penchmark 0.0.1

Python code benchmark library

Python benchmark library

Installation

pip install penchmark
pip install penchmark[charts]

Example

from penchmark import benchmark_and_print, Callee, InData

callees = (
    Callee(callee_name='mul', callee=lambda x: x[0] * x[1]),
    Callee(callee_name='nop', callee=lambda x: x)
)
dataset = (
    InData(name='small-data', data=(2, 1), count_of_call=100000),
    InData(name='big-data', data=(200, 10), count_of_call=1000),
    InData(name='skipped-data', data=(1, 1), count_of_call=0)
)
benchmark_and_print(callees, dataset)

or

from penchmark import benchmark_and_print

callees = (
    ('mul', lambda x: x[0] * x[1]),
    ('nop', lambda x: x)
)
dataset = (
    ('small-data', (2, 1), 100000),
    ('big-data', (200, 10), 1000),
    ('skipped-data', (1, 1), 0)
)
benchmark_and_print(callees, dataset)

or

from penchmark import benchmark_and_print

def mul(x): return x[0] * x[1]
def nop(x): return x

dataset = (
    ('small-data', (2, 1), 100000),
    ('big-data', (200, 10), 1000),
    ('skipped-data', (1, 1), 0)
)
benchmark_and_print((mul, nop), dataset)

Markdown result

small-data

callee_name	elapsed	ratio
nop	0.0050	1.0000
mul	0.0080	1.5842

big-data

callee_name	elapsed	ratio
nop	0.0000	1.0000
mul	0.0001	1.7201

Summary

callee_name	mean	median
nop	1.0000	1.0000
mul	1.6521	1.6521

or

...
benchmark_and_print((mul, nop), dataset, markdown=False)

Result

SMALL-DATA

callee_name      elapsed    ratio
-------------  ---------  -------
nop               0.0050   1.0000
mul               0.0079   1.5944

BIG-DATA

callee_name      elapsed    ratio
-------------  ---------  -------
nop               0.0001   1.0000
mul               0.0001   1.7565

SUMMARY

callee_name      mean    median
-------------  ------  --------
nop            1.0000    1.0000
mul            1.6754    1.6754