pyprofmem

A simple utility decorator to helps developers to see the memory usage of a determined function and its internal calls. It also provides a decorator to see the profile stats usage with memory. In this case, the cProfile implementation will be used.

usage

@profile_with_memory_usage

sort_type parameter options:

• PROFILE_SORT_CUMULATIVE
• PROFILE_SORT_CALLS
• PROFILE_SORT_STDNAME
• PROFILE_SORT_TIME

by default, the option PROFILE_SORT_CUMULATIVE will be used

from concurrent import futures
from time import sleep
 
from decorators import profile_with_memory_usage, PROFILE_SORT_STDNAME
 
 
@profile_with_memory_usage(sort_type=PROFILE_SORT_STDNAME)
def execute():
    with futures.ProcessPoolExecutor() as executor:
        l = [executor.submit(sleep, 1) for _ in range(10)]
        results = []
        for f in futures.as_completed(l):
            results.append(f.result())
 
  
if __name__ == '__main__':
    execute()

The profile_with_memory_usage decorator will print

         1955 function calls in 3.021 seconds
 
   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        3    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:102(release)
        3    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:142(__init__)
        3    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:146(__enter__)
        3    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:153(__exit__)
        3    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:159(_get_module_lock)
        3    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:173(cb)
        3    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:197(_call_with_frames_removed)
      
        ...
 
       42    0.000    0.000    0.000    0.000 {method 'rstrip' of 'str' objects}
        2    0.000    0.000    0.000    0.000 {method 'setter' of 'property' objects}
       11    0.000    0.000    0.000    0.000 {method 'update' of 'dict' objects}
 
 
Ran profile_with_memory_usage to execute
Initial RAM: 13.1 MiB
Final RAM: 13.5 MiB

@memory_usage

 
@memory_usage
def execute():
    for _ in range(10):
        pass
 
if __name__ == '__main__':
    execute()

The memory_usage decorator will print

Ran memory_usage to execute in 1.00135 seconds
Initial RAM: 12.9 MiB
Final RAM: 12.9 MiB