Benchmark (ONNX) for MLPClassifier#

Overview#

(Source code, png, hires.png, pdf)

../_images/onnxruntime_mlp-1.png

Detailed graphs#

(Source code, png, hires.png, pdf)

../_images/onnxruntime_mlp-2.png

Configuration#

<<<

from pyquickhelper.pandashelper import df2rst
import pandas
name = os.path.join(
    __WD__, "../../onnx/results/bench_plot_onnxruntime_mlp.time.csv")
df = pandas.read_csv(name)
print(df2rst(df, number_format=4))

>>>

name

version

value

date

2020-01-28

python

3.7.2 (default, Mar 1 2019, 18:34:21) [GCC 6.3.0 20170516]

platform

linux

OS

Linux-4.9.0-8-amd64-x86_64-with-debian-9.6

machine

x86_64

processor

release

4.9.0-8-amd64

architecture

(‘64bit’, ‘’)

mlprodict

0.3

numpy

1.18.1

openblas, language=c

onnx

1.6.37

opset=12

onnxruntime

1.1.995

CPU-DNNL-MKL-ML

pandas

0.25.3

skl2onnx

1.6.994

sklearn

0.22.1

Raw results#

bench_plot_onnxruntime_mlp.csv

<<<

from pyquickhelper.pandashelper import df2rst
from pymlbenchmark.benchmark.bench_helper import bench_pivot
import pandas
name = os.path.join(
    __WD__, "../../onnx/results/bench_plot_onnxruntime_mlp.perf.csv")
df = pandas.read_csv(name)
piv = bench_pivot(df).reset_index(drop=False)
piv['speedup_py'] = piv['skl'] / piv['onxpython_compiled']
piv['speedup_ort'] = piv['skl'] / piv['onxonnxruntime1']
print(df2rst(piv, number_format=4))