在python中多处理iterable

Question

我正在尝试将以下代码拆分以允许在python中进行多处理，这对我来说真的是一个令人沮丧的任务-我对多处理是新手，已经阅读了文档和尽可能多的示例，但仍然没有找到一个解决方案，使它能够同时在所有的cpu核心上运行。在

我想把它分成四分之一，让它用parralell计算测试。在

我的单线程示例：

import itertools as it
import numpy as np

wmod = np.array([[0,1,2],[3,4,5],[6,7,3]])
pmod = np.array([[0,1,2],[3,4,5],[6,7,3]])

plines1 = it.product(wmod[0],wmod[1],wmod[2])
plines2 = it.product(pmod[0],pmod[1],pmod[2])

check = .915
result = []

for count, (A,B) in enumerate(zip(plines1,plines2)):
    pass

    test = (sum(B)+10)/(sum(A)+12)
    if test > check:
        result = np.append(result,[A,B])
print('results: ',result)

我知道这是一对3x3矩阵的一个很小的例子，但是我想把它应用到一对更大的矩阵上，大约需要一个小时来计算。我很感激你给我的建议。在

Answer 1

我建议使用队列来转储您的可移植文件。像这样：

import multiprocessing as mp
import numpy as np
import itertools as it


def worker(in_queue, out_queue):
    check = 0.915
    for a in iter(in_queue.get, 'STOP'):
        A = a[0]
        B = a[1]
        test = (sum(B)+10)/(sum(A)+12)
        if test > check:
            out_queue.put([A,B])
        else:
            out_queue.put('')

if __name__ == "__main__":
    wmod = np.array([[0,1,2],[3,4,5],[6,7,3]])
    pmod = np.array([[0,1,2],[3,4,5],[6,7,3]])

    plines1 = it.product(wmod[0],wmod[1],wmod[2])
    plines2 = it.product(pmod[0],pmod[1],pmod[2])

    # determine length of your iterator
    counts = 26

    # setup iterator
    it = zip(plines1,plines2)

    in_queue = mp.Queue()
    out_queue = mp.Queue()

    # setup workers
    numProc = 2
    process = [mp.Process(target=worker,
                          args=(in_queue, out_queue), daemon=True) for x in range(numProc)]

    # run processes
    for p in process:
        p.start()

    results = []
    control = True

    # fill queue and get data
    # code fills the queue until a new element is available in the output
    # fill blocks if no slot is available in the in_queue
    for idx in range(counts):
        while out_queue.empty() and control:
            # fill the queue
            try:
                in_queue.put(next(it), block=True) 
            except StopIteration:
                # signals for processes stop
                for p in process:
                    print('stopping')
                    in_queue.put('STOP')
                control = False
                break
        results.append(out_queue.get(timeout=10))

    # wait for processes to finish
    for p in process:
        p.join()

    print(results)

    print('finished')

但是，您必须首先确定任务列表的长度。在