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   <div class="section" id="module-apache_beam.ml.inference.vertex_ai_inference">
 <span id="apache-beam-ml-inference-vertex-ai-inference-module"></span><h1>apache_beam.ml.inference.vertex_ai_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.vertex_ai_inference" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.vertex_ai_inference.</code><code class="descname">VertexAIModelHandlerJSON</code><span class="sig-paren">(</span><em>endpoint_id: str</em>, <em>project: str</em>, <em>location: str</em>, <em>experiment: Optional[str] = None</em>, <em>network: Optional[str] = None</em>, <em>private: bool = False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/vertex_ai_inference.html#VertexAIModelHandlerJSON"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON" 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 Vertex AI.
 <strong>NOTE:</strong> This API and its implementation are under development and
 do not provide backward compatibility guarantees.
 Unlike other ModelHandler implementations, this does not load the model
 being used onto the worker and instead makes remote queries to a
 Vertex AI endpoint. In that way it functions more like a mid-pipeline
 IO. Public Vertex AI endpoints have a maximum request size of 1.5 MB.
 If you wish to make larger requests and use a private endpoint, provide
 the Compute Engine network you wish to use and set <cite>private=True</cite></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>endpoint_id</strong> – the numerical ID of the Vertex AI endpoint to query</li>
 <li><strong>project</strong> – the GCP project name where the endpoint is deployed</li>
 <li><strong>location</strong> – the GCP location where the endpoint is deployed</li>
 <li><strong>experiment</strong> – optional. experiment label to apply to the
 queries. See
 <a class="reference external" href="https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments">https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments</a>
 for more information.</li>
 <li><strong>network</strong> – optional. the full name of the Compute Engine
 network the endpoint is deployed on; used for private
 endpoints. The network or subnetwork Dataflow pipeline
 option must be set and match this network for pipeline
 execution.
 Ex: “projects/12345/global/networks/myVPC”</li>
 <li><strong>private</strong> – optional. if the deployed Vertex AI endpoint is
 private, set to true. Requires a network to be provided
 as well.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; google.cloud.aiplatform.models.Endpoint<a class="reference internal" href="_modules/apache_beam/ml/inference/vertex_ai_inference.html#VertexAIModelHandlerJSON.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.load_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads the Endpoint object used to build and send prediction request to
 Vertex AI.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.get_request">
 <code class="descname">get_request</code><span class="sig-paren">(</span><em>batch: Sequence[Any], model: google.cloud.aiplatform.models.Endpoint, throttle_delay_secs: int, inference_args: Optional[Dict[str, Any]]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/vertex_ai_inference.html#VertexAIModelHandlerJSON.get_request"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.get_request" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[Any], model: google.cloud.aiplatform.models.Endpoint, 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/vertex_ai_inference.html#VertexAIModelHandlerJSON.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sends a prediction request to a Vertex AI endpoint containing batch
 of inputs and matches that input with the prediction response from
 the endpoint as an iterable of PredictionResults.</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 any values to be passed to the Vertex AI endpoint.
 Should be encoded as the model expects.</li>
 <li><strong>model</strong> – an aiplatform.Endpoint object configured to access the desired
 model.</li>
 <li><strong>inference_args</strong> – any additional arguments to send as part of the
 prediction request.</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.vertex_ai_inference.VertexAIModelHandlerJSON.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/vertex_ai_inference.html#VertexAIModelHandlerJSON.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

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	<div class="section" id="module-apache_beam.ml.inference.vertex_ai_inference">
	<span id="apache-beam-ml-inference-vertex-ai-inference-module"></span><h1>apache_beam.ml.inference.vertex_ai_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.vertex_ai_inference" title="Permalink to this headline">¶</a></h1>
	<dl class="class">
	<dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON">
	<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.vertex_ai_inference.</code><code class="descname">VertexAIModelHandlerJSON</code><span class="sig-paren">(</span><em>endpoint_id: str</em>, <em>project: str</em>, <em>location: str</em>, <em>experiment: Optional[str] = None</em>, <em>network: Optional[str] = None</em>, <em>private: bool = False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/vertex_ai_inference.html#VertexAIModelHandlerJSON"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON" 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 Vertex AI.
	<strong>NOTE:</strong> This API and its implementation are under development and
	do not provide backward compatibility guarantees.
	Unlike other ModelHandler implementations, this does not load the model
	being used onto the worker and instead makes remote queries to a
	Vertex AI endpoint. In that way it functions more like a mid-pipeline
	IO. Public Vertex AI endpoints have a maximum request size of 1.5 MB.
	If you wish to make larger requests and use a private endpoint, provide
	the Compute Engine network you wish to use and set <cite>private=True</cite></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>endpoint_id</strong> – the numerical ID of the Vertex AI endpoint to query</li>
	<li><strong>project</strong> – the GCP project name where the endpoint is deployed</li>
	<li><strong>location</strong> – the GCP location where the endpoint is deployed</li>
	<li><strong>experiment</strong> – optional. experiment label to apply to the
	queries. See
	<a class="reference external" href="https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments">https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments</a>
	for more information.</li>
	<li><strong>network</strong> – optional. the full name of the Compute Engine
	network the endpoint is deployed on; used for private
	endpoints. The network or subnetwork Dataflow pipeline
	option must be set and match this network for pipeline
	execution.
	Ex: “projects/12345/global/networks/myVPC”</li>
	<li><strong>private</strong> – optional. if the deployed Vertex AI endpoint is
	private, set to true. Requires a network to be provided
	as well.</li>
	</ul>
	</td>
	</tr>
	</tbody>
	</table>
	<dl class="method">
	<dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.load_model">
	<code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → google.cloud.aiplatform.models.Endpoint<a class="reference internal" href="_modules/apache_beam/ml/inference/vertex_ai_inference.html#VertexAIModelHandlerJSON.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.load_model" title="Permalink to this definition">¶</a></dt>
	<dd><p>Loads the Endpoint object used to build and send prediction request to
	Vertex AI.</p>
	</dd></dl>

	<dl class="method">
	<dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.get_request">
	<code class="descname">get_request</code><span class="sig-paren">(</span><em>batch: Sequence[Any], model: google.cloud.aiplatform.models.Endpoint, throttle_delay_secs: int, inference_args: Optional[Dict[str, Any]]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/vertex_ai_inference.html#VertexAIModelHandlerJSON.get_request"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.get_request" title="Permalink to this definition">¶</a></dt>
	<dd></dd></dl>

	<dl class="method">
	<dt id="apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.run_inference">
	<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[Any], model: google.cloud.aiplatform.models.Endpoint, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> → Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/vertex_ai_inference.html#VertexAIModelHandlerJSON.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.run_inference" title="Permalink to this definition">¶</a></dt>
	<dd><p>Sends a prediction request to a Vertex AI endpoint containing batch
	of inputs and matches that input with the prediction response from
	the endpoint as an iterable of PredictionResults.</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 any values to be passed to the Vertex AI endpoint.
	Should be encoded as the model expects.</li>
	<li><strong>model</strong> – an aiplatform.Endpoint object configured to access the desired
	model.</li>
	<li><strong>inference_args</strong> – any additional arguments to send as part of the
	prediction request.</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.vertex_ai_inference.VertexAIModelHandlerJSON.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/vertex_ai_inference.html#VertexAIModelHandlerJSON.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON.validate_inference_args" title="Permalink to this definition">¶</a></dt>
	<dd></dd></dl>

	</dd></dl>

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