frommultiprocessingimportPipe#创建管道conn1, conn2 =Pipe()#进程1发送数据conn1.send(data)#进程2接收数据data = conn2.recv() 3、共享内存(Value、Array) 共享内存是一种在多进程之间共享数据的高效方式。Python的multiprocessing模块提供了Value和Array类来实现进程间共享数据。Value用于共享单个值,而Array用于共享...
importloggingimport osimport timefrom multiprocessing.poolimportPoolfrom timeimportsleepdeff():sleep(1)return'%s finish f_call at %s'%(os.getpid(),time.strftime('%Y-%m-%d %H:%M:%S'))if__name__=='__main__':result=[]withPool(4)aspool:result.append(pool.apply_async(f))result.append(p...
#Pool内的进程数默认是cpu核数,假设为4(查看方法os.cpu_count())#开启6个客户端,会发现2个客户端处于等待状态#在每个进程内查看pid,会发现pid使用为4个,即多个客户端公用4个进程fromsocketimport*frommultiprocessingimportPoolimportos server=socket(AF_INET,SOCK_STREAM)server.setsockopt(SOL_SOCKET,SO_REUSEADDR...
对于同时运行多个同质任务来讲,采用multiprocessing.Pool进程池去管理是最方便的。Pool的用法如下: frommultiprocessingimportPool,processimportosimportpprintdef_test_func(a,b):result=a+bprint(f'{os.getpid()}: {result}')returnresultdeftest_pool():test_data=[(2*i,2*i+1)foriinrange(16)]withPool(4...
num_processes = multiprocessing.cpu_count() # 将数据集切分为对应数量的块 data_chunks = split_data(data, num_processes) # 创建进程池 with Pool(processes=num_processes) as pool: # 并行计算每个数据块的平均值 averages = pool.map(calculate_average, data_chunks) # 计算总体...
直接上代码 import math from multiprocessing import Pool def is_prime(number: int) -> bool: if number < 2: return False if number == 2: return True if number % 2 == 0: return False for i in ran…
进程池Pool 进程池pool可以存放我们要运行的程序; AI检测代码解析 import multiprocessing as mp def job(a): # print("执行{}^{}={}".format(a, a, a**a)) return a**a def multi_core(): # 定义一个pool,并定义CPU的核数量为3 pool = mp.Pool(processes=3) ...
pool 子进程比较多的话可以使用进程池 from multiprocessing import Pool import os, time, random def long_time_task(name): print('Run task %s (%s)...' % (name, os.getpid())) start = time.time() time.sleep(random.random() * 3) ...
():# 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: {time.time(...
multiprocessing.Pool类的实例: importtimefrommultiprocessingimportPooldefrun(fn):#fn: 函数参数是数据列表的一个元素time.sleep(1)returnfn*fnif__name__ =="__main__": testFL = [1,2,3,4,5,6]print'shunxu:'#顺序执行(也就是串行执行,单进程)s = time.time()forfnintestFL: ...