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| <h1>Source code for airflow.providers.google.cloud.hooks.vertex_ai.auto_ml</h1><div class="highlight"><pre> |
| <span></span><span class="c1">#</span> |
| <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| <span class="c1">#</span> |
| <span class="sd">"""</span> |
| <span class="sd">This module contains a Google Cloud Vertex AI hook.</span> |
| |
| <span class="sd">.. spelling::</span> |
| |
| <span class="sd"> aiplatform</span> |
| <span class="sd"> au</span> |
| <span class="sd"> codepoints</span> |
| <span class="sd"> milli</span> |
| <span class="sd"> mae</span> |
| <span class="sd"> quantile</span> |
| <span class="sd"> quantiles</span> |
| <span class="sd"> Quantiles</span> |
| <span class="sd"> rmse</span> |
| <span class="sd"> rmsle</span> |
| <span class="sd"> rmspe</span> |
| <span class="sd"> wape</span> |
| <span class="sd"> prc</span> |
| <span class="sd"> roc</span> |
| <span class="sd"> Jetson</span> |
| <span class="sd"> forecasted</span> |
| <span class="sd"> Struct</span> |
| <span class="sd"> sentimentMax</span> |
| <span class="sd"> TrainingPipeline</span> |
| <span class="sd"> targetColumn</span> |
| <span class="sd"> optimizationObjective</span> |
| <span class="sd">"""</span> |
| |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span> |
| |
| <span class="kn">from</span> <span class="nn">google.api_core.client_options</span> <span class="kn">import</span> <span class="n">ClientOptions</span> |
| <span class="kn">from</span> <span class="nn">google.api_core.gapic_v1.method</span> <span class="kn">import</span> <span class="n">DEFAULT</span><span class="p">,</span> <span class="n">_MethodDefault</span> |
| <span class="kn">from</span> <span class="nn">google.api_core.operation</span> <span class="kn">import</span> <span class="n">Operation</span> |
| <span class="kn">from</span> <span class="nn">google.api_core.retry</span> <span class="kn">import</span> <span class="n">Retry</span> |
| <span class="kn">from</span> <span class="nn">google.cloud.aiplatform</span> <span class="kn">import</span> <span class="p">(</span> |
| <span class="n">AutoMLForecastingTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLImageTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLTabularTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLTextTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLVideoTrainingJob</span><span class="p">,</span> |
| <span class="n">datasets</span><span class="p">,</span> |
| <span class="n">models</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="kn">from</span> <span class="nn">google.cloud.aiplatform_v1</span> <span class="kn">import</span> <span class="n">JobServiceClient</span><span class="p">,</span> <span class="n">PipelineServiceClient</span> |
| <span class="kn">from</span> <span class="nn">google.cloud.aiplatform_v1.services.pipeline_service.pagers</span> <span class="kn">import</span> <span class="n">ListTrainingPipelinesPager</span> |
| <span class="kn">from</span> <span class="nn">google.cloud.aiplatform_v1.types</span> <span class="kn">import</span> <span class="n">TrainingPipeline</span> |
| |
| <span class="kn">from</span> <span class="nn">airflow</span> <span class="kn">import</span> <span class="n">AirflowException</span> |
| <span class="kn">from</span> <span class="nn">airflow.providers.google.common.hooks.base_google</span> <span class="kn">import</span> <span class="n">GoogleBaseHook</span> |
| |
| |
| <div class="viewcode-block" id="AutoMLHook"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook">[docs]</a><span class="k">class</span> <span class="nc">AutoMLHook</span><span class="p">(</span><span class="n">GoogleBaseHook</span><span class="p">):</span> |
| <span class="sd">"""Hook for Google Cloud Vertex AI Auto ML APIs."""</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">gcp_conn_id</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"google_cloud_default"</span><span class="p">,</span> |
| <span class="n">delegate_to</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">impersonation_chain</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> |
| <span class="n">gcp_conn_id</span><span class="o">=</span><span class="n">gcp_conn_id</span><span class="p">,</span> |
| <span class="n">delegate_to</span><span class="o">=</span><span class="n">delegate_to</span><span class="p">,</span> |
| <span class="n">impersonation_chain</span><span class="o">=</span><span class="n">impersonation_chain</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span> |
| <span class="n">Union</span><span class="p">[</span> |
| <span class="n">AutoMLForecastingTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLImageTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLTabularTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLTextTrainingJob</span><span class="p">,</span> |
| <span class="n">AutoMLVideoTrainingJob</span><span class="p">,</span> |
| <span class="p">]</span> |
| <span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="AutoMLHook.get_pipeline_service_client"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_pipeline_service_client">[docs]</a> <span class="k">def</span> <span class="nf">get_pipeline_service_client</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">PipelineServiceClient</span><span class="p">:</span> |
