Python多处理和共享内存-为什么我得到不同的结果?

Question

我正在测试一些python代码在两个进程之间共享numpy数组。

from multiprocessing import Process, Semaphore, shared_memory
import numpy as np
import time
def reader(id, a, shm):
exst_shm = shared_memory.SharedMemory(name=shm)
b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf)

time.sleep(2)    
print('FUNCTION VERSION: ', b[0])   

def worker(id, a, shm):
exst_shm = shared_memory.SharedMemory(name=shm)
b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf) 
b[0] += 10 

if __name__ == "__main__":
a = np.array([0])
shm = shared_memory.SharedMemory(create=True, size=a.nbytes) 
c = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)

th1 = Process(target=reader, args=(1, a, shm.name))
th2 = Process(target=worker, args=(2, a, shm.name))

th1.start()
th2.start()
th1.join()
th2.join()

上面的代码工作得很好，它打印出:

from multiprocessing import Process, Semaphore, shared_memory
import numpy as np
import time
class Reader(Process):
def __init__(self, id, a, shm):
Process.__init__(self)

exst_shm = shared_memory.SharedMemory(name=shm)
b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf) 

time.sleep(2)    
print('SUBCLASS VERSION: ', b[0])   

class Worker(Process):
def __init__(self, id, a, shm):
Process.__init__(self)

exst_shm = shared_memory.SharedMemory(name=shm) 
b = np.ndarray(a.shape, dtype=a.dtype, buffer=exst_shm.buf) 
b[0] += 10

if __name__ == "__main__":
a = np.array([0])
shm = shared_memory.SharedMemory(create=True, size=a.nbytes) 
c = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)

th1 = Reader(1, a, shm.name)
th2 = Worker(2, a, shm.name)
th1.start()
th2.start()
th1.join()
th2.join()

然而，当它被写成类时，它打印出:SUBCLASS VERSION: 0。差异从何而来?代码有什么问题?

Answer 1

您以错误的方式创建类。

正常情况下，start()运行run()方法在新进程中执行target。

当你创建自己的类，然后你必须覆盖方法run()和把你的代码在run()，然后它会运行这段代码，当你使用start()。

在你的版本中，它在主进程中运行所有代码，而不是在新进程中运行。首先，您创建Reader()，它休眠2s并打印结果b[0]，然后您创建Worker()，它添加b[0] += 10。然后是start()进程——但是它们没有什么关系，因为你没有覆盖run()，而且你在类中没有target。

from multiprocessing import Process, Semaphore, shared_memory
import numpy as np
import time
class Reader(Process):
def __init__(self, id, a, shm):
Process.__init__(self)

self.exst_shm = shared_memory.SharedMemory(name=shm)
self.b = np.ndarray(a.shape, dtype=a.dtype, buffer=self.exst_shm.buf) 

def run(self):            
time.sleep(2)
print('SUBCLASS VERSION: ', self.b[0])   

class Worker(Process):
def __init__(self, id, a, shm):
Process.__init__(self)

self.exst_shm = shared_memory.SharedMemory(name=shm) 
self.b = np.ndarray(a.shape, dtype=a.dtype, buffer=self.exst_shm.buf) 
def run(self):            
self.b[0] += 10

if __name__ == "__main__":
a = np.array([0])
shm = shared_memory.SharedMemory(create=True, size=a.nbytes) 
c = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)

th1 = Reader(1, a, shm.name)
th2 = Worker(2, a, shm.name)
th1.start()
th2.start()
th1.join()
th2.join()

---

顺便说一句:

使用

import multiprocessing
print(multiprocessing.__file__)

你可以在process.py中找到源代码并看到它是如何工作的。

def run(self):
'''
Method to be run in sub-process; can be overridden in sub-class
'''
if self._target:
self._target(*self._args, **self._kwargs)