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<!DOCTYPE html>
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<title>API Summary</title>
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<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 current"><a class="current reference internal" href="#">API Summary</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#device">Device</a></li>
<li class="toctree-l2"><a class="reference internal" href="#examples-and-datasets">Examples and datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="#load-and-run-a-model">Load and run a model</a></li>
<li class="toctree-l2"><a class="reference internal" href="#backend">Backend</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Gallery of examples</a><ul>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_pipeline.html">Draw a pipeline</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/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="auto_examples/plot_profiling.html">Profile the execution of a simple model</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_backend.html">ONNX Runtime Backend for ONNX</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_metadata.html">Metadata</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_dl_keras.html">ONNX Runtime for Keras</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_convert_pipeline_vectorizer.html">Train, convert and predict with ONNX Runtime</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_common_errors.html">Common errors with onnxruntime</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_train_convert_predict.html">Train, convert and predict with ONNX Runtime</a></li>
</ul>
</li>
</ul>
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</div>
</div>
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<div class="section" id="api-summary">
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<h1>API Summary<a class="headerlink" href="#api-summary" title="Permalink to this headline"></a></h1>
<p>Summary of public functions and classes exposed
in <em>ONNX Runtime</em>.</p>
<div class="contents local topic" id="contents">
<ul class="simple">
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<li><a class="reference internal" href="#device" id="id1">Device</a></li>
<li><a class="reference internal" href="#examples-and-datasets" id="id2">Examples and datasets</a></li>
<li><a class="reference internal" href="#load-and-run-a-model" id="id3">Load and run a model</a></li>
<li><a class="reference internal" href="#backend" id="id4">Backend</a></li>
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</ul>
</div>
<div class="section" id="device">
<h2><a class="toc-backref" href="#id1">Device</a><a class="headerlink" href="#device" title="Permalink to this headline"></a></h2>
<p>The package is compiled for a specific device, GPU or CPU.
The CPU implementation includes optimizations
such as MKL (Math Kernel Libary). The following function
indicates the chosen option:</p>
<dl class="function">
<dt id="onnxruntime.get_device">
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<code class="descclassname">onnxruntime.</code><code class="descname">get_device</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; str<a class="headerlink" href="#onnxruntime.get_device" title="Permalink to this definition"></a></dt>
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<dd><p>Return the device used to compute the prediction (CPU, MKL, …)</p>
</dd></dl>
</div>
<div class="section" id="examples-and-datasets">
<h2><a class="toc-backref" href="#id2">Examples and datasets</a><a class="headerlink" href="#examples-and-datasets" title="Permalink to this headline"></a></h2>
<p>The package contains a few models stored in ONNX format
used in the documentation. These don’t need to be downloaded
as they are installed with the package.</p>
<dl class="function">
<dt id="onnxruntime.datasets.get_example">
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<code class="descclassname">onnxruntime.datasets.</code><code class="descname">get_example</code><span class="sig-paren">(</span><em>name</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/datasets.html#get_example"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.datasets.get_example" title="Permalink to this definition"></a></dt>
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<dd><p>Retrieves the absolute file name of an example.</p>
</dd></dl>
</div>
<div class="section" id="load-and-run-a-model">
<h2><a class="toc-backref" href="#id3">Load and run a model</a><a class="headerlink" href="#load-and-run-a-model" title="Permalink to this headline"></a></h2>
<p><em>ONNX Runtime</em> reads a model saved in ONNX format.
The main class <em>InferenceSession</em> wraps these functionalities
in a single place.</p>
<dl class="class">
<dt id="onnxruntime.ModelMetadata">
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<em class="property">class </em><code class="descclassname">onnxruntime.</code><code class="descname">ModelMetadata</code><a class="headerlink" href="#onnxruntime.ModelMetadata" title="Permalink to this definition"></a></dt>
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<dd><p>Pre-defined and custom metadata about the model.
