bma-benchmark 0.0.4

Python benchmark tool

bma-benchmark-py

Python benchmark library

Installation

pip3 install bma_benchmark

Usage

from bma_benchmark import benchmark

@benchmark(base=True)
def my_function1():
    # do some job
    1 + 1

@benchmark(name='benchmark2')
def my_function2():
    # do some job
    1 * 1

benchmark.run()
# equal to
#benchmark.run(
#    number=100, precision=3, units='s', print_result=True, sort_result='desc')

Decorator arguments

• name override benchmark name (default: function name)

• base use as the base benchmark to measure difference from (True/False)

Benchmark.run() arguments

• number number of function executions

• precision digits after comma for numbers

• units min/max/avg units (s, ms, us or ns)

• print_result automatically print results (True/False)

• sort_result sort result in ascending (asc/a) or descending(desc/d) order, None to keep unsorted

Calling sub-processes

The module can call sub-processes to measure difference between different library versions in different virtual environments or between different versions of Python interpreter.

Primary script

from bma_benchmark import benchmark

# define some local benchmarks if required
@benchmark
def bench1():
    pass

benchmark.append_sub('./sub1.py')
benchmark.run()

Secondary scripts

make sure the scripts have execution permission (chmod +x):

#!/path/to/some/other/python

from bma_benchmark import benchmark

# define benchmarks
@benchmark
def bench2():
    pass

benchmark.sub()

The secondary scripts can contain "base" argument in a decorator as well. The "sub()" method outputs benchmark results to stdout in JSON format to synchronize with the primary, so secondary benchmarks must not print anything.

Multiple benchmarks in the same script

"benchmark" is the default benchmark object. Custom objects can be created from the "Benchmark class".

from bma_benchmark import Benchmark

my_bench = Benchmark()

@my_bench
def bench():
    pass