pool.map 单个参数 其实,还有一种写法,使用pool.map,语法如下: pool.map(func,iterator) 比如: pool.map(self.get_kernel, NODE_LIST...,语法:pool.map(func,iterator) pool.map(self.check_ping, ip_list) if __name__ == '__main__': ...pool.map 多参数 如果方法,有多个参数,需要借用偏函数实现。 完整代码如下: #!/usr/bin/env python3 # coding: utf-8 #!...语法: pool.map(func,iterator) # partial使用偏函数传递参数 # 注意:func第一个参数,必须是迭代器遍历的值。...后面的参数,必须使用有命名传参 pool.map(partial(self.check_ping, timeout=1), ip_list) if __name__ == '__main
def my_print(x): print(x) if __name__ == "__main__": x = [1, 2, 3, 4, 5] pool = Pool() pool.map...比如现在my_print新增一个参数y: def my_print(x, y): print(x + y) 查看pool.map的函数说明: def map(self, func, iterable...main__": x = [1, 2, 3, 4, 5] y = [1, 1, 1, 1, 1] zip_args = list(zip(x, y)) pool = Pool() pool.map...partial版本 x = [1, 2, 3, 4, 5] y = 1 partial_func = partial(my_print, y=y) pool = Pool() pool.map..._serve() return _pool.map(star(f), zip(*args)) # chunksize 2.4 使用starmap函数 if __name__ == '__main__
exercise_middleware_ip/3', 'http://exercise.kingname.info/exercise_middleware_ip/4' ] pool = Pool(3) result = pool.map...pool = Pool(3) result = pool.map(test, ()) 运行以后发现,什么都没有打印出来,也就是说 test()函数根本没有运行。...\n') pool = Pool(3) result = pool.map(test, (0, ) * 3) 运行效果如下图所示 ?...所以你隐隐觉得,如果 pool.map的第二个参数是空的可迭代对象,那么函数就不会运行。...到此为止,在 pool.map的第二个参数为空的可迭代对象时,所有的流程就走完了。整个过程中,没有涉及到任何调用 func的过程。所以原有的函数不会被执行。
Pool.map(func, iterable[, chunksize=None]) 2个参数, 第一个参数是函数, 第二个参数是需要可迭代的变量, 作为参数传递到func 如果func含有的参数多于一个...pool = Pool() str_1 = "This" str_2 = "is" func = partial(somefunc, str_1, str_2) pool.map
return pool = Pool(3) # result = pool.map(tiny_png, pics) result = pool.map(convert, pics
multiprocessing库,使用方法如下: import re from multiprocessing import Pool pool = Pool(processes = 4) # 这个4代表着进程数 pool.map...time.time() print("单进程:",end_1 - start_1) start_2 = time.time() pool = Pool(processes = 2) pool.map...time.time() print("2进程:",end_2 - start_2) start_3 = time.time() pool = Pool(processes = 4) pool.map
(os.path.join(folder, SAVE_DIRECTORY)) images = get_image_paths(folder) pool = Pool() pool.map...) # # ------- VERSUS ------- # # # ------- 4 Pool ------- # # pool = ThreadPool(4) # results = pool.map...(urllib2.urlopen, urls) # # ------- 8 Pool ------- # # pool = ThreadPool(8) # results = pool.map(...urllib2.urlopen, urls) # # ------- 13 Pool ------- # # pool = ThreadPool(13) # results = pool.map...(os.path.join(folder, SAVE_DIRECTORY)) images = get_image_paths(folder) pool = Pool() pool.map
workers pool = ThreadPool(4) # Open the urls in their own threads # and return the results results = pool.map...(urllib2.urlopen, urls) # # ------- 8 Pool ------- # # pool = ThreadPool(8) # results = pool.map(urllib2....urlopen, urls) # # ------- 13 Pool ------- # # pool = ThreadPool(13) # results = pool.map(urllib2....coding=utf8 from multiprocessing import Pool def do_add(n1, n2): return n1+n2 pool = Pool(5) print pool.map...: time.sleep(1) print n1 except: return n1 pool = Pool(5) print pool.map
