嘗試使用多填寫蟒蛇

Question

數組我有這樣

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的價值不是用多線程改變了代碼，可能是什麼問題？

謝謝

Answer 1

你在這裏使用多個進程，而不是多線程。因此，local_func的每個實例都獲得自己的ar單獨副本。您可以使用custom Manager創建共享numpy的陣列，它可以傳遞給每個子進程，讓你期望的結果：

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，多線程和多處理是不同的東西。

多線程線程共享對同一陣列的訪問。

多處理每個進程都有自己的數組副本。

Answer 3

multiprocessing.Pool是進程池，而不是線程池。

如果你想線程池，使用multiprocess.pool.ThreadPool：

---

替換：

from multiprocessing import Pool 

有：

from multiprocessing.pool import ThreadPool as Pool