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<title>API Summary &#8212; ONNX Runtime 1.7.0 documentation</title>
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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><p><a class="reference internal" href="#ortvalue" id="id1">OrtValue</a></p></li>
<li><p><a class="reference internal" href="#iobinding" id="id2">IOBinding</a></p></li>
<li><p><a class="reference internal" href="#device" id="id3">Device</a></p></li>
<li><p><a class="reference internal" href="#examples-and-datasets" id="id4">Examples and datasets</a></p></li>
<li><p><a class="reference internal" href="#load-and-run-a-model" id="id5">Load and run a model</a></p></li>
<li><p><a class="reference internal" href="#backend" id="id6">Backend</a></p></li>
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</ul>
</div>
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<div class="section" id="ortvalue">
<h2><a class="toc-backref" href="#id1">OrtValue</a><a class="headerlink" href="#ortvalue" title="Permalink to this headline"></a></h2>
<p><em>ONNX Runtime</em> works with native Python data structures which are mapped into ONNX data formats :
Numpy arrays (tensors), dictionaries (maps), and a list of Numpy arrays (sequences).
The data backing these are on CPU.</p>
<p><em>ONNX Runtime</em> supports a custom data structure that supports all ONNX data formats that allows users
to place the data backing these on a device, for example, on a CUDA supported device. This allows for
interesting <em>IOBinding</em> scenarios (discussed below). In addition, <em>ONNX Runtime</em> supports directly
working with <em>OrtValue</em> (s) while inferencing a model if provided as part of the input feed.</p>
<p>Below is an example showing creation of an <em>OrtValue</em> from a Numpy array while placing its backing memory
on a CUDA device:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1">#X is numpy array on cpu, create an OrtValue and place it on cuda device id = 0</span>
<span class="n">ortvalue</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">OrtValue</span><span class="o">.</span><span class="n">ortvalue_from_numpy</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">ortvalue</span><span class="o">.</span><span class="n">device_name</span><span class="p">()</span> <span class="c1"># &#39;cuda&#39;</span>
<span class="n">ortvalue</span><span class="o">.</span><span class="n">shape</span><span class="p">()</span> <span class="c1"># shape of the numpy array X</span>
<span class="n">ortvalue</span><span class="o">.</span><span class="n">data_type</span><span class="p">()</span> <span class="c1"># &#39;tensor(float)&#39;</span>
<span class="n">ortvalue</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">()</span> <span class="c1"># &#39;True&#39;</span>
<span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">ortvalue</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">X</span><span class="p">)</span> <span class="c1"># &#39;True&#39;</span>
<span class="c1">#ortvalue can be provided as part of the input feed to a model</span>
<span class="n">ses</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">&#39;model.onnx&#39;</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="s2">&quot;Y&quot;</span><span class="p">],</span> <span class="p">{</span><span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">ortvalue</span><span class="p">})</span>
</pre></div>
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</div>
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<div class="section" id="iobinding">
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<h2><a class="toc-backref" href="#id2">IOBinding</a><a class="headerlink" href="#iobinding" title="Permalink to this headline"></a></h2>
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<p>By default, <em>ONNX Runtime</em> always places input(s) and output(s) on CPU, which
is not optimal if the input or output is consumed and produced on a device
other than CPU because it introduces data copy between CPU and the device.
<em>ONNX Runtime</em> provides a feature, <em>IO Binding</em>, which addresses this issue by
enabling users to specify which device to place input(s) and output(s) on.
