Tutorial#

The following examples explores the training of a deep or not so deep machine learning model and the combination of pytorch and :epkg:`onnxruntime_training`.

Training
- ONNX in a torch function
- Pytorch and onnxruntime
Benchmarks and profilings
- Compares implementations of Einsum
- Profling functions while using ONNX to extend pytorch

The tutorial was tested with following version:

<<<

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

print("python {}".format(sys.version_info))
mods = [numpy, scipy, sklearn, lightgbm, xgboost,
        onnx, onnxmltools, onnxruntime, onnxcustom,
        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))

>>>

    python sys.version_info(major=3, minor=9, micro=1, releaselevel='final', serial=0)
    lightgbm      3.3.2
    mlprodict     0.8.1826
    numpy         1.22.4
    onnx          1.11.0
    onnxcustom    0.4.344
    onnxmltools   1.11.0
    onnxruntime   1.12.993+cpu
    pyquickhelper 1.11.3733
    scipy         1.8.1
    skl2onnx      1.12.999
    sklearn       1.1.1
    xgboost       1.6.1

deeponnxcustom: inference, training, deep learning, onnxruntime

Training