The tutorial goes from a simple example which converts a pipeline to a more complex example involving operator not actually implemented in ONNX operators or ONNX ML operators.

The tutorial was tested with following version:


import numpy
import scipy
import sklearn
import lightgbm
import onnx
import onnxmltools
import onnxruntime
import xgboost
import skl2onnx
import mlprodict
import pyquickhelper

mods = [numpy, scipy, sklearn, lightgbm, xgboost,
        onnx, onnxmltools, onnxruntime,
        skl2onnx, mlprodict, pyquickhelper]
mods = [(m.__name__, m.__version__) for m in mods]
mx = max(len(_[0]) for _ in mods) + 1
for name, vers in sorted(mods):
    print("%s%s%s" % (name, " " * (mx - len(name)), vers))


    lightgbm      3.3.2
    mlprodict     0.8.1697
    numpy         1.21.3
    onnx          1.11.0
    onnxmltools   1.10.0
    onnxruntime   1.10.0
    pyquickhelper 1.11.3697
    scipy         1.7.1
    skl2onnx      1.11.1
    sklearn       1.0
    xgboost       1.5.2