multiprocessing.Pool() # 提交任务 tasks = [(i, j) for i in range(1, 11) for j in range(1, 11)] results = pool.map...然后,main 函数使用 pool.map 方法来获取任务的结果。pool.map 方法会将 tasks 序列中的每个任务提交到多进程池,并返回一个包含任务结果的列表。
17 # Time the code below. 18 19 for _ in range(10): 20 image = np.zeros((3000, 3000)) 21 pool.map...np.random.bytes(20) for _ in range(10000)] 25 for _ in range(num_cpus)] 26 results = pool.map...这里的挑战是pool.map执行无状态函数,这意味着要在另一个pool.map调用中使用的pool.map调用中生成的任何变量都需要从第一个调用返回并传递到第二个调用。...在本例中,我们将pool.map进行比较,因为它提供了最接近的API比较。在本例中,应该可以通过启动不同的进程并在它们之间设置多个多进程队列来获得更好的性能,但是这会导致复杂而脆弱的设计。
item in parse_one_page(html): print(item) write_one_page(item) 步骤五:多进程爬取,定义进程池,并调用 Pool.map...() 方法进行多进程爬取,提高爬取效率: if __name__ == "__main__": pool = Pool() pool.map(main, [i*10 for i in range
in range(num_cpus)] # Time the code below. for _ in range(10): image = np.zeros((3000, 3000)) pool.map...[[np.random.bytes(20) for _ in range(10000)] for _ in range(num_cpus)] results = pool.map...()for prefixes in running_popular_prefixes: popular_prefixes |= prefixes 使用多处理的玩具流处理示例的代码 这里的挑战是pool.map...执行无状态函数,这意味着pool.map您希望在另一个pool.map调用中使用的一个调用中生成的任何变量需要从第一个调用返回并传递给第二个调用。...在这个例子中进行比较,Pool.map因为它提供了最接近的API比较。通过启动不同的进程并在它们之间设置多个多处理队列,应该可以在此示例中实现更好的性能,但这会导致复杂和脆弱的设计。
format(i) 那么我们定制两个函数一个用于爬取并且解析页面(spider),一个用于下载数据 (download),开启线程池,使用for循环构建13页的url,储存在列表中,作为url队列,使用pool.map...timeout=None, chunksize=1): """Returns an iterator equivalent to map(fn, iter)”“” 这里我们的使用是:pool.map...newpage = 'http://www.bizhi88.com/s/470/{}.html'.format(i) page.append(newpage) result = pool.map
return result if name=="main": inputs=list(range(100)) pool=multiprocessing.Pool(processes=4) outs=pool.map
resource2", "http://example.com/resource3"] 创建进程池 pool = Pool(processes=4) 使用进程池并发发送请求 results = pool.map...resource2", "http://example.com/resource3"] 创建进程池 pool = Pool(processes=4) 使用进程池并发执行协程任务 results = pool.map
logger = None pool = None def main(): global pool logger.info('@@@@@@@@@@@@@@@@@@@@@@') pool.map...logger = None pool = None def main(): global pool logger.info('@@@@@@@@@@@@@@@@@@@@@@') pool.map
#使用多线程 start = time.time() pool = ThreadPoolExecutor(max_workers=3) results = list(pool.map...#使用多进程 start = time.time() pool = ProcessPoolExecutor(max_workers=3) results = list(pool.map...pool = ThreadPoolExecutor(max_workers = 3) start = time.time() result = list(pool.map...pool = ProcessPoolExecutor(max_workers = 3) start = time.time() result = list(pool.map
re.S)[0] video_url_list.append(video_url) pool = Pool(4) #使用线程池将视频的二进制数据下载下来 content_list = pool.map...(request_video, video_url_list) # 使用线程池将视频的二进制数据保存到本地 pool.map(save_video, content_list) print(
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