Multiprocessing Questions

(i'm using the pyprocessing module in this example, but

Consider simplified example using multiprocessing inside a class that use cupy for simulation. this part

I have some programm in which multiple processes try to finish some function. my aim now is to stop all the other processes after one process has

Why dose the program print "hello world" 2 times rather than only 1 time? the console.log is excuted before cluster.fork(). import

i have a list of filenames and use it to generate string which will be the new variable to store dataframe. the code below it's not

I am not an experienced programmer and i am trying to create a sort of datalogger program in python using qt for python (pyside2) to build the

I want to create a queue system that allows me to enqueue some commands to be executed from the command console. they are neural nets training

I have 2 functions def print_out(string): typing_speed = engine.getproperty('rate') #wpm

I am having trouble with the multiple processing model. when passing a single argument with a for loop, my code works: def job(a):

For an mcmc implementation, i want to calculate the covariance tensor c in numpy. working single-threaded code the distance

I have a task in a python script that was previously mostly io-bound, so i used threadpools and everything worked fine. now my task is becoming

Here's a minimal example of what i want to achieve. the global init_* values should be incremented by each process and made

I'm trying to learn multiprocessing library. this way it's working: def generate_files(file_number, directory): var02 =

I've got two python programs running. program a connects to program b with the multiprocessing module:

I have 3 dimensional dataset (100,64,3000), and i am finding features using multiprocessing. i am doing multiprocessing across

Import multiprocessing from threading import thread import speech_recognition as sr def

I am trying to do feature extraction for my dataset using multiprocessing, each processor for each subject. i have 12 logical processors , and i

I am trying to parallelize a function that takes multiple constant arguments. so far i have been able to run it, but it is not parallelizing the

) i'm a beginner in python and django and try to run a external code with multiprocessing. on my first try import a .txt with

I have been trying to learn to use the multiprocessing module in python. i seem to find a little interesting fact in this module, if using pool()

I am doing some parallel processing, as follows: with mp.pool(8) as tmppool: results = tmppool.starmap(my_function,

I got two configuration file for different environments which share the same source file source.py, the source code is about seven

I am using a jupyter notebook to launch all manner of nonsense out of a multiprocessing.pool. unfortunately, sometimes my workers

Re-asking this with a more specific question as my

New to python. i am trying to create multiple consumer for a rabbitmq client. i am using pika and trying to do with multiprocessing. it seems

It was a task from a book called "node.js 8 the right way". you can see it below:

Using python's multiprocessing on windows will require many arguments to be "picklable" while passing them to child

It seems obvious that writing from multiple process to the same file may cause corrupted data if the write() calls are not somehow

So recently i wanted to try using multiprocessing in my work script, so i made this dummy just to see if i can make it work with a class and i am

I think i need some suggestion here. below is my code: from multiprocessing import pool import time import sys def

1. why does the following python code using the concurrent.futures module hang forever? import

I tried to use multithread pool as in this question. but i want pack all logic to my own

This is a sample code i am running, which generates a matrix of dimension (size x size). the matrix is sent to fft (over multiple

I am working on python code using multiprocessing. below is the code import multiprocessing import os def square(n):

An exception is raised in threading._wait_for_tstate_lock when i transfere hugh data between a process and a

I'm using pytesseract to do some ocr with a multiprocessing approach. the approach looks the following: tess_api =

I'm trying to build semantic segmentation model in keras. since i use a custom data, i decided to write a custom generator to feed it to the keras

The source code is as follows: const listener = new listener (); listener.start (); process.on ('sigterm', () => {

I am trying to use multiprocessing.map on an iterable of iterables. i am using partial because i have other arguments that need to be input, but

I wrote a program to crawl a website and because of amount of link that i have to crawl i use python multiprocessing. when my program starts

I am trying to add a couple of million rows to a table in exasol one column is supposed have increasing integer values (1 - xmio). i can't get my

I want to do some large matrix multiplications using multiprocessing.pool. suddenly, when the dimension is higher than 50, it

I want to parallelize the execution of tests on 2 or more devices. i have a list with tests, and i want to distribute them across all

I have a function that takes 5 or more parameters as inputs, these parameters have different types and sizes, how do i apply multiprocessing in

I have a function that processes one url at a time: def sanity(url): try: if 'media' in url[:10]: url =

I have a program written in fortran and compiled (with -fpic) as a dynamic library. i load with cdll in python to perform some numerical

The following code is runnable, you can just copy/paste: from tkinter import * import multiprocessing startingwin = tk() def

I run a script that executes multiple processes, that are executed in different classes. the processes do not end unless the user asks for it. so

I can't seem to get this to work, i'm trying to use multiprocessing to call my evaluate function for all combinations of a list;

From multiprocessing import process from threading import thread def main(): thread1 = myclass(vara) #subclassed threading.thread

