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<div class="section" id="module-apache_beam.ml.inference.tensorflow_inference">
<span id="apache-beam-ml-inference-tensorflow-inference-module"></span><h1>apache_beam.ml.inference.tensorflow_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.tensorflow_inference" title="Permalink to this headline"></a></h1>
<dl class="class">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.tensorflow_inference.</code><code class="descname">TFModelHandlerNumpy</code><span class="sig-paren">(</span><em>model_uri: str, model_type: apache_beam.ml.inference.tensorflow_inference.ModelType = &lt;ModelType.SAVED_MODEL: 1&gt;, create_model_fn: Optional[Callable] = None, *, inference_fn: Callable[[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e484e20&gt;, Sequence[Union[numpy.ndarray, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e484e80&gt;]], Dict[str, Any], Optional[str]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_numpy_inference_fn&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerNumpy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy" 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 Tensorflow.</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">TFModelHandlerNumpy</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>See <a class="reference external" href="https://www.tensorflow.org/tutorials/keras/save_and_load">https://www.tensorflow.org/tutorials/keras/save_and_load</a> for details.</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> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – path to the trained model.</li>
<li><strong>model_type</strong> – type of model to be loaded. Defaults to SAVED_MODEL.</li>
<li><strong>create_model_fn</strong> – a function that creates and returns a new
tensorflow model to load the saved weights.
It should be used with ModelType.SAVED_WEIGHTS.</li>
<li><strong>inference_fn</strong> – inference function to use during RunInference.
Defaults to default_numpy_inference_fn.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
with Tensorflow 2.9, 2.10, 2.11.</p>
<dl class="method">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.load_model">
<code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e4841c0&gt;<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerNumpy.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.load_model" title="Permalink to this definition"></a></dt>
<dd><p>Loads and initializes a Tensorflow model for processing.</p>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.update_model_path" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray], model: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e484070&gt;, 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/tensorflow_inference.html#TFModelHandlerNumpy.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of numpy array and returns an Iterable of
numpy array Predictions.</p>
<p>This method stacks the n-dimensional numpy array in a vectorized format to
optimize the inference call.</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 numpy nd-array. These should be batchable, as this
method will call <cite>numpy.stack()</cite> and pass in batched numpy nd-array
with dimensions (batch_size, n_features, etc.) into the model’s
predict() function.</li>
<li><strong>model</strong> – A Tensorflow model.</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.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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 numpy arrays.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.validate_inference_args" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="class">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.tensorflow_inference.</code><code class="descname">TFModelHandlerTensor</code><span class="sig-paren">(</span><em>model_uri: str, model_type: apache_beam.ml.inference.tensorflow_inference.ModelType = &lt;ModelType.SAVED_MODEL: 1&gt;, create_model_fn: Optional[Callable] = None, *, inference_fn: Callable[[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e484e20&gt;, Sequence[Union[numpy.ndarray, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e484e80&gt;]], Dict[str, Any], Optional[str]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_tensor_inference_fn&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerTensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor" 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 Tensorflow.</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">TFModelHandlerTensor</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>See <a class="reference external" href="https://www.tensorflow.org/tutorials/keras/save_and_load">https://www.tensorflow.org/tutorials/keras/save_and_load</a> for details.</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> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – path to the trained model.</li>
<li><strong>model_type</strong> – type of model to be loaded.
Defaults to SAVED_MODEL.</li>
<li><strong>create_model_fn</strong> – a function that creates and returns a new
tensorflow model to load the saved weights.
It should be used with ModelType.SAVED_WEIGHTS.</li>
<li><strong>inference_fn</strong> – inference function to use during RunInference.
Defaults to default_numpy_inference_fn.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
with Tensorflow 2.11.</p>
<dl class="method">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.load_model">
<code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e52e7f0&gt;<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerTensor.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.load_model" title="Permalink to this definition"></a></dt>
<dd><p>Loads and initializes a tensorflow model for processing.</p>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.update_model_path" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e492100&gt;], model: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e492160&gt;, 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/tensorflow_inference.html#TFModelHandlerTensor.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of tf.Tensor and returns an Iterable of
Tensor Predictions.</p>
<p>This method stacks the list of Tensors in a vectorized format to optimize
the inference call.</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 Tensors. These Tensors should be batchable, as this
method will call <cite>tf.stack()</cite> and pass in batched Tensors with
dimensions (batch_size, n_features, etc.) into the model’s predict()
function.</li>
<li><strong>model</strong> – A Tensorflow model.</li>
<li><strong>inference_args</strong> – Non-batchable arguments required as inputs to the model’s
forward() function. Unlike Tensors in <cite>batch</cite>, these parameters will
not be dynamically batched</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.tensorflow_inference.TFModelHandlerTensor.get_num_bytes">
<code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f2e2e492250&gt;]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerTensor.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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>
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<dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.validate_inference_args" title="Permalink to this definition"></a></dt>
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