| <span class="sd">"""Returns PipelineServiceClient."""</span> |
| <span class="k">if</span> <span class="n">region</span> <span class="ow">and</span> <span class="n">region</span> <span class="o">!=</span> <span class="s1">'global'</span><span class="p">:</span> |
| <span class="n">client_options</span> <span class="o">=</span> <span class="n">ClientOptions</span><span class="p">(</span><span class="n">api_endpoint</span><span class="o">=</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">region</span><span class="si">}</span><span class="s1">-aiplatform.googleapis.com:443'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">client_options</span> <span class="o">=</span> <span class="n">ClientOptions</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">PipelineServiceClient</span><span class="p">(</span> |
| <span class="n">credentials</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_credentials</span><span class="p">(),</span> <span class="n">client_info</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">client_info</span><span class="p">,</span> <span class="n">client_options</span><span class="o">=</span><span class="n">client_options</span></div> |
| <span class="p">)</span> |
| |
| <div class="viewcode-block" id="AutoMLHook.get_job_service_client"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_job_service_client">[docs]</a> <span class="k">def</span> <span class="nf">get_job_service_client</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">JobServiceClient</span><span class="p">:</span> |
| <span class="sd">"""Returns JobServiceClient"""</span> |
| <span class="k">if</span> <span class="n">region</span> <span class="ow">and</span> <span class="n">region</span> <span class="o">!=</span> <span class="s1">'global'</span><span class="p">:</span> |
| <span class="n">client_options</span> <span class="o">=</span> <span class="n">ClientOptions</span><span class="p">(</span><span class="n">api_endpoint</span><span class="o">=</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">region</span><span class="si">}</span><span class="s1">-aiplatform.googleapis.com:443'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">client_options</span> <span class="o">=</span> <span class="n">ClientOptions</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">JobServiceClient</span><span class="p">(</span> |
| <span class="n">credentials</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_credentials</span><span class="p">(),</span> <span class="n">client_info</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">client_info</span><span class="p">,</span> <span class="n">client_options</span><span class="o">=</span><span class="n">client_options</span></div> |
| <span class="p">)</span> |
| |
| <div class="viewcode-block" id="AutoMLHook.get_auto_ml_tabular_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_tabular_training_job">[docs]</a> <span class="k">def</span> <span class="nf">get_auto_ml_tabular_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">optimization_prediction_type</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">optimization_objective_recall_value</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">optimization_objective_precision_value</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">project</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">location</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">AutoMLTabularTrainingJob</span><span class="p">:</span> |
| <span class="sd">"""Returns AutoMLTabularTrainingJob object"""</span> |
| <span class="k">return</span> <span class="n">AutoMLTabularTrainingJob</span><span class="p">(</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">optimization_prediction_type</span><span class="o">=</span><span class="n">optimization_prediction_type</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="o">=</span><span class="n">optimization_objective</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="o">=</span><span class="n">column_specs</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="o">=</span><span class="n">column_transformations</span><span class="p">,</span> |
| <span class="n">optimization_objective_recall_value</span><span class="o">=</span><span class="n">optimization_objective_recall_value</span><span class="p">,</span> |
| <span class="n">optimization_objective_precision_value</span><span class="o">=</span><span class="n">optimization_objective_precision_value</span><span class="p">,</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> |
| <span class="n">credentials</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_credentials</span><span class="p">(),</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span></div> |
| <span class="p">)</span> |
| |
| <div class="viewcode-block" id="AutoMLHook.get_auto_ml_forecasting_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_forecasting_training_job">[docs]</a> <span class="k">def</span> <span class="nf">get_auto_ml_forecasting_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">project</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">location</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">AutoMLForecastingTrainingJob</span><span class="p">:</span> |
