.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_metadata.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_metadata.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_metadata.py:


Metadata
========

.. index:: metadata

ONNX format contains metadata related to how the
model was produced. It is useful when the model
is deployed to production to keep track of which
instance was used at a specific time.
Let's see how to do that with a simple
logistic regression model trained with
*scikit-learn*.

.. GENERATED FROM PYTHON SOURCE LINES 18-39

.. code-block:: default


    import skl2onnx
    import onnxruntime
    import sklearn
    import numpy
    from onnxruntime import InferenceSession
    import onnx
    from onnxruntime.datasets import get_example

    example = get_example("logreg_iris.onnx")

    model = onnx.load(example)

    print("doc_string={}".format(model.doc_string))
    print("domain={}".format(model.domain))
    print("ir_version={}".format(model.ir_version))
    print("metadata_props={}".format(model.metadata_props))
    print("model_version={}".format(model.model_version))
    print("producer_name={}".format(model.producer_name))
    print("producer_version={}".format(model.producer_version))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    doc_string=
    domain=onnxml
    ir_version=3
    metadata_props=[]
    model_version=0
    producer_name=OnnxMLTools
    producer_version=1.2.0.0116




.. GENERATED FROM PYTHON SOURCE LINES 40-41

With *ONNX Runtime*:

.. GENERATED FROM PYTHON SOURCE LINES 41-52

.. code-block:: default


    sess = InferenceSession(example)
    meta = sess.get_modelmeta()

    print("custom_metadata_map={}".format(meta.custom_metadata_map))
    print("description={}".format(meta.description))
    print("domain={}".format(meta.domain))
    print("graph_name={}".format(meta.graph_name))
    print("producer_name={}".format(meta.producer_name))
    print("version={}".format(meta.version))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    custom_metadata_map={}
    description=
    domain=onnxml
    graph_name=3c59201b940f410fa29dc71ea9d5767d
    producer_name=OnnxMLTools
    version=0




.. GENERATED FROM PYTHON SOURCE LINES 53-54

**Versions used for this example**

.. GENERATED FROM PYTHON SOURCE LINES 54-60

.. code-block:: default


    print("numpy:", numpy.__version__)
    print("scikit-learn:", sklearn.__version__)
    print("onnx: ", onnx.__version__)
    print("onnxruntime: ", onnxruntime.__version__)
    print("skl2onnx: ", skl2onnx.__version__)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    numpy: 1.23.5
    scikit-learn: 1.2.2
    onnx:  1.13.1
    onnxruntime:  1.14.1
    skl2onnx:  1.14.0





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.049 seconds)


.. _sphx_glr_download_auto_examples_plot_metadata.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_metadata.py <plot_metadata.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_metadata.ipynb <plot_metadata.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_