It is usually used to identify the model used to run the prediction and
facilitate the comparison.</p>
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<dl class="attribute">
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<dt id="onnxruntime.ModelMetadata.custom_metadata_map">
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<code class="descname">custom_metadata_map</code><a class="headerlink" href="#onnxruntime.ModelMetadata.custom_metadata_map" title="Permalink to this definition"></a></dt>
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<dd><p>additional metadata</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.ModelMetadata.description">
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<code class="descname">description</code><a class="headerlink" href="#onnxruntime.ModelMetadata.description" title="Permalink to this definition"></a></dt>
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<dd><p>description of the model</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.ModelMetadata.domain">
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<code class="descname">domain</code><a class="headerlink" href="#onnxruntime.ModelMetadata.domain" title="Permalink to this definition"></a></dt>
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<dd><p>ONNX domain</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.ModelMetadata.graph_name">
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<code class="descname">graph_name</code><a class="headerlink" href="#onnxruntime.ModelMetadata.graph_name" title="Permalink to this definition"></a></dt>
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<dd><p>graph name</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.ModelMetadata.producer_name">
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<code class="descname">producer_name</code><a class="headerlink" href="#onnxruntime.ModelMetadata.producer_name" title="Permalink to this definition"></a></dt>
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<dd><p>producer name</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.ModelMetadata.version">
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<code class="descname">version</code><a class="headerlink" href="#onnxruntime.ModelMetadata.version" title="Permalink to this definition"></a></dt>
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<dd><p>version of the model</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="onnxruntime.InferenceSession">
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<em class="property">class </em><code class="descclassname">onnxruntime.</code><code class="descname">InferenceSession</code><span class="sig-paren">(</span><em>path_or_bytes</em>, <em>sess_options=None</em>, <em>providers=[]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession" title="Permalink to this definition"></a></dt>
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<dd><p>This is the main class used to run a model.</p>
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<dl class="method">
<dt id="onnxruntime.InferenceSession.disable_fallback">
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<code class="descname">disable_fallback</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.disable_fallback"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.disable_fallback" title="Permalink to this definition"></a></dt>
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<dd><p>Disable session.run() fallback mechanism.</p>
</dd></dl>
<dl class="method">
<dt id="onnxruntime.InferenceSession.enable_fallback">
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<code class="descname">enable_fallback</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.enable_fallback"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.enable_fallback" title="Permalink to this definition"></a></dt>
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<dd><p>Enable session.Run() fallback mechanism. If session.Run() fails due to an internal Execution Provider failure, reset the Execution Providers
enabled for this session.
If GPU is enabled, fall back to CUDAExecutionProvider.
otherwise fall back to CPUExecutionProvider.</p>
</dd></dl>
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<dl class="method">
<dt id="onnxruntime.InferenceSession.end_profiling">
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<code class="descname">end_profiling</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.end_profiling"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.end_profiling" title="Permalink to this definition"></a></dt>
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<dd><p>End profiling and return results in a file.</p>
<p>The results are stored in a filename if the option
<a class="reference internal" href="#onnxruntime.SessionOptions.enable_profiling" title="onnxruntime.SessionOptions.enable_profiling"><code class="xref py py-meth docutils literal notranslate"><span class="pre">onnxruntime.SessionOptions.enable_profiling()</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="onnxruntime.InferenceSession.get_inputs">
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<code class="descname">get_inputs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.get_inputs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.get_inputs" title="Permalink to this definition"></a></dt>
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<dd><p>Return the inputs metadata as a list of <a class="reference internal" href="#onnxruntime.NodeArg" title="onnxruntime.NodeArg"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.NodeArg</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="onnxruntime.InferenceSession.get_modelmeta">
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<code class="descname">get_modelmeta</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.get_modelmeta"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.get_modelmeta" title="Permalink to this definition"></a></dt>
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<dd><p>Return the metadata. See <a class="reference internal" href="#onnxruntime.ModelMetadata" title="onnxruntime.ModelMetadata"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.ModelMetadata</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="onnxruntime.InferenceSession.get_outputs">
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<code class="descname">get_outputs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.get_outputs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.get_outputs" title="Permalink to this definition"></a></dt>
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<dd><p>Return the outputs metadata as a list of <a class="reference internal" href="#onnxruntime.NodeArg" title="onnxruntime.NodeArg"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.NodeArg</span></code></a>.</p>
</dd></dl>
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<dl class="method">
<dt id="onnxruntime.InferenceSession.get_overridable_initializers">
