multiprocessing.map 的替代方案，不会存储函数的返回值

Question

我习惯于multiprocessing.imap_unordered同时运行一个函数，但我的 RAM 使用量不断增加。问题如下：我有数百万个itertools.product数据组合（使用 创建）需要传递给函数。然后，该函数将使用 SVM 计算分数，然后存储分数和当前组合。该函数不会返回任何值，它只会计算分数并将其存储在共享值中。我不需要所有其他组合，只需要最好的组合。通过使用imap_unorderedRAM 使用量不断增加，直到由于 RAM 不足而崩溃。我想发生这种情况是因为它将imap存储函数的结果，它不会返回任何值，但可能会保留 aNone或Null值？这是一个示例代码：from functools import partialimport itertoolsimport multiprocessingimport timedef svm(input_data, params):    # Copy the data to avoid changing the original data    # as input_data is a reference to a pandas dataframe    # and I need to shift columns up and down.    dataset = input_data.copy()    # Use svm here to analyse data    score = sum(dataset) + sum(params)  # simulate score of svm    # Simulate a process that takes a bit of time    time.sleep(0.5)    return (score, params)if __name__ == "__main__":        # Without this, multiprocessing gives error    multiprocessing.freeze_support()    # Set the number of worker processes    # Empty for all the cores    # Int for number of processes    pool = multiprocessing.Pool()    # iterable settings    total_combinations = 2    total_features = 45    # Keep track of best score    best_score = -1000    best_param = [0 for _ in range(total_features)]    input_data = [x * x for x in range(10000)]    # Create a partial function with the necessary args    func = partial(svm, input_data)    params = itertools.product(range(total_combinations), repeat=total_features)在此示例中，您会注意到 RAM 使用量随着时间的推移而增加。尽管在本例中它并不多，但如果您单独放置一天或其他时间（通过增加可迭代的范围），它将达到 GB 的 RAM。正如我所说，我有数百万种组合。我应该如何解决这个问题？是否有替代方案，imap根本不会存储有关该功能的任何内容？我应该只创建Processes而不是使用吗Pool？难道是因为我正在复制数据集，后来垃圾收集器没有清理它？

Answer 1

您可以使用apply或apply_async

Answer 2

import objgraph我已经使用和打印跟踪了内存使用情况objgraph.show_most_common_types(limit=20)。我注意到元组和列表的数量在子进程的持续时间内不断增加。为了解决这个问题，我更改了maxtasksperchild在Pool一段时间后强制关闭进程并因此释放内存。

from functools import partial

import itertools

import multiprocessing

import random

import time

# Tracing memory leaks

import objgraph

def svm(input_data, params):

    # Copy the data to avoid changing the original data

    # as input_data is a reference to a pandas dataframe.

    dataset = input_data.copy()

    # Use svm here to analyse data

    score = sum(dataset) + sum(params)  # simulate score of svm

    # Simulate a process that takes a bit of time

    time.sleep(0.5)

    return (score, params)

if __name__ == "__main__":

    # iterable settings

    total_combinations = 2

    total_features = 12

    # Keep track of best score

    best_score = -1000

    best_param = [0 for _ in range(total_features)]

    # Simulate a dataframe with random data

    input_data = [random.random() for _ in range(100000)]

    # Create a partial function with the necessary args

    func = partial(svm, input_data)

    params = itertools.product(range(total_combinations), repeat=total_features)

    # Without this, multiprocessing gives error

    multiprocessing.freeze_support()

    # Set the number of worker processes

    # Empty for all the cores

    # Int for number of processes

    with multiprocessing.Pool(maxtasksperchild=5) as pool:

        # Calculate scores concurrently

        # As the iterable is in the order of millions, this value

        # will get continuously large until it uses all available

        # memory as the map stores the results, that in this case

        # it's not needed.

        for score, param in pool.imap_unordered(func, iterable=params, chunksize=10):

            if score > best_score:

                best_score = score

                best_param = param

                # print(best_score)

            # Count the number of objects in the memory

            # If the number of objects keep increasing, it's a memory leak

            print(objgraph.show_most_common_types(limit=20))

    # Wait for all the processes to terminate their tasks

    pool.close()

    pool.join()

    print(best_score)

    print(best_param)