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<div class="section" id="module-airflow.providers.google.cloud.hooks.vertex_ai.auto_ml">
<span id="airflow-providers-google-cloud-hooks-vertex-ai-auto-ml"></span><h1><a class="reference internal" href="#module-airflow.providers.google.cloud.hooks.vertex_ai.auto_ml" title="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml"><code class="xref py py-mod docutils literal notranslate"><span class="pre">airflow.providers.google.cloud.hooks.vertex_ai.auto_ml</span></code></a><a class="headerlink" href="#module-airflow.providers.google.cloud.hooks.vertex_ai.auto_ml" title="Permalink to this heading"></a></h1>
<p>This module contains a Google Cloud Vertex AI hook.</p>
<div class="section" id="module-contents">
<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading"></a></h2>
<div class="section" id="classes">
<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading"></a></h3>
<table class="autosummary longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook" title="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AutoMLHook</span></code></a></p></td>
<td><p>Hook for Google Cloud Vertex AI Auto ML APIs.</p></td>
</tr>
</tbody>
</table>
<dl class="py class">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.</span></span><span class="sig-name descname"><span class="pre">AutoMLHook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gcp_conn_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'google_cloud_default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delegate_to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">impersonation_chain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="../../../../common/hooks/base_google/index.html#airflow.providers.google.common.hooks.base_google.GoogleBaseHook" title="airflow.providers.google.common.hooks.base_google.GoogleBaseHook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">airflow.providers.google.common.hooks.base_google.GoogleBaseHook</span></code></a></p>
<p>Hook for Google Cloud Vertex AI Auto ML APIs.</p>
<dl class="field-list simple">
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_pipeline_service_client">
<span class="sig-name descname"><span class="pre">get_pipeline_service_client</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">region</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_pipeline_service_client"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_pipeline_service_client" title="Permalink to this definition"></a></dt>
<dd><p>Returns PipelineServiceClient.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_job_service_client">
<span class="sig-name descname"><span class="pre">get_job_service_client</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">region</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_job_service_client"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_job_service_client" title="Permalink to this definition"></a></dt>
<dd><p>Returns JobServiceClient</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_tabular_training_job">
<span class="sig-name descname"><span class="pre">get_auto_ml_tabular_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_prediction_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_specs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_transformations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective_recall_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective_precision_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">project</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_auto_ml_tabular_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_tabular_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Returns AutoMLTabularTrainingJob object</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_forecasting_training_job">
<span class="sig-name descname"><span class="pre">get_auto_ml_forecasting_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_specs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_transformations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">project</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_auto_ml_forecasting_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_forecasting_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Returns AutoMLForecastingTrainingJob object</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_image_training_job">
<span class="sig-name descname"><span class="pre">get_auto_ml_image_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CLOUD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">project</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_auto_ml_image_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_image_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Returns AutoMLImageTrainingJob object</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_text_training_job">
<span class="sig-name descname"><span class="pre">get_auto_ml_text_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sentiment_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">project</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_auto_ml_text_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_text_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Returns AutoMLTextTrainingJob object</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_video_training_job">
<span class="sig-name descname"><span class="pre">get_auto_ml_video_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CLOUD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">project</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_auto_ml_video_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_video_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Returns AutoMLVideoTrainingJob object</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.extract_model_id">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">extract_model_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.extract_model_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.extract_model_id" title="Permalink to this definition"></a></dt>
<dd><p>Returns unique id of the Model.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.wait_for_operation">
<span class="sig-name descname"><span class="pre">wait_for_operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.wait_for_operation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.wait_for_operation" title="Permalink to this definition"></a></dt>
<dd><p>Waits for long-lasting operation to complete.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.cancel_auto_ml_job">
<span class="sig-name descname"><span class="pre">cancel_auto_ml_job</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.cancel_auto_ml_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.cancel_auto_ml_job" title="Permalink to this definition"></a></dt>
<dd><p>Cancel Auto ML Job for training pipeline</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_tabular_training_job">
<span class="sig-name descname"><span class="pre">create_auto_ml_tabular_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_prediction_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_specs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_transformations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective_recall_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective_precision_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predefined_split_column_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp_split_column_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_column</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">budget_milli_node_hours</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_display_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_evaluated_data_items</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_evaluated_data_items_bigquery_destination_uri</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_evaluated_data_items_override_destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.create_auto_ml_tabular_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_tabular_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Create an AutoML Tabular Training Job.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Project to run training in.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Location to run training in.</p></li>
<li><p><strong>display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The user-defined name of this TrainingPipeline.</p></li>
<li><p><strong>dataset</strong> (<em>google.cloud.aiplatform.datasets.TabularDataset</em>) – Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline’s [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular
Datasets, all their data is exported to training, to pick and choose from.</p></li>
<li><p><strong>target_column</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The name of the column values of which the Model is to predict.</p></li>
<li><p><strong>optimization_prediction_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – The type of prediction the Model is to produce.
