Simple Function Profiler

With a limited amount of time to do performance profiling and instead of researching and using a ready made tool, this utility was written to meet a few very basic needs.

• passive profiling, rather than designing experiments, collect data as functions are being used.
• Decorator style tags to mark a function as worth profiling
• Keeping a history or record of the time it takes to run a function
• options for simple reductions along runtime history like mean, std, sum, max, min
• options to load and save history.

Usage

The history of a function is captured in an instance of the PerformanceProfiler class.

import profiler

fun_profiler = profiler.PerformanceProfiler()

To profile a function, use the time_function wrapper that belongs to the profiler,

@fun_profiler.time_function
def count_to_ten_thousand():
    for x in range(10000):
        pass

@fun_profiler.time_function
def count_to_on_million():
    for x in range(1000000):
        pass

Run the function a few times. Below we run each function 10 times. Every time a function is run the profiler keeps record of the time it took to run each function

for x in range(10):
    count_to_ten_thousand()
    count_to_on_million()

History

To print a datastructure containing the execution time of each function, you can access the history attribute of the fun profiler.

fun_profiler.history

>> {    'count_to_on_million': [0.026912450790405273,
                                0.026929140090942383,
                                0.025929927825927734,
                                0.028921127319335938,
                                0.027927398681640625,
                                0.02695941925048828,
                                0.02589893341064453,
                                0.02596569061279297,
                                0.028912067413330078,
                                0.028897762298583984],
        'count_to_ten_thousand': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

Aggregate Stats

A few aggregate stats options are provided to glimpse brief summaries of the dataset.

mean

fun_profiler.mean()
>>  {'count_to_on_million': 0.026030373573303223,
     'count_to_ten_thousand': 0.0001995086669921875}

standard deviation

fun_profiler.std()
>> {'count_to_on_million': 0.0013784160946390387,
    'count_to_ten_thousand': 0.0003990173482302267}

min

fun_profiler.min()
>> {'count_to_on_million': 0.02489495277404785,
    'count_to_ten_thousand': 0.0}

max

fun_profiler.max()
>> {'count_to_on_million': 0.026927709579467773,
 'count_to_ten_thousand': 0.000997781753540039}

IO

pandas

fun_profiler.to_dataframe()

>>   durrations function
0    0.000000   pass_n
1    0.000997   pass_n
2    0.000000   pass_n
3    0.001081   pass_n
4    0.000000   pass_n

json save

fun_profiler.to_json("function_stats.json") 

json load

new_profiler = PerformanceProfiler()
new_profiler.from_json("function_stats.json")