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   <div class="section" id="module-apache_beam.ml.inference.sklearn_inference">
 <span id="apache-beam-ml-inference-sklearn-inference-module"></span><h1>apache_beam.ml.inference.sklearn_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.sklearn_inference" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.sklearn_inference.</code><code class="descname">SklearnModelHandlerNumpy</code><span class="sig-paren">(</span><em>model_uri: str, model_file_type: apache_beam.ml.inference.sklearn_inference.ModelFileType = &lt;ModelFileType.PICKLE: 1&gt;, *, inference_fn: Callable[[sklearn.base.BaseEstimator, Sequence[numpy.ndarray], Optional[Dict[str, Any]]], Any] = &lt;function _default_numpy_inference_fn&gt;, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, max_batch_duration_secs: Optional[int] = None, large_model: bool = False, model_copies: Optional[int] = None, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy" 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 scikit-learn
 using numpy arrays as input.</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">SklearnModelHandlerNumpy</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="s2">&quot;my_uri&quot;</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"><ul class="first last simple">
 <li><strong>model_uri</strong> – The URI to where the model is saved.</li>
 <li><strong>model_file_type</strong> – The method of serialization of the argument.
 default=pickle</li>
 <li><strong>inference_fn</strong> – The inference function to use.
 default=_default_numpy_inference_fn</li>
 <li><strong>min_batch_size</strong> – the minimum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Numpy
 ndarrays.</li>
 <li><strong>max_batch_size</strong> – the maximum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Numpy
 ndarrays.</li>
 <li><strong>max_batch_duration_secs</strong> – the maximum amount of time to buffer a batch
 before emitting; used in streaming contexts.</li>
 <li><strong>large_model</strong> – set to true if your model is large enough to run into
 memory pressure if you load multiple copies. Given a model that
 consumes N memory and a machine with W cores and M memory, you should
 set this to True if N*W &gt; M.</li>
 <li><strong>model_copies</strong> – The exact number of models that you would like loaded
 onto your machine. This can be useful if you exactly know your CPU or
 GPU capacity and want to maximize resource utilization.</li>
 <li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
 before loading the model.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; sklearn.base.BaseEstimator<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.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.sklearn_inference.SklearnModelHandlerNumpy.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/sklearn_inference.html#SklearnModelHandlerNumpy.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray], model: sklearn.base.BaseEstimator, 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/sklearn_inference.html#SklearnModelHandlerNumpy.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of numpy arrays.</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 as numpy arrays. They should
 be single examples.</li>
 <li><strong>model</strong> – A numpy model or pipeline. Must implement predict(X).
 Where the parameter X is a numpy array.</li>
 <li><strong>inference_args</strong> – Any additional arguments for an inference.</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.sklearn_inference.SklearnModelHandlerNumpy.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/sklearn_inference.html#SklearnModelHandlerNumpy.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.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.sklearn_inference.SklearnModelHandlerNumpy.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/sklearn_inference.html#SklearnModelHandlerNumpy.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.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.sklearn_inference.SklearnModelHandlerNumpy.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/sklearn_inference.html#SklearnModelHandlerNumpy.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.share_model_across_processes">
 <code class="descname">share_model_across_processes</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; bool<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.share_model_across_processes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.share_model_across_processes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.model_copies">
 <code class="descname">model_copies</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.model_copies"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.model_copies" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.sklearn_inference.</code><code class="descname">SklearnModelHandlerPandas</code><span class="sig-paren">(</span><em>model_uri: str, model_file_type: apache_beam.ml.inference.sklearn_inference.ModelFileType = &lt;ModelFileType.PICKLE: 1&gt;, *, inference_fn: Callable[[sklearn.base.BaseEstimator, Sequence[pandas.core.frame.DataFrame], Optional[Dict[str, Any]]], Any] = &lt;function _default_pandas_inference_fn&gt;, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, max_batch_duration_secs: Optional[int] = None, large_model: bool = False, model_copies: Optional[int] = None, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas" 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 scikit-learn that
 supports pandas dataframes.</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">SklearnModelHandlerPandas</span><span class="p">(</span><span class="n">model_uri</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>model_uri</strong> – The URI to where the model is saved.</li>
 <li><strong>model_file_type</strong> – The method of serialization of the argument.
 default=pickle</li>
 <li><strong>inference_fn</strong> – The inference function to use.
 default=_default_pandas_inference_fn</li>
 <li><strong>min_batch_size</strong> – the minimum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Pandas
 Dataframes.</li>
 <li><strong>max_batch_size</strong> – the maximum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Pandas
 Dataframes.</li>
 <li><strong>max_batch_duration_secs</strong> – the maximum amount of time to buffer a batch
 before emitting; used in streaming contexts.</li>
 <li><strong>large_model</strong> – set to true if your model is large enough to run into
 memory pressure if you load multiple copies. Given a model that
 consumes N memory and a machine with W cores and M memory, you should
 set this to True if N*W &gt; M.</li>
 <li><strong>model_copies</strong> – The exact number of models that you would like loaded
 onto your machine. This can be useful if you exactly know your CPU or
 GPU capacity and want to maximize resource utilization.</li>
 <li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
 before loading the model.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; sklearn.base.BaseEstimator<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.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.sklearn_inference.SklearnModelHandlerPandas.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/sklearn_inference.html#SklearnModelHandlerPandas.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[pandas.core.frame.DataFrame], model: sklearn.base.BaseEstimator, 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/sklearn_inference.html#SklearnModelHandlerPandas.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of pandas dataframes.</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 as numpy arrays. They should
 be single examples.</li>
 <li><strong>model</strong> – A dataframe model or pipeline. Must implement predict(X).
 Where the parameter X is a pandas dataframe.</li>
 <li><strong>inference_args</strong> – Any additional arguments for an inference.</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.sklearn_inference.SklearnModelHandlerPandas.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[pandas.core.frame.DataFrame]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.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.sklearn_inference.SklearnModelHandlerPandas.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/sklearn_inference.html#SklearnModelHandlerPandas.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.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.sklearn_inference.SklearnModelHandlerPandas.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/sklearn_inference.html#SklearnModelHandlerPandas.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.share_model_across_processes">
 <code class="descname">share_model_across_processes</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; bool<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.share_model_across_processes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.share_model_across_processes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.model_copies">
 <code class="descname">model_copies</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.model_copies"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.model_copies" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

 </div>


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