multiprocessing

问题 I am having 1 queue which is accessed by 2 multiprocessing functions. Both these processes and consuming the same item in the queue and then clearing it. I want each one to take one unique value only. What am I doing wrong? import time import queue import multiprocessing import threading q = queue.Queue(maxsize=0) run_1 = 1 run_2 = 1 def multi_one(): while run_1 == 1: item = q.get() q.task_done() time.sleep(2) print(item) def multi_two(): while run_2 == 1: item = q.get() q.task_done() time

问题 I am creating multiple processes which are running crawlers separately. I want to ensure that if there is some exception in crawler process, I am able to catch it in Parent Process. Here is the process creation code: try: caching_process = Process(target=run_crawler_process, args=(Config.CRAWLER_NAME, locations, city_payloads_map, cycle_count)) caching_process.start() except Exception as processException: raise processException 回答1: You cannot do that with Process objects. The multiprocessing

问题 This question concerns multiprocessing in python. I want to execute some code when I terminate the process, to be more specific just before it will be terminated. I'm looking for a solution which works as atexit.register for the python program. I have a method worker which looks: def worker(): while True: print('work') time.sleep(2) return I run it by: proc = multiprocessing.Process(target=worker, args=()) proc.start() My goal is to execute some extra code just before terminating it, which I

问题 I have now tried to resolve this issue for multiple hours but no matter what I do, I never get the thing to work. My project tracks live data and provides an endpoint for other services to get the latest(ish) measurement. But no matter what I do, the queue.get() always returns nothing. Here is my code: from collections import deque import numpy as np import argparse import imutils import cv2 from flask import Flask from multiprocessing import Queue import threading import Queue as Q app =

问题 I have simple UDPServer , which works with multiprocessing . I want to create a list, that contains information about all clients. I use Manager , but I don't understand, how to append information in list - I need transfer Manager`s object to handle, but how? My way with new attribute does not work. import multiprocessing from socketserver import UDPServer, ForkingMixIn, DatagramRequestHandler from socket import socket, AF_INET, SOCK_DGRAM from settings import host, port, number_of

问题 I have a dictionary that looks like this: sampleData = {'x1': [1,2,3], 'x2': [4,5,6], 'x3': [7,8,9]} I need to do some calculation for each key and value pair by passing data to a blackBoxFunction . This function takes time to do the processing. The final output is stored in a separate dictionary finalValue = {} . This is the code for doing it sequentially: for key in sampleData.keys(): finalValue[key] = [] for i in range(0,len(sampleData[key])): for j in range(i,len(sampleData[key])): if(i!

问题 I wrote some code to break up a for loop into multiple processes to speed up calculations. import numpy as np import formfactors from subdivide_loop import subdivide_loop import multiprocessing def worker(start, end, triangleI, areaI, scene, kdtree, samples, output): form_factors = np.zeros(end-start) for j in range(start, end): triangleJ = np.array(scene[j][0:4]) form_factors[start] = formfactors.uniform(triangleJ, triangleI, areaI, kdtree, samples) result = output.get(block=True) for j in

问题 At the bottom is the code I have now. It seems to work fine. However, I don't completely understand it. I thought without .join() , I'd risking the code going onto the next for-loop before the pool finishes executing. Wouldn't we need those 3 commented-out lines? On the other hand, if I were to go with the .close() and .join() way, is there any way to 'reopen' that closed pool instead of Pool(6) every time? import multiprocessing as mp import random as rdm from statistics import stdev, mean

问题 I'm pretty new to Python and completely new to parallel processing. I've been writing code to analyze punctate image data (think PALM lite) and trying to speed up my analysis code using the multiprocessing module. For small data sets I see a pretty decent speed-up up to four cores. For large datasets I start getting an AssertionError. I tried to make a boiled down example which produces the same error, see below: import numpy as np import multiprocessing as mp import os class TestClass(object

问题 I'm pretty new to Python and completely new to parallel processing. I've been writing code to analyze punctate image data (think PALM lite) and trying to speed up my analysis code using the multiprocessing module. For small data sets I see a pretty decent speed-up up to four cores. For large datasets I start getting an AssertionError. I tried to make a boiled down example which produces the same error, see below: import numpy as np import multiprocessing as mp import os class TestClass(object