multiprocessing

问题 Is it possible to have a few child processes running some calculations, then send the result to main process (e.g. update PyQt ui), but the processes are still running, after a while they send back data and update ui again? With multiprocessing.queue, it seems like the data can only be sent back after process is terminated. So I wonder whether this case is possible or not. 回答1: I don't know what you mean by "With multiprocessing.queue, it seems like the data can only be sent back after

问题 Quite new to multiprocessing here. I have a code that runs two processes. One to continuously receive data blocks from the server and put it inside a queue and the other to remove the data blocks from the queue and process it. Below is my client code: import socket import turtle import multiprocessing from multiprocessing import Process, Queue from tkinter import * class GUI: def __init__(self, master): rec_data = recv_data() self.master = master master.title("Collision Detection") self.input

问题 Quite new to multiprocessing here. I have a code that runs two processes. One to continuously receive data blocks from the server and put it inside a queue and the other to remove the data blocks from the queue and process it. Below is my client code: import socket import turtle import multiprocessing from multiprocessing import Process, Queue from tkinter import * class GUI: def __init__(self, master): rec_data = recv_data() self.master = master master.title("Collision Detection") self.input

问题 Quite new to multiprocessing here. I have a code that runs two processes. One to continuously receive data blocks from the server and put it inside a queue and the other to remove the data blocks from the queue and process it. Below is my client code: import socket import turtle import multiprocessing from multiprocessing import Process, Queue from tkinter import * class GUI: def __init__(self, master): rec_data = recv_data() self.master = master master.title("Collision Detection") self.input

问题 I would like to create objects of class Training and create multiple processes which call the print() function. I have a class Training : class Training(): def __init__(self, param1, param2): self.param1 = param1 self.param2 = param2 def print(self): print(self.param1) print(self.param2) I have tried to use the starmap function to create 5 processes in the following way: import multiprocessing as mp num_devices = 5 func_args = [] for i in range (0, num_devices): func_args.append((i, i*10))

问题 I'm trying to learn the basics of multi-processing in python, and found the following example online which I wanted to practice with. import concurrent.futures import time def do_something(seconds): print(f' Sleeping {seconds} seconds') time.sleep(seconds) return f'Done Sleeping {seconds}' with concurrent.futures.ProcessPoolExecutor() as executor: f1 = executor.submit(do_something, 1) print(f1.result()) Fairly simple, I know. However, for some reason when I try and run this, I get the

问题 I'm trying to learn the basics of multi-processing in python, and found the following example online which I wanted to practice with. import concurrent.futures import time def do_something(seconds): print(f' Sleeping {seconds} seconds') time.sleep(seconds) return f'Done Sleeping {seconds}' with concurrent.futures.ProcessPoolExecutor() as executor: f1 = executor.submit(do_something, 1) print(f1.result()) Fairly simple, I know. However, for some reason when I try and run this, I get the

问题 Keras have very little info about keras.utils.Sequence, actually the only reason I want to derive my batch generator from keras.utils.Sequence is that I want to not to write thread pool with queue by myself, but I'm not sure if it's best choice for my task, here is my questions: What should __len__ return if I have random generator and I don't have any predefined 'list' with samples. How keras.utils.Sequence should be used with fit_generator , I'm interested in max_queue_size , workers , use

问题 Keras have very little info about keras.utils.Sequence, actually the only reason I want to derive my batch generator from keras.utils.Sequence is that I want to not to write thread pool with queue by myself, but I'm not sure if it's best choice for my task, here is my questions: What should __len__ return if I have random generator and I don't have any predefined 'list' with samples. How keras.utils.Sequence should be used with fit_generator , I'm interested in max_queue_size , workers , use

问题 I have to process a huge pandas.DataFrame (several tens of GB) on a row by row bases, where each row operation is quite lengthy (a couple of tens of milliseconds). So I had the idea to split up the frame into chunks and process each chunk in parallel using multiprocessing . This does speed-up the task, but the memory consumption is a nightmare. Although each child process should in principle only consume a tiny chunk of the data, it needs (almost) as much memory as the original parent process