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 of orca. When not to use the function, you do not need to install orca separately.
Compatibility
perfbench works with Python 3.5 or higher.
Dependencies
- tqdm(4.6.1 or higher.)
- cerberus(1.1 or higher.)
- plotly(3.0.0 or higher)
- notebook(6.0 or higher.)
- ipywidgets(7.2 or higher.)
Installation
pip install perfbench
Usage
Plotting a single figure. Here is the demonstration.
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()
Plotting multiple plots on a single figure. Here is the demonstration.
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)Other Here are a few examples.
License
This software is released under the MIT License, see LICENSE.
