Tutorial

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 easy case
  • Train and deploy a scikit-learn pipeline
  • Benchmark ONNX conversion
  • What is the opset number?
  • One model, many possible conversions with options
  • Choose appropriate output of a classifier
  • Black list operators when converting
  • Issues when switching to float
  • Intermediate results and investigation
  • Store arrays in one onnx graph
  • Dataframe as an input
  • Modify the ONNX graph
  • Transfer Learning with ONNX
• Using converters from other libraries
  • Convert a pipeline with a LightGBM classifier
  • Convert a pipeline with a LightGBM regressor
  • Convert a pipeline with a XGBoost model
  • Convert a pipeline with a CatBoost classifier
• A custom converter for a custom model
  • Implement a new converter
  • Two ways to implement a converter
  • Implement a new converter using other converters
  • A new converter with options
  • Change the number of outputs by adding a parser
• Extend ONNX, extend runtime
  • Fast design with a python runtime
  • Fast runtime with onnxruntime
• Advanced scenarios
  • Tricky issue when converting CountVectorizer or TfidfVectorizer
  • TfIdf and sparse matrices
  • Converter for WOE
• Write converters for other libraries
  • Converter for pyod.models.iforest.IForest

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      3.3.4
    mlprodict     0.9.1887
    numpy         1.23.5
    onnx          1.14.0
    onnxmltools   1.11.2
    onnxruntime   1.15.0+cpu
    pyquickhelper 1.11.3776
    scipy         1.10.0
    skl2onnx      1.14.0
    sklearn       1.3.dev0
    xgboost       1.7.3