“classification” - Predict one out of multiple target values is picked for each row.
“regression” - Predict a value based on its relation to other values. This type is available only
to columns that contain semantically numeric values, i.e. integers or floating point number, even
if stored as e.g. strings.</p></li>
<li><p><strong>optimization_objective</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – <p>Optional. Objective function the Model is to be optimized towards.
The training task creates a Model that maximizes/minimizes the value of the objective function
over the validation set.</p>
<p>The supported optimization objectives depend on the prediction type, and in the case of
classification also the number of distinct values in the target column (two distinct values
-&gt; binary, 3 or more distinct values -&gt; multi class). If the field is not set, the default
objective function is used.</p>
<p>Classification (binary):
“maximize-au-roc” (default) - Maximize the area under the receiver operating characteristic (ROC)
curve.
“minimize-log-loss” - Minimize log loss.
“maximize-au-prc” - Maximize the area under the precision-recall curve.
“maximize-precision-at-recall” - Maximize precision for a specified recall value.
“maximize-recall-at-precision” - Maximize recall for a specified precision value.</p>
<p>Classification (multi class):
“minimize-log-loss” (default) - Minimize log loss.</p>
<p>Regression:
“minimize-rmse” (default) - Minimize root-mean-squared error (RMSE).
“minimize-mae” - Minimize mean-absolute error (MAE).
“minimize-rmsle” - Minimize root-mean-squared log error (RMSLE).</p>
</p></li>
<li><p><strong>column_specs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. Alternative to column_transformations where the keys of the dict are
column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types.
When creating transformation for BigQuery Struct column, the column should be flattened using “.”
as the delimiter. Only columns with no child should have a transformation. If an input column has
no transformations on it, such a column is ignored by the training, except for the targetColumn,
which should have no transformations defined on. Only one of column_transformations or
column_specs should be passed.</p></li>
<li><p><strong>column_transformations</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em><em>]</em><em>] </em><em>| </em><em>None</em>) – Optional. Transformations to apply to the input columns (i.e. columns
other than the targetColumn). Each transformation may produce multiple result values from the
column’s value, and all are used for training. When creating transformation for BigQuery Struct
column, the column should be flattened using “.” as the delimiter. Only columns with no child
should have a transformation. If an input column has no transformations on it, such a column is
ignored by the training, except for the targetColumn, which should have no transformations
defined on. Only one of column_transformations or column_specs should be passed. Consider using
column_specs as column_transformations will be deprecated eventually.</p></li>
<li><p><strong>optimization_objective_recall_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. Required when maximize-precision-at-recall
optimizationObjective was picked, represents the recall value at which the optimization is done.
The minimum value is 0 and the maximum is 1.0.</p></li>
<li><p><strong>optimization_objective_precision_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. Required when maximize-recall-at-precision
optimizationObjective was picked, represents the precision value at which the optimization is
done.
The minimum value is 0 and the maximum is 1.0.</p></li>
<li><p><strong>labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>training_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>. The key needs to be
in the same region as where the compute resource is created. If set, this TrainingPipeline will
be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if <code class="docutils literal notranslate"><span class="pre">model_to_upload</span></code>
is not set separately.</p></li>
<li><p><strong>model_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>. The key needs to be
in the same region as where the compute resource is created. If set, the trained Model will be
secured by this key.</p></li>
<li><p><strong>training_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>validation_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>predefined_split_column_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The key is a name of one of the Dataset’s data
columns. The value of the key (either the label’s value or value in the column) must be one of
{<code class="docutils literal notranslate"><span class="pre">training</span></code>, <code class="docutils literal notranslate"><span class="pre">validation</span></code>, <code class="docutils literal notranslate"><span class="pre">test</span></code>}, and it defines to which set the given piece of data is
assigned. If for a piece of data the key is not present or has an invalid value, that piece is
ignored by the pipeline. Supported only for tabular and time series Datasets.</p></li>
<li><p><strong>timestamp_split_column_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The key is a name of one of the Dataset’s data columns.
