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   <div class="section" id="module-apache_beam.ml.inference.tensorrt_inference">
 <span id="apache-beam-ml-inference-tensorrt-inference-module"></span><h1>apache_beam.ml.inference.tensorrt_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.tensorrt_inference" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngine">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.tensorrt_inference.</code><code class="descname">TensorRTEngine</code><span class="sig-paren">(</span><em>engine: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2ddb4d60&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngine" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Implementation of the TensorRTEngine class which handles
 allocations associated with TensorRT engine.</p>
 <p>Example Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TensorRTEngine</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
 </pre></div>
 </div>
 <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>engine</strong> – trt.ICudaEngine object that contains TensorRT engine</td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngine.get_engine_attrs">
 <code class="descname">get_engine_attrs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngine.get_engine_attrs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngine.get_engine_attrs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns TensorRT engine attributes.</p>
 </dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.tensorrt_inference.</code><code class="descname">TensorRTEngineHandlerNumPy</code><span class="sig-paren">(</span><em>min_batch_size: int, max_batch_size: int, *, inference_fn: Callable[[Sequence[numpy.ndarray], apache_beam.ml.inference.tensorrt_inference.TensorRTEngine, Optional[Dict[str, Any]]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function _default_tensorRT_inference_fn&gt;, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler" title="apache_beam.ml.inference.base.ModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.base.ModelHandler</span></code></a></p>
 <p>Implementation of the ModelHandler interface for TensorRT.</p>
 <p>Example Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
     <span class="n">TensorRTEngineHandlerNumPy</span><span class="p">(</span>
       <span class="n">min_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
       <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
       <span class="n">engine_path</span><span class="o">=</span><span class="s2">&quot;my_uri&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
 <p><strong>NOTE:</strong> This API and its implementation are under development and
 do not provide backward compatibility guarantees.</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>min_batch_size</strong> – minimum accepted batch size.</li>
 <li><strong>max_batch_size</strong> – maximum accepted batch size.</li>
 <li><strong>inference_fn</strong> – the inference function to use on RunInference calls.
 default: _default_tensorRT_inference_fn</li>
 <li><strong>kwargs</strong> – Additional arguments like ‘engine_path’ and ‘onnx_path’ are
 currently supported.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <p>See <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/">https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/</a>
 for details</p>
 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.batch_elements_kwargs">
 <code class="descname">batch_elements_kwargs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets min_batch_size and max_batch_size of a TensorRT engine.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; apache_beam.ml.inference.tensorrt_inference.TensorRTEngine<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.load_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads and initializes a TensorRT engine for processing.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.load_onnx">
 <code class="descname">load_onnx</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; Tuple[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2ddb4f70&gt;, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2ddb4c10&gt;]<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy.load_onnx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.load_onnx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads and parses an onnx model for processing.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.build_engine">
 <code class="descname">build_engine</code><span class="sig-paren">(</span><em>network: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e4684f0&gt;</em>, <em>builder: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e468f70&gt;</em><span class="sig-paren">)</span> &#x2192; apache_beam.ml.inference.tensorrt_inference.TensorRTEngine<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy.build_engine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.build_engine" title="Permalink to this definition">¶</a></dt>
 <dd><p>Build an engine according to parsed/created network.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray], engine: apache_beam.ml.inference.tensorrt_inference.TensorRTEngine, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of Tensors and returns an Iterable of
 TensorRT 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 simple">
 <li><strong>batch</strong> – A np.ndarray or a np.ndarray that represents a concatenation
 of multiple arrays as a batch.</li>
 <li><strong>engine</strong> – A TensorRT engine.</li>
 <li><strong>inference_args</strong> – Any additional arguments for an inference
 that are not applicable to TensorRT.</li>
 </ul>
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.get_num_bytes" title="Permalink to this definition">¶</a></dt>
 <dd><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">Returns:</th><td class="field-body">The number of bytes of data for a batch of Tensors.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.get_metrics_namespace">
 <code class="descname">get_metrics_namespace</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorrt_inference.html#TensorRTEngineHandlerNumPy.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy.get_metrics_namespace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a namespace for metrics collected by the RunInference transform.</p>
 </dd></dl>

 </dd></dl>

 <dl class="function">
 <dt id="apache_beam.ml.inference.tensorrt_inference.experimental">
 <code class="descclassname">apache_beam.ml.inference.tensorrt_inference.</code><code class="descname">experimental</code><span class="sig-paren">(</span><em>*</em>, <em>label='experimental'</em>, <em>since=None</em>, <em>current=None</em>, <em>extra_message=None</em>, <em>custom_message=None</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.ml.inference.tensorrt_inference.experimental" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorates an API with a deprecated or experimental annotation.</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>label</strong> – the kind of annotation (‘deprecated’ or ‘experimental’).</li>
 <li><strong>since</strong> – the version that causes the annotation.</li>
 <li><strong>current</strong> – the suggested replacement function.</li>
 <li><strong>extra_message</strong> – an optional additional message.</li>
 <li><strong>custom_message</strong> – if the default message does not suffice, the message
 can be changed using this argument. A string
 whit replacement tokens.
 A replecement string is were the previus args will
 be located on the custom message.
 The following replacement strings can be used:
 %name% -&gt; API.__name__
 %since% -&gt; since (Mandatory for the decapreted annotation)
 %current% -&gt; current
 %extra% -&gt; extra_message</li>
 </ul>
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The decorator for the API.</p>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 </div>


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