count，lock）： ''循环处理重图像'' 因为我在射程内(计数值): #对输出给定变量的数组执行处理

sample_variable=count*10#我想监视这个变量

    with lock:
        s.value= i*10

如果名称=='main'：

val = Value('i', 1000)
sample_variable = Value('i', 0)
lock = Lock()

p = Process(target=sampleprocess, args=(sample_variable, val, lock))
p.start()

# continuously monitor the dummy variable that is being computed on the process
while sample_variable.value < 1000:
    time.sleep(0.1)
    print ' Still less than 1000'

Answer 2

以下几个程序可能会演示如何实现解决方案：

例1

from multiprocessing import *
import time


def main():
    array, loops = list(range(1000)), 1000
    variable = Value('I')
    p = Process(target=sample, args=(array, loops, variable))
    p.start()
    while variable.value < 1000:
        print('Still less than 1000')
        time.sleep(0.005)
    print('Must be at least 1000')
    p.join()
    print('Value is', variable.value)


def sample(array, loops, variable):
    for number in range(loops):
        variable.value = number * 10
    print('Sample is done')

if __name__ == '__main__':
    main()

例2

from multiprocessing import *
import time


def main():
    processes = 10
    array, loops = list(range(1000)), 1000
    shared = Array('I', processes)
    p_array = []
    for index in range(processes):
        p = Process(target=sample, args=(array, loops, shared, index))
        p.start()
        p_array.append(p)
    while True:
        less_than_1000 = [p for p in enumerate(shared[:]) if p[1] < 1000]
        if less_than_1000:
            print(less_than_1000)
            time.sleep(0.001)
        else:
            break
    print('No process in less than 1000')
    for p in p_array:
        p.join()
    print(shared[:])


def sample(array, loops, p_array, index):
    time.sleep(1)
    for number in range(loops):
        time.sleep(0.001)
        p_array[index] = number * 10
    print('Sample is done')

if __name__ == '__main__':
    main()

例3

from multiprocessing import *
import time


def main():
    array, loops = list(range(1000)), 1000
    with Manager() as manager:
        variable = manager.Value('I', 0)
        p = Process(target=sample, args=(array, loops, variable))
        p.start()
        while variable.value < 1000:
            print('Still less than 1000')
            time.sleep(0.005)
        print('Must be at least 1000')
        p.join()
        print('Value is', variable.value)


def sample(array, loops, variable):
    for number in range(loops):
        variable.value = number * 10
    print('Sample is done')

if __name__ == '__main__':
    main()

例4

from multiprocessing import *
import time


def main():
    array, loops = list(range(1000)), 1000
    event = Event()
    p = Process(target=sample, args=(array, loops, event))
    p.start()
    event.wait()
    print('Must be at least 1000')
    p.join()


def sample(array, loops, event):
    for number in range(loops):
        if number >= 100 and not event.is_set():
            event.set()
            time.sleep(0.001)
    print('Sample is done')

if __name__ == '__main__':
    main()

正如你所看到的，有各种各样的方法来完成你所要求的任务。你知道吗