进程池中的进程之间共享的类属性和内存?

Question

我有一个类A，当启动时会更改可变类属性nums。

当通过具有maxtasksperchild= 1的进程池启动类时，我注意到nums具有几个不同进程的值。 这对我来说是一种不良行为。

我的问题是：

• 进程是否共享内存？
• 我是否正确理解maxtasksperchild和进程池的工作原理？

编辑：我猜池会腌制它启动的先前进程(而不是原始进程(，从而保存nums的值，这是正确的吗？ 如果是这样，我如何强制它使用原始进程？

下面是一个示例代码：

from multiprocessing import Pool

class A:
nums = []
def __init__(self, num=None):
self.__class__.nums.append(num)  # I use 'self.__class__' for the sake of explicitly
print(self.__class__.nums)
assert len(self.__class__.nums) < 2  # checking that they don't share memory

if __name__ == '__main__':
with Pool(maxtasksperchild=1) as pool:
pool.map(A, range(99))  # the assert is being raised

编辑，因为 k.wahome 的回答：使用实例属性并不能回答我的问题 我需要使用类属性，因为在我的原始代码中(此处未显示(每个进程都有几个实例。 我的问题专门涉及多处理池的工作原理。

---

顺便说一句，执行以下操作确实有效

from multiprocessing import Process
if __name__ == '__main__':
prs = []
for i in range(99):
pr = Process(target=A, args=[i])
pr.start()
prs.append(pr)
[pr.join() for pr in prs]
# the assert was not raised

Answer 1

你的观察还有另一个原因。nums中的值不是来自其他进程，而是来自开始托管多个 A 实例时的同一进程。发生这种情况是因为您没有在pool.map调用中将chunksize设置为 1。 在您的情况下，设置maxtasksperchild=1是不够的，因为一个任务仍然会消耗整个可迭代对象。

此方法将可迭代对象切成多个块，并将其作为单独的任务提交到进程池。可以通过将块大小设置为正整数来指定这些块的(近似(大小。关于地图的文档

Answer 2

共享很可能是通过具有类属性nums的映射类A进入的

。 类属性是类绑定的，因此属于类本身，在加载类时创建，它们将由所有实例共享。所有对象都将具有对类属性的相同内存引用。

与类属性不同，实例属性是实例绑定的，不由各种实例共享。每个实例都有自己的实例属性副本。

查看类与实例属性效果：

1. 使用nums作为类属性class_num.py

from multiprocessing import Pool

class A:
nums = []
def __init__(self, num=None):
# I use 'self.__class__' for the sake of explicitly
self.__class__.nums.append(num)
print("nums:", self.__class__.nums)
# checking that they don't share memory
assert len(self.__class__.nums) < 2

if __name__ == '__main__':
with Pool(maxtasksperchild=1) as pool:
print(pool)
pool.map(A, range(99))  # the assert is being raised

运行此脚本

>>> python class_num.py
nums: [0]
nums: [0, 1]
nums: [4]
nums: [4, 5]
nums: [8]
nums: [8, 9]
nums: [12]
nums: [12, 13]
nums: [16]
nums: [16, 17]
nums: [20]
nums: [20, 21]
nums: [24]
nums: [24, 25]
nums: [28]
nums: [28, 29]
nums: [32]
nums: [32, 33]
nums: [36]
nums: [36, 37]
nums: [40]
nums: [40, 41]
nums: [44]
nums: [44, 45]
nums: [48]
nums: [48, 49]
nums: [52]
nums: [52, 53]
nums: [56]
nums: [56, 57]
nums: [60]
nums: [60, 61]
nums: [64]
nums: [64, 65]
nums: [68]
nums: [68, 69]
nums: [72]
nums: [72, 73]
nums: [76]
nums: [76, 77]
nums: [80]
nums: [80, 81]
nums: [84]
nums: [84, 85]
nums: [88]
nums: [88, 89]
nums: [92]
nums: [92, 93]
nums: [96]
nums: [96, 97]
multiprocessing.pool.RemoteTraceback: 
"""
Traceback (most recent call last):
File "/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py", line 44, in mapstar
return list(map(*args))
File "class_num.py", line 12, in __init__
assert len(self.__class__.nums) < 2
AssertionError
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "class_num.py", line 18, in <module>
pool.map(A, range(99))  # the assert is being raised
File "/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py", line 260, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py", line 608, in get
raise self._value
AssertionError

2. 使用nums作为实例属性instance_num.py

from multiprocessing import Pool

class A:
def __init__(self, num=None):
self.nums = []
if num is not None:
self.nums.append(num)
print("nums:", self.nums)
# checking that they don't share memory
assert len(self.nums) < 2

if __name__ == '__main__':
with Pool(maxtasksperchild=1) as pool:
pool.map(A, range(99))  # the assert is being raised

运行此脚本

>>> python instance_num.py
nums: [0]
nums: [1]
nums: [2]
nums: [3]
nums: [4]
nums: [5]
nums: [6]
nums: [7]
nums: [8]
nums: [9]
nums: [10]
nums: [11]
nums: [12]
nums: [13]
nums: [14]
nums: [15]
nums: [16]
nums: [17]
nums: [18]
nums: [19]
nums: [20]
nums: [21]
nums: [22]
nums: [23]
nums: [24]
nums: [25]
nums: [26]
nums: [27]
nums: [28]
nums: [29]
nums: [30]
nums: [31]
nums: [32]
nums: [33]
nums: [34]
nums: [35]
nums: [36]
nums: [37]
nums: [38]
nums: [39]
nums: [40]
nums: [41]
nums: [42]
nums: [43]
nums: [44]
nums: [45]
nums: [46]
nums: [47]
nums: [48]
nums: [49]
nums: [50]
nums: [51]
nums: [52]
nums: [53]
nums: [54]
nums: [55]
nums: [56]
nums: [57]
nums: [58]
nums: [59]
nums: [60]
nums: [61]
nums: [62]
nums: [63]
nums: [64]
nums: [65]
nums: [66]
nums: [67]
nums: [68]
nums: [69]
nums: [70]
nums: [71]
nums: [72]
nums: [73]
nums: [74]
nums: [75]
nums: [76]
nums: [77]
nums: [78]
nums: [79]
nums: [80]
nums: [81]
nums: [82]
nums: [83]
nums: [84]
nums: [85]
nums: [86]
nums: [87]
nums: [88]
nums: [89]
nums: [90]
nums: [91]
nums: [92]
nums: [93]
nums: [94]
nums: [95]
nums: [96]
nums: [97]
nums: [98]