1、multiprocessing簡介

　　multiprocessing模塊提供本地和遠程並發性，通過使用子進程而不是線程來有效地避開全局解釋器鎖。由於這個原因，多處理模塊允許程序員在給定的機器上充分利用多個處理器。它在Unix和Windows上運行。

2、進程的創建

2.1、創建一個進程

import multiprocessing,time


def run(name):
    time.sleep(2)
    print("hello", name)

if __name__ == "__main__":
    p = multiprocessing.Process(target=run, args=(‘
 
bob‘,))
    p.start()
    p.join()

2.2、創建多個進程

import multiprocessing,time


def run(name):
    time.sleep(2)
    print("hello", name)

if __name__ == "__main__":
    for i in range(10):
        p = multiprocessing.Process(target=run, args=(‘bob %s‘ % i,))
        p.start()
#         p.join()

2.3、進程中創建線程

import multiprocessing,time,threading

def thread_run():
    print("threading id :%s" %threading.get_ident())

def run(name):
    time.sleep(2)
    print("hello", name)
    t = threading.Thread(target=thread_run,)
    t.start()

if __name__ == "__main__":
    for i in range(10):
        p = multiprocessing.Process(target=run, args=(‘
 
bob %s‘ % i,))
        p.start()

2.4、獲取進程id

from multiprocessing import Process
import os


def info(title):
    print(title)
    print(‘module name:‘, __name__)
    print(‘parent process:‘, os.getppid())
    print(‘process id:‘, os.getpid())
    print("\n\n")


def f(name):
    info(‘\033[31;1mcalled from child process function f\033[0m‘)
    print(‘hello‘, name)

if __name__ == ‘__main__‘:
    info(‘\033[32;1mmain process line\033[0m‘)

　　輸出結果：

main process line
module name: __main__
parent process: 6024
process id: 30756

2.5、獲取進程id和子進程id

from multiprocessing import Process
import os


def info(title):
    print(title)
    print(‘module name:‘, __name__)
    print(‘parent process:‘, os.getppid())
    print(‘process id:‘, os.getpid())
    print("\n\n")


def f(name):
    info(‘\033[31;1mcalled from child process function f\033[0m‘)
    print(‘hello‘, name)

if __name__ == ‘__main__‘:
    info(‘\033[32;1mmain process line\033[0m‘)
    p = Process(target=f, args=(‘bob‘,))
    p.start()
    # p.join()

　　輸出結果：

main process line
module name: __main__
parent process: 6024
process id: 30756

3、進程間通信

　　不同進程間內存是不共享的，要實現兩個進程間的數據交換，可以使用一下方法：Queue,Pipes,Manager

3.1、Queue

from multiprocessing import Process, Queue

def f(q):
    q.put([42, None, ‘hello‘])

if __name__ == ‘__main__‘:
    q = Queue()
    p = Process(target=f, args=(q,))
    p.start()
    
    print(q.get())

3.2、Pipes

from multiprocessing import Process, Pipe


def f(conn):
    conn.send([42, None, ‘hello from child‘])
    conn.send([42, None, ‘hello from child3‘])
    print("",conn.recv())
    conn.close()


if __name__ == ‘__main__‘:
    parent_conn, child_conn = Pipe()
    p = Process(target=f, args=(child_conn,))
    p.start()
    print("parent",parent_conn.recv())  # prints "[42, None, ‘hello‘]"
    print("parent",parent_conn.recv())  # prints "[42, None, ‘hello‘]"
    parent_conn.send(" from hshs")  # prints "[42, None, ‘hello‘]"
    p.join()

3.3、Manager

from multiprocessing import Process, Manager
import os
def f(d, l):
    d[os.getpid()] =os.getpid()
    l.append(os.getpid())
    print(l)

if __name__ == ‘__main__‘:
    with Manager() as manager:
        d = manager.dict() #{} #生成一個字典，可在多個進程間共享和傳遞

        l = manager.list(range(5))#生成一個列表，可在多個進程間共享和傳遞
        p_list = []
        for i in range(10):
            p = Process(target=f, args=(d, l))
            p.start()
            p_list.append(p)
        for res in p_list: #等待結果
            res.join()

        print(d)
        print(l)

3.4、進程同步

from multiprocessing import Process, Lock


def f(l, i):
    l.acquire()
    print(‘hello world‘, i)
    l.release()


if __name__ == ‘__main__‘:
    lock = Lock()

    for num in range(100):
        Process(target=f, args=(lock, num)).start()

4、進程池

4.1、串行：apply

from  multiprocessing import Process, Pool,freeze_support
import time
import os

def Foo(i):
    time.sleep(2)
    print("in process",os.getpid())
    return i + 100

def Bar(arg):
    print(‘-->exec done:‘, arg,os.getpid())

if __name__ == ‘__main__‘:
    #freeze_support()
    pool = Pool(processes=3) #允許進程池同時放入5個進程
    print("主進程",os.getpid())
    for i in range(10):
        pool.apply(func=Foo, args=(i,)) #串行
    print(‘end‘)
    pool.close()
    pool.join() #進程池中進程執行完畢後再關閉，如果註釋，那麽程序直接關閉。.join()

4.2、並行：apply_async

from  multiprocessing import Process, Pool,freeze_support
import time
import os

def Foo(i):
    time.sleep(2)
    print("in process",os.getpid())
    return i + 100

def Bar(arg):
    print(‘-->exec done:‘, arg,os.getpid())

if __name__ == ‘__main__‘:
    #freeze_support()
    pool = Pool(processes=3) #允許進程池同時放入5個進程
    print("主進程",os.getpid())
    for i in range(10):
        pool.apply_async(func=Foo, args=(i,), callback=Bar) #callback=回調
        #pool.apply_async(func=Foo, args=(i,)) #串行
    print(‘end‘)
    pool.close()
    pool.join() #進程池中進程執行完畢後再關閉，如果註釋，那麽程序直接關閉。.join()

python之multiprocessing