2.pool.map应用 举个例子说明: 首先先定义一个列表,里面存放着整数,之后计算这个列表的均值,用多进程判断列表里的每个数字与均值的大小,比均值大输出1,反之输出0. #-*- coding: utf-8 -*-frommultiprocessingimportPool#导入偏函数fromfunctoolsimportpartialimportnumpy as npdefadjust(mean, number):ifnumber >m...
Pool.apply_async() 区别:map和starmap的参数都是一个迭代器,但starmap可以接受多个迭代器的list作为参数,也就是说,starmap可以接受更多参数,而map不能。map需要一些特殊操作才可以接受多个参数,如下: from functools import partial from itertools import repeat from multiprocessing import Pool, freeze_support def...
在python中经常会到用多线程处理某个函数来缩短运行时间,但通常multiprocessing.Pool的map函数只接受一个可迭代参数。 frommultiprocessingimportPooldefwork(x):returnx+1pool= Pool(processes=4)#4个线程x = [1,2,3,4,5,6] results=pool.map(work, x)printresults 运行上面的程序就会多线程处理并打印出输入x...
ddict = multiprocessing.Manager().dict() llock = multiprocessing.Manager().Lock() rrlock = multiprocessing.Manager().RLock() qqueue = multiprocessing.Manager().Queue() vvalue = multiprocessing.Manager().Value('d', 0) aarray = multiprocessing.Manager().Array('i', range(10)) manager支持以下...
importmultiprocessingfromfunctoolsimportpartial# 定义一个处理函数defadd(a,b):returna+b# 主程序if__name__=='__main__':# 创建进程池withmultiprocessing.Pool(processes=4)aspool:# 准备参数numbers_1=[1,2,3,4]numbers_2=[10,20,30,40]# 使用 partial 固定一个参数add_partial=partial(add,b=5)...
from functoolsimportpartial from multiprocessing.poolimportPool from timeimporttime from downloadimportsetup_download_dir,get_links,download_link logging.basicConfig(level=logging.DEBUG,format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')logging.getLogger('requests').setLevel(logging.CRIT...
results=pool.map(partial_work,x) 2). 将多个输入变量打包到一个参数 x=[1,2,3,4,5,6]y=[1,1,1,1,1,1]x_y=zip(x,y)results=pool.map(work,x_y) 3). 使用pathos包下的multiprocessing 这个包是使用dill的multiprocessing的一个fork,允许多参数输入: ...
from multiprocessing import Pool from tqdm import tqdm import numpy as np import math import time from functools import partial def func(x,y): time.sleep(0.1) return math.sin(x)+math.cos(y) with Pool(processes = 20) as pool: result = list(tqdm(pool.imap(partial(func,y = math.pi),...
defrun__pool():# main process from multiprocessingimportPool cpu_worker_num=3process_args=[(1,1),(9,9),(4,4),(3,3),]print(f'| inputs: {process_args}')start_time=time.time()withPool(cpu_worker_num)asp:outputs=p.map(func2,process_args)print(f'| outputs: {outputs} TimeUsed:...
让我们先了解multiprocessing.Pool的基本用法。multiprocessing是Python标准库中的一个模块,允许我们创建一个进程池来并行执行任务,以下是一个简单的示例: from multiprocessing import Pool import time def work(n): print(f"Processing {n}") time.sleep(2) ...