尝试使用多填写蟒蛇

Question

数组我有这样

x = 3; 
y = 3; 
z = 10; 
ar = np.zeros((x,y,z)) 

from multiprocessing import Process, Pool 

para = [] 
process = [] 
def local_func(section): 
    print "section %s" % str(section) 
    ar[2,2,section] = 255 
    print "value set %d", ar[2,2,section] 

pool = Pool(1) 

run_list = range(0,10) 
list_of_results = pool.map(local_func, run_list) 

print ar 

在AR的价值不是用多线程改变了代码，可能是什么问题？

谢谢

Answer 1

你在这里使用多个进程，而不是多线程。因此，local_func的每个实例都获得自己的ar单独副本。您可以使用custom Manager创建共享numpy的阵列，它可以传递给每个子进程，让你期望的结果：

import numpy as np 
from functools import partial 
from multiprocessing import Process, Pool 
import multiprocessing.managers 

x = 3; 
y = 3; 
z = 10; 

class MyManager(multiprocessing.managers.BaseManager): 
    pass 
MyManager.register('np_zeros', np.zeros, multiprocessing.managers.ArrayProxy) 


para = [] 
process = [] 
def local_func(ar, section): 
    print "section %s" % str(section) 
    ar[2,2,section] = 255 
    print "value set %d", ar[2,2,section] 

if __name__ == "__main__": 
    m = MyManager() 
    m.start() 
    ar = m.np_zeros((x,y,z)) 

    pool = Pool(1) 

    run_list = range(0,10) 
    func = partial(local_func, ar) 
    list_of_results = pool.map(func, run_list) 

    print ar 

Answer 2

好吧，多线程和多处理是不同的东西。

多线程线程共享对同一阵列的访问。

多处理每个进程都有自己的数组副本。

Answer 3

multiprocessing.Pool是进程池，而不是线程池。

如果你想线程池，使用multiprocess.pool.ThreadPool：

---

替换：

from multiprocessing import Pool 

有：

from multiprocessing.pool import ThreadPool as Pool