#import threading import multiprocessing import time condition = True finish_state = False x = 0 all_processes =  def operation_process(x): #many operations with x value that if they take a long time will be closed because the other thread will set the boolean to True and close all open threads time.sleep(20) #for example these operations ..
I’m trying to use multi-processing on windows 10 with Python 3.10.1. If the method has a decoration, then this code fails: from multiprocessing import Process def profiler(func): def wrap(*args, **kwargs): result = func(*args, **kwargs) return result return wrap @profiler def go_around(num): print(num) if __name__ == ‘__main__’: p = Process(target=go_around, args=(1,)) p.start() p.join() I’m getting this ..
Desired goal I am working on a project. What I would like to do is run a single python file every day, fetch and process data. During processing of said data, if some certain parameters are true, send data to another sub process for further processing. While that data is being processed "further", I would ..
python is definitely one of the worst languages for parallel processing just finna get the parent process to throw an error when the child process fails. instead, the parent process hangs at the recv() method some code in the child process: try: condition.acquire() x = 0/0 pipeConn2.send(‘test data’) condition.notify_all() condition.release() except: pipeConn2.close() # expect this ..
i’m trying to handle multiprocessing in python, however, i think i might did not understand it properly. To start with, i have dataframe, which contains texts as string, on which i want to perform some regex. The code looks as follows: import multiprocess from threading import Thread def clean_qa(): for index, row in data.iterrows(): data["qa"].loc[index] ..
I am trying to make a soundboard that plays after a certain keystroke. I didn’t find an efficient way to make the code online ,so I created a function that runs in parallel to record keystrokes. but now although it works I think that there might be a more efficient way to implement the idea. ..
Here is some test of multiprocessing.Pool vs multiprocessing.pool.ThreadPool vs sequential version, I wonder why multiprocessing.pool.ThreadPool version is slower than sequential version? Is it true that multiprocessing.Pool is faster because it use processes (i.e. without GIL) and multiprocessing.pool.ThreadPool use threads(i.e. with GIL) despite the name of the package multiprocessing? import time def test_1(job_list): from multiprocessing import ..
I’m running a webdriver with selenium to scrape items from a page after sending keys and clicking on a button. However, my data is fairly large ~ 28000 rows and the time it takes to complete a single page is ~0.8-1.3 seconds. Are there ways in improving the speed so it drops down to <0.5 ..
I have a image-processing service containing two methods, which I want to execute in parallel using the multiprocessing library in Python. The first-method does an api call in order to fetch image metadata from an external service. The second method uses an object of a class which performs certain complex operations such as reading an ..
Ok, so I have 2 different programs, both async. I need to share data from one of them to the other, I’ve tried with multiprocessing but it didn’t work with async (idk if I did it right or not). I have no more ideas of what to do. (Comment if I’m not explaining my self ..