Metadata-Version: 2.1
Name: onnxcustom
Version: 0.2.122
Summary: Extends scikit-learn with a couple of new models, transformers, metrics, plotting.
Home-page: http://www.xavierdupre.fr/app/onnxcustom/helpsphinx/index.html
Author: Xavier Dupré
Author-email: xavier.dupre@gmail.com
License: MIT
Download-URL: https://github.com/sdpython/onnxcustom/
Keywords: onnxcustom,Xavier Dupré
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 5 - Production/Stable
Requires-Dist: fire
Requires-Dist: numpy
Requires-Dist: onnx (>=1.10.1)
Requires-Dist: scipy
Provides-Extra: all
Requires-Dist: fire ; extra == 'all'
Requires-Dist: numpy ; extra == 'all'
Requires-Dist: onnx (>=1.10.1) ; extra == 'all'
Requires-Dist: scipy ; extra == 'all'
Requires-Dist: pandas-streaming (>=0.3) ; extra == 'all'
Requires-Dist: cxxfilt ; extra == 'all'


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onnxcustom: custom ONNX
=======================

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    :width: 50

`documentation <http://www.xavierdupre.fr/app/onnxcustom/helpsphinx/index.html>`_

Tutorial on how to convert machine learned models into ONNX,
implement your own converter or runtime.
The module must be compiled to be used inplace:

::

    python setup.py build_ext --inplace

Generate the setup in subfolder ``dist``:

::

    python setup.py sdist

Generate the documentation in folder ``dist/html``:

::

    python setup.py build_sphinx

Run the unit tests:

::

    python setup.py unittests

To check style:

::

    python -m flake8 onnxcustom tests examples

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()

This tutorial has been merged into `sklearn-onnx documentation
<http://onnx.ai/sklearn-onnx/index_tutorial.html>`_.


