https://github.com/sdpython/onnxcustom

Tutorial on how to convert machine learned models into ONNX
https://github.com/sdpython/onnxcustom

Last synced: about 4 hours ago
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Tutorial on how to convert machine learned models into ONNX

• Host: GitHub
• URL: https://github.com/sdpython/onnxcustom
• Owner: sdpython
• License: mit
• Created: 2020-06-30T11:33:46.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-03-11T20:52:31.000Z (over 1 year ago)
• Last Synced: 2024-09-05T14:04:42.391Z (28 days ago)
• Language: Python
• Homepage: http://www.xavierdupre.fr/app/onnxcustom/helpsphinx/index.html
• Size: 6.04 MB
• Stars: 15
• Watchers: 4
• Forks: 3
• Open Issues: 5
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • License: LICENSE.txt

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onnxcustom: custom ONNX

=======================

.. image:: https://raw.githubusercontent.com/sdpython/onnxcustom/master/_doc/sphinxdoc/source/_static/project_ico.png

    :width: 50

`documentation `_

Examples, tutorial on how to convert machine learned models into ONNX,

implement your own converter or runtime, or even train with ONNX / onnxruntime.

The function *check* or the command line ``python -m onnxcustom check``

checks the module is properly installed and returns processing

time for a couple of functions or simply:

::

    import onnxcustom

    onnxcustom.check()

The documentation also introduces *onnx*, *onnxruntime* for

inference and training.

The tutorial related to *scikit-learn*

has been merged into `sklearn-onnx documentation

`_.

Among the tools this package implements, you may find:

* a tool to convert NVidia Profilder logs into a dataframe,

* a SGD optimizer similar to what *scikit-learn* implements but

  based on *onnxruntime-training* and able to train an CPU and GPU,

* functions to manipulate *onnx* graph.

**Installation of onnxruntime-training**

onnxruntime-training is only available on Linux. The CPU

can be installed with the following instruction.

::

    pip install onnxruntime-training --extra-index-url https://download.onnxruntime.ai/onnxruntime_nightly_cpu.html

Versions using GPU with CUDA or ROCm are available. Check

`download.onnxruntime.ai `_

to find a specific version.

You can use it on Windows

inside WSL (Windows Linux Subsystem) or compile it for CPU:

::

    python tools\ci_build\build.py --skip_tests --build_dir .\build\Windows --config Release --build_shared_lib --build_wheel --numpy_version= --cmake_generator="Visual Studio 16 2019" --enable_training --enable_training_ops

GPU versions work better on WSL, see `Build onnxruntime on WSL (Windows Linux Subsystem)

`_.

*onnxcustom* can be installed from pypi.