Python 多处理 Pool.map

Question

我尝试在 python 中使用多处理读取文件。这是一个小例子：

import multiprocessing
from time import *

class class1():
    def function(self, datasheetname):
        #here i start reading my datasheet

if __name__ == '__main__':
    #Test with multiprosessing
    pool = multiprocessing.Pool(processes=4)
    pool.map(class1("Datasheetname"))
    pool.close()

现在我得到以下错误：

TypeError: map() missing 1 required positional argument: 'iterable'

在此版块的另一个帖子中，我得到了使用 ThreadPool 执行此操作的提示，但我不知道该怎么做。有什么想法吗？

Answer 1

Pool.map:

map(func, iterable[, chunksize])

A parallel equivalent of the map() built-in function (it supports only one iterable argument though). It blocks until the result is ready.

This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer.

您需要传递一个可迭代对象，其中每个元素都作为每个进程中的参数传递给目标func。

示例：

def function(sheet):
    # do something with sheet
    return "foo"

pool = Pool(processes=4)
result = pool.map(function, ['sheet1', 'sheet2', 'sheet3', 'sheet4'])
# result will be ['foo', 'foo', 'foo', 'foo']

Python 多处理 Pool.map

Python Multiprocessing Pool.map

python

multiprocessing

pool