Here are scenarios to use this feature.</p>
<p>(In the following code snippets, <em>model.onnx</em> is the model to execute,
<em>X</em> is the input data to feed, and <em>Y</em> is the output data.)</p>
<p>Scenario 1:</p>
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<p>A graph is executed on a device other than CPU, for instance CUDA. Users can
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use IOBinding to put input on CUDA as the follows.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1">#X is numpy array on cpu</span>
<span class="n">session</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">&#39;model.onnx&#39;</span><span class="p">)</span>
<span class="n">io_binding</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">io_binding</span><span class="p">()</span>
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<span class="c1"># OnnxRuntime will copy the data over to the CUDA device if &#39;input&#39; is consumed by nodes on the CUDA device</span>
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<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_cpu_input</span><span class="p">(</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_output</span><span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">)</span>
<span class="n">session</span><span class="o">.</span><span class="n">run_with_iobinding</span><span class="p">(</span><span class="n">io_binding</span><span class="p">)</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">io_binding</span><span class="o">.</span><span class="n">copy_outputs_to_cpu</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
<p>Scenario 2:</p>
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<p>The input data is on a device, users directly use the input. The output data is on CPU.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1">#X is numpy array on cpu</span>
<span class="n">X_ortvalue</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">OrtValue</span><span class="o">.</span><span class="n">ortvalue_from_numpy</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">session</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">&#39;model.onnx&#39;</span><span class="p">)</span>
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<span class="n">io_binding</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">io_binding</span><span class="p">()</span>
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<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_input</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">device_type</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">device_name</span><span class="p">(),</span> <span class="n">device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">shape</span><span class="p">(),</span> <span class="n">buffer_ptr</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
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<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_output</span><span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">)</span>
<span class="n">session</span><span class="o">.</span><span class="n">run_with_iobinding</span><span class="p">(</span><span class="n">io_binding</span><span class="p">)</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">io_binding</span><span class="o">.</span><span class="n">copy_outputs_to_cpu</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
<p>Scenario 3:</p>
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<p>The input data and output data are both on a device, users directly use the input and also place output on the device.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1">#X is numpy array on cpu</span>
<span class="n">X_ortvalue</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">OrtValue</span><span class="o">.</span><span class="n">ortvalue_from_numpy</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">Y_ortvalue</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">OrtValue</span><span class="o">.</span><span class="n">ortvalue_from_shape_and_type</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># Change the shape to the actual shape of the output being bound</span>
<span class="n">session</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">&#39;model.onnx&#39;</span><span class="p">)</span>
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<span class="n">io_binding</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">io_binding</span><span class="p">()</span>
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<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_input</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">device_type</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">device_name</span><span class="p">(),</span> <span class="n">device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">shape</span><span class="p">(),</span> <span class="n">buffer_ptr</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_output</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;output&#39;</span><span class="p">,</span> <span class="n">device_type</span><span class="o">=</span><span class="n">Y_ortvalue</span><span class="o">.</span><span class="n">device_name</span><span class="p">(),</span> <span class="n">device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">Y_ortvalue</span><span class="o">.</span><span class="n">shape</span><span class="p">(),</span> <span class="n">buffer_ptr</span><span class="o">=</span><span class="n">Y_ortvalue</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
<span class="n">session</span><span class="o">.</span><span class="n">run_with_iobinding</span><span class="p">(</span><span class="n">io_binding</span><span class="p">)</span>
</pre></div>
</div>
<p>Scenario 4:</p>
<p>Users can request <em>ONNX Runtime</em> to allocate an output on a device. This is particularly useful for dynamic shaped outputs.
Users can use the <em>get_outputs()</em> API to get access to the <em>OrtValue</em> (s) corresponding to the allocated output(s).
Users can thus consume the <em>ONNX Runtime</em> allocated memory for the output as an <em>OrtValue</em>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1">#X is numpy array on cpu</span>
<span class="n">X_ortvalue</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">OrtValue</span><span class="o">.</span><span class="n">ortvalue_from_numpy</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">session</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">&#39;model.onnx&#39;</span><span class="p">)</span>
<span class="n">io_binding</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">io_binding</span><span class="p">()</span>
<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_input</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">device_type</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">device_name</span><span class="p">(),</span> <span class="n">device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">shape</span><span class="p">(),</span> <span class="n">buffer_ptr</span><span class="o">=</span><span class="n">X_ortvalue</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
<span class="c1">#Request ONNX Runtime to bind and allocate memory on CUDA for &#39;output&#39;</span>
<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_output</span><span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
<span class="n">session</span><span class="o">.</span><span class="n">run_with_iobinding</span><span class="p">(</span><span class="n">io_binding</span><span class="p">)</span>
<span class="c1"># The following call returns an OrtValue which has data allocated by ONNX Runtime on CUDA</span>
<span class="n">ort_output</span> <span class="o">=</span> <span class="n">io_binding</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
</pre></div>
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<p>Scenario 5:</p>
<p>Users can bind <em>OrtValue</em> (s) directly.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1">#X is numpy array on cpu</span>
<span class="c1">#X is numpy array on cpu</span>