The code below worked in windows but in linux is hanging: from impala.dbapi import connect from multiprocessing import pool conn =

My script is calling librosa module to compute mel-frequency cepstral

Say i have the below code, a function that does something, which is initiated in a process, and returns a value. from

I have a python code where the main process creates a child process. there is a shared queue between the two processes. the child process writes

I have two functions that are going to do something. i need them to run simultaneously until the except case happen, so i have used

So i have the following code import time from multiprocessing import process from multiprocessing import joinablequeue as queue

I'm trying to reduce the processing time of reading a database with roughly 100,000 entries, but i need them to be formatted a specific way, in an

Code: from multiprocessing import pool print ('parent') max_processes = 4 def foo(result): print (result) def main(): pool

Is there any map-like method which doesn't load all sub-processes in memory at once, instead, if total cpu threads is four, it firstly load four

I have a function that does the following : take a file as input and does basic cleaning. extract the required items

I have one python script which is generating data and one which is training a neural network with tensorflow and keras on this data. both need an

There is a bloom filter object that created by pybloom, a python module. assume that i have over 10 million strings

I'm found the module multiprocessing_generator. i

I was using multiprocessing library in python. i have python 3.6. whenever i try to create the multiprocessing. queue() object i get an

I have 2 processes, initiator and responder, they are supposed to work on parallel and repeatedly sending and receiving values (the values are

Why can't i put process in pool into a queue? here my code works when using pool and can

Grpc request in process from multiprocessing import process, manager def frames_process(self): # fork process

I got an error for this code: from pathos.multiprocessing import processingpool def dieplz(im): print('whoopdepoop!') def

I'm trying to fill a numpy array using multiprocessing, following

I am developing a program that does a polling and i need the polling to be done in a separate process for performance reasons. the program is

I want to encapsulate a multiprocessing task into a class. both control and worker functions are members of the class. the workers are run using

I use jupyter notebook to play with the data that i store in django/postgres. i initialize my project this way:

I am getting following error: df_op = pd.dataframe([excel_write_data[idx].__dict__ for idx in range(0, len(excel_write_data))])

I am having some trouble using a tuple globally with the multiprocessing class. i have a code as produced below: from

I tried to speed up a calculation using pool from the multiprocessing package. while i did get a significant speedup i'm missing more and more

We've recently started running into crashes in our android app due to the app being open in multiple processes. several different errors point

I would like to use dask to parallelize a numbercrunching task. this task utilizes only one of the cores in my computer. as a

I've created a script in python using multiprocessing library to scrape certain fields from a webpage. as i don't have any knowledge

I was trying to make my code parallel and i run into a strange thing that i am not able to explain. let me define the context. i have a

I have a heavy batch work so i sliced it into 30(number of my cpus) mini batch and i made 30 multiprocessing.process to do them(for

I'm using multiprocessing pool to run a function for multiple arguments over and over. i use a list for jobs that filled by another thread and a

I am doing some multiprocessing in linux, and i am using shared memory that is currently not explicitly passed to the child processes (not via an

During testing i find out in the following, mp method run a bit slower def eat_time(j): result = [] for j in range(10**4):

I have a cpu intensive task running in a loop that calls opencv functions. i tried to parallelize the task using multiprocessing, but the

I have a main thread running cmd from cmd2. this allows me to interactively start new threads using

What am i missing or doing wrong in the following python file using multiprocessing? when i run it, nothing happens and i have to restart the

So this is more or less a theoretical question. i have a single core machine which is supposedly powerful but nevertheless only one core. now i

I'm trying to parallelize a for loop to speed-up my code, since the loop processing operations are all independent. following online

The real code i want to optimize is too complicated to be included here, so here is a simplified example: def enumerate_paths(n,

I have some background task that i want to start an be able to safely quit by user input. to do that i have a thread in which a process pool with

What's the proper way of aborting multiprocessing when one of the child aborts and/or throw an exception? i found various questions around

I've made a lot of studying about multiprocessing! basically i'm downloading data from an api and insert in a database. i made a pool and

I want to decrease training time of my models by using a high end ec2 instance. so i tried c5.18xlarge instance with 2 cpus and run a

Pythonists, here is my class definition, class test(): def __init__(self, num): self.num = num

I was trying out multiprocessing because i was trying to imitate an internet and how it's nodes work. i am fine with the basic functionality so i

Much appreciated in advance ! task description: i would like to use python to collect the free https proxy server

I have a class a that when initiated changes a mutable class attribute nums. when initiating the class via a

I'm trying to implement logging in a multiprocessing class within flask but i'm encountering some errors. is it possible to log individually in

I'm using pool from multiprocessing package (from multiprocessing.dummy import pool). i wrote a function

I am trying to figure out how to run a large problem on multiple cores. i am struggling with splitting a dataframe to the different processes.