.. 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:: pytb

    Traceback (most recent call last):
      File "/home/xadupre/github/sklearn-onnx/docs/examples/plot_metadata.py", line 42, in <module>
        sess = InferenceSession(example)
      File "/home/xadupre/github/onnxruntime/build/linux_cuda/Release/onnxruntime/capi/onnxruntime_inference_collection.py", line 432, in __init__
        raise e
      File "/home/xadupre/github/onnxruntime/build/linux_cuda/Release/onnxruntime/capi/onnxruntime_inference_collection.py", line 419, in __init__
        self._create_inference_session(providers, provider_options, disabled_optimizers)
      File "/home/xadupre/github/onnxruntime/build/linux_cuda/Release/onnxruntime/capi/onnxruntime_inference_collection.py", line 451, in _create_inference_session
        raise ValueError(
    ValueError: This ORT build has ['CUDAExecutionProvider', 'CPUExecutionProvider'] enabled. Since ORT 1.9, you are required to explicitly set the providers parameter when instantiating InferenceSession. For example, onnxruntime.InferenceSession(..., providers=['CUDAExecutionProvider', 'CPUExecutionProvider'], ...)




.. 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-timing

   **Total running time of the script:** (0 minutes 0.008 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>`_