问题描述
我编写了一个使用multiprocessing
的小python脚本(请参阅 https://stackoverflow.com/a/41875711/1878788 ).当我对其进行测试时,它会起作用:
I've written a small python script that uses multiprocessing
(See https://stackoverflow.com/a/41875711/1878788). It works when I test it:
$ ./forkiter.py
0
1
2
3
4
sum of x+1: 15
sum of 2*x: 20
sum of x*x: 30
但是当我尝试使用cProfile
对其进行概要分析时,我得到以下信息:
But when I try to profile it with cProfile
, I get the following:
$ 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
会发生什么?
这是脚本:
#!/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())
一些酸洗测试
一些研究建议我,有时对象需要可拾取的__setstate__
和__getstate__
方法.这对于某些酸洗协议很有帮助,但是在cProfile
情况下似乎无法解决问题.请参见下面的测试.
Some pickling tests
Some research suggested me that sometimes objects needed a __setstate__
and __getstate__
methods to be picklable. This helps for some pickling protocols, but this doesn't seem to solve the problem in the cProfile
case.See the tests below.
更新的脚本:
#!/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())
在没有cProfile
的情况下进行测试似乎可以:
Test without cProfile
seems OK:
$ ./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
在cProfile下进行的测试在每种酸洗协议下均失败(因此在多处理中也是如此):
The test under cProfile fails at every pickling protocol (and consequently in multiprocessing also):
$ 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
推荐答案
看来cProfile
根本不能用于多处理.
It seems cProfile
simply doesn't work with multiprocessing.
如果您乐意修改代码,仅对主流程进行配置(或为子流程添加特定的配置文件),则cProfile.run()
似乎在一定程度上可以正常工作.
If you are happy to modify the code only profile the main process (or add specific profiling for the subprocesses), cProfile.run()
seems to work to a degree.
在您的示例中,替换
exit(main())
使用
exit(cProfile.run('main()')
至少在并行函数是全局作用域函数的情况下有效,不确定像您所使用的类那样也是正确的.
That at least works if the parallel function is a global scope function, not sure that is also true for a class like in your case.
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