Question

我想在给定文章中搜索预定义的关键字列表，如果在文章中找到关键字，则将得分增加1。我想使用多处理，因为预定义的关键字列表非常大--10k关键字和文章数量是100k。

我遇到了this问题，但它没有解决我的问题。

我尝试了此实现，但结果是None。

keywords = ["threading", "package", "parallelize"]

def search_worker(keyword):
    score = 0
    article = """
    The multiprocessing package also includes some APIs that are not in the threading module at all. For example, there is a neat Pool class that you can use to parallelize executing a function across multiple inputs."""

   if keyword in article:
        score += 1
    return score

我在下面尝试了两种方法，但结果却得到了三个None。

方法一：

 pool = mp.Pool(processes=4)
 result = [pool.apply(search_worker, args=(keyword,)) for keyword in keywords]

方法2：

result = pool.map(search_worker, keywords)
print(result)

实际输出： [无，无，无]

预期输出： 3

我想向工作人员发送预定义的关键字列表和文章，但我不确定我是否正在朝着正确的方向前进，因为我之前没有多处理经验。

提前致谢。

Answer 1

这是使用Pool的功能。您可以传递text和keyword_list，它会起作用。您可以使用Pool.starmap传递(text, keyword)的元组，但是您需要处理一个对text有10k引用的迭代。

from functools import partial
from multiprocessing import Pool

def search_worker(text, keyword):
    return int(keyword in text)

def parallel_search_text(text, keyword_list):
    processes = 4
    chunk_size = 10
    total = 0
    func = partial(search_worker, text)
    with Pool(processes=processes) as pool:
        for result in pool.imap_unordered(func, keyword_list, chunksize=chunk_size):
            total += result

    return total

if __name__ == '__main__':
    texts = []  # a list of texts
    keywords = []  # a list of keywords
    for text in texts:
        print(parallel_search_text(text, keywords))

创建工作池会产生开销。对于简单的单进程文本搜索功能，可能值得进行时间测试。通过创建Pool的一个实例并将其传递给函数，可以加快重复调用。

def parallel_search_text2(text, keyword_list, pool):
    chunk_size = 10
    results = 0
    func = partial(search_worker, text)

    for result in pool.imap_unordered(func, keyword_list, chunksize=chunk_size):
        results += result
    return results

if __name__ == '__main__':
    pool = Pool(processes=4)
    texts = []  # a list of texts
    keywords = []  # a list of keywords
    for text in texts:
        print(parallel_search_text2(text, keywords, pool))

Answer 2

用户e.s已在其评论中解决了主要问题，但我发布了Om Prakash评论请求传入的解决方案：

文章和预定义的关键字工作方法列表

这是一种简单的方法。您需要做的就是构造一个元组，其中包含您希望工作者处理的参数：

from multiprocessing import Pool

def search_worker(article_and_keyword):
    # unpack the tuple
    article, keyword = article_and_keyword

    # count occurrences
    score = 0
    if keyword in article:
        score += 1

    return score

if __name__ == "__main__":
    # the article and the keywords
    article = """The multiprocessing package also includes some APIs that are not in the threading module at all. For example, there is a neat Pool class that you can use to parallelize executing a function across multiple inputs."""
    keywords = ["threading", "package", "parallelize"]

    # construct the arguments for the search_worker; one keyword per worker but same article
    args = [(article, keyword) for keyword in keywords]

    # construct the pool and map to the workers
    with Pool(3) as pool:
        result = pool.map(search_worker, args)
    print(result)

如果您使用的是更高版本的python，我建议您尝试使用starmap，因为这会让它更清洁。

如何在Python多处理中的所有进程之间共享数据？

2 个答案: