Python多進程multiprocessing使用示例

mutilprocess簡介

像線程同樣管理進程，這個是mutilprocess的核心，他與threading非常相像，對多核CPU的利用率會比threading好的多。

import multiprocessing

def worker(num):
    """thread worker function"""
    print 'Worker:', num
    return

if __name__ == '__main__':
    jobs = []
    for i in range(5):
        p = multiprocessing.Process(target=worker, args=(i,))
        jobs.append(p)
        p.start()

簡單的建立進程

肯定當前的進程，便是給進程命名，方便標識區分，跟蹤

import multiprocessing
import time

def worker():
    name = multiprocessing.current_process().name
    print name, 'Starting'
    time.sleep(2)
    print name, 'Exiting'

def my_service():
    name = multiprocessing.current_process().name
    print name, 'Starting'
    time.sleep(3)
    print name, 'Exiting'

if __name__ == '__main__':
    service = multiprocessing.Process(name='my_service',
                                      target=my_service)
    worker_1 = multiprocessing.Process(name='worker 1',
                                       target=worker)
    worker_2 = multiprocessing.Process(target=worker) # default name

    worker_1.start()
    worker_2.start()
    service.start()

View Code

守護進程就是不阻擋主程序退出，本身幹本身的 mutilprocess.setDaemon(True)就這句等待守護進程退出，要加上join,join能夠傳入浮點數值，等待n久就不等了

import multiprocessing
import time
import sys

def daemon():
    name = multiprocessing.current_process().name
    print 'Starting:', name
    time.sleep(2)
    print 'Exiting :', name

def non_daemon():
    name = multiprocessing.current_process().name
    print 'Starting:', name
    print 'Exiting :', name

if __name__ == '__main__':
    d = multiprocessing.Process(name='daemon',
                                target=daemon)
    d.daemon = True

    n = multiprocessing.Process(name='non-daemon',
                                target=non_daemon)
    n.daemon = False

    d.start()
    n.start()

    d.join(1)
    print 'd.is_alive()', d.is_alive()
    n.join()

守護進程

最好使用 poison pill，強制的使用terminate()注意 terminate以後要join，使其能夠更新狀態

import multiprocessing
import time

def slow_worker():
    print 'Starting worker'
    time.sleep(0.1)
    print 'Finished worker'

if __name__ == '__main__':
    p = multiprocessing.Process(target=slow_worker)
    print 'BEFORE:', p, p.is_alive()

    p.start()
    print 'DURING:', p, p.is_alive()

    p.terminate()
    print 'TERMINATED:', p, p.is_alive()

    p.join()
    print 'JOINED:', p, p.is_alive()

終止進程

1. == 0 未生成任何錯誤  
2. 0 進程有一個錯誤，並以該錯誤碼退出
3. < 0 進程由一個-1 * exitcode信號結束

import multiprocessing
import sys
import time

def exit_error():
    sys.exit(1)

def exit_ok():
    return

def return_value():
    return 1

def raises():
    raise RuntimeError('There was an error!')

def terminated():
    time.sleep(3)

if __name__ == '__main__':
    jobs = []
    for f in [exit_error, exit_ok, return_value, raises, terminated]:
        print 'Starting process for', f.func_name
        j = multiprocessing.Process(target=f, name=f.func_name)
        jobs.append(j)
        j.start()

    jobs[-1].terminate()

    for j in jobs:
        j.join()
        print '%15s.exitcode = %s' % (j.name, j.exitcode)

進程的退出狀態

方便的調試，能夠用logging

import multiprocessing
import logging
import sys

def worker():
    print 'Doing some work'
    sys.stdout.flush()

if __name__ == '__main__':
    multiprocessing.log_to_stderr()
    logger = multiprocessing.get_logger()
    logger.setLevel(logging.INFO)
    p = multiprocessing.Process(target=worker)
    p.start()
    p.join()

日誌

利用class來建立進程，定製子類

import multiprocessing

class Worker(multiprocessing.Process):

    def run(self):
        print 'In %s' % self.name
        return

if __name__ == '__main__':
    jobs = []
    for i in range(5):
        p = Worker()
        jobs.append(p)
        p.start()
    for j in jobs:
        j.join()

派生進程

import multiprocessing

class MyFancyClass(object):

    def __init__(self, name):
        self.name = name

    def do_something(self):
        proc_name = multiprocessing.current_process().name
        print 'Doing something fancy in %s for %s!' % \
            (proc_name, self.name)

def worker(q):
    obj = q.get()
    obj.do_something()

if __name__ == '__main__':
    queue = multiprocessing.Queue()

    p = multiprocessing.Process(target=worker, args=(queue,))
    p.start()

    queue.put(MyFancyClass('Fancy Dan'))