<span class="n">X_ortvalue</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">OrtValue</span><span class="o">.</span><span class="n">ortvalue_from_numpy</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">Y_ortvalue</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">OrtValue</span><span class="o">.</span><span class="n">ortvalue_from_shape_and_type</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># Change the shape to the actual shape of the output being bound</span>
<span class="n">session</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">&#39;model.onnx&#39;</span><span class="p">)</span>
<span class="n">io_binding</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">io_binding</span><span class="p">()</span>
<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_ortvalue_input</span><span class="p">(</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">X_ortvalue</span><span class="p">)</span>
<span class="n">io_binding</span><span class="o">.</span><span class="n">bind_ortvalue_output</span><span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">,</span> <span class="n">Y_ortvalue</span><span class="p">)</span>
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<span class="n">session</span><span class="o">.</span><span class="n">run_with_iobinding</span><span class="p">(</span><span class="n">io_binding</span><span class="p">)</span>
</pre></div>
</div>
</div>
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<div class="section" id="device">
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<h2><a class="toc-backref" href="#id3">Device</a><a class="headerlink" href="#device" title="Permalink to this headline"></a></h2>
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<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>
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<dl class="py function">
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<dt id="onnxruntime.get_device">
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<code class="sig-prename descclassname"><span class="pre">onnxruntime.</span></code><code class="sig-name descname"><span class="pre">get_device</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a><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">
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<h2><a class="toc-backref" href="#id4">Examples and datasets</a><a class="headerlink" href="#examples-and-datasets" title="Permalink to this headline"></a></h2>
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<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>
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<dl class="py function">
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<dt id="onnxruntime.datasets.get_example">
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<code class="sig-prename descclassname"><span class="pre">onnxruntime.datasets.</span></code><code class="sig-name descname"><span class="pre">get_example</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/onnxruntime/datasets.html#get_example"><span class="viewcode-link"><span class="pre">[source]</span></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">
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<h2><a class="toc-backref" href="#id5">Load and run a model</a><a class="headerlink" href="#load-and-run-a-model" title="Permalink to this headline"></a></h2>
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<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>
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<dl class="py class">
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<dt id="onnxruntime.ModelMetadata">
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<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">onnxruntime.</span></code><code class="sig-name descname"><span class="pre">ModelMetadata</span></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="py method">
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<dt id="onnxruntime.ModelMetadata.custom_metadata_map">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">custom_metadata_map</span></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="py method">
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<dt id="onnxruntime.ModelMetadata.description">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">description</span></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="py method">
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<dt id="onnxruntime.ModelMetadata.domain">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">domain</span></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="py method">
<dt id="onnxruntime.ModelMetadata.graph_description">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">graph_description</span></code><a class="headerlink" href="#onnxruntime.ModelMetadata.graph_description" title="Permalink to this definition"></a></dt>
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<dd><p>description of the graph hosted in the model</p>
</dd></dl>
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<dl class="py method">
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<dt id="onnxruntime.ModelMetadata.graph_name">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">graph_name</span></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="py method">
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<dt id="onnxruntime.ModelMetadata.producer_name">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">producer_name</span></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="py method">
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<dt id="onnxruntime.ModelMetadata.version">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">version</span></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>
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<dl class="py class">
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<dt id="onnxruntime.InferenceSession">
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<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">onnxruntime.</span></code><code class="sig-name descname"><span class="pre">InferenceSession</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_or_bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sess_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">providers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">provider_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/onnxruntime/capi/onnxruntime_inference_collection.html#InferenceSession"><span class="viewcode-link"><span class="pre">[source]</span></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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</dd></dl>
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<dl class="py class">
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<dt id="onnxruntime.NodeArg">
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<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">onnxruntime.</span></code><code class="sig-name descname"><span class="pre">NodeArg</span></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="py method">
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<dt id="onnxruntime.NodeArg.name">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">name</span></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="py method">
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<dt id="onnxruntime.NodeArg.shape">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">shape</span></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="py method">
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<dt id="onnxruntime.NodeArg.type">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">type</span></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>
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<dl class="py class">
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<dt id="onnxruntime.RunOptions">
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<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">onnxruntime.</span></code><code class="sig-name descname"><span class="pre">RunOptions</span></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="py method">
