perfbench is a perfomance benchmarking module for Python code.
Project description
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perfbench
About
perfbench measures execution time of code snippets with Timeit and uses Plotly to visualize the results.
Feature
It is possible to select measurement modes.
It is possible to switch between layout sizes dynamically.
It is possible to switch between axes scales dynamically.
It is possible to switch between subplots dynamically.
The result of the benchmark can be saved locally as a html.
The result of the benchmark can be saved locally as a png. Requires installation oforca. When not to use the function, you do not need to install orca separately.
Compatibility
perfbench works with Python 3.3 or higher.
Dependencies
Installation
pip install perfbench
Usage
import numpy as np
from perfbench import *
bm = Benchmark(
datasets=[
Dataset(
factories=[
lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float64),
],
title='float64'
)
],
dataset_sizes=[2 ** n for n in range(26)],
kernels=[
Kernel(
stmt='np.around(DATASET)',
setup='import numpy as np',
label='around'
),
Kernel(
stmt='np.rint(DATASET)',
setup='import numpy as np',
label='rint'
)
],
xlabel='dataset sizes',
title='around vs rint',
)
bm.run()
bm.plot()
import numpy as np
from perfbench import *
bm = Benchmark(
datasets=[
Dataset(
factories=[
lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float16),
],
title='float16'
),
Dataset(
factories=[
lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float32),
],
title='float32'
),
Dataset(
factories=[
lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float64),
],
title='float64'
)
],
dataset_sizes=[2 ** n for n in range(26)],
kernels=[
Kernel(
stmt='np.around(DATASET)',
setup='import numpy as np',
label='around'
),
Kernel(
stmt='np.rint(DATASET)',
setup='import numpy as np',
label='rint'
),
],
xlabel='dataset sizes',
title='around vs rint',
)
bm.run()
bm.plot()
Switching between layout sizes.
import numpy as np
from perfbench import *
bm = Benchmark(
datasets=[
Dataset(
factories=[
lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float64),
],
title='float64'
)
],
dataset_sizes=[2 ** n for n in range(26)],
kernels=[
Kernel(
stmt='np.around(DATASET)',
setup='import numpy as np',
label='around'
),
Kernel(
stmt='np.rint(DATASET)',
setup='import numpy as np',
label='rint'
)
],
xlabel='dataset sizes',
title='around vs rint',
layout_sizes=[
LayoutSize(width=640, height=480, label='VGA'),
LayoutSize(width=800, height=600, label='SVGA'),
LayoutSize(width=1024, height=768, label='XGA'),
LayoutSize(width=1280, height=960, label='HD 720p'),
]
)
bm.run()
bm.plot()
Save as a html.
# same as above
bm.save_as_html(filepath='/path/to/file')
Save as a png.
# same as above
bm.save_as_png(filepath='/path/to/file', width=1280, height=960)
License
This software is released under the MIT License, see LICENSE.
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