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onnxruntime/python/auto_examples/plot_metadata.html
Ryan Hill 5ef84bf0f8 Update to v1.1.0 (#2706)
2019-12-20 13:35:58 -08:00

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<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>Metadata</title>
<link rel="stylesheet" href="../_static/pyramid.css" type="text/css"/>
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../_static/gallery.css" type="text/css" />
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<div id="sidebar" class="col-xs-12 col-sm-3">
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<img style="margin-bottom: 0.5rem;" class="img-fluid" src="../_static/ONNX_Runtime_icon.png"/>
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<div id="searchbox" style="display: none" role="search">
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<input type="hidden" name="check_keywords" value="yes"/>
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<hr>
<div id="toc">
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-1-train-a-model-using-your-favorite-framework">Step 1: Train a model using your favorite framework</a></li>
<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format">Step 2: Convert or export the model into ONNX format</a></li>
<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime">Step 3: Load and run the model using ONNX Runtime</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../api_summary.html">API Summary</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#device">Device</a></li>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#examples-and-datasets">Examples and datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#load-and-run-a-model">Load and run a model</a></li>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#backend">Backend</a></li>
</ul>
</li>
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Gallery of examples</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="plot_pipeline.html">Draw a pipeline</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_load_and_predict.html">Load and predict with ONNX Runtime and a very simple model</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_profiling.html">Profile the execution of a simple model</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_backend.html">ONNX Runtime Backend for ONNX</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Metadata</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_dl_keras.html">ONNX Runtime for Keras</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_convert_pipeline_vectorizer.html">Train, convert and predict with ONNX Runtime</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_common_errors.html">Common errors with onnxruntime</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_train_convert_predict.html">Train, convert and predict with ONNX Runtime</a></li>
</ul>
</li>
</ul>
</div>
</div>
<div class="col-xs-12 col-sm-9">
<div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-metadata-py"><span class="std std-ref">here</span></a> to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="metadata">
<span id="sphx-glr-auto-examples-plot-metadata-py"></span><h1>Metadata<a class="headerlink" href="#metadata" title="Permalink to this headline"></a></h1>
<p>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
<em>scikit-learn</em> and converted with <em>sklearn-onnx</em>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnxruntime.datasets</span> <span class="kn">import</span> <span class="n">get_example</span>
<span class="n">example</span> <span class="o">=</span> <span class="n">get_example</span><span class="p">(</span><span class="s2">&quot;logreg_iris.onnx&quot;</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">onnx</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">onnx</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">example</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;doc_string={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">doc_string</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;domain={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">domain</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;ir_version={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">ir_version</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;metadata_props={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">metadata_props</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;model_version={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">model_version</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;producer_name={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">producer_name</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;producer_version={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">producer_version</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>doc_string=
domain=onnxml
ir_version=3
metadata_props=[]
model_version=0
producer_name=OnnxMLTools
producer_version=1.2.0.0116
</pre></div>
</div>
<p>With <em>ONNX Runtime</em>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnxruntime</span> <span class="kn">import</span> <span class="n">InferenceSession</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">InferenceSession</span><span class="p">(</span><span class="n">example</span><span class="p">)</span>
<span class="n">meta</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_modelmeta</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;custom_metadata_map={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">meta</span><span class="o">.</span><span class="n">custom_metadata_map</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;description={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">meta</span><span class="o">.</span><span class="n">description</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;domain={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">meta</span><span class="o">.</span><span class="n">domain</span><span class="p">,</span> <span class="n">meta</span><span class="o">.</span><span class="n">domain</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;graph_name={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">meta</span><span class="o">.</span><span class="n">graph_name</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;producer_name={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">meta</span><span class="o">.</span><span class="n">producer_name</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;version={}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">meta</span><span class="o">.</span><span class="n">version</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>custom_metadata_map={}
description=
domain=onnxml
graph_name=3c59201b940f410fa29dc71ea9d5767d
producer_name=OnnxMLTools
version=0
</pre></div>
</div>
<p><strong>Total running time of the script:</strong> ( 0 minutes 0.016 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-metadata-py">
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../_downloads/plot_metadata.py" download=""><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_metadata.py</span></code></a></div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../_downloads/plot_metadata.ipynb" download=""><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_metadata.ipynb</span></code></a></div>
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