The value of the key values of the key (the values in the column) must be in RFC 3339 <cite>date-time</cite>
format, where <cite>time-offset</cite> = <cite>“Z”</cite> (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the
key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only
for tabular and time series Datasets. This parameter must be used with training_fraction_split,
validation_fraction_split and test_fraction_split.</p></li>
<li><p><strong>weight_column</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. Name of the column that should be used as the weight column. Higher
values in this column give more importance to the row during Model training. The column must have
numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the
weight column field is not set, then all rows are assumed to have equal weight of 1.</p></li>
<li><p><strong>(</strong><strong>int</strong><strong>)</strong> (<em>budget_milli_node_hours</em>) – Optional. The train budget of creating this Model, expressed in
milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model
will not exceed this budget. The final cost will be attempted to be close to the budget, though
may end up being (even) noticeably smaller - at the backend’s discretion. This especially may
happen when further model training ceases to provide any improvements. If the budget is set to a
value known to be insufficient to train a Model for the given training set, the training won’t be
attempted and will error. The minimum value is 1000 and the maximum is 72000.</p></li>
<li><p><strong>model_display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. If the script produces a managed Vertex AI Model. The display
name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8
characters. If not provided upon creation, the job’s display_name is used.</p></li>
<li><p><strong>model_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>disable_early_stopping</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Required. If true, the entire budget is used. This disables the early
stopping feature. By default, the early stopping feature is enabled, which means that training
might stop before the entire training budget has been used, if further training does no longer
brings significant improvement to the model.</p></li>
<li><p><strong>export_evaluated_data_items</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to export the test set predictions to a BigQuery table.
If False, then the export is not performed.</p></li>
<li><p><strong>export_evaluated_data_items_bigquery_destination_uri</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – <p>Optional. URI of desired destination
BigQuery table for exported test set predictions.</p>
<p>Expected format: <code class="docutils literal notranslate"><span class="pre">bq://&lt;project_id&gt;:&lt;dataset_id&gt;:&lt;table&gt;</span></code></p>
<p>If not specified, then results are exported to the following auto-created BigQuery table:
<code class="docutils literal notranslate"><span class="pre">&lt;project_id&gt;:export_evaluated_examples_&lt;model_name&gt;_&lt;yyyy_MM_dd'T'HH_mm_ss_SSS'Z'&gt;</span>
<span class="pre">.evaluated_examples</span></code></p>
<p>Applies only if [export_evaluated_data_items] is True.</p>
</p></li>
<li><p><strong>export_evaluated_data_items_override_destination</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to override the contents of
[export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test
set predictions. If False, and the table exists, then the training job will fail. Applies only if
[export_evaluated_data_items] is True and [export_evaluated_data_items_bigquery_destination_uri]
is specified.</p></li>
<li><p><strong>sync</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_forecasting_training_job">
<span class="sig-name descname"><span class="pre">create_auto_ml_forecasting_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_series_identifier_column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unavailable_at_forecast_columns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">available_at_forecast_columns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecast_horizon</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_granularity_unit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_granularity_count</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_objective</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_specs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_transformations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predefined_split_column_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_column</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_series_attribute_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_evaluated_data_items</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_evaluated_data_items_bigquery_destination_uri</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_evaluated_data_items_override_destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">budget_milli_node_hours</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_display_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.create_auto_ml_forecasting_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_forecasting_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Create an AutoML Forecasting Training Job.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Project to run training in.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Location to run training in.</p></li>
<li><p><strong>display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The user-defined name of this TrainingPipeline.</p></li>
<li><p><strong>dataset</strong> (<em>google.cloud.aiplatform.datasets.TimeSeriesDataset</em>) – Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline’s [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For time series
Datasets, all their data is exported to training, to pick and choose from.</p></li>
<li><p><strong>target_column</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Name of the column that the Model is to predict values for.</p></li>
<li><p><strong>time_column</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Name of the column that identifies time order in the time series.</p></li>
<li><p><strong>time_series_identifier_column</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Name of the column that identifies the time series.</p></li>
<li><p><strong>unavailable_at_forecast_columns</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em>) – Required. Column names of columns that are unavailable at
forecast. Each column contains information for the given entity (identified by the
[time_series_identifier_column]) that is unknown before the forecast (e.g. population of a city
in a given year, or weather on a given day).</p></li>
<li><p><strong>available_at_forecast_columns</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em>) – Required. Column names of columns that are available at
forecast. Each column contains information for the given entity (identified by the
[time_series_identifier_column]) that is known at forecast.</p></li>
<li><p><strong>forecast_horizon</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Required. The amount of time into the future for which forecasted values for
the target are returned. Expressed in number of units defined by the [data_granularity_unit] and
[data_granularity_count] field. Inclusive.</p></li>
<li><p><strong>data_granularity_unit</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The data granularity unit. Accepted values are <code class="docutils literal notranslate"><span class="pre">minute</span></code>,
<code class="docutils literal notranslate"><span class="pre">hour</span></code>, <code class="docutils literal notranslate"><span class="pre">day</span></code>, <code class="docutils literal notranslate"><span class="pre">week</span></code>, <code class="docutils literal notranslate"><span class="pre">month</span></code>, <code class="docutils literal notranslate"><span class="pre">year</span></code>.</p></li>
<li><p><strong>data_granularity_count</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Required. The number of data granularity units between data points in
the training data. If [data_granularity_unit] is <cite>minute</cite>, can be 1, 5, 10, 15, or 30. For all
other values of [data_granularity_unit], must be 1.</p></li>
<li><p><strong>optimization_objective</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. Objective function the model is to be optimized towards. The
training process creates a Model that optimizes the value of the objective function over the
validation set. The supported optimization objectives:
“minimize-rmse” (default) - Minimize root-mean-squared error (RMSE).
