我目前正在学习如何使用cProfile
,但我有一些疑问。
我目前正在尝试分析以下脚本:
import time
def fast():
print("Fast!")
def slow():
time.sleep(3)
print("Slow!")
def medium():
time.sleep(0.5)
print("Medium!")
fast()
slow()
medium()
我执行命令python -m cProfile test_cprofile.py
,结果如下:
Fast!
Slow!
Medium!
7 function calls in 3.504 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 3.504 3.504 test_cprofile.py:1(<module>)
1 0.000 0.000 0.501 0.501 test_cprofile.py:10(medium)
1 0.000 0.000 0.000 0.000 test_cprofile.py:3(fast)
1 0.000 0.000 3.003 3.003 test_cprofile.py:6(slow)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
2 3.504 1.752 3.504 1.752 {time.sleep}
但是,当我使用顶部的pylab导入(import pylab
)编辑脚本时,cProfile
的输出非常大。我尝试使用python -m cProfile test_cprofile.py | head -n 10
限制行数,但是收到以下错误:
Traceback (most recent call last):
File "/home/user/anaconda/lib/python2.7/runpy.py", line 162, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/home/user/anaconda/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/home/user/anaconda/lib/python2.7/cProfile.py", line 199, in <module>
main()
File "/home/user/anaconda/lib/python2.7/cProfile.py", line 192, in main
runctx(code, globs, None, options.outfile, options.sort)
File "/home/user/anaconda/lib/python2.7/cProfile.py", line 56, in runctx
result = prof.print_stats(sort)
File "/home/user/anaconda/lib/python2.7/cProfile.py", line 81, in print_stats
pstats.Stats(self).strip_dirs().sort_stats(sort).print_stats()
File "/home/user/anaconda/lib/python2.7/pstats.py", line 360, in print_stats
self.print_line(func)
File "/home/user/anaconda/lib/python2.7/pstats.py", line 438, in print_line
print >> self.stream, c.rjust(9),
IOError: [Errno 32] Broken pipe
有人可以帮助解决与这种情况类似的正确程序吗?在这种情况下,我们有一个import pylab
或另一个模块会在cProfile
上生成如此高的输出信息?
我不知道如何通过直接从命令行运行cProfile
模块来执行选择性分析,就像您想要的一样。
但是,您可以通过修改代码以显式import
该模块来完成此操作,但是您必须自己做所有事情。这是对示例代码的处理方式:
((注:以下代码与Python 2和3兼容。)
from cProfile import Profile
from pstats import Stats
prof = Profile()
prof.disable() # i.e. don't time imports
import time
prof.enable() # profiling back on
def fast():
print("Fast!")
def slow():
time.sleep(3)
print("Slow!")
def medium():
time.sleep(0.5)
print("Medium!")
fast()
slow()
medium()
prof.disable() # don't profile the generation of stats
prof.dump_stats('mystats.stats')
with open('mystats_output.txt', 'wt') as output:
stats = Stats('mystats.stats', stream=output)
stats.sort_stats('cumulative', 'time')
stats.print_stats()
[mystats_output.txt
文件之后的内容:
Sun Aug 02 16:55:38 2015 mystats.stats
6 function calls in 3.522 seconds
Ordered by: cumulative time, internal time
ncalls tottime percall cumtime percall filename:lineno(function)
2 3.522 1.761 3.522 1.761 {time.sleep}
1 0.000 0.000 3.007 3.007 cprofile-with-imports.py:15(slow)
1 0.000 0.000 0.515 0.515 cprofile-with-imports.py:19(medium)
1 0.000 0.000 0.000 0.000 cprofile-with-imports.py:12(fast)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
更新:
您可以使用Profile
方法派生您自己的context manager类来使事情自动化,从而使启用配置文件更加容易。我已经实现了它,而不是添加一个像enable_profiling()
这样的名称的方法,以便您可以在with
语句中调用类实例。每当退出with
语句控制的上下文时,分析将自动关闭。
这里是课程:
with
使用它代替库存from contextlib import contextmanager
from cProfile import Profile
from pstats import Stats
class Profiler(Profile):
""" Custom Profile class with a __call__() context manager method to
enable profiling.
"""
def __init__(self, *args, **kwargs):
super(Profile, self).__init__(*args, **kwargs)
self.disable() # Profiling initially off.
@contextmanager
def __call__(self):
self.enable()
yield # Execute code to be profiled.
self.disable()
对象看起来像这样:
Profile
因为它是profiler = Profiler() # Create class instance.
import time # Import won't be profiled since profiling is initially off.
with profiler(): # Call instance to enable profiling.
def fast():
print("Fast!")
def slow():
time.sleep(3)
print("Slow!")
def medium():
time.sleep(0.5)
print("Medium!")
fast()
slow()
medium()
profiler.dump_stats('mystats.stats') # Stats output generation won't be profiled.
with open('mystats_output.txt', 'wt') as output:
stats = Stats('mystats.stats', stream=output)
stats.strip_dirs().sort_stats('cumulative', 'time')
stats.print_stats()
# etc...
子类,所以所有基类的方法,例如Profile
都仍然可以使用,如图所示。
当然,您可以将其进一步加上例如一种生成统计信息并以自定义方式格式化它们的方法。
如果您稍稍更改脚本,则无需对导入进行概要分析就可以更轻松地分析脚本。
dump_stats()
import time
import pylab
def fast():
print("Fast!")
def slow():
time.sleep(3)
print("Slow!")
def medium():
time.sleep(0.5)
print("Medium!")
def main():
fast()
slow()
medium()
if __name__ == "__main__":
main()
运行方式:
import cProfile
import test_cprofiler
cProfile.run("test_cprofiler.main()")
将产生以下输出:
python profiler.py