multiprocessing

问题 While I have found numerous answers to questions similar to mine, I don't believe it has been directly addressed here--and I have several additional questions. The motivation for sharing contiguous numpy arrays is as follows: I'm using a convolutional neural network run on Caffe to perform a regression on images to a series of continuous-value labels. The images require specific preprocessing and data augmentation. The constraints of (1) the continuous nature of the labels (they're floats)

问题 While I have found numerous answers to questions similar to mine, I don't believe it has been directly addressed here--and I have several additional questions. The motivation for sharing contiguous numpy arrays is as follows: I'm using a convolutional neural network run on Caffe to perform a regression on images to a series of continuous-value labels. The images require specific preprocessing and data augmentation. The constraints of (1) the continuous nature of the labels (they're floats)

问题 I'm trying to figure out how could I run the same function multiple times in the same time. I could implement something with the multiprocessing that based on other SO questions, but unfortunately it doesn't work as I wanted. Actually when I run it I get something like this (the functions are running after each osther): Worker1 0 1 1 1 2 1 Worker2 0 2 1 2 2 2 Worker3 0 3 1 3 2 3 And I would like to achieve this (or something like this): Worker1 Worker2 Worker3 0 1 0 2 0 3 1 1 1 2 1 3 2 1 2 2

问题 I'm trying to figure out how could I run the same function multiple times in the same time. I could implement something with the multiprocessing that based on other SO questions, but unfortunately it doesn't work as I wanted. Actually when I run it I get something like this (the functions are running after each osther): Worker1 0 1 1 1 2 1 Worker2 0 2 1 2 2 2 Worker3 0 3 1 3 2 3 And I would like to achieve this (or something like this): Worker1 Worker2 Worker3 0 1 0 2 0 3 1 1 1 2 1 3 2 1 2 2

问题 I'm making the classic atari snake game in python3 using Pygame. I want to spawn a subprocess to listen for key strokes so that whenever the player enters a key (UP, DOWN, LEFT, or RIGHT), the subprocess sends the parent process the key. But this pipe should not be blocking, so that the snake can travel in the direction it was traveling until the key is received. I found Python's official documentation on multi-processes, but it does not describe the behavior I want, or at least doesn't

问题 Trying to find out the correct number of parallel processes to run with python multiprocessing. Scripts below are run on an 8-core, 32 GB (Ubuntu 18.04) machine. (There were only system processes and basic user processes running while the below was tested.) Tested multiprocessing.Pool and apply_async with the following: from multiprocessing import current_process, Pool, cpu_count from datetime import datetime import time num_processes = 1 # vary this print(f"Starting at {datetime.now()}")

问题 Often there is a need for the program to wait for a function to complete its work. Sometimes it is opposite: there is no need for a main program to wait. I've put a simple example. There are four buttons. Clicking each will call the same calculate() function. The only difference is the way the function is called. "Call Directly" button calls calculate() function directly. Since there is a 'Function End' print out it is evident that the program is waiting for the calculate function to complete

问题 I used multiprocessing.Pool to imporove the performance of my Python server. But I found that, if I create a Pool with processes=100, when the server is started and the task has not yet started running, there will be 100+ processes while executing the command "pstree |grep python | wc -l". Does that mean all the process will be initialized when the pool is initialized? Will it result in a waste of server resources? Is there a way to control the timing of process initialization in Python

问题 I used multiprocessing.Pool to imporove the performance of my Python server. But I found that, if I create a Pool with processes=100, when the server is started and the task has not yet started running, there will be 100+ processes while executing the command "pstree |grep python | wc -l". Does that mean all the process will be initialized when the pool is initialized? Will it result in a waste of server resources? Is there a way to control the timing of process initialization in Python

问题 from multiprocessing.dummy import Pool as ThreadPool import multiprocessing as mp def func(a): pthData = "C:/temp/temp.txt" with open(pthData, 'r') as file: done = file.read().splitlines() if a in done: return 'done' q.put(a) return a def listener(q): pthData = "C:/temp/temp.txt" m = q.get() with open(pthData, 'a') as the_file: the_file.write( m + '\n') #he_file.write(str(m) + '\n') a = ['a', 'b', 'c', 'd', 'a', 'b'] # Make the Pool of workers pool = ThreadPool(4) #must use Manager queue here