“minimize-mae” - Minimize mean-absolute error (MAE).
“minimize-rmsle” - Minimize root-mean-squared log error (RMSLE).
“minimize-rmspe” - Minimize root-mean-squared percentage error (RMSPE).
“minimize-wape-mae” - Minimize the combination of weighted absolute percentage error (WAPE) and
mean-absolute-error (MAE).
“minimize-quantile-loss” - Minimize the quantile loss at the defined quantiles. (Set this
objective to build quantile forecasts.)</p></li>
<li><p><strong>column_specs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. Alternative to column_transformations where the keys of the dict are
column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types.
When creating transformation for BigQuery Struct column, the column should be flattened using “.”
as the delimiter. Only columns with no child should have a transformation. If an input column has
no transformations on it, such a column is ignored by the training, except for the targetColumn,
which should have no transformations defined on. Only one of column_transformations or
column_specs should be passed.</p></li>
<li><p><strong>column_transformations</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em><em>]</em><em>] </em><em>| </em><em>None</em>) – Optional. Transformations to apply to the input columns (i.e. columns
other than the targetColumn). Each transformation may produce multiple result values from the
column’s value, and all are used for training. When creating transformation for BigQuery Struct
column, the column should be flattened using “.” as the delimiter. Only columns with no child
should have a transformation. If an input column has no transformations on it, such a column is
ignored by the training, except for the targetColumn, which should have no transformations
defined on. Only one of column_transformations or column_specs should be passed. Consider using
column_specs as column_transformations will be deprecated eventually.</p></li>
<li><p><strong>labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>training_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>. The key needs to be
in the same region as where the compute resource is created. If set, this TrainingPipeline will
be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if <code class="docutils literal notranslate"><span class="pre">model_to_upload</span></code>
is not set separately.</p></li>
<li><p><strong>model_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>. The key needs to be
in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.</p></li>
<li><p><strong>training_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>validation_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>predefined_split_column_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The key is a name of one of the Dataset’s data
columns. The value of the key (either the label’s value or value in the column) must be one of
{<code class="docutils literal notranslate"><span class="pre">TRAIN</span></code>, <code class="docutils literal notranslate"><span class="pre">VALIDATE</span></code>, <code class="docutils literal notranslate"><span class="pre">TEST</span></code>}, and it defines to which set the given piece of data is
assigned. If for a piece of data the key is not present or has an invalid value, that piece is
ignored by the pipeline.
Supported only for tabular and time series Datasets.</p></li>
<li><p><strong>weight_column</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. Name of the column that should be used as the weight column. Higher
values in this column give more importance to the row during Model training. The column must have
numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the
weight column field is not set, then all rows are assumed to have equal weight of 1.</p></li>
<li><p><strong>time_series_attribute_columns</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. Column names that should be used as attribute
columns. Each column is constant within a time series.</p></li>
<li><p><strong>context_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Optional. The amount of time into the past training and prediction data is
used for model training and prediction respectively. Expressed in number of units defined by the
[data_granularity_unit] and [data_granularity_count] fields. When not provided uses the default
value of 0 which means the model sets each series context window to be 0 (also known as “cold
start”). Inclusive.</p></li>
<li><p><strong>export_evaluated_data_items</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to export the test set predictions to a BigQuery table.
