He escrito un pequeño script de Python que usamultiprocessing (Verhttps://stackoverflow.com/a/41875711/1878788) Funciona cuando lo pruebo:

$ ./forkiter.py
0
1
2
3
4
sum of x+1: 15
sum of 2*x: 20
sum of x*x: 30

Pero cuando trato de perfilarlo concProfile, Me sale lo siguiente:

$ python3.6 -m cProfile -o forkiter.prof ./forkiter.py
0
1
2
3
4
Traceback (most recent call last):
  File "/home/bli/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/bli/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/bli/lib/python3.6/cProfile.py", line 160, in <module>
    main()
  File "/home/bli/lib/python3.6/cProfile.py", line 153, in main
    runctx(code, globs, None, options.outfile, options.sort)
  File "/home/bli/lib/python3.6/cProfile.py", line 20, in runctx
    filename, sort)
  File "/home/bli/lib/python3.6/profile.py", line 64, in runctx
    prof.runctx(statement, globals, locals)
  File "/home/bli/lib/python3.6/cProfile.py", line 100, in runctx
    exec(cmd, globals, locals)
  File "./forkiter.py", line 71, in <module>
    exit(main())
  File "./forkiter.py", line 67, in main
    sum_tuples, results_generator))
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 699, in next
    raise value
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 385, in _handle_tasks
    put(task)
  File "/home/bli/lib/python3.6/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/home/bli/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed

¿Lo que pasa?

Aquí está el guión:

#!/usr/bin/env python3
"""This script tries to work around some limitations of multiprocessing."""

from itertools import repeat, starmap
from multiprocessing import Pool
from functools import reduce
from operator import add
from time import sleep

# Doesn't work because local functions can't be pickled:
# def make_tuple_func(funcs):
#     def tuple_func(args_list):
#         return tuple(func(args) for func, args in zip(funcs, args_list))
#     return tuple_func
#
# test_tuple_func = make_tuple_func((plus_one, double, square))

class FuncApplier(object):
    """This kind of object can be used to group functions and call them on a
    tuple of arguments."""
    __slots__ = ("funcs", )

    def __init__(self, funcs):
        self.funcs = funcs

    def __len__(self):
        return len(self.funcs)

    def __call__(self, args_list):
        return tuple(func(args) for func, args in zip(self.funcs, args_list))

    def fork_args(self, args_list):
        """Takes an arguments list and repeat them in a n-tuple."""
        return tuple(repeat(args_list, len(self)))


def sum_tuples(*tuples):
    """Element-wise sum of tuple items."""
    return tuple(starmap(add, zip(*tuples)))


# Can't define these functions in main:
# They wouldn't be pickleable.
def plus_one(x):
    return x + 1

def double(x):
    return 2 * x

def square(x):
    return x * x

def main():
    def my_generator():
        for i in range(5):
            print(i)
            yield i


    test_tuple_func = FuncApplier((plus_one, double, square))

    with Pool(processes=5) as pool:
        results_generator = pool.imap_unordered(
            test_tuple_func,
            (test_tuple_func.fork_args(args_list) for args_list in my_generator()))
        print("sum of x+1:\t%s\nsum of 2*x:\t%s\nsum of x*x:\t%s" % reduce(
            sum_tuples, results_generator))
    exit(0)

if __name__ == "__main__":
    exit(main())

Algunas pruebas de decapado

Algunas investigaciones me sugirieron que a veces los objetos necesitaban un__setstate__ y__getstate__ métodos para ser picklable. Esto ayuda para algunos protocolos de decapado, pero esto no parece resolver el problema en elcProfile caso. Ver las pruebas a continuación.

El script actualizado:

#!/usr/bin/env python3
"""This script tries to work around some limitations of multiprocessing."""

from itertools import repeat, starmap
from multiprocessing import Pool
from functools import reduce
from operator import add
from time import sleep
import pickle

# Doesn't work because local functions can't be pickled:
# def make_tuple_func(funcs):
#     def tuple_func(args_list):
#         return tuple(func(args) for func, args in zip(funcs, args_list))
#     return tuple_func
#
# test_tuple_func = make_tuple_func((plus_one, double, square))

class FuncApplier(object):
    """This kind of object can be used to group functions and call them on a
    tuple of arguments."""
    __slots__ = ("funcs", )

    def __init__(self, funcs):
        self.funcs = funcs

    def __len__(self):
        return len(self.funcs)

    def __call__(self, args_list):
        return tuple(func(args) for func, args in zip(self.funcs, args_list))

    # Attempt to make it pickleable when under cProfile (doesn't help)
    def __getstate__(self):
        return self.funcs

    def __setstate__(self, state):
        self.funcs = state

    def fork_args(self, args_list):
        """Takes an arguments list and repeat them in a n-tuple."""
        return tuple(repeat(args_list, len(self)))


def sum_tuples(*tuples):
    """Element-wise sum of tuple items."""
    return tuple(starmap(add, zip(*tuples)))


# Can't define these functions in main:
# They wouldn't be pickleable.
def plus_one(x):
    return x + 1

def double(x):
    return 2 * x

def square(x):
    return x * x

def main():
    def my_generator():
        for i in range(5):
            print(i)
            yield i


    test_tuple_func = FuncApplier((plus_one, double, square))

    print("protocol 0")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 1")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 2")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 3")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 4")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)

    with Pool(processes=5) as pool:
        results_generator = pool.imap_unordered(
            test_tuple_func,
            (test_tuple_func.fork_args(args_list) for args_list in my_generator()))
        print("sum of x+1:\t%s\nsum of 2*x:\t%s\nsum of x*x:\t%s" % reduce(
            sum_tuples, results_generator))
    exit(0)

if __name__ == "__main__":
    exit(main())

Prueba sincProfile parece bien:

$ ./forkiter.py
protocol 0
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 1
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 2
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 3
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 4
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
0
1
2
3
4
sum of x+1: 15
sum of 2*x: 20
sum of x*x: 30

La prueba bajo cProfile falla en cada protocolo de decapado (y, en consecuencia, también en multiprocesamiento):

$ python3.6 -m cProfile -o forkiter.prof ./forkiter.py
protocol 0
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 1
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 2
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 3
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 4
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
0
1
2
3
4
Traceback (most recent call last):
  File "/home/bli/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/bli/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/bli/lib/python3.6/cProfile.py", line 160, in <module>
    main()
  File "/home/bli/lib/python3.6/cProfile.py", line 153, in main
    runctx(code, globs, None, options.outfile, options.sort)
  File "/home/bli/lib/python3.6/cProfile.py", line 20, in runctx
    filename, sort)
  File "/home/bli/lib/python3.6/profile.py", line 64, in runctx
    prof.runctx(statement, globals, locals)
  File "/home/bli/lib/python3.6/cProfile.py", line 100, in runctx
    exec(cmd, globals, locals)
  File "./forkiter.py", line 105, in <module>
    exit(main())
  File "./forkiter.py", line 101, in main
    sum_tuples, results_generator))
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 699, in next
    raise value
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 385, in _handle_tasks
    put(task)
  File "/home/bli/lib/python3.6/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/home/bli/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed