.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_backend.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_backend.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_backend.py:


.. _l-example-backend-api:

ONNX Runtime Backend for ONNX
=============================

.. index:: backend

*ONNX Runtime* extends the
`onnx backend API <https://github.com/onnx/onnx/blob/main/docs/
ImplementingAnOnnxBackend.md>`_
to run predictions using this runtime.
Let's use the API to compute the prediction
of a simple logistic regression model.

.. GENERATED FROM PYTHON SOURCE LINES 20-32

.. code-block:: Python

    import skl2onnx
    import onnxruntime
    import onnx
    import sklearn
    from sklearn.datasets import load_iris
    from sklearn.linear_model import LogisticRegression
    import numpy
    from onnxruntime import get_device
    import numpy as np
    import onnxruntime.backend as backend









.. GENERATED FROM PYTHON SOURCE LINES 33-34

Let's create an ONNX graph first.

.. GENERATED FROM PYTHON SOURCE LINES 34-43

.. code-block:: Python


    data = load_iris()
    X, Y = data.data, data.target
    logreg = LogisticRegression(C=1e5).fit(X, Y)
    model = skl2onnx.to_onnx(logreg, X.astype(np.float32))
    name = "logreg_iris.onnx"
    with open(name, "wb") as f:
        f.write(model.SerializeToString())








.. GENERATED FROM PYTHON SOURCE LINES 44-45

Let's use ONNX backend API to test it.

.. GENERATED FROM PYTHON SOURCE LINES 45-56

.. code-block:: Python


    model = onnx.load(name)
    rep = backend.prepare(model)
    x = np.array(
        [[-1.0, -2.0, 5.0, 6.0], [-1.0, -2.0, -3.0, -4.0], [-1.0, -2.0, 7.0, 8.0]],
        dtype=np.float32,
    )
    label, proba = rep.run(x)
    print("label={}".format(label))
    print("probabilities={}".format(proba))





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

 .. code-block:: none

    label=[2 0 2]
    probabilities=[{0: 0.0, 1: 0.0, 2: 1.0}, {0: 1.0, 1: 1.9515885113950192e-38, 2: 0.0}, {0: 0.0, 1: 0.0, 2: 1.0}]




.. GENERATED FROM PYTHON SOURCE LINES 57-59

The device depends on how the package was compiled,
GPU or CPU.

.. GENERATED FROM PYTHON SOURCE LINES 59-61

.. code-block:: Python

    print(get_device())





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

 .. code-block:: none

    GPU




.. GENERATED FROM PYTHON SOURCE LINES 62-64

The backend can also directly load the model
without using *onnx*.

.. GENERATED FROM PYTHON SOURCE LINES 64-74

.. code-block:: Python


    rep = backend.prepare(name)
    x = np.array(
        [[-1.0, -2.0, -3.0, -4.0], [-1.0, -2.0, -3.0, -4.0], [-1.0, -2.0, -3.0, -4.0]],
        dtype=np.float32,
    )
    label, proba = rep.run(x)
    print("label={}".format(label))
    print("probabilities={}".format(proba))





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

 .. code-block:: none

    label=[0 0 0]
    probabilities=[{0: 1.0, 1: 1.9515885113950192e-38, 2: 0.0}, {0: 1.0, 1: 1.9515885113950192e-38, 2: 0.0}, {0: 1.0, 1: 1.9515885113950192e-38, 2: 0.0}]




.. GENERATED FROM PYTHON SOURCE LINES 75-78

The backend API is implemented by other frameworks
and makes it easier to switch between multiple runtimes
with the same API.

.. GENERATED FROM PYTHON SOURCE LINES 80-81

**Versions used for this example**

.. GENERATED FROM PYTHON SOURCE LINES 81-87

.. code-block:: Python


    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: 1.26.4
    scikit-learn: 1.6.dev0
    onnx:  1.17.0
    onnxruntime:  1.18.0+cu118
    skl2onnx:  1.17.0





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

   **Total running time of the script:** (0 minutes 17.934 seconds)


.. _sphx_glr_download_auto_examples_plot_backend.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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