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<dt id="onnxruntime.RunOptions.log_severity_level">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">log_severity_level</span></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="py method">
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<dt id="onnxruntime.RunOptions.log_verbosity_level">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">log_verbosity_level</span></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="py method">
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<dt id="onnxruntime.RunOptions.logid">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">logid</span></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="py method">
<dt id="onnxruntime.RunOptions.only_execute_path_to_fetches">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">only_execute_path_to_fetches</span></code><a class="headerlink" href="#onnxruntime.RunOptions.only_execute_path_to_fetches" title="Permalink to this definition"></a></dt>
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<dd><p>Only execute the nodes needed by fetch list</p>
</dd></dl>
<dl class="py method">
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<dt id="onnxruntime.RunOptions.terminate">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">terminate</span></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>
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<dl class="py class">
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<dt id="onnxruntime.SessionOptions">
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<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">onnxruntime.</span></code><code class="sig-name descname"><span class="pre">SessionOptions</span></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="py method">
<dt id="onnxruntime.SessionOptions.add_free_dimension_override_by_denotation">
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<code class="sig-name descname"><span class="pre">add_free_dimension_override_by_denotation</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">onnxruntime.capi.onnxruntime_pybind11_state.SessionOptions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><span class="pre">None</span></a><a class="headerlink" href="#onnxruntime.SessionOptions.add_free_dimension_override_by_denotation" title="Permalink to this definition"></a></dt>
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<dd><p>Specify the dimension size for each denotation associated with an input’s free dimension.</p>
</dd></dl>
<dl class="py method">
<dt id="onnxruntime.SessionOptions.add_free_dimension_override_by_name">
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<code class="sig-name descname"><span class="pre">add_free_dimension_override_by_name</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">onnxruntime.capi.onnxruntime_pybind11_state.SessionOptions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><span class="pre">None</span></a><a class="headerlink" href="#onnxruntime.SessionOptions.add_free_dimension_override_by_name" title="Permalink to this definition"></a></dt>
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<dd><p>Specify values of named dimensions within model inputs.</p>
</dd></dl>
<dl class="py method">
<dt id="onnxruntime.SessionOptions.add_initializer">
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<code class="sig-name descname"><span class="pre">add_initializer</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">onnxruntime.capi.onnxruntime_pybind11_state.SessionOptions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><span class="pre">object</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><span class="pre">None</span></a><a class="headerlink" href="#onnxruntime.SessionOptions.add_initializer" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py method">
<dt id="onnxruntime.SessionOptions.add_session_config_entry">
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<code class="sig-name descname"><span class="pre">add_session_config_entry</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">onnxruntime.capi.onnxruntime_pybind11_state.SessionOptions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><span class="pre">None</span></a><a class="headerlink" href="#onnxruntime.SessionOptions.add_session_config_entry" title="Permalink to this definition"></a></dt>
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<dd><p>Set a single session configuration entry as a pair of strings.</p>
</dd></dl>
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<dl class="py method">
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<dt id="onnxruntime.SessionOptions.enable_cpu_mem_arena">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">enable_cpu_mem_arena</span></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="py method">
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<dt id="onnxruntime.SessionOptions.enable_mem_pattern">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">enable_mem_pattern</span></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="py method">
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<dt id="onnxruntime.SessionOptions.enable_profiling">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">enable_profiling</span></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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<dt id="onnxruntime.SessionOptions.execution_mode">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">execution_mode</span></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="py method">
<dt id="onnxruntime.SessionOptions.execution_order">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">execution_order</span></code><a class="headerlink" href="#onnxruntime.SessionOptions.execution_order" title="Permalink to this definition"></a></dt>
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<dd><p>Sets the execution order. Default is basic topological order.</p>
</dd></dl>
<dl class="py method">
<dt id="onnxruntime.SessionOptions.get_session_config_entry">
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<code class="sig-name descname"><span class="pre">get_session_config_entry</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">onnxruntime.capi.onnxruntime_pybind11_state.SessionOptions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a><a class="headerlink" href="#onnxruntime.SessionOptions.get_session_config_entry" title="Permalink to this definition"></a></dt>
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<dd><p>Get a single session configuration value using the given configuration key.</p>
</dd></dl>
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<dl class="py method">
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<dt id="onnxruntime.SessionOptions.graph_optimization_level">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">graph_optimization_level</span></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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<dt id="onnxruntime.SessionOptions.inter_op_num_threads">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">inter_op_num_threads</span></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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<dl class="py method">
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<dt id="onnxruntime.SessionOptions.intra_op_num_threads">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">intra_op_num_threads</span></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="py method">
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<dt id="onnxruntime.SessionOptions.log_severity_level">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">log_severity_level</span></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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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">log_verbosity_level</span></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_severity_level is 0.