If False, then the export is not performed.</p></li>
<li><p><strong>export_evaluated_data_items_bigquery_destination_uri</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. URI of desired destination
BigQuery table for exported test set predictions. Expected format:
<code class="docutils literal notranslate"><span class="pre">bq://&lt;project_id&gt;:&lt;dataset_id&gt;:&lt;table&gt;</span></code>
If not specified, then results are exported to the following auto-created BigQuery table:
<code class="docutils literal notranslate"><span class="pre">&lt;project_id&gt;:export_evaluated_examples_&lt;model_name&gt;_&lt;yyyy_MM_dd'T'HH_mm_ss_SSS'Z'&gt;</span>
<span class="pre">.evaluated_examples</span></code>
Applies only if [export_evaluated_data_items] is True.</p></li>
<li><p><strong>export_evaluated_data_items_override_destination</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to override the contents of
[export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test
set predictions. If False, and the table exists, then the training job will fail.
Applies only if [export_evaluated_data_items] is True and
[export_evaluated_data_items_bigquery_destination_uri] is specified.</p></li>
<li><p><strong>quantiles</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>] </em><em>| </em><em>None</em>) – Quantiles to use for the <cite>minizmize-quantile-loss</cite>
[AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in this case.
Accepts up to 5 quantiles in the form of a double from 0 to 1, exclusive. Each quantile must be
unique.</p></li>
<li><p><strong>validation_options</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Validation options for the data validation component. The available
options are: “fail-pipeline” - (default), will validate against the validation and fail the
pipeline if it fails. “ignore-validation” - ignore the results of the validation and continue the
pipeline</p></li>
<li><p><strong>budget_milli_node_hours</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Optional. The train budget of creating this Model, expressed in milli
node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will
not exceed this budget. The final cost will be attempted to be close to the budget, though may
end up being (even) noticeably smaller - at the backend’s discretion. This especially may happen
when further model training ceases to provide any improvements. If the budget is set to a value
known to be insufficient to train a Model for the given training set, the training won’t be
attempted and will error. The minimum value is 1000 and the maximum is 72000.</p></li>
<li><p><strong>model_display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. If the script produces a managed Vertex AI Model. The display
name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8
characters. If not provided upon creation, the job’s display_name is used.</p></li>
<li><p><strong>model_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>sync</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_image_training_job">
<span class="sig-name descname"><span class="pre">create_auto_ml_image_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CLOUD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">budget_milli_node_hours</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_display_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.create_auto_ml_image_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_image_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Create an AutoML Image Training Job.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Project to run training in.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Location to run training in.</p></li>
<li><p><strong>display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The user-defined name of this TrainingPipeline.</p></li>
<li><p><strong>dataset</strong> (<em>google.cloud.aiplatform.datasets.ImageDataset</em>) – Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline’s [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular
Datasets, all their data is exported to training, to pick and choose from.</p></li>
<li><p><strong>prediction_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – The type of prediction the Model is to produce, one of:
“classification” - Predict one out of multiple target values is picked for each row.
“object_detection” - Predict a value based on its relation to other values. This type is
available only to columns that contain semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.</p></li>
<li><p><strong>multi_label</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Required. Default is False. If false, a single-label (multi-class) Model will be
trained (i.e. assuming that for each image just up to one annotation may be applicable). If true,
a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may
be applicable).
This is only applicable for the “classification” prediction_type and will be ignored otherwise.</p></li>
<li><p><strong>model_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. One of the following:
“CLOUD” - Default for Image Classification. A Model best tailored to be used within Google Cloud,
and which cannot be exported.
“CLOUD_HIGH_ACCURACY_1” - Default for Image Object Detection. A model best tailored to be used
within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should
also have a higher prediction quality than other cloud models.
“CLOUD_LOW_LATENCY_1” - A model best tailored to be used within Google Cloud, and which cannot be
exported. Expected to have a low latency, but may have lower prediction quality than other cloud
models.
“MOBILE_TF_LOW_LATENCY_1” - A model that, in addition to being available within Google Cloud, can
also be exported as TensorFlow or Core ML model and used on a mobile or edge device afterwards.
Expected to have low latency, but may have lower prediction quality than other mobile models.
“MOBILE_TF_VERSATILE_1” - A model that, in addition to being available within Google Cloud, can
also be exported as TensorFlow or Core ML model and used on a mobile or edge device with
afterwards.
