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为了multiprocessing在 windows (miniconda) 上的交互式 python 中使用，我发现了一个非常有用的代码，效果很好。但是，代码不能将self类内的参数传递给要池化的函数。这是我在 google colab 上运行但从未在 windows iPython 上完成的代码：

import multiprocessing
from multiprocessing import Pool
from poolable import make_applicable, make_mappable

def worker(d):
    """worker function"""
    for i in range(10000):
      j = i **(1/3) + d.bias
    return j

class dummy():
  def __init__(self):
    self.bias = 1000
  def calc(self):
    pool = Pool(processes=12)
    results = {}
    for i in range(5):
        results[i] = (pool.apply_async(*make_applicable(worker,self)))
    pool.close()
    pool.join()
    print([results[i].get() for i in range(5)])

d=dummy()
d.calc()

如果我传递其他类型的变量，该代码在 Windows 上运行良好，例如：

results[i] = (pool.apply_async(*make_applicable(worker,self.bias)))

但是当我传递self给函数时，这个过程永远不会结束。我不知道该怎么做。

poolable.py从这里：

from types import FunctionType
import marshal

def _applicable(*args, **kwargs):
  name = kwargs['__pw_name']
  code = marshal.loads(kwargs['__pw_code'])
  gbls = globals() #gbls = marshal.loads(kwargs['__pw_gbls'])
  defs = marshal.loads(kwargs['__pw_defs'])
  clsr = marshal.loads(kwargs['__pw_clsr'])
  fdct = marshal.loads(kwargs['__pw_fdct'])
  func = FunctionType(code, gbls, name, defs, clsr)
  func.fdct = fdct
  del kwargs['__pw_name']
  del kwargs['__pw_code']
  del kwargs['__pw_defs']
  del kwargs['__pw_clsr']
  del kwargs['__pw_fdct']
  return func(*args, **kwargs)

def make_applicable(f, *args, **kwargs):
  if not isinstance(f, FunctionType): raise ValueError('argument must be a function')
  kwargs['__pw_name'] = f.__name__  # edited
  kwargs['__pw_code'] = marshal.dumps(f.__code__)   # edited
  kwargs['__pw_defs'] = marshal.dumps(f.__defaults__)  # edited
  kwargs['__pw_clsr'] = marshal.dumps(f.__closure__)  # edited
  kwargs['__pw_fdct'] = marshal.dumps(f.__dict__)   # edited
  return _applicable, args, kwargs

def _mappable(x):
  x,name,code,defs,clsr,fdct = x
  code = marshal.loads(code)
  gbls = globals() #gbls = marshal.loads(gbls)
  defs = marshal.loads(defs)
  clsr = marshal.loads(clsr)
  fdct = marshal.loads(fdct)
  func = FunctionType(code, gbls, name, defs, clsr)
  func.fdct = fdct
  return func(x)

def make_mappable(f, iterable):
  if not isinstance(f, FunctionType): raise ValueError('argument must be a function')
  name = f.__name__    # edited
  code = marshal.dumps(f.__code__)   # edited
  defs = marshal.dumps(f.__defaults__)  # edited
  clsr = marshal.dumps(f.__closure__)  # edited
  fdct = marshal.dumps(f.__dict__)  # edited
  return _mappable, ((i,name,code,defs,clsr,fdct) for i in iterable)

编辑：

似乎问题不仅存在于传递给该函数self的任何其他类中，也存在于其他类中。make_applicable以下代码也没有完成：

class dummy2():
  def __init__(self):
    self.bias = 1000  

class dummy():
  def __init__(self):
    self.bias = 1000
  def copy(self):
    return copy.deepcopy(self)
  def calc(self):
    pool = Pool(processes=12)
    results = {}
    for i in range(5):
      d = dummy2()
      results[i] = pool.apply_async(*make_applicable(worker,d))
    pool.close()
    pool.join()
    print([results[i].get() for i in range(5)])
4

2 回答 2

1

使用 IPython 控制台：将代码放入模块 ( mp.py) 中，确保类实例化和方法调用在if __name__ == '__main__':有条件的情况下执行

import multiprocessing
from multiprocessing import Pool
from poolable import make_applicable, make_mappable

def worker(d):
    """worker function"""
    for i in range(10000):
      j = i **(1/3) + d.bias
    return j

class Dummy():
  def __init__(self):
#    self.bias = 1000
      self.bias = 10
  def calc(self):
    pool = Pool(processes=12)
    results = {}
    for i in range(5):
        results[i] = (pool.apply_async(*make_applicable(worker,self)))
    pool.close()
    pool.join()
    return [results[i].get() for i in range(5)]

if __name__ == '__main__':
    d=Dummy()
    print(d.calc())

然后在控制台中运行模块：

In [1]: runfile('P:/pyProjects3/mp.py', wdir='P:/pyProjects3')
[31.543628731482663, 31.543628731482663, 31.543628731482663, 31.543628731482663, 31.543628731482663]

我有 Jupyter Notebook (Anaconda) 但不知道如何使用它，如果我弄清楚了，我会更新这个答案。

于 2019-10-10T15:28:05.847 回答
0

尝试使用此代码进行测试

import pickle

def worker(d):
    """worker function"""
    d = pickle.loads(d)
    for i in range(10000):
        j = i **(1/3) + d.bias
    return j


class dummy():
    def __init__(self):
    self.bias = 1000
  def calc(self):
      pool = Pool(processes=12)
      results = {}
      for i in range(5):
          x = pickle.dumps(self)
          results[i] = (pool.apply_async(*make_applicable(worker,x)))
      pool.close()
      pool.join()
      print([results[i].get() for i in range(5)])
于 2019-10-10T13:40:39.090 回答