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   <div class="section" id="module-apache_beam.ml.inference.base">
 <span id="apache-beam-ml-inference-base-module"></span><h1>apache_beam.ml.inference.base module<a class="headerlink" href="#module-apache_beam.ml.inference.base" title="Permalink to this headline">¶</a></h1>
 <p>An extensible run inference transform.</p>
 <p>Users of this module can extend the ModelHandler class for any machine learning
 framework. A ModelHandler implementation is a required parameter of
 RunInference.</p>
 <p>The transform handles standard inference functionality, like metric
 collection, sharing model between threads, and batching elements.</p>
 <dl class="class">
 <dt id="apache_beam.ml.inference.base.PredictionResult">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.base.</code><code class="descname">PredictionResult</code><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#PredictionResult"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.PredictionResult" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#apache_beam.ml.inference.base.PredictionResult" title="apache_beam.ml.inference.base.PredictionResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.base.PredictionResult</span></code></a></p>
 <p>A NamedTuple containing both input and output
 from the inference.</p>
 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.base.ModelMetadata">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.base.</code><code class="descname">ModelMetadata</code><span class="sig-paren">(</span><em>model_id</em>, <em>model_name</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelMetadata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelMetadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 <p>Create new instance of ModelMetadata(model_id, model_name)</p>
 <dl class="attribute">
 <dt id="apache_beam.ml.inference.base.ModelMetadata.model_id">
 <code class="descname">model_id</code><a class="headerlink" href="#apache_beam.ml.inference.base.ModelMetadata.model_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unique identifier for the model. This can be
 a file path or a URL where the model can be accessed. It is used to load
 the model for inference.</p>
 </dd></dl>