“MOBILE_TF_HIGH_ACCURACY_1” - A model that, in addition to being available within Google Cloud,
can also be exported as TensorFlow or Core ML model and used on a mobile or edge device
afterwards. Expected to have a higher latency, but should also have a higher prediction quality
than other mobile models.</p></li>
<li><p><strong>base_model</strong> (<em>models.Model</em><em> | </em><em>None</em>) – Optional. Only permitted for Image Classification models. If it is specified, the
new model will be trained based on the <cite>base</cite> model. Otherwise, the new model will be trained
from scratch. The <cite>base</cite> model must be in the same Project and Location as the new Model to
train, and have the same model_type.</p></li>
<li><p><strong>labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>training_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>. The key needs to be
in the same region as where the compute resource is created. If set, this TrainingPipeline will
be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if <code class="docutils literal notranslate"><span class="pre">model_to_upload</span></code>
is not set separately.</p></li>
<li><p><strong>model_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>.
The key needs to be in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.</p></li>
<li><p><strong>training_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>validation_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>training_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to train the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>validation_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to validate the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match this
filter are used to test the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>budget_milli_node_hours</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Optional. The train budget of creating this Model, expressed in milli
node hours i.e. 1,000 value in this field means 1 node hour.
Defaults by <cite>prediction_type</cite>:
<cite>classification</cite> - For Cloud models the budget must be: 8,000 - 800,000 milli node hours
(inclusive). The default value is 192,000 which represents one day in wall time, assuming 8 nodes
are used.
<cite>object_detection</cite> - For Cloud models the budget must be: 20,000 - 900,000 milli node hours
(inclusive). The default value is 216,000 which represents one day in wall time, assuming 9 nodes
are used.
The training cost of the model will not exceed this budget. The final cost will be attempted to
be close to the budget, though may end up being (even) noticeably smaller - at the backend’s
discretion. This especially may happen when further model training ceases to provide any
improvements. If the budget is set to a value known to be insufficient to train a Model for the
given training set, the training won’t be attempted and will error.</p></li>
<li><p><strong>model_display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The display name of the managed Vertex AI Model. The name can be
up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon
creation, the job’s display_name is used.</p></li>
<li><p><strong>model_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>disable_early_stopping</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Required. If true, the entire budget is used. This disables the early
stopping feature. By default, the early stopping feature is enabled, which means that training
might stop before the entire training budget has been used, if further training does no longer
brings significant improvement to the model.</p></li>
<li><p><strong>sync</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_text_training_job">
<span class="sig-name descname"><span class="pre">create_auto_ml_text_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sentiment_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_display_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.create_auto_ml_text_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_text_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Create an AutoML Text Training Job.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Project to run training in.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Location to run training in.</p></li>
<li><p><strong>display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The user-defined name of this TrainingPipeline.</p></li>
<li><p><strong>dataset</strong> (<em>google.cloud.aiplatform.datasets.TextDataset</em>) – Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline’s [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].</p></li>
<li><p><strong>prediction_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – The type of prediction the Model is to produce, one of:
“classification” - A classification model analyzes text data and returns a list of categories
that apply to the text found in the data. Vertex AI offers both single-label and multi-label text
classification models.
“extraction” - An entity extraction model inspects text data for known entities referenced in the
data and labels those entities in the text.
“sentiment” - A sentiment analysis model inspects text data and identifies the prevailing
emotional opinion within it, especially to determine a writer’s attitude as positive, negative,
or neutral.</p></li>
<li><p><strong>multi_label</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Required and only applicable for text classification task. If false, a
single-label (multi-class) Model will be trained (i.e. assuming that for each text snippet just
up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e.
assuming that for each text snippet multiple annotations may be applicable).</p></li>
<li><p><strong>sentiment_max</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Required and only applicable for sentiment task. A sentiment is expressed as an
integer ordinal, where higher value means a more positive sentiment. The range of sentiments that
will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the
range must be represented in the dataset before a model can be created. Only the Annotations with
this sentimentMax will be used for training. sentimentMax value must be between 1 and 10
(inclusive).</p></li>
<li><p><strong>labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>training_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>.
The key needs to be in the same region as where the compute resource is created.
If set, this TrainingPipeline will be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if <code class="docutils literal notranslate"><span class="pre">model_to_upload</span></code>
is not set separately.</p></li>
<li><p><strong>model_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>.
The key needs to be in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.</p></li>
<li><p><strong>training_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>validation_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>training_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to train the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>validation_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to validate the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match this
filter are used to test the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>model_display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The display name of the managed Vertex AI Model. The name can be
up to 128 characters long and can consist of any UTF-8 characters.
