multiprocessing

问题 I have 5 different connection strings which is stored in connstr list and I have a table list which contains data stored in templst. So the contents looks like templst = [lineitem, orders, partsupp, region, cur_cur, T1, T2] connstr = [DRIVER={libdbodbc17.so};host=lint16muthab.phl.sap.corp:8766;UID=dba;PWD=sql;CharSet=utf8, DRIVER={libdbodbc17.so};host=localhost:8767;UID=dba;PWD=sql;CharSet=utf8, DRIVER={libdbodbc17.so};host=localhost:8768;UID=dba;PWD=sql;CharSet=utf8, DRIVER={libdbodbc17.so}

问题 I am using the below sample code for OneMax problem (maximizing the number of ones of a bitstring) using DEAP package and multiprocessing. I am unable to speed up the process using multiprocessing. I want to use this for a more complex problem before finding out what is the issue here. Thank you. import array import multiprocessing from multiprocessing import Pool import random import time import numpy as np from deap import algorithms from deap import base from deap import creator from deap

问题 Il posed question, I did not understand the ture cause of the issue (it seems to have been related to my usage of flask in one of the subprocesses). PLEASE IGNORE THIS (can't delete due to bounty) Essentially, I have to start some Processes and or a pool when running a python library as a module. However, since __name__ == '__main__' is always true in __main__.py this proves to be an issue (see multiprocessing docs: https://docs.python.org/3/library/multiprocessing.html) I've attempted

问题 I'm trying to implement a pool of two processes that run in parallel and communicate through a queue. The goal is to have a writer process that passes a message to a reader process by using a queue . Each process is printing a feedback on the terminal in order to have a feedback. Here is the code: #!/usr/bin/env python import os import time import multiprocessing as mp import Queue def writer(queue): pid = os.getpid() for i in range(1,4): msg = i print "### writer ", pid, " -> ", msg queue

问题 I have a python tool, that has basically this kind of setup: main process (P1) -> spawns a process (P2) that starts a tcp connection -> spawns a thread (T1) that starts a loop to receive messages that are sent from P2 to P1 via a Queue (Q1) server process (P2) -> spawns two threads (T2 and T3) that start loops to receive messages that are sent from P1 to P2 via Queues (Q2 and Q3) The problem I'm having is that when I stop my program (with Ctrl+C), it doesn't quit. The server process is ended,

问题 I have a python tool, that has basically this kind of setup: main process (P1) -> spawns a process (P2) that starts a tcp connection -> spawns a thread (T1) that starts a loop to receive messages that are sent from P2 to P1 via a Queue (Q1) server process (P2) -> spawns two threads (T2 and T3) that start loops to receive messages that are sent from P1 to P2 via Queues (Q2 and Q3) The problem I'm having is that when I stop my program (with Ctrl+C), it doesn't quit. The server process is ended,

问题 I recently set up a new machine to aid in decreasing run times for fitting models and data wrangling. I did some preliminary benchmarks and everything is mostly smoothe, but I ran into a snag when I tried enabling multi-process workers with in scikit learn. I've simplified the error to not be associated with my original code as I enabled this feature without a problem on a different machine and a VM. I've also done memory allocation checks to make sure my machine wasn't running out of

问题 I recently set up a new machine to aid in decreasing run times for fitting models and data wrangling. I did some preliminary benchmarks and everything is mostly smoothe, but I ran into a snag when I tried enabling multi-process workers with in scikit learn. I've simplified the error to not be associated with my original code as I enabled this feature without a problem on a different machine and a VM. I've also done memory allocation checks to make sure my machine wasn't running out of

问题 I'm doing a fair amount of parallel processing in Python using the multiprocessing module. I know certain objects CAN be pickle (thus passed as arguments in multi-p) and others can't. E.g. class abc(): pass a=abc() pickle.dumps(a) 'ccopy_reg\n_reconstructor\np1\n(c__main__\nabc\np2\nc__builtin__\nobject\np3\nNtRp4\n.' But I have some larger classes in my code (a dozen methods, or so), and this happens: a=myBigClass() pickle.dumps(a) Traceback (innermost last): File "<stdin>", line 1, in

问题 I'm running Jupyter 5.0.0 notebook with Python 3.5.3 on Windows 10. The following example code fails to run: from concurrent.futures import as_completed, ProcessPoolExecutor import time import numpy as np def do_work(idx1, idx2): time.sleep(0.2) return np.mean([idx1, idx2]) with ProcessPoolExecutor(max_workers=4) as executor: futures = set() for idx in range(32): future = winprocess.submit( executor, do_work, idx, idx * 2 ) futures.add(future) for future in as_completed(futures): print(future