multiprocessing

问题 I've been browsing for some time but couldn't find any constructive answer that I could comprehend. How should I paralellize the following code: import random import math import numpy as np import sys import multiprocessing boot = 20#number of iterations to be performed def myscript(iteration_number): #stuff that the code actually does def main(unused_command_line_args): for i in xrange(boot): myscript(i) return 0 if __name__ == '__main__': sys.exit(main(sys.argv)) or where can I read about

问题 I have a simple dataframe with two columns. +---------+-------+ | subject | score | +---------+-------+ | wow | 0 | +---------+-------+ | cool | 0 | +---------+-------+ | hey | 0 | +---------+-------+ | there | 0 | +---------+-------+ | come on | 0 | +---------+-------+ | welcome | 0 | +---------+-------+ For every record in 'subject' column, I am calling a function and updating the results in column 'score' : df['score'] = df['subject'].apply(find_score) Here find_score is a function, which

问题 Hi everybody I took Jiaaro's solution as a template to convert it from threading to multiprocessing: import multiprocessing from function_repo import run from time import time vitems = ['02','63','25','0'] num_processes = (multiprocessing.cpu_count()/1) threads = [] if __name__ == '__main__': begin = time() print begin # run until all the threads are done, and there is no data left while threads or vitems: if( len(threads) < (num_processes -1) ): p = multiprocessing.Process(target=run,args=

问题 If I need to share a multiprocessing.Queue or a multiprocessing.Manager (or any of the other synchronization primitives), is there any difference in doing it by defining them at the global (module) level, versus passing them as an argument to the function executed in a different process? For example, here are three possible ways I can imagine a queue could be shared: # works fine on both Windows and Linux from multiprocessing import Process, Queue def f(q): q.put([42, None, 'hello']) def main

问题 If I need to share a multiprocessing.Queue or a multiprocessing.Manager (or any of the other synchronization primitives), is there any difference in doing it by defining them at the global (module) level, versus passing them as an argument to the function executed in a different process? For example, here are three possible ways I can imagine a queue could be shared: # works fine on both Windows and Linux from multiprocessing import Process, Queue def f(q): q.put([42, None, 'hello']) def main

问题 whenever I try to run my program on WSL, I get the following error. I'm pretty new to pytorch and vizdoom, so I don't know how to solve this problem. Setup - Windows 10 x64 - Ubuntu 14 (on WSL) - Python 2.7.14 (Anaconda 2) - OpenAI Gym 0.9.5 - Vizdoom 1.1.4 - doom-py 0.0.14 - ppaquette/gym-doom - pytorch 0.0.12 (doomenv) hybridsyntax@Blacklynx:/mnt/f/_TUTORIALS/ai/doom/code$ python ai.py > wsl.log [2018-05-25 18:21:44,354] Making new env: ppaquette/DoomCorridor-v0 [2018-05-25 18:21:44,365]

问题 I found this why multiple processes have the same object id in python, but I do not quite understand what does it mean "because both processes execute the same code", I try the code, it seems the outputs are always the same. ➜ ~ python test2.py 4419085696 4419085696 ➜ ~ python test2.py 4342830464 4342830464 ➜ ~ python test2.py 4510156160 4510156160 ➜ ~ python test2.py 4329948544 4329948544 ➜ ~ python test2.py 4468004224 4468004224 ➜ ~ python test2.py 4326647168 4326647168 ➜ ~ python test2.py

问题 The real code I want to optimize is too complicated to be included here, so here is a simplified example: def enumerate_paths(n, k): """ John want to go up a flight of stairs that has N steps. He can take up to K steps each time. This function enumerate all different ways he can go up this flight of stairs. """ paths = [] to_analyze = [(0,)] while to_analyze: path = to_analyze.pop() last_step = path[-1] if last_step >= n: # John has reach the top paths.append(path) continue for i in range(1,

问题 Trying to use the code here https://stackoverflow.com/a/15390953/378594 to convert a numpy array into a shared memory array and back. Running the following code: shared_array = shmarray.ndarray_to_shm(my_numpy_array) and then passing the shared_array as an argument in the list of argument for a multiprocessing pool: pool.map(my_function, list_of_args_arrays) Where list_of_args_arrays contains my shared array and other arguments. It results in the following error PicklingError: Can't pickle

问题 I have created a small script in python where I want to execute two function on the same time using multiprocessing. The first function would do a directory recursive search and the second one will display some questions to the user. Although the .txt file is created the question doesn't appear. I have seen this question: Python command line input in a process but as a beginner I did not understand what is the problem and how to solve it. Here's my script: import os import thread import time