问题描述
我正在尝试按照以下代码片段使用多处理来生成复杂的、不可选择的对象：
void removeAll(vector<int>& v,const int& x)
{
int count = 0;
for (vector<int>::iterator it = v.begin(); it < v.end(); it++)
{
if (*it == x && count > 0)
{
v.erase(it);
}
if (*it == x)
{
count++;
}
}
}
（努力用更简单的代码重现这个，所以我展示了实际的代码）
我需要更新一个可共享的变量，所以我使用 from multiprocessing import Manager
from pathos.multiprocessing import ProcessingPool
class Facility:
def __init__(self):
self.blocks = Manager().list()
def __process_blocks(self,block):
designer = block["designer"]
apply_terrain = block["terrain"]
block_type = self.__block_type_to_string(block["type"])
block = designer.generate_block(block_id=block["id"],block_type=block_type,anchor=Point(float(block["anchor_x"]),float(block["anchor_y"]),float(block["anchor_z"])),pcu_anchor=Point(float(block["pcu_x"]),float(block["pcu_y"]),0),corridor_width=block["corridor"],jb_height=block["jb_connect_height"],min_boxes=block["min_boxes"],apply_terrain=apply_terrain)
self.blocks.append(block)
def design(self,apply_terrain=False):
designer = FacilityBuilder(string_locator=self._string_locator,string_router=self._string_router,box_router=self._box_router,sorter=self._sorter,tracker_configurator=self._tracker_configurator,config=self._config)
blocks = [block.to_dict() for index,block in self._store.get_blocks().iterrows()]
for block in blocks:
block["designer"] = designer
block["terrain"] = apply_terrain
with ProcessingPool() as pool:
pool.map(self.__process_blocks,blocks)
初始化一个类级变量，如下所示：
multiprocessing.Manager这给我留下了以下错误（仅部分堆栈跟踪）：
self.blocks = Manager().list()
作为最后的手段，我尝试使用 File "C:\Users\Paul.Nel\Documents\repos\autoPV\.autopv\lib\site-packages\dill\_dill.py",line 481,in load
obj = StockUnpickler.load(self)
File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\managers.py",line 933,in RebuildProxy
return func(token,serializer,incref=incref,**kwds)
File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\managers.py",line 783,in __init__
self._incref()
File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\managers.py",line 837,in _incref
conn = self._Client(self._token.address,authkey=self._authkey)
File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\connection.py",line 513,in Client
answer_challenge(c,authkey)
File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\connection.py",line 764,in answer_challe
nge
raise AuthenticationError('digest sent was rejected')
multiprocessing.context.AuthenticationError: digest sent was rejected
的标准 python 实现来尝试规避 ThreadPool 问题，但这也不太顺利。我已经阅读了许多类似的问题，但还没有找到解决这个特定问题的方法。是 pickle 的问题还是 dill 与 pathos 的接口方式的问题？
编辑：所以我设法用示例代码复制了它，如下所示：
mulitprocessing.Manager添加 import os
import math
from multiprocessing import Manager
from pathos.multiprocessing import ProcessingPool
class MyComplex:
def __init__(self,x):
self._z = x * x
def me(self):
return math.sqrt(self._z)
class Starter:
def __init__(self):
manager = Manager()
self.my_list = manager.list()
def _f(self,value):
print(f"{value.me()} on {os.getpid()}")
self.my_list.append(value.me)
def start(self):
names = [MyComplex(x) for x in range(100)]
with ProcessingPool() as pool:
pool.map(self._f,names)
if __name__ == '__main__':
starter = Starter()
starter.start()
时发生错误。
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
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