from multiprocessing import Process,Pipedef f1(conn):from_zhujincheng = conn.recv()print('子进程')print('来自主进程的消息:',from_zhujincheng)if __name__ == '__main__':conn1,conn2 = Pipe()  #创建一个管道对象,全双工,返回管道的两端,但是一端发送的消息,只能另外一端接收,自己这一端是不能接收的p1 = Process(target=f1,args=(conn2,))p1.start()conn1.send('出来吧')print('主进程')

from multiprocessing import Process,Evente = Event()  #创建事件对象,这个对象的初识状态为False
print('e的状态是:',e.is_set())  #  Falseprint('进程运行到这里了')
e.set()  #将e的状态改为True
print('e的状态是:',e.is_set())  #  True

e.clear()  #将e的状态改为False

e.wait()  #e这个事件对象如果值为False,就在我加wait的地方等待print('进程过了wait')

import time,random
from multiprocessing import Process,Semaphoredef f1(i,s):s.acquire()print('%s男嘉宾到了'%i)time.sleep(random.randint(1,3))s.release()if __name__ == '__main__':s = Semaphore(4)  #计数器4,acquire一次减一,为0 ,其他人等待,release加1for i in range(10):p = Process(target=f1,args=(i,s))p.start()

import time
from multiprocessing import Process,Pool#对比多进程和进程池的效率
def f1(n):for i in range(5):n = n + iif __name__ == '__main__':#统计进程池执行100个任务的时间s_time = time.time()pool = Pool(4)  pool.map(f1,range(100))  e_time = time.time()dif_time = e_time - s_time#统计100个进程,来执行100个任务的执行时间p_s_t = time.time() #多进程起始时间p_list = []for i in range(100):p = Process(target=f1,args=(i,))p.start()p_list.append(p)[pp.join() for pp in p_list]p_e_t = time.time()p_dif_t = p_e_t - p_s_tprint('进程池的时间:',dif_time)print('多进程的执行时间:',p_dif_t)# 结果:  进程池的时间: 0.40102291107177734    多进程的执行时间: 9.247529029846191     # 可以看出进程池运行效率远远大于创建多进程

import time
from multiprocessing import Process,Pooldef f1(n):time.sleep(1)return n*nif __name__ == '__main__':pool = Pool(4)for i in range(10):res = pool.apply(f1,args=(i,))print(res)

import time
from multiprocessing import Process,Pooldef f1(n):time.sleep(0.5)return n*nif __name__ == '__main__':pool = Pool(4)res_list = []for i in range(10):res = pool.apply_async(f1,args=(i,)) # 不能直接打印返回值,因为直接返回结果对象,进程还没执行完,结果对象里没有数据
        res_list.append(res)pool.close()  pool.join()#打印结果,异步提交之后的结果对象for i in res_list:print(i.get())

import os
from multiprocessing import Pool,Processdef f1(n):print('传入的函数',n)return n*ndef call_back_func(asdf):print('回调函数',asdf)if __name__ == '__main__':pool = Pool(4)res = pool.apply_async(f1,args=(5,),callback=call_back_func)pool.close()pool.join()