    # Wait for the worker to finish
    queue.close()
    queue.join_thread()
    p.join()

import multiprocessing
import time

class Consumer(multiprocessing.Process):

    def __init__(self, task_queue, result_queue):
        multiprocessing.Process.__init__(self)
        self.task_queue = task_queue
        self.result_queue = result_queue

    def run(self):
        proc_name = self.name
        while True:
            next_task = self.task_queue.get()
            if next_task is None:
                # Poison pill means shutdown
                print '%s: Exiting' % proc_name
                self.task_queue.task_done()
                break
            print '%s: %s' % (proc_name, next_task)
            answer = next_task()
            self.task_queue.task_done()
            self.result_queue.put(answer)
        return

class Task(object):
    def __init__(self, a, b):
        self.a = a
        self.b = b
    def __call__(self):
        time.sleep(0.1) # pretend to take some time to do the work
        return '%s * %s = %s' % (self.a, self.b, self.a * self.b)
    def __str__(self):
        return '%s * %s' % (self.a, self.b)

if __name__ == '__main__':
    # Establish communication queues
    tasks = multiprocessing.JoinableQueue()
    results = multiprocessing.Queue()

    # Start consumers
    num_consumers = multiprocessing.cpu_count() * 2
    print 'Creating %d consumers' % num_consumers
    consumers = [ Consumer(tasks, results)
                  for i in xrange(num_consumers) ]
    for w in consumers:
        w.start()

    # Enqueue jobs
    num_jobs = 10
    for i in xrange(num_jobs):
        tasks.put(Task(i, i))

    # Add a poison pill for each consumer
    for i in xrange(num_consumers):
        tasks.put(None)

    # Wait for all of the tasks to finish
    tasks.join()

    # Start printing results
    while num_jobs:
        result = results.get()
        print 'Result:', result
        num_jobs -= 1

python進程間傳遞消息

Event提供一種簡單的方法，能夠在進程間傳遞狀態信息。事件能夠切換設置和未設置狀態。經過使用一個可選的超時值，時間對象的用戶能夠等待其狀態從未設置變爲設置。

import multiprocessing
import time

def wait_for_event(e):
    """Wait for the event to be set before doing anything"""
    print 'wait_for_event: starting'
    e.wait()
    print 'wait_for_event: e.is_set()->', e.is_set()

def wait_for_event_timeout(e, t):
    """Wait t seconds and then timeout"""
    print 'wait_for_event_timeout: starting'
    e.wait(t)
    print 'wait_for_event_timeout: e.is_set()->', e.is_set()

if __name__ == '__main__':
    e = multiprocessing.Event()
    w1 = multiprocessing.Process(name='block', 
                                 target=wait_for_event,
                                 args=(e,))
    w1.start()

    w2 = multiprocessing.Process(name='nonblock', 
                                 target=wait_for_event_timeout, 
                                 args=(e, 2))
    w2.start()

    print 'main: waiting before calling Event.set()'
    time.sleep(3)
    e.set()
    print 'main: event is set'

進程間信號傳遞

Python多進程，通常的狀況是Queue來傳遞。

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()    # prints "[42, None, 'hello']"
    p.join()

Queue

import Queue
import threading
import time

exitFlag = 0

class myThread (threading.Thread):
    def __init__(self, threadID, name, q):
        threading.Thread.__init__(self)
        self.threadID = threadID
        self.name = name
        self.q = q
    def run(self):
        print "Starting " + self.name
        process_data(self.name, self.q)
        print "Exiting " + self.name

def process_data(threadName, q):
    while not exitFlag:
        queueLock.acquire()
        if not workQueue.empty():
            data = q.get()
            queueLock.release()
            print "%s processing %s" % (threadName, data)
        else:
            queueLock.release()
        time.sleep(1)

threadList = ["Thread-1", "Thread-2", "Thread-3"]
nameList = ["One", "Two", "Three", "Four", "Five"]
queueLock = threading.Lock()
workQueue = Queue.Queue(10)
threads = []
threadID = 1

# Create new threads
for tName in threadList:
    thread = myThread(threadID, tName, workQueue)
    thread.start()
    threads.append(thread)
    threadID += 1

# Fill the queue
queueLock.acquire()
for word in nameList:
    workQueue.put(word)
queueLock.release()

# Wait for queue to empty
while not workQueue.empty():
    pass

# Notify threads it's time to exit
exitFlag = 1

# Wait for all threads to complete
for t in threads:
    t.join()
print "Exiting Main Thread"

多線程優先隊列Queue

多進程使用Queue通訊的例子

import time
from multiprocessing import Process,Queue

MSG_QUEUE = Queue(5)

def startA(msgQueue):
    while True:
        if msgQueue.empty() > 0:
            print ('queue is empty %d' % (msgQueue.qsize()))
        else:
            msg = msgQueue.get()
            print( 'get msg %s' % (msg,))
        time.sleep(1)

def startB(msgQueue):
    while True:
        msgQueue.put('hello world')
        print( 'put hello world queue size is %d' % (msgQueue.qsize(),))
        time.sleep(3)

if __name__ == '__main__':
    processA = Process(target=startA,args=(MSG_QUEUE,))
    processB = Process(target=startB,args=(MSG_QUEUE,))

    processA.start()
    print( 'processA start..')

View Code

主進程定義了一個Queue類型的變量，並做爲Process的args參數傳給子進程processA和processB，兩個進程一個向隊列中寫數據，一個讀數據。