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Applies to session load, initialization, etc. Default is 0.</p>
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</dd></dl>
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<dl class="py method">
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<dt id="onnxruntime.SessionOptions.logid">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">logid</span></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="py method">
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<dt id="onnxruntime.SessionOptions.optimized_model_filepath">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">optimized_model_filepath</span></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 to.
Optimized model is not serialized unless optimized_model_filepath is set.
Serialized model format will default to ONNX unless:</p>
<blockquote>
<div><ul class="simple">
<li><p>add_session_config_entry is used to set ‘session.save_model_format’ to ‘ORT’, or</p></li>
<li><p>there is no ‘session.save_model_format’ config entry and optimized_model_filepath ends in ‘.ort’ (case insensitive)</p></li>
</ul>
</div></blockquote>
</dd></dl>
<dl class="py method">
<dt id="onnxruntime.SessionOptions.profile_file_prefix">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">profile_file_prefix</span></code><a class="headerlink" href="#onnxruntime.SessionOptions.profile_file_prefix" title="Permalink to this definition"></a></dt>
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<dd><p>The prefix of the profile file. The current time will be appended to the file name.</p>
</dd></dl>
<dl class="py method">
<dt id="onnxruntime.SessionOptions.register_custom_ops_library">
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<code class="sig-name descname"><span class="pre">register_custom_ops_library</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">onnxruntime.capi.onnxruntime_pybind11_state.SessionOptions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><span class="pre">None</span></a><a class="headerlink" href="#onnxruntime.SessionOptions.register_custom_ops_library" title="Permalink to this definition"></a></dt>
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<dd><p>Specify the path to the shared library containing the custom op kernels required to run a model.</p>
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</dd></dl>
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<dl class="py method">
<dt id="onnxruntime.SessionOptions.use_deterministic_compute">
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<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">use_deterministic_compute</span></code><a class="headerlink" href="#onnxruntime.SessionOptions.use_deterministic_compute" title="Permalink to this definition"></a></dt>
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<dd><p>Whether to use deterministic compute. Default is false.</p>
</dd></dl>
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</dd></dl>
</div>
<div class="section" id="backend">
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<h2><a class="toc-backref" href="#id6">Backend</a><a class="headerlink" href="#backend" title="Permalink to this headline"></a></h2>
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<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>
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<dl class="py function">
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<dt id="onnxruntime.backend.is_compatible">
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<code class="sig-prename descclassname"><span class="pre">onnxruntime.backend.</span></code><code class="sig-name descname"><span class="pre">is_compatible</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – unused</p></li>
<li><p><strong>device</strong> – None to use the default device or a string (ex: <cite>‘CPU’</cite>)</p></li>
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</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>boolean</p>
</dd>
</dl>
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</dd></dl>
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<dl class="py function">
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<dt id="onnxruntime.backend.prepare">
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<code class="sig-prename descclassname"><span class="pre">onnxruntime.backend.</span></code><code class="sig-name descname"><span class="pre">prepare</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – ModelProto (returned by <cite>onnx.load</cite>),
string for a filename or bytes for a serialized model</p></li>
<li><p><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</p></li>
<li><p><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></p></li>
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</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><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>
</dd>
</dl>
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</dd></dl>
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<dt id="onnxruntime.backend.run">
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<code class="sig-prename descclassname"><span class="pre">onnxruntime.backend.</span></code><code class="sig-name descname"><span class="pre">run</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><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></p></li>
<li><p><strong>inputs</strong> – inputs</p></li>
<li><p><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</p></li>
<li><p><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></p></li>
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</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>predictions</p>
</dd>
</dl>
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</dd></dl>
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<dt id="onnxruntime.backend.supports_device">
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<code class="sig-prename descclassname"><span class="pre">onnxruntime.backend.</span></code><code class="sig-name descname"><span class="pre">supports_device</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.supports_device" title="Permalink to this definition"></a></dt>
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<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>
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