@profile def allocate_memory(n, count): The @profile decorator is applied to the allocate_memory function to enable memory profiling. large_string = "a" * (n * n) A large string is created in each iteration, causing memory usage to increase. $ python -m memory_profiler mem_prof.py ...
rgc@rgc:~/baidu_eye/carrier/test$ python -m memory_profiler memory_profiler_test.py Filename: memory_profiler_test.py Line# Mem usage Increment Line Contents===21.492MiB21.492MiB @profiledeftest1():21.492MiB0.000MiB c=021.492MiB0.000MiBforiteminxrange(100000):21.492MiB0.000MiB c+=121.492MiB0.00...
time.sleep(1)returnbfrommemory_profilerimportmemory_usageprintmemory_usage((f, (2,), {'n': int(1e6)})) 这里执行了f(1, n=int(1e6)),并返回在执行此函数时的内存消耗。 回到顶部 9. 优化实例 对比str & int from datetime import datetime @profile def my_func(): beg=datetime.now() a={...
memory_profiler的输出将显示调用create_leak时的内存使用情况。例如,可能形如: Line # Mem usage Increment Occurrences Line Contents === 7 16.6 MiB 16.6 MiB 1 @profile 8 def create_leak(self): 9 16.6 MiB 0.0 MiB 1 """模拟内存泄漏""" 10 16.6 MiB 0.0 MiB 1 for _ in range(10000): 11 ...
%%file demo.py from memory_profiler import profile @profile def addition(): a = [1] * (10 ** 1) b = [2] * (3 * 10 ** 2) sum = a+b return sum 现在,我们可以调用该函数 from demo import addition %memit addition() #Output Line # Mem usage Increment Line Contents === 2 36....
python-mmemory_profiler your_script.py 1. 你应该会看到类似如下的输出: Line # Mem usage Increment Occurrences Line Contents === 4 14.2 MiB 14.2 MiB 1 @profile 5 def memory_leak_example(): 6 14.2 MiB 0.0 MiB 1 a = [] 7 14.2 MiB 0.0 MiB 1 for i ...
python -m memory_profiler memory_profiler_test.py windows: 直接运行py文件就行 log文件中是这样的: Filename: memory_profiler_test.py Line # Mem usage Increment Line Contents === 3 @profile 4 5.97 MB 0.00 MB def my_func(): 5 13.61 MB 7.64 MB a = [1] * (10 ** 6) 6 166.20 MB...
pip install -U memory_profiler 然后用profile修饰想要查看的函数名:如: @profiledefmy_func(): a = [1] * (10**6) b = [2] * (2*10**7)delbreturnaif__name__ =='__main__': my_func() AI代码助手复制代码 输出结果: Line # Mem usage Increment Line Contents ...
To create a report that combines memory usage of all the children and the parent, use theinclude-childrenflag in either theprofiledecorator or as a command line argument tomprof: mprof run --include-children The second method tracks each child independently of the main process, serializing ...
@profile def my_func(): a = [1] * (10 ** 6) b = [2] * (2 * 10 ** 7) del b return a if __name__ == '__main__': my_func() 执行结果: #python -m memory_profiler example.py Filename: example.py Line # Mem usage Increment Line Contents === 1 10.539 MiB 0.000 Mi...