If not provided upon creation, the job’s display_name is used.</p></li>
<li><p><strong>model_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>sync</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_video_training_job">
<span class="sig-name descname"><span class="pre">create_auto_ml_video_training_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CLOUD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_encryption_spec_key_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_fraction_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_filter_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_display_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.create_auto_ml_video_training_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_video_training_job" title="Permalink to this definition"></a></dt>
<dd><p>Create an AutoML Video Training Job.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Project to run training in.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. Location to run training in.</p></li>
<li><p><strong>display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The user-defined name of this TrainingPipeline.</p></li>
<li><p><strong>dataset</strong> (<em>google.cloud.aiplatform.datasets.VideoDataset</em>) – Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline’s [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular
Datasets, all their data is exported to training, to pick and choose from.</p></li>
<li><p><strong>prediction_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – The type of prediction the Model is to produce, one of:
“classification” - A video classification model classifies shots and segments in your videos
according to your own defined labels.
“object_tracking” - A video object tracking model detects and tracks multiple objects in shots
and segments. You can use these models to track objects in your videos according to your own
pre-defined, custom labels.
“action_recognition” - A video action recognition model pinpoints the location of actions with
short temporal durations (~1 second).</p></li>
<li><p><strong>model_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. One of the following:
“CLOUD” - available for “classification”, “object_tracking” and “action_recognition” A Model best
tailored to be used within Google Cloud, and which cannot be exported.
“MOBILE_VERSATILE_1” - available for “classification”, “object_tracking” and “action_recognition”
A model that, in addition to being available within Google Cloud, can also be exported (see
ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge
device with afterwards.
“MOBILE_CORAL_VERSATILE_1” - available only for “object_tracking” A versatile model that is meant
to be exported (see ModelService.ExportModel) and used on a Google Coral device.
“MOBILE_CORAL_LOW_LATENCY_1” - available only for “object_tracking” A model that trades off
quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral
device.
“MOBILE_JETSON_VERSATILE_1” - available only for “object_tracking” A versatile model that is
meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.
“MOBILE_JETSON_LOW_LATENCY_1” - available only for “object_tracking” A model that trades off
quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA
Jetson device.</p></li>
<li><p><strong>labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>training_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>.
The key needs to be in the same region as where the compute resource is created.
If set, this TrainingPipeline will be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if <code class="docutils literal notranslate"><span class="pre">model_to_upload</span></code>
is not set separately.</p></li>
<li><p><strong>model_encryption_spec_key_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
<code class="docutils literal notranslate"><span class="pre">projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key</span></code>.
The key needs to be in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.</p></li>
<li><p><strong>training_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_fraction_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>training_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to train the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>test_filter_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. A filter on DataItems of the Dataset. DataItems that match this
filter are used to test the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.</p></li>
<li><p><strong>model_display_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The display name of the managed Vertex AI Model. The name can be
up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon
creation, the job’s display_name is used.</p></li>
<li><p><strong>model_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See <a class="reference external" href="https://goo.gl/xmQnxf">https://goo.gl/xmQnxf</a> for more information and examples of labels.</p></li>
<li><p><strong>sync</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.delete_training_pipeline">
<span class="sig-name descname"><span class="pre">delete_training_pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_pipeline</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DEFAULT</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.delete_training_pipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.delete_training_pipeline" title="Permalink to this definition"></a></dt>
<dd><p>Deletes a TrainingPipeline.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The ID of the Google Cloud project that the service belongs to.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The ID of the Google Cloud region that the service belongs to.</p></li>
<li><p><strong>training_pipeline</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The name of the TrainingPipeline resource to be deleted.</p></li>
<li><p><strong>retry</strong> (<em>Retry</em><em> | </em><em>_MethodDefault</em>) – Designation of what errors, if any, should be retried.</p></li>
<li><p><strong>timeout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – The timeout for this request.</p></li>
<li><p><strong>metadata</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em><em>]</em>) – Strings which should be sent along with the request as metadata.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_training_pipeline">
<span class="sig-name descname"><span class="pre">get_training_pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_pipeline</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DEFAULT</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.get_training_pipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_training_pipeline" title="Permalink to this definition"></a></dt>
<dd><p>Gets a TrainingPipeline.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The ID of the Google Cloud project that the service belongs to.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The ID of the Google Cloud region that the service belongs to.</p></li>
<li><p><strong>training_pipeline</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The name of the TrainingPipeline resource.</p></li>
<li><p><strong>retry</strong> (<em>Retry</em><em> | </em><em>_MethodDefault</em>) – Designation of what errors, if any, should be retried.</p></li>
<li><p><strong>timeout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – The timeout for this request.</p></li>
<li><p><strong>metadata</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em><em>]</em>) – Strings which should be sent along with the request as metadata.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.list_training_pipelines">
