python-multiprocessing

I am trying to read and process 1000s of files, but unfortunately it takes about 3x as long to process the file as it does to read it in from disk, so I would like to process these files as they are read in (and while I am continuing to read in additional files). In a perfect world, I have a generator which reads one file at a time, and I would like to pass this generator to a pool of workers which process items from the generator as they are (slowly) generated. Here's an example: def process_file(file_string): ... return processed_file pool = Pool(processes=4) path = 'some/path/' results =

We are running an script ussing the multiprocessing library ( python 3.6 ), where big pd.DataFrames are passed through a process: from multiprocessing import Pool import time def f(x): # do something time conssuming time.sleep(50) if __name__ == '__main__': with Pool(10) as p: res = {} output = {} for id, big_df in some_dict_of_big_dfs: res[id] = p.apply_async(f,(big_df ,)) output = {u : res[id].get() for id in id_list} The problem is that we are getting an error from the pickle library. Reason: 'OverflowError('cannot serialize a bytes objects larger than 4GiB',)' We are aware than pickle v4

I am trying to pass a list as a parameter to the pool.map(co_refresh, input_list) . However, pool.map didn't trigger the function co_refresh . And also no error returned. It looks like the process hung in there. Original Code: from multiprocessing import Pool import pandas as pd import os account='xxx' password='xxx' threads=5 co_links='file.csv' input_list=[] pool = Pool(processes=threads) def co_refresh(url, account, password, outputfile): print(url + ' : ' + account + ' : ' + password + ' : ' + outputfile) return; link_pool = pd.read_csv(co_links, skipinitialspace = True) for i, row in link

I use multiprocessing.connection.Listener for communication between processes, and it works as a charm for me. Now i would really love my mainloop to do something else between commands from client. Unfortunately listener.accept() blocks execution until connection from client process is established. Is there a simple way of managing non blocking check for multiprocessing.connection? Timeout? Or shall i use a dedicated thread? # Simplified code: from multiprocessing.connection import Listener def mainloop(): listener = Listener(address=(localhost, 6000), authkey=b'secret') while True: conn =

You can see the full here . A simplified version of my code follows: executor = ProcessPoolExecutor(10) try: coro = bot.loop.run_in_executor(executor, processUserInput, userInput) result = await asyncio.wait_for(coro, timeout=10.0, loop=bot.loop) except asyncio.TimeoutError: result="Operation took longer than 10 seconds. Aborted." Unfortunately, when an operation times out, that process is still running, even though the future has been cancelled. How do I cancel that process/task so that it actually stops running? ProcessPoolExecutor uses the multiprocessing module. Instead of canceling the

Using python multiprocessing and curses, it appears that terminating a Process interferes with curses display. For example, in the following code, why does terminating the process stops curses from displaying the text ? (pressing b after pressing a) More precisely, it seems that not only the string "hello" is not displayed anymore but also the whole curses window. import curses from multiprocessing import Process from time import sleep def display(stdscr): stdscr.clear() curses.newwin(0,0) stdscr.timeout(500) p = None while True: stdscr.addstr(1, 1, "hello") stdscr.refresh() key = stdscr.getch

For my college project I am trying to develop a python based traffic generator.I have created 2 CentOS machines on vmware and I am using 1 as my client and 1 as my server machine. I have used IP aliasing technique to increase number of clients and severs using just single client/server machine. Upto now I have created 50 IP alias on my client machine and 10 IP alias on my server machine. I am also using multiprocessing module to generate traffic concurrently from all 50 clients to all 10 servers. I have also developed few profiles(1kb,10kb,50kb,100kb,500kb,1mb) on my server(in /var/www/html