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<code class="descname">get_overridable_initializers</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.get_overridable_initializers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.get_overridable_initializers" title="Permalink to this definition"></a></dt>
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<dd><p>Return the inputs (including initializers) metadata as a list of <a class="reference internal" href="#onnxruntime.NodeArg" title="onnxruntime.NodeArg"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.NodeArg</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="onnxruntime.InferenceSession.get_providers">
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<code class="descname">get_providers</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.get_providers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.get_providers" title="Permalink to this definition"></a></dt>
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<dd><p>Return list of registered execution providers.</p>
</dd></dl>
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<dl class="method">
<dt id="onnxruntime.InferenceSession.get_session_options">
<code class="descname">get_session_options</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.get_session_options"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.get_session_options" title="Permalink to this definition"></a></dt>
<dd><p>Return the session options. See <a class="reference internal" href="#onnxruntime.SessionOptions" title="onnxruntime.SessionOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.SessionOptions</span></code></a>.</p>
</dd></dl>
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<dl class="method">
<dt id="onnxruntime.InferenceSession.run">
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<code class="descname">run</code><span class="sig-paren">(</span><em>output_names</em>, <em>input_feed</em>, <em>run_options=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.run" title="Permalink to this definition"></a></dt>
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<dd><p>Compute the predictions.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>output_names</strong> – name of the outputs</li>
<li><strong>input_feed</strong> – dictionary <code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">input_name:</span> <span class="pre">input_value</span> <span class="pre">}</span></code></li>
<li><strong>run_options</strong> – See <a class="reference internal" href="#onnxruntime.RunOptions" title="onnxruntime.RunOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.RunOptions</span></code></a>.</li>
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</ul>
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</td>
</tr>
</tbody>
</table>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">output_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
</pre></div>
</div>
</dd></dl>
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<dl class="method">
<dt id="onnxruntime.InferenceSession.set_providers">
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<code class="descname">set_providers</code><span class="sig-paren">(</span><em>providers</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/onnxruntime/capi/session.html#InferenceSession.set_providers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#onnxruntime.InferenceSession.set_providers" title="Permalink to this definition"></a></dt>
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<dd><p>Register the input list of execution providers. The underlying session is re-created.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>providers</strong> – list of execution providers</td>
</tr>
</tbody>
</table>
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<p>The list of providers is ordered by Priority. For example [‘CUDAExecutionProvider’, ‘CPUExecutionProvider’] means
execute a node using CUDAExecutionProvider if capable, otherwise execute using CPUExecutionProvider.</p>
</dd></dl>
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</dd></dl>
<dl class="class">
<dt id="onnxruntime.NodeArg">
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<em class="property">class </em><code class="descclassname">onnxruntime.</code><code class="descname">NodeArg</code><a class="headerlink" href="#onnxruntime.NodeArg" title="Permalink to this definition"></a></dt>
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<dd><p>Node argument definition, for both input and output,
including arg name, arg type (contains both type and shape).</p>
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<dl class="attribute">
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<dt id="onnxruntime.NodeArg.name">
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<code class="descname">name</code><a class="headerlink" href="#onnxruntime.NodeArg.name" title="Permalink to this definition"></a></dt>
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<dd><p>node name</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.NodeArg.shape">
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<code class="descname">shape</code><a class="headerlink" href="#onnxruntime.NodeArg.shape" title="Permalink to this definition"></a></dt>
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<dd><p>node shape (assuming the node holds a tensor)</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.NodeArg.type">
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<code class="descname">type</code><a class="headerlink" href="#onnxruntime.NodeArg.type" title="Permalink to this definition"></a></dt>
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<dd><p>node type</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="onnxruntime.RunOptions">
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<em class="property">class </em><code class="descclassname">onnxruntime.</code><code class="descname">RunOptions</code><a class="headerlink" href="#onnxruntime.RunOptions" title="Permalink to this definition"></a></dt>
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<dd><p>Configuration information for a single Run.</p>
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<dl class="attribute">
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<dt id="onnxruntime.RunOptions.log_severity_level">
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<code class="descname">log_severity_level</code><a class="headerlink" href="#onnxruntime.RunOptions.log_severity_level" title="Permalink to this definition"></a></dt>
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<dd><p>Log severity level for a particular Run() invocation. 0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.RunOptions.log_verbosity_level">
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<code class="descname">log_verbosity_level</code><a class="headerlink" href="#onnxruntime.RunOptions.log_verbosity_level" title="Permalink to this definition"></a></dt>
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<dd><p>VLOG level if DEBUG build and run_log_severity_level is 0.
