[英]how profiling class method using IPython %lprun magic function
How can I profile a method of an object called inside a function? 如何剖析在函数内部调用的对象的方法? I am using the %lprun
magic in a jupyter notebook. 我在jupyter笔记本中使用%lprun
魔术。 Please see the following ex.py
example file: 请参阅以下ex.py
示例文件:
class foo():
def __init__(self, a=0, n=1):
self.a=a
self.n=n
def compute(self):
result = 0
for i in range(self.n):
result += self.a
return result
def my_func():
a = 1
n = 1000
my_foo = foo(a, n)
result = my_foo.compute()
print(result)
Then, from my jupyter notebook, i can profile my_func
: 然后,从我的jupyter笔记本中,我可以分析my_func
:
from ex import my_func
%lprun -f my_func my_func()
but I cannot profile my compute
method: 但我无法分析我的compute
方法:
from ex import my_func
%lprun -f my_foo.compute my_func()
Is what I want even possible? 我想要的甚至有可能吗? How would I have to fill the class method in the -f
argument for it to work? 我必须如何在-f
参数中填充类方法才能起作用?
According to the documentation , "cProfile only times explicit function calls, not special methods called because of syntax", ... so it should work. 根据文档 ,“ cProfile仅对显式函数调用进行排序,而不是由于语法而对特殊方法进行调用”,因此它应该可以工作。
A (maybe) related question that I found is here . 我发现的一个(也许)相关问题在这里 。
TL;DR: Use foo in %lprun -f foo.compute my_func()
, not my_foo as in your example. TL; DR:在%lprun -f foo.compute my_func()
使用foo , 而不是在您的示例中使用my_foo 。
Given the current example, you can profile your class and method as such: 给定当前示例,您可以按如下方式配置您的类和方法:
%load_ext line_profiler
Profile your function in which you call your class: %lprun -f my_func my_func()
, which returns: %lprun -f my_func my_func()
调用类的函数: %lprun -f my_func my_func()
,该%lprun -f my_func my_func()
返回:
Timer unit: 1e-06 s
Total time: 0.000363 s
File: <ipython-input-111-dedac733c95b>
Function: my_func at line 12
Line # Hits Time Per Hit % Time Line Contents
==============================================================
12 def my_func():
13 1 2.0 2.0 0.6 a = 1
14 1 1.0 1.0 0.3 n = 1000
15 1 4.0 4.0 1.1 my_foo = foo(a, n)
16 1 278.0 278.0 76.6 result = my_foo.compute()
17 1 78.0 78.0 21.5 print(result)
my_foo.compute()
. 然后,通过检查,您发现大部分时间都在方法my_foo.compute()
。 my_foo
is an instance of the the foo
class, so you make a further and more specific profiler call %lprun -f foo.compute my_func()
, which returns: my_foo
是foo
类的实例,因此您可以进行进一步更具体的探查器调用%lprun -f foo.compute my_func()
,该方法返回: Timer unit: 1e-06 s
Total time: 0.001566 s
File: <ipython-input-12-e96be9cf3108>
Function: compute at line 6
Line # Hits Time Per Hit % Time Line Contents
==============================================================
6 def compute(self):
7 1 3.0 3.0 0.2 result = 0
8 1001 765.0 0.8 48.9 for i in range(self.n):
9 1000 797.0 0.8 50.9 result += self.a
10 1 1.0 1.0 0.1 return result
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