Python性能分析工具Line_profiler

介绍

profile和line_profiler两个模块都是性能分析工具。有时候需要找到代码中运行速度较慢处或瓶颈，可以通过这两模块实现，而不再使用time计时。
line_profiler模块可以记录每行代码的运行时间和耗时百分比。

安装

输入以下命令安装即可

pip install line_profiler

使用

方法1 ：生成一个Line_profiler类（推荐）

简单版本

from line_profiler import LineProfiler

def do_stuff(numbers):
    s = sum(numbers)
    l = [numbers[i] / 43 for i in range(len(numbers))]
    m = ['hello' + str(numbers[i]) for i in range(len(numbers))]


if __name__ == '__main__':
    number = [1,2,3,4,5,6]
    p = LineProfiler()
    p_wrap = p(do_stuff)
    p_wrap(number)
    p.print_stats()    # 控制台打印相关信息
    p.dump_stats('saveName.lprof')   # 当前项目根目录下保存文件

输出结果：

"D:\Program Files\Anaconda3\envs\tensorflow2.3\python.exe" "C:/Users/admin/Desktop/xxxx/temp.py"
Timer unit: 1e-07 s

Total time: 1.08e-05 s
File: C:/Users/admin/Desktop/GPflowMPC_cui _contract - profile/temp.py
Function: do_stuff at line 193

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   193                                           def do_stuff(numbers):
   194         1         21.0     21.0     19.4      s = sum(numbers)
   195         1         45.0     45.0     41.7      l = [numbers[i] / 43 for i in range(len(numbers))]
   196         1         42.0     42.0     38.9      m = ['hello' + str(numbers[i]) for i in range(len(numbers))]

多函数调用

当你需要调用不止一个函数的时候，就可以使用add_function函数去添加额外需要监控的函数

from line_profiler import LineProfiler

def second_function():
    # just for test
    i = 5
    pass

def do_stuff(numbers):
    s = sum(numbers)
    l = [numbers[i] / 43 for i in range(len(numbers))]
    m = ['hello' + str(numbers[i]) for i in range(len(numbers))]
    for i in range(5):
        second_function()


if __name__ == '__main__':
    number = [1,2,3,4,5,6]
    p = LineProfiler()
    p.add_function(second_function)
    p_wrap = p(do_stuff)
    p_wrap(number)
    p.print_stats()
    p.dump_stats('saveName.lprof')

输出结果

"D:\Program Files\Anaconda3\envs\tensorflow2.3\python.exe" "C:/Users/admin/Desktop/xxxx/temp.py"
Timer unit: 1e-07 s

Total time: 2.4e-06 s
File: C:/Users/admin/Desktop/GPflowMPC_cui _contract - profile/temp.py
Function: second_function at line 193

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   193                                           def second_function():
   194                                               # just for test
   195         5         14.0      2.8     58.3      i = 5
   196         5         10.0      2.0     41.7      pass

Total time: 2.44e-05 s
File: C:/Users/admin/Desktop/GPflowMPC_cui _contract - profile/temp.py
Function: do_stuff at line 198

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   198                                           def do_stuff(numbers):
   199         1         22.0     22.0      9.0      s = sum(numbers)
   200         1         48.0     48.0     19.7      l = [numbers[i] / 43 for i in range(len(numbers))]
   201         1         45.0     45.0     18.4      m = ['hello' + str(numbers[i]) for i in range(len(numbers))]
   202         6         32.0      5.3     13.1      for i in range(5):
   203         5         97.0     19.4     39.8          second_function()

方法2：使用装饰器@profile

在需要检测的函数上面添加@profile装饰符号, (此时调包是调用profile 而不是line_profiler)。
在命令行中使用kernprof -l -v test.py启动，结束会在窗口打印逐行信息以及生成一个lprof文件。 与方法1 相似。
问题在于每次不在使用profile 查看性能时，需要将函数上的装饰类注释掉

读取lprof 文件: 进入当前目录后，在命令行中使用

python -m line_profiler saveName.lprof
# saveName.lprof 读取指定文件

资料来源 https://blog.csdn.net/weixin_44613728/article/details/120411325