mirror of
https://github.com/saymrwulf/onnxruntime.git
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a0f3e30de6
* initial setup and rename "how to" to "setup" * move API to main nav * move api to main nav * add get starated, rework nav order * rename to install move mds out of install section * update api nav and home page * add install docs and python qs updates * python get started work * remove c and obj c for now * move java, python, and obj-c docs under api folder * move java api html to iframe (ugh) * remove api docs w/o details, move api text getstar * remove api docs wo detail updates get started * remvoe iframes * move eco system to main nav * fix api buttons * added more examples moved intro to ORT * fix links * fix get started titles * fix get started titles * fix more links * fix more links * more link fixes * fix nav remove inferencing and training subnav * fix top nav remove inference and training nav * fix title * fix tutorials nav hierarchy * fix python api button * add tenorflow keras example * fix quickstart toc * add imports fix spacing * fix links * update nav and python get started page * move ort training example, add coming soon for iot * update C# get started * fix spacing on quantization * Add some js get started content * fix formatting * fix typo * removed onnx-pytorch and onnx-tf * updated pip install torch and added links iot page * added pytorch tutorial heirarchy * updated web to docs soon added release blog link * add web link
142 lines
3.1 KiB
ReStructuredText
142 lines
3.1 KiB
ReStructuredText
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.. DO NOT EDIT.
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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
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.. "auto_examples/plot_metadata.py"
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.. LINE NUMBERS ARE GIVEN BELOW.
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.. only:: html
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.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_auto_examples_plot_metadata.py>`
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to download the full example code
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.. rst-class:: sphx-glr-example-title
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.. _sphx_glr_auto_examples_plot_metadata.py:
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Metadata
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========
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ONNX format contains metadata related to how the
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model was produced. It is useful when the model
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is deployed to production to keep track of which
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instance was used at a specific time.
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Let's see how to do that with a simple
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logistic regression model trained with
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*scikit-learn* and converted with *sklearn-onnx*.
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.. GENERATED FROM PYTHON SOURCE LINES 16-31
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.. code-block:: default
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from onnxruntime.datasets import get_example
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example = get_example("logreg_iris.onnx")
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import onnx
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model = onnx.load(example)
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print("doc_string={}".format(model.doc_string))
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print("domain={}".format(model.domain))
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print("ir_version={}".format(model.ir_version))
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print("metadata_props={}".format(model.metadata_props))
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print("model_version={}".format(model.model_version))
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print("producer_name={}".format(model.producer_name))
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print("producer_version={}".format(model.producer_version))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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doc_string=
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domain=onnxml
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ir_version=3
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metadata_props=[]
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model_version=0
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producer_name=OnnxMLTools
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producer_version=1.2.0.0116
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.. GENERATED FROM PYTHON SOURCE LINES 32-33
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With *ONNX Runtime*:
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.. GENERATED FROM PYTHON SOURCE LINES 33-44
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.. code-block:: default
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from onnxruntime import InferenceSession
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sess = InferenceSession(example)
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meta = sess.get_modelmeta()
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print("custom_metadata_map={}".format(meta.custom_metadata_map))
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print("description={}".format(meta.description))
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print("domain={}".format(meta.domain, meta.domain))
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print("graph_name={}".format(meta.graph_name))
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print("producer_name={}".format(meta.producer_name))
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print("version={}".format(meta.version))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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custom_metadata_map={}
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description=
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domain=onnxml
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graph_name=3c59201b940f410fa29dc71ea9d5767d
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producer_name=OnnxMLTools
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version=0
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 0.006 seconds)
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.. _sphx_glr_download_auto_examples_plot_metadata.py:
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.. only :: html
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.. container:: sphx-glr-footer
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:class: sphx-glr-footer-example
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.. container:: sphx-glr-download sphx-glr-download-python
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:download:`Download Python source code: plot_metadata.py <plot_metadata.py>`
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.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: plot_metadata.ipynb <plot_metadata.ipynb>`
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.. only:: html
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.. rst-class:: sphx-glr-signature
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
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