process-pool

问题 I used multiprocessing.Pool to imporove the performance of my Python server. But I found that, if I create a Pool with processes=100, when the server is started and the task has not yet started running, there will be 100+ processes while executing the command "pstree |grep python | wc -l". Does that mean all the process will be initialized when the pool is initialized? Will it result in a waste of server resources? Is there a way to control the timing of process initialization in Python

问题 I used multiprocessing.Pool to imporove the performance of my Python server. But I found that, if I create a Pool with processes=100, when the server is started and the task has not yet started running, there will be 100+ processes while executing the command "pstree |grep python | wc -l". Does that mean all the process will be initialized when the pool is initialized? Will it result in a waste of server resources? Is there a way to control the timing of process initialization in Python

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))

问题 Can anyone please tell me how to create a WKProcessPool in Swift? I'm not familiar with Objective-C. I have to create a WKProcessPool in order to have shared cookies with all WKWebViews. I want to keep cookies even when showing another viewcontroller with same class. I tried the following but it's not working. import UIKit import WebKit class ViewController: UIViewController, WKNavigationDelegate { var webView = WKWebView() override func viewDidLoad() { super.viewDidLoad() let processPool =

问题 Can anyone please tell me how to create a WKProcessPool in Swift? I'm not familiar with Objective-C. I have to create a WKProcessPool in order to have shared cookies with all WKWebViews. I want to keep cookies even when showing another viewcontroller with same class. I tried the following but it's not working. import UIKit import WebKit class ViewController: UIViewController, WKNavigationDelegate { var webView = WKWebView() override func viewDidLoad() { super.viewDidLoad() let processPool =

问题 Following code not executing as expected. import multiprocessing lock = multiprocessing.Lock() def dummy(): def log_results_l1(results): lock.acquire() print("Writing results", results) lock.release() def mp_execute_instance_l1(cmd): print(cmd) return cmd cmds = [x for x in range(10)] pool = multiprocessing.Pool(processes=8) for c in cmds: pool.apply_async(mp_execute_instance_l1, args=(c, ), callback=log_results_l1) pool.close() pool.join() print("done") dummy() But it does work if the

问题 Similar Question (but answer does not work for me): How to cancel long-running subprocesses running using concurrent.futures.ProcessPoolExecutor? Unlike the question linked above and the solution provided, in my case the computation itself is rather long (CPU bound) and cannot be run in a loop to check if some event has happened. Reduced version of the code below: import asyncio import concurrent.futures as futures import time class Simulator: def __init__(self): self._loop = None self._lmz

问题 I was using this answer in order to run parallel commands with multiprocessing in Python on a Linux box. My code did something like: import multiprocessing import logging def cycle(offset): # Do stuff def run(): for nprocess in process_per_cycle: logger.info("Start cycle with %d processes", nprocess) offsets = list(range(nprocess)) pool = multiprocessing.Pool(nprocess) pool.map(cycle, offsets) But I was getting this error: OSError: [Errno 24] Too many open files So, the code was opening too

问题 I am doing some parallel processing, as follows: with mp.Pool(8) as tmpPool: results = tmpPool.starmap(my_function, inputs) where inputs look like: [(1,0.2312),(5,0.52) ...] i.e., tuples of an int and a float. The code runs nicely, yet I cannot seem to wrap it around a loading bar (tqdm), such as can be done with e.g., imap method as follows: tqdm.tqdm(mp.imap(some_function,some_inputs)) Can this be done for starmap also? Thanks! 回答1: It's not possible with starmap() , but it's possible with