| <span class="sd">"""Returns AutoMLForecastingTrainingJob object"""</span> |
| <span class="k">return</span> <span class="n">AutoMLForecastingTrainingJob</span><span class="p">(</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="o">=</span><span class="n">optimization_objective</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="o">=</span><span class="n">column_specs</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="o">=</span><span class="n">column_transformations</span><span class="p">,</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> |
| <span class="n">credentials</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_credentials</span><span class="p">(),</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span></div> |
| <span class="p">)</span> |
| |
| <div class="viewcode-block" id="AutoMLHook.get_auto_ml_image_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_image_training_job">[docs]</a> <span class="k">def</span> <span class="nf">get_auto_ml_image_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"classification"</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"CLOUD"</span><span class="p">,</span> |
| <span class="n">base_model</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">project</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">location</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">AutoMLImageTrainingJob</span><span class="p">:</span> |
| <span class="sd">"""Returns AutoMLImageTrainingJob object"""</span> |
| <span class="k">return</span> <span class="n">AutoMLImageTrainingJob</span><span class="p">(</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="o">=</span><span class="n">prediction_type</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="o">=</span><span class="n">multi_label</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span> |
| <span class="n">base_model</span><span class="o">=</span><span class="n">base_model</span><span class="p">,</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> |
| <span class="n">credentials</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_credentials</span><span class="p">(),</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span></div> |
| <span class="p">)</span> |
| |
| <div class="viewcode-block" id="AutoMLHook.get_auto_ml_text_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_text_training_job">[docs]</a> <span class="k">def</span> <span class="nf">get_auto_ml_text_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">sentiment_max</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> |
| <span class="n">project</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">location</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">AutoMLTextTrainingJob</span><span class="p">:</span> |
| <span class="sd">"""Returns AutoMLTextTrainingJob object"""</span> |
| <span class="k">return</span> <span class="n">AutoMLTextTrainingJob</span><span class="p">(</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="o">=</span><span class="n">prediction_type</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="o">=</span><span class="n">multi_label</span><span class="p">,</span> |
| <span class="n">sentiment_max</span><span class="o">=</span><span class="n">sentiment_max</span><span class="p">,</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> |
| <span class="n">credentials</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_credentials</span><span class="p">(),</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span></div> |
| <span class="p">)</span> |
| |
| <div class="viewcode-block" id="AutoMLHook.get_auto_ml_video_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_auto_ml_video_training_job">[docs]</a> <span class="k">def</span> <span class="nf">get_auto_ml_video_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"classification"</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"CLOUD"</span><span class="p">,</span> |
| <span class="n">project</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">location</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">AutoMLVideoTrainingJob</span><span class="p">:</span> |
| <span class="sd">"""Returns AutoMLVideoTrainingJob object"""</span> |
| <span class="k">return</span> <span class="n">AutoMLVideoTrainingJob</span><span class="p">(</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="o">=</span><span class="n">prediction_type</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> |
| <span class="n">credentials</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_credentials</span><span class="p">(),</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span></div> |
| <span class="p">)</span> |
| |
| <span class="nd">@staticmethod</span> |
| <div class="viewcode-block" id="AutoMLHook.extract_model_id"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.extract_model_id">[docs]</a> <span class="k">def</span> <span class="nf">extract_model_id</span><span class="p">(</span><span class="n">obj</span><span class="p">:</span> <span class="n">Dict</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="sd">"""Returns unique id of the Model."""</span> |
| <span class="k">return</span> <span class="n">obj</span><span class="p">