onnxruntime/api_summary.html
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<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>
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<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>
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<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_backend.html">ONNX Runtime Backend for ONNX</a></li>
<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_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>
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<div class="col-xs-12 col-sm-9">
<div class="section" id="api-summary">
<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">
<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>
</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">
<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>
<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 dont need to be downloaded
as they are installed with the package.</p>
<dl class="function">
<dt id="onnxruntime.datasets.get_example">
<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>
<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">
<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>
<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>
<dl class="attribute">
<dt id="onnxruntime.ModelMetadata.custom_metadata_map">
<code class="descname">custom_metadata_map</code><a class="headerlink" href="#onnxruntime.ModelMetadata.custom_metadata_map" title="Permalink to this definition"></a></dt>
<dd><p>additional metadata</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.ModelMetadata.description">
<code class="descname">description</code><a class="headerlink" href="#onnxruntime.ModelMetadata.description" title="Permalink to this definition"></a></dt>
<dd><p>description of the model</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.ModelMetadata.domain">
<code class="descname">domain</code><a class="headerlink" href="#onnxruntime.ModelMetadata.domain" title="Permalink to this definition"></a></dt>
<dd><p>ONNX domain</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.ModelMetadata.graph_name">
<code class="descname">graph_name</code><a class="headerlink" href="#onnxruntime.ModelMetadata.graph_name" title="Permalink to this definition"></a></dt>
<dd><p>graph name</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.ModelMetadata.producer_name">
<code class="descname">producer_name</code><a class="headerlink" href="#onnxruntime.ModelMetadata.producer_name" title="Permalink to this definition"></a></dt>
<dd><p>producer name</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.ModelMetadata.version">
<code class="descname">version</code><a class="headerlink" href="#onnxruntime.ModelMetadata.version" title="Permalink to this definition"></a></dt>
<dd><p>version of the model</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="onnxruntime.InferenceSession">
<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><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>
<dd><p>This is the main class used to run a model.</p>
<dl class="method">
<dt id="onnxruntime.InferenceSession.end_profiling">
<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>
<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">
<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>
<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">
<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>
<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">
<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>
<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>
<dl class="method">
<dt id="onnxruntime.InferenceSession.run">
<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>
<dd><p>Compute the predictions.</p>
<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>
</ul>
</td>
</tr>
</tbody>
</table>
<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>
</dd></dl>
<dl class="class">
<dt id="onnxruntime.NodeArg">
<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>
<dd><p>Node argument definition, for both input and output,
including arg name, arg type (contains both type and shape).</p>
<dl class="attribute">
<dt id="onnxruntime.NodeArg.name">
<code class="descname">name</code><a class="headerlink" href="#onnxruntime.NodeArg.name" title="Permalink to this definition"></a></dt>
<dd><p>node name</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.NodeArg.shape">
<code class="descname">shape</code><a class="headerlink" href="#onnxruntime.NodeArg.shape" title="Permalink to this definition"></a></dt>
<dd><p>node shape (assuming the node holds a tensor)</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.NodeArg.type">
<code class="descname">type</code><a class="headerlink" href="#onnxruntime.NodeArg.type" title="Permalink to this definition"></a></dt>
<dd><p>node type</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="onnxruntime.RunOptions">
<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>
<dd><p>Configuration information for a single Run.</p>
<dl class="attribute">
<dt id="onnxruntime.RunOptions.run_log_verbosity_level">
<code class="descname">run_log_verbosity_level</code><a class="headerlink" href="#onnxruntime.RunOptions.run_log_verbosity_level" title="Permalink to this definition"></a></dt>
<dd><p>Applies to a particular Run() invocation.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.RunOptions.run_tag">
<code class="descname">run_tag</code><a class="headerlink" href="#onnxruntime.RunOptions.run_tag" title="Permalink to this definition"></a></dt>
<dd><p>To identify logs generated by a particular Run() invocation.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.RunOptions.terminate">
<code class="descname">terminate</code><a class="headerlink" href="#onnxruntime.RunOptions.terminate" title="Permalink to this definition"></a></dt>
<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">
<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>
<dd><p>Configuration information for a session.</p>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.enable_cpu_mem_arena">
<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>
<dd><p>Enables the memory arena on CPU. Arena may pre-allocate memory for future usage.
