如何实现并行延迟，使得并行化的 for 循环在输出低于阈值时停止？

Question

假设我有以下代码：

from scipy import *
import multiprocessing as mp
num_cores = mp.cpu_count()
from joblib import Parallel, delayed
import matplotlib.pyplot as plt

def func(x,y):
    return y/x
def main(y, xmin,xmax, dx):
    x = arange(xmin,xmax,dx)
    output = Parallel(n_jobs=num_cores)(delayed(func)(i, y) for i in x)
    return x, asarray(output)
def demo():
    x,z = main(2.,1.,30.,.1)
    plt.plot(x,z, label='All values')
    plt.plot(x[z>.1],z[z>.1], label='desired range') ## This is better to do in main()
    plt.show()

demo()

我只想计算输出直到输出 > 给定数字（可以假设输出的元素随着 x 的增加而单调减少）然后停止（不计算 x 的所有值然后排序，这是低效的为了我的目的）。有没有办法使用并行、延迟或任何其他多处理来做到这一点？

Answer 1

我会使用 Dask 并行执行，特别是 futures 接口，用于在完成时实时反馈结果。完成后，您可以取消剩余的 futures，出租不需要的 futures 以异步完成或关闭集群。

from dask.distributed import Client, as_completed
client = Client()  # defaults to ncores workers, one thread each
y, xmin, xmax, dx = 2.,1.,30.,.1

def func(x, y):
    return x, y/x
x = arange(xmin,xmax,dx)
outx = []
output = []
futs = [client.submit(func, val, y) for val in x]
for future in as_completed(futs):
    outs = future.result()
    outx.append(outs[0])
    output.append(outs[1])
    if outs[1] < 0.1:
        break

备注： - 我假设你的意思是 "less than"，否则第一个值已经通过 (y / xmin > 0.1) - 如果您想在结果准备就绪时获取结果，则不能保证输出按照您输入的顺序排列，但是通过如此快速的计算，也许它们总是如此（这就是为什么我有 func return the也有输入值） - 如果你停止计算，输出将比完整的输入集短，所以我不太确定你想打印什么。

Answer 2

没有指定 output > a given number 所以我只是编了一个。测试后我不得不扭转正常运行的条件 output < a given number.

我会使用池，启动带有回调函数的进程来检查停止条件，然后终止池准备好后。但这会导致竞争条件，从而允许从运行进程中省略结果不允许完成。我认为此方法对您的代码的修改最小，并且非常易于阅读。这不保证列表的顺序。

优点：开销很小
缺点：可能缺少结果。

方法 1)

from scipy import *
import multiprocessing

import matplotlib.pyplot as plt


def stop_condition_callback(ret):
        output.append(ret)
        if ret < stop_condition:
            worker_pool.terminate()


def func(x, y, ):
    return y / x


def main(y, xmin, xmax, dx):
    x = arange(xmin, xmax, dx)
    print("Number of calculations: %d" % (len(x)))

    # add calculations to the pool
    for i in x:
        worker_pool.apply_async(func, (i, y,), callback=stop_condition_callback)

    # wait for the pool to finish/terminate
    worker_pool.close()
    worker_pool.join()

    print("Number of results: %d" % (len(output)))
    return x, asarray(output)


def demo():
    x, z_list = main(2., 1., 30., .1)
    plt.plot(z_list, label='desired range')
    plt.show()


output = []
stop_condition = 0.1

worker_pool = multiprocessing.Pool()
demo()

此方法有更多的开销，但将允许已经开始的进程完成。方法 2)

from scipy import *
import multiprocessing

import matplotlib.pyplot as plt


def stop_condition_callback(ret):
    if ret is not None:
        if ret < stop_condition:
            worker_stop.value = 1
        else:
            output.append(ret)


def func(x, y, ):
    if worker_stop.value != 0:
        return None
    return y / x


def main(y, xmin, xmax, dx):
    x = arange(xmin, xmax, dx)
    print("Number of calculations: %d" % (len(x)))

    # add calculations to the pool
    for i in x:
        worker_pool.apply_async(func, (i, y,), callback=stop_condition_callback)

    # wait for the pool to finish/terminate
    worker_pool.close()
    worker_pool.join()

    print("Number of results: %d" % (len(output)))
    return x, asarray(output)


def demo():
    x, z_list = main(2., 1., 30., .1)
    plt.plot(z_list, label='desired range')
    plt.show()


output = []
worker_stop = multiprocessing.Value('i', 0)
stop_condition = 0.1

worker_pool = multiprocessing.Pool()
demo()

方法 3) 优点：不会遗漏任何结果
缺点：这超出了您通常会做的事情。

采用方法 1 并添加

def stopPoolButLetRunningTaskFinish(pool):
    # Pool() shutdown new task from being started, by emptying the query all worker processes draw from
    while pool._task_handler.is_alive() and pool._inqueue._reader.poll():
        pool._inqueue._reader.recv()
    # Send sentinels to all worker processes
    for a in range(len(pool._pool)):
            pool._inqueue.put(None)

然后改stop_condition_callback

def stop_condition_callback(ret):
    if ret[1] < stop_condition:
        #worker_pool.terminate()
        stopPoolButLetRunningTaskFinish(worker_pool)
    else:
        output.append(ret)

如何实现并行延迟，使得并行化的 for 循环在输出低于阈值时停止？

How to implement parallel, delayed in such a way that the parallelized for loop stops when output goes below a threshold?

python

python-3.x

python-multiprocessing

joblib