python 中的多处理可迭代

Question

我正在尝试拆分以下代码以允许在 python 中进行多处理，这对我来说确实是一项令人沮丧的任务 - 我是多处理的新手，已经阅读了文档和尽可能多的示例可以找到但仍未找到可以同时在所有 cpu 核心上运行的解决方案。

我想将可迭代对象分成四部分并让它并行计算测试。

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 矩阵的一个非常小的例子，但我想将它应用于一对更大的矩阵，并且需要大约一个小时的时间来计算。我很感激任何建议。

Answer 1

我建议使用队列来转储可迭代对象。类似的东西：

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。

python 中的多处理可迭代

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iterable

multiprocessing

itertools

python-3.x