perfbench 1.0.0

perfbench is a perfomance benchmarking module for Python code.

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perfbench

About

perfbench is a perfomance benchmarking module for Python code.

Feature

• 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

• ipython(6.0.0 or higher.)

• tqdm(4.6.1 or higher.)

• cerberus(1.1 or higher.)

• plotly(3.0.0 or lower)

• notebook(5.3 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.process import *


bm = Benchmark(
    datasets=[
        dict(
            stmt=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=[
        dict(
            stmt=lambda x: np.around(x),
            label='around'
        ),
        dict(
            stmt=lambda x: np.rint(x),
            label='rint'
        )
    ],
    xlabel='dataset sizes',
    title='around vs rint',
)
bm.run()
bm.plot()

plot1

Plotting multiple plots on a single figure.

Here is the demonstration.

import numpy as np
from perfbench.process import *


bm = Benchmark(
    datasets=[
        dict(
            stmt=lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float16),
            title='float16'
        ),
        dict(
            stmt=lambda n: np.random.uniform(low=-1., high=1., size=n).astype(np.float32),
            title='float32'
        ),
        dict(
            stmt=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=[
        dict(
            stmt=lambda x: np.around(x),
            label='around'
        ),
        dict(
            stmt=lambda x: np.rint(x),
            label='rint'
        )
    ],
    xlabel='dataset sizes',
    title='around vs rint',
)
bm.run()
bm.plot()

plot2

plot2

Switching between layout sizes.

import numpy as np
from perfbench.process import *


bm = Benchmark(
    datasets=[
        dict(
            stmt=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=[
        dict(
            stmt=lambda x: np.around(x),
            label='around'
        ),
        dict(
            stmt=lambda x: np.rint(x),
            label='rint'
        )
    ],
    xlabel='dataset sizes',
    title='around vs rint',
    layout_sizes=[
        dict(label='VGA', width=640, height=480),
        dict(label='SVGA', width=800, height=600),
        dict(label='XGA', width=1024, height=768),
        dict(label='HD 720p', width=1280, height=960),
    ]
)
bm.run()
bm.plot()

plot3

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.