Python中运行并行任务技巧

程序员文章站 2022-04-21 08:48:01

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示例

标准线程多进程，生产者/消费者示例：
Worker越多,问题越大

复制代码代码如下:

# -*- coding: utf8 -*-

import os
import time
import Queue
import threading
from PIL import Image

def create_thumbnail(filename, size=(128, 128)):
try:
fp, fmt = filename.rsplit('.', 1)
im = Image.open(filename)
im.thumbnail(size, Image.ANTIALIAS)
im.save((fp + '_'+'x'.join(str(i) for i in size) + '.'+fmt), im.format)
return '%s thumbnail success!' % filename
except Exception:
return '%s thumbnail failed!' % filename

def get_image_paths(folder):
return [os.path.join(folder, f) for f in os.listdir(folder) if 'png' in f]

class Consumer(threading.Thread):
def __init__(self, queue):
threading.Thread.__init__(self)
self._queue = queue

def run(self):
while True:
content = self._queue.get()
if isinstance(content, str) and content == 'quit':
break
respone = create_thumbnail(content)
print 'Bye bye!'

def Producer():
filenames = get_image_paths('images')
queue = Queue.Queue()
worker_threads = build_worker_pool(queue, 4)
start_time = time.time()

for filename in filenames:
queue.put(filename)
for worker in worker_threads:
queue.put('quit')
for worker in worker_threads:
worker.join()

print time.time() - start_time

def build_worker_pool(queue, size):
workers = []
for _ in range(size):
worker = Consumer(queue)
worker.start()
workers.append(worker)
return workers

if __name__ == '__main__':
Producer()

map

Map能够处理集合按顺序遍历，最终将调用产生的结果保存在一个简单的集合当中。

Python中运行并行任务技巧

复制代码代码如下:

# -*- coding: utf8 -*-

import os
import time
from multiprocessing import Pool
from PIL import Image

def get_image_paths(folder):
return [os.path.join(folder, f) for f in os.listdir(folder) if 'png' in f]

def main():
filenames = get_image_paths('images')
start_time = time.time()

pool = Pool(4)
pool.map(create_thumbnail, filenames)
pool.close()
pool.join()

print time.time() - start_time

if __name__ == '__main__':
main()