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Applies to a particular Run() invocation. Default is 0.</p>
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</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.RunOptions.logid">
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<code class="descname">logid</code><a class="headerlink" href="#onnxruntime.RunOptions.logid" title="Permalink to this definition"></a></dt>
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<dd><p>To identify logs generated by a particular Run() invocation.</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.RunOptions.terminate">
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<code class="descname">terminate</code><a class="headerlink" href="#onnxruntime.RunOptions.terminate" title="Permalink to this definition"></a></dt>
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<dd><p>Set to True to terminate any currently executing calls that are using this
RunOptions instance. The individual calls will exit gracefully and return an error status.</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="onnxruntime.SessionOptions">
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<em class="property">class </em><code class="descclassname">onnxruntime.</code><code class="descname">SessionOptions</code><a class="headerlink" href="#onnxruntime.SessionOptions" title="Permalink to this definition"></a></dt>
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<dd><p>Configuration information for a session.</p>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.enable_cpu_mem_arena">
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<code class="descname">enable_cpu_mem_arena</code><a class="headerlink" href="#onnxruntime.SessionOptions.enable_cpu_mem_arena" title="Permalink to this definition"></a></dt>
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<dd><p>Enables the memory arena on CPU. Arena may pre-allocate memory for future usage.
Set this option to false if you don’t want it. Default is True.</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.enable_mem_pattern">
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<code class="descname">enable_mem_pattern</code><a class="headerlink" href="#onnxruntime.SessionOptions.enable_mem_pattern" title="Permalink to this definition"></a></dt>
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<dd><p>Enable the memory pattern optimization. Default is true.</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.enable_profiling">
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<code class="descname">enable_profiling</code><a class="headerlink" href="#onnxruntime.SessionOptions.enable_profiling" title="Permalink to this definition"></a></dt>
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<dd><p>Enable profiling for this session. Default is false.</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.execution_mode">
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<code class="descname">execution_mode</code><a class="headerlink" href="#onnxruntime.SessionOptions.execution_mode" title="Permalink to this definition"></a></dt>
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<dd><p>Sets the execution mode. Default is sequential.</p>
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</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.graph_optimization_level">
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<code class="descname">graph_optimization_level</code><a class="headerlink" href="#onnxruntime.SessionOptions.graph_optimization_level" title="Permalink to this definition"></a></dt>
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<dd><p>Graph optimization level for this session.</p>
</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.inter_op_num_threads">
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<code class="descname">inter_op_num_threads</code><a class="headerlink" href="#onnxruntime.SessionOptions.inter_op_num_threads" title="Permalink to this definition"></a></dt>
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<dd><p>Sets the number of threads used to parallelize the execution of the graph (across nodes). Default is 0 to let onnxruntime choose.</p>
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</dd></dl>
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<dt id="onnxruntime.SessionOptions.intra_op_num_threads">
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<code class="descname">intra_op_num_threads</code><a class="headerlink" href="#onnxruntime.SessionOptions.intra_op_num_threads" title="Permalink to this definition"></a></dt>
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<dd><p>Sets the number of threads used to parallelize the execution within nodes. Default is 0 to let onnxruntime choose.</p>
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</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.log_severity_level">
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<code class="descname">log_severity_level</code><a class="headerlink" href="#onnxruntime.SessionOptions.log_severity_level" title="Permalink to this definition"></a></dt>
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<dd><p>Log severity level. Applies to session load, initialization, etc.
0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.</p>
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</dd></dl>
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<dt id="onnxruntime.SessionOptions.log_verbosity_level">
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<code class="descname">log_verbosity_level</code><a class="headerlink" href="#onnxruntime.SessionOptions.log_verbosity_level" title="Permalink to this definition"></a></dt>
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<dd><p>VLOG level if DEBUG build and session_log_verbosity_level is 0.
Applies to session load, initialization, etc. Default is 0.</p>
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</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.logid">
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<code class="descname">logid</code><a class="headerlink" href="#onnxruntime.SessionOptions.logid" title="Permalink to this definition"></a></dt>
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<dd><p>Logger id to use for session output.</p>
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</dd></dl>
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<dl class="attribute">
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<dt id="onnxruntime.SessionOptions.optimized_model_filepath">
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<code class="descname">optimized_model_filepath</code><a class="headerlink" href="#onnxruntime.SessionOptions.optimized_model_filepath" title="Permalink to this definition"></a></dt>
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<dd><p>File path to serialize optimized model. By default, optimized model is not serialized if optimized_model_filepath is not provided.</p>
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</dd></dl>
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</dd></dl>
</div>
<div class="section" id="backend">
<h2><a class="toc-backref" href="#id4">Backend</a><a class="headerlink" href="#backend" title="Permalink to this headline"></a></h2>
<p>In addition to the regular API which is optimized for performance and usability,
<em>ONNX Runtime</em> also implements the
<a class="reference external" href="https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md">ONNX backend API</a>
for verification of <em>ONNX</em> specification conformance.