<span class="sig-name descname"><span class="pre">list_training_pipelines</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">project_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">region</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">page_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">page_token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">read_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DEFAULT</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../../../../_modules/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.html#AutoMLHook.list_training_pipelines"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.list_training_pipelines" title="Permalink to this definition"></a></dt>
<dd><p>Lists TrainingPipelines in a Location.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>project_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The ID of the Google Cloud project that the service belongs to.</p></li>
<li><p><strong>region</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Required. The ID of the Google Cloud region that the service belongs to.</p></li>
<li><p><strong>filter</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – <p>Optional. The standard list filter. Supported fields:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">display_name</span></code> supports = and !=.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">state</span></code> supports = and !=.</p></li>
</ul>
<p>Some examples of using the filter are:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">state=&quot;PIPELINE_STATE_SUCCEEDED&quot;</span> <span class="pre">AND</span> <span class="pre">display_name=&quot;my_pipeline&quot;</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">state=&quot;PIPELINE_STATE_RUNNING&quot;</span> <span class="pre">OR</span> <span class="pre">display_name=&quot;my_pipeline&quot;</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">NOT</span> <span class="pre">display_name=&quot;my_pipeline&quot;</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">state=&quot;PIPELINE_STATE_FAILED&quot;</span></code></p></li>
</ul>
</p></li>
<li><p><strong>page_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Optional. The standard list page size.</p></li>
<li><p><strong>page_token</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. The standard list page token. Typically obtained via
[ListTrainingPipelinesResponse.next_page_token][google.cloud.aiplatform.v1.ListTrainingPipelinesResponse.next_page_token]
of the previous
[PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines]
call.</p></li>
<li><p><strong>read_mask</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – Optional. Mask specifying which fields to read.</p></li>
<li><p><strong>retry</strong> (<em>Retry</em><em> | </em><em>_MethodDefault</em>) – Designation of what errors, if any, should be retried.</p></li>
<li><p><strong>timeout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – The timeout for this request.</p></li>
<li><p><strong>metadata</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em><em>]</em>) – Strings which should be sent along with the request as metadata.</p></li>
</ul>
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</dl>
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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">airflow.providers.google.cloud.hooks.vertex_ai.auto_ml</span></code></a><ul>
<li><a class="reference internal" href="#module-contents">Module Contents</a><ul>
<li><a class="reference internal" href="#classes">Classes</a><ul>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook"><code class="docutils literal notranslate"><span class="pre">AutoMLHook</span></code></a><ul>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_pipeline_service_client"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_pipeline_service_client()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_job_service_client"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_job_service_client()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_tabular_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_auto_ml_tabular_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_forecasting_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_auto_ml_forecasting_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_image_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_auto_ml_image_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_text_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_auto_ml_text_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_video_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_auto_ml_video_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.extract_model_id"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.extract_model_id()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.wait_for_operation"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.wait_for_operation()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.cancel_auto_ml_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.cancel_auto_ml_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_tabular_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.create_auto_ml_tabular_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_forecasting_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.create_auto_ml_forecasting_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_image_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.create_auto_ml_image_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_text_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.create_auto_ml_text_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_video_training_job"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.create_auto_ml_video_training_job()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.delete_training_pipeline"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.delete_training_pipeline()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_training_pipeline"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.get_training_pipeline()</span></code></a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.list_training_pipelines"><code class="docutils literal notranslate"><span class="pre">AutoMLHook.list_training_pipelines()</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook">AutoMLHook</a><ul>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_pipeline_service_client">get_pipeline_service_client</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_job_service_client">get_job_service_client</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_tabular_training_job">get_auto_ml_tabular_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_forecasting_training_job">get_auto_ml_forecasting_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_image_training_job">get_auto_ml_image_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_text_training_job">get_auto_ml_text_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_video_training_job">get_auto_ml_video_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.extract_model_id">extract_model_id</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.wait_for_operation">wait_for_operation</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.cancel_auto_ml_job">cancel_auto_ml_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_tabular_training_job">create_auto_ml_tabular_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_forecasting_training_job">create_auto_ml_forecasting_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_image_training_job">create_auto_ml_image_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_text_training_job">create_auto_ml_text_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_video_training_job">create_auto_ml_video_training_job</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.delete_training_pipeline">delete_training_pipeline</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_training_pipeline">get_training_pipeline</a></li>
<li><a class="reference internal" href="#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.list_training_pipelines">list_training_pipelines</a></li>
</ul>
</li>
</ul>
</li>
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