onnxcustom: deploy, train machine learned models ================================================ .. image:: https://circleci.com/gh/sdpython/onnxcustom/tree/master.svg?style=svg :target: https://circleci.com/gh/sdpython/onnxcustom/tree/master .. image:: https://travis-ci.com/sdpython/onnxcustom.svg?branch=master :target: https://app.travis-ci.com/github/sdpython/onnxcustom :alt: Build status .. image:: https://ci.appveyor.com/api/projects/status/a3sn45a2fayoxb5q?svg=true :target: https://ci.appveyor.com/project/sdpython/onnxcustom :alt: Build Status Windows .. image:: https://codecov.io/gh/sdpython/onnxcustom/branch/master/graph/badge.svg :target: https://codecov.io/gh/sdpython/onnxcustom .. image:: https://badge.fury.io/py/onnxcustom.svg :target: http://badge.fury.io/py/onnxcustom .. image:: http://img.shields.io/github/issues/sdpython/onnxcustom.png :alt: GitHub Issues :target: https://github.com/sdpython/onnxcustom/issues .. image:: https://img.shields.io/badge/license-MIT-blue.svg :alt: MIT License :target: http://opensource.org/licenses/MIT .. image:: https://pepy.tech/badge/onnxcustom/month :target: https://pepy.tech/project/onnxcustom/month :alt: Downloads .. image:: https://img.shields.io/github/forks/sdpython/onnxcustom.svg :target: https://github.com/sdpython/onnxcustom/ :alt: Forks .. image:: https://img.shields.io/github/stars/sdpython/onnxcustom.svg :target: https://github.com/sdpython/onnxcustom/ :alt: Stars .. image:: https://img.shields.io/github/repo-size/sdpython/onnxcustom :target: https://github.com/sdpython/onnxcustom/ :alt: size Examples, tutorial on how to convert machine learned models into ONNX, implement your own converter or runtime, or even train with :epkg:`ONNX`, :epkg:`onnxruntime`. The documentation introduces :epkg:`onnx`, :epkg:`onnxruntime` for inference and training. It implements training classes following :epkg:`scikit-learn` based on :epkg:`onnxruntime-training` enabling training linear models, neural networks on CPU or GPU. It implements tools to manipulate logs produced NVidia Profiler logs (:func:`convert_trace_to_json `), tools to manipulate :epkg:`onnx` graphs. Section :ref:`l-apis` summarizes APIs for :epkg:`onnx`, :epkg:`onnxruntime`, and this package. Section :ref:`l-tutorials` explains the logic behind :epkg:`onnx`, :epkg:`onnxruntime` and this package. It guides the user through all the examples this documentation contains. **Contents** .. toctree:: :maxdepth: 1 installation tutorials/index api/apis gyexamples/index all_notebooks other_pages blog/blogindex Sources are available on `github/onnxcustom `_. Package is available on `pypi `_, :ref:`l-README`, and a blog for unclassified topics :ref:`blog `. The tutorial related to :epkg:`scikit-learn` has been merged into `sklearn-onnx documentation `_. This package supports ONNX opsets to the latest opset stored in `onnxcustom.__max_supported_opset__` which is: .. runpython:: :showcode: import onnxcustom print(onnxcustom.__max_supported_opset__) Any opset beyond that value is not supported and could fail. That's for the main set of ONNX functions or domain. Converters for :epkg:`scikit-learn` requires another domain, `'ai.onnxml'` to implement tree. Latest supported options are defined here: .. runpython:: :showcode: import pprint import onnxcustom pprint.pprint(onnxcustom.__max_supported_opsets__) +----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+ | :ref:`l-modules` | :ref:`l-functions` | :ref:`l-classes` | :ref:`l-methods` | :ref:`l-staticmethods` | :ref:`l-properties` | +----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+ | :ref:`modindex` | :ref:`l-EX2` | :ref:`search` | :ref:`l-license` | :ref:`l-changes` | :ref:`l-README` | +----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+ | :ref:`genindex` | :ref:`l-FAQ2` | :ref:`l-notebooks` | :ref:`l-NB2` | :ref:`l-statcode` | `Unit Test Coverage `_ | +----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+