 <dl class="attribute">
 <dt id="apache_beam.ml.inference.base.ModelMetadata.model_name">
 <code class="descname">model_name</code><a class="headerlink" href="#apache_beam.ml.inference.base.ModelMetadata.model_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Human-readable name for the model. This
 can be used to identify the model in the metrics generated by the
 RunInference transform.</p>
 </dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.base.ModelHandler">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.base.</code><code class="descname">ModelHandler</code><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Generic" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">typing.Generic</span></code></a></p>
 <p>Has the ability to load and apply an ML model.</p>
 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; ModelT<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.load_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads and initializes a model for processing.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[ExampleT], model: ModelT, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[PredictionT]<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of examples.</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 sequence of examples or features.</li>
 <li><strong>model</strong> – The model used to make inferences.</li>
 <li><strong>inference_args</strong> – Extra arguments for models whose inference call requires
 extra parameters.</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 Predictions.</p>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[ExampleT]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.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.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.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/base.html#ModelHandler.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.get_metrics_namespace" 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">A namespace for metrics collected by the RunInference transform.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.get_resource_hints">
 <code class="descname">get_resource_hints</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; dict<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler.get_resource_hints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.get_resource_hints" 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">Resource hints for the transform.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.batch_elements_kwargs">
 <code class="descname">batch_elements_kwargs</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; Mapping[str, Any]<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.batch_elements_kwargs" 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">kwargs suitable for beam.BatchElements.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.validate_inference_args">
 <code class="descname">validate_inference_args</code><span class="sig-paren">(</span><em>inference_args: Optional[Dict[str, Any]]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Validates inference_args passed in the inference call.</p>
 <p>Because most frameworks do not need extra arguments in their predict() call,
 the default behavior is to error out if inference_args are present.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.ModelHandler.update_model_path">
 <code class="descname">update_model_path</code><span class="sig-paren">(</span><em>model_path: Optional[str] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#ModelHandler.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.ModelHandler.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update the model paths produced by side inputs.</p>
 </dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.base.</code><code class="descname">KeyedModelHandler</code><span class="sig-paren">(</span><em>unkeyed: apache_beam.ml.inference.base.ModelHandler[~ExampleT, ~PredictionT, ~ModelT][ExampleT, PredictionT, ModelT]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#KeyedModelHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#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>A ModelHandler that takes keyed examples and returns keyed predictions.</p>
 <p>For example, if the original model is used with RunInference to take a
 PCollection[E] to a PCollection[P], this ModelHandler would take a
 PCollection[Tuple[K, E]] to a PCollection[Tuple[K, P]], making it possible
 to use the key to associate the outputs with the inputs.</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"><strong>unkeyed</strong> – An implementation of ModelHandler that does not require keys.</td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; ModelT<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#KeyedModelHandler.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.load_model" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[Tuple[KeyT, ExampleT]], model: ModelT, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[Tuple[KeyT, PredictionT]]<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#KeyedModelHandler.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[Tuple[KeyT, ExampleT]]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#KeyedModelHandler.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.get_num_bytes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.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/base.html#KeyedModelHandler.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.get_metrics_namespace" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.get_resource_hints">
 <code class="descname">get_resource_hints</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#KeyedModelHandler.get_resource_hints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.get_resource_hints" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.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/base.html#KeyedModelHandler.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.validate_inference_args">
 <code class="descname">validate_inference_args</code><span class="sig-paren">(</span><em>inference_args: Optional[Dict[str, Any]]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#KeyedModelHandler.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.KeyedModelHandler.update_model_path">
 <code class="descname">update_model_path</code><span class="sig-paren">(</span><em>model_path: Optional[str] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#KeyedModelHandler.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.KeyedModelHandler.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.base.</code><code class="descname">MaybeKeyedModelHandler</code><span class="sig-paren">(</span><em>unkeyed: apache_beam.ml.inference.base.ModelHandler[~ExampleT, ~PredictionT, ~ModelT][ExampleT, PredictionT, ModelT]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#MaybeKeyedModelHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#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>A ModelHandler that takes examples that might have keys and returns
 predictions that might have keys.</p>
 <p>For example, if the original model is used with RunInference to take a
 PCollection[E] to a PCollection[P], this ModelHandler would take either
 PCollection[E] to a PCollection[P] or PCollection[Tuple[K, E]] to a
 PCollection[Tuple[K, P]], depending on the whether the elements are
 tuples. This pattern makes it possible to associate the outputs with the
 inputs based on the key.</p>
 <p>Note that you cannot use this ModelHandler if E is a tuple type.
 In addition, either all examples should be keyed, or none of them.</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"><strong>unkeyed</strong> – An implementation of ModelHandler that does not require keys.</td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; ModelT<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#MaybeKeyedModelHandler.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.load_model" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[Union[ExampleT, Tuple[KeyT, ExampleT]]], model: ModelT, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Union[Iterable[PredictionT], Iterable[Tuple[KeyT, PredictionT]]]<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#MaybeKeyedModelHandler.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[Union[ExampleT, Tuple[KeyT, ExampleT]]]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#MaybeKeyedModelHandler.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.get_num_bytes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.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/base.html#MaybeKeyedModelHandler.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.get_metrics_namespace" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.get_resource_hints">
 <code class="descname">get_resource_hints</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#MaybeKeyedModelHandler.get_resource_hints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.get_resource_hints" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.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/base.html#MaybeKeyedModelHandler.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.validate_inference_args">
 <code class="descname">validate_inference_args</code><span class="sig-paren">(</span><em>inference_args: Optional[Dict[str, Any]]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#MaybeKeyedModelHandler.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.MaybeKeyedModelHandler.update_model_path">
 <code class="descname">update_model_path</code><span class="sig-paren">(</span><em>model_path: Optional[str] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#MaybeKeyedModelHandler.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.MaybeKeyedModelHandler.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.base.RunInference">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.base.</code><code class="descname">RunInference</code><span class="sig-paren">(</span><em>model_handler: apache_beam.ml.inference.base.ModelHandler[~ExampleT, ~PredictionT, typing.Any][ExampleT, PredictionT, Any], clock=&lt;module 'time' (built-in)&gt;, inference_args: Optional[Dict[str, Any]] = None, metrics_namespace: Optional[str] = None, *, model_metadata_pcoll: apache_beam.pvalue.PCollection[apache_beam.ml.inference.base.ModelMetadata][apache_beam.ml.inference.base.ModelMetadata] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#RunInference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.RunInference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.ptransform.PTransform</span></code></a></p>
 <p>A transform that takes a PCollection of examples (or features) for use
 on an ML model. The transform then outputs inferences (or predictions) for
 those examples in a PCollection of PredictionResults that contains the input
 examples and the output inferences.</p>
 <p>Models for supported frameworks can be loaded using a URI. Supported
 services can also be used.</p>
 <p>This transform attempts to batch examples using the beam.BatchElements
 transform. Batching can be configured using the ModelHandler.</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>model_handler</strong> – An implementation of ModelHandler.</li>
 <li><strong>clock</strong> – A clock implementing time_ns. <em>Used for unit testing.</em></li>
 <li><strong>inference_args</strong> – Extra arguments for models whose inference call requires
 extra parameters.</li>
 <li><strong>metrics_namespace</strong> – Namespace of the transform to collect metrics.</li>
 <li><strong>model_metadata_pcoll</strong> – PCollection that emits Singleton ModelMetadata
 containing model path and model name, that is used as a side input
 to the _RunInferenceDoFn.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="classmethod">
 <dt id="apache_beam.ml.inference.base.RunInference.from_callable">
 <em class="property">classmethod </em><code class="descname">from_callable</code><span class="sig-paren">(</span><em>model_handler_provider</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#RunInference.from_callable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.RunInference.from_callable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Multi-language friendly constructor.</p>
 <p>Use this constructor with fully_qualified_named_transform to
 initialize the RunInference transform from PythonCallableSource provided
 by foreign SDKs.</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>model_handler_provider</strong> – A callable object that returns ModelHandler.</li>
 <li><strong>kwargs</strong> – Keyword arguments for model_handler_provider.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.base.RunInference.expand">
 <code class="descname">expand</code><span class="sig-paren">(</span><em>pcoll: apache_beam.pvalue.PCollection[~ExampleT][ExampleT]</em><span class="sig-paren">)</span> &#x2192; apache_beam.pvalue.PCollection[~PredictionT][PredictionT]<a class="reference internal" href="_modules/apache_beam/ml/inference/base.html#RunInference.expand"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.base.RunInference.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

 </div>


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