scikit-learn to ONNX Tutorial ============================= .. index:: tutorial The tutorial goes from a simple example which converts a pipeline to a more complex example involving operator not actually implemented in :epkg:`ONNX operators` or :epkg:`ONNX ML Operators`. .. toctree:: :maxdepth: 2 tutorial_1_simple tutorial_1-5_external tutorial_2_new_converter tutorial_3_new_operator tutorial_4_complex The tutorial was tested with following version: .. runpython:: :showcode: import sys import numpy import scipy import onnx import onnxruntime import lightgbm import xgboost import sklearn import onnxconverter_common import onnxmltools import skl2onnx import pyquickhelper import mlprodict import onnxcustom print("python {}".format(sys.version_info)) mods = [numpy, scipy, sklearn, lightgbm, xgboost, onnx, onnxmltools, onnxruntime, onnxcustom, onnxconverter_common, 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("{}{}{}".format(name, " " * (mx - len(name)), vers))