Set this option to false if you dont want it. Default is True.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.enable_mem_pattern">
<code class="descname">enable_mem_pattern</code><a class="headerlink" href="#onnxruntime.SessionOptions.enable_mem_pattern" title="Permalink to this definition"></a></dt>
<dd><p>Enables the memory pattern optimization.
The idea is if the input shapes are the same, we could trace the internal memory allocation
and generate a memory pattern for future request. So next time we could just do one allocation
with a big chunk for all the internal memory allocation. Default is true.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.enable_profiling">
<code class="descname">enable_profiling</code><a class="headerlink" href="#onnxruntime.SessionOptions.enable_profiling" title="Permalink to this definition"></a></dt>
<dd><p>Enable profiling for this session. Default is false.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.enable_sequential_execution">
<code class="descname">enable_sequential_execution</code><a class="headerlink" href="#onnxruntime.SessionOptions.enable_sequential_execution" title="Permalink to this definition"></a></dt>
<dd><p>Enables sequential execution, disables parallel execution. Default is true.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.max_num_graph_transformation_steps">
<code class="descname">max_num_graph_transformation_steps</code><a class="headerlink" href="#onnxruntime.SessionOptions.max_num_graph_transformation_steps" title="Permalink to this definition"></a></dt>
<dd><p>Runs optimization steps on the execution graph. Default is 5.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.session_log_verbosity_level">
<code class="descname">session_log_verbosity_level</code><a class="headerlink" href="#onnxruntime.SessionOptions.session_log_verbosity_level" title="Permalink to this definition"></a></dt>
<dd><p>Applies to session load, initialization, etc. Default is 0.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.session_logid">
<code class="descname">session_logid</code><a class="headerlink" href="#onnxruntime.SessionOptions.session_logid" title="Permalink to this definition"></a></dt>
<dd><p>Logger id to use for session output.</p>
</dd></dl>
<dl class="attribute">
<dt id="onnxruntime.SessionOptions.session_thread_pool_size">
<code class="descname">session_thread_pool_size</code><a class="headerlink" href="#onnxruntime.SessionOptions.session_thread_pool_size" title="Permalink to this definition"></a></dt>
<dd><p>How many threads in the session thread pool. Default is 0 to let onnxruntime choose.
This parameter is unused unless <em>enable_sequential_execution</em> is false.</p>
</dd></dl>
</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">
<code class="descclassname">onnxruntime.backend.</code><code class="descname">is_compatible</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.is_compatible" title="Permalink to this definition"></a></dt>
<dd><p>Return whether the model is compatible with the backend.</p>
<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>
</ul>
</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>
</dd></dl>
<dl class="function">
<dt id="onnxruntime.backend.prepare">
<code class="descclassname">onnxruntime.backend.</code><code class="descname">prepare</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.prepare" title="Permalink to this definition"></a></dt>
<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>
<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,
None means the default one which depends on
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>
</ul>
</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>
</dd></dl>
<dl class="function">
<dt id="onnxruntime.backend.run">
<code class="descclassname">onnxruntime.backend.</code><code class="descname">run</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.run" title="Permalink to this definition"></a></dt>
<dd><p>Compute the prediction.</p>
<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,
None means the default one which depends on
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>
</ul>
</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>
</dd></dl>
<dl class="function">
<dt id="onnxruntime.backend.supports_device">
<code class="descclassname">onnxruntime.backend.</code><code class="descname">supports_device</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#onnxruntime.backend.supports_device" title="Permalink to this definition"></a></dt>
<dd><p>Check whether the backend is compiled with particular device support.
In particular its used in the testing suite.</p>
</dd></dl>
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