python-multiprocessing

问题 I am trying to run multiple instances of a console-based game (dungeon crawl stone soup -- for research purposes naturally) using a multiprocessing pool to evaluate each run. In the past when I've used a pool to evaluate similar code (genetic algorithms), I've used subprocess.call to split off each process. However, with dcss being quite interactive having a shared subshell seems to be problematic. I have the code I normally use for this kind of thing, with crawl replacing other applications

问题 I have a project for my college regarding multiprocessing in python.For my python projects I use spyder in windows. Therefore im trying to run a very trivial multi - processing code in spyder but every time i run it spyder console freezes and never finishes. This is my code: from multiprocessing import Pool, freeze_support import multiprocessing def f(i): return i+1 def main(): pool = multiprocessing.Pool(4) result = pool.map(f, range(4)) pool.close() pool.join() print result if __name__ == '

问题 I now need to request arrays in python, and pass them into a Cpp program, yet python still needs to process them. But I found that when I use multiprocessing, the array's adderess would be changed. Below are my codes: //export dll in test.h, this is test.cpp #include "test.h" #include <iostream> using namespace std; void someWork(double* data, long* flag){ cout << "cpp flag address:" << flag << endl; //do some work } # test.py import multiprocessing as mp from multiprocessing.sharedctypes

问题 I now need to request arrays in python, and pass them into a Cpp program, yet python still needs to process them. But I found that when I use multiprocessing, the array's adderess would be changed. Below are my codes: //export dll in test.h, this is test.cpp #include "test.h" #include <iostream> using namespace std; void someWork(double* data, long* flag){ cout << "cpp flag address:" << flag << endl; //do some work } # test.py import multiprocessing as mp from multiprocessing.sharedctypes

问题 I am building a GUI that takes in sensor data from the raspberry pi and displays it onto a window via matplotlib animation. The code works fine, except when being run on raspberry pi, the matplotlib animation takes some time to execute, which momentarily blocks the sensor reading GetCPM that I'm interested in. How can I make both these programs run simultaneously without one clogging the other, I've tried the multiprocessing library, but I can't seem to get it to work. Note: The sensor data

问题 Note that I have to sweep through more argument sets than available CPUs, so I'm not sure if Python will automatically schedule the use of the CPUs depending on their availability or what. Here is what I tried, but I get an error about the arguments: import random import multiprocessing from train_nodes import run import itertools envs = ["AntBulletEnv-v0", "HalfCheetahBulletEnv-vo", "HopperBulletEnv-v0", "ReacherBulletEnv-v0", "Walker2DBulletEnv-v0", "InvertedDoublePendulumBulletEnv-v0"]

问题 I'm trying to run a bunch of simulations in Python, so I tried implementing it with multiprocessing. import numpy as np import matplotlib.pyplot as plt import multiprocessing as mp import psutil from Functions import hist, exp_fit, exponential N = 100000 # Number of observations tau = 13362.525 # decay rate to simulate iterations = 1 # Number of iterations for each process bin_size = 1*1e9 # in nanoseconds def spawn(queue): results = [] procs = list() n_cpus = psutil.cpu_count() for cpu in

问题 I'm trying to run a bunch of simulations in Python, so I tried implementing it with multiprocessing. import numpy as np import matplotlib.pyplot as plt import multiprocessing as mp import psutil from Functions import hist, exp_fit, exponential N = 100000 # Number of observations tau = 13362.525 # decay rate to simulate iterations = 1 # Number of iterations for each process bin_size = 1*1e9 # in nanoseconds def spawn(queue): results = [] procs = list() n_cpus = psutil.cpu_count() for cpu in

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))