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.
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
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