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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