The following functions are supported:</p>
<dl class="function">
<dt id="onnxruntime.backend.is_compatible">
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<code class="descclassname">onnxruntime.backend.</code><code class="descname">is_compatible</code><span class="sig-paren">(</span><em>model</em>, <em>device=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.is_compatible" title="Permalink to this definition"></a></dt>
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<dd><p>Return whether the model is compatible with the backend.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>model</strong> – unused</li>
<li><strong>device</strong> – None to use the default device or a string (ex: <cite>‘CPU’</cite>)</li>
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</ul>
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</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">boolean</p>
</td>
</tr>
</tbody>
</table>
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</dd></dl>
<dl class="function">
<dt id="onnxruntime.backend.prepare">
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<code class="descclassname">onnxruntime.backend.</code><code class="descname">prepare</code><span class="sig-paren">(</span><em>model</em>, <em>device=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.prepare" title="Permalink to this definition"></a></dt>
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<dd><p>Load the model and creates a <a class="reference internal" href="#onnxruntime.InferenceSession" title="onnxruntime.InferenceSession"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.InferenceSession</span></code></a>
ready to be used as a backend.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>model</strong> – ModelProto (returned by <cite>onnx.load</cite>),
string for a filename or bytes for a serialized model</li>
<li><strong>device</strong> – requested device for the computation,
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None means the default one which depends on
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the compilation settings</li>
<li><strong>kwargs</strong> – see <a class="reference internal" href="#onnxruntime.SessionOptions" title="onnxruntime.SessionOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.SessionOptions</span></code></a></li>
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</ul>
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</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#onnxruntime.InferenceSession" title="onnxruntime.InferenceSession"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.InferenceSession</span></code></a></p>
</td>
</tr>
</tbody>
</table>
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</dd></dl>
<dl class="function">
<dt id="onnxruntime.backend.run">
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<code class="descclassname">onnxruntime.backend.</code><code class="descname">run</code><span class="sig-paren">(</span><em>model</em>, <em>inputs</em>, <em>device=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.run" title="Permalink to this definition"></a></dt>
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<dd><p>Compute the prediction.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>model</strong> – <a class="reference internal" href="#onnxruntime.InferenceSession" title="onnxruntime.InferenceSession"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.InferenceSession</span></code></a> returned
by function <em>prepare</em></li>
<li><strong>inputs</strong> – inputs</li>
<li><strong>device</strong> – requested device for the computation,
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None means the default one which depends on
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the compilation settings</li>
<li><strong>kwargs</strong> – see <a class="reference internal" href="#onnxruntime.RunOptions" title="onnxruntime.RunOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">onnxruntime.RunOptions</span></code></a></li>
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</ul>
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</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">predictions</p>
</td>
</tr>
</tbody>
</table>
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</dd></dl>
<dl class="function">
<dt id="onnxruntime.backend.supports_device">
Update to v1.1.0 (#2706)
2019-12-20 21:35:58 +00:00
<code class="descclassname">onnxruntime.backend.</code><code class="descname">supports_device</code><span class="sig-paren">(</span><em>device</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.supports_device" title="Permalink to this definition"></a></dt>
First version of the documentation (#312) * clear branch * First version of the documentation on github
2019-01-11 19:08:33 +00:00
<dd><p>Check whether the backend is compiled with particular device support.
In particular it’s used in the testing suite.</p>
</dd></dl>
</div>
</div>
</div>
Update to v1.1.0 (#2706)
2019-12-20 21:35:58 +00:00
</div>
First version of the documentation (#312) * clear branch * First version of the documentation on github
2019-01-11 19:08:33 +00:00
</div>
Update to v1.1.0 (#2706)
2019-12-20 21:35:58 +00:00
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First version of the documentation (#312) * clear branch * First version of the documentation on github
2019-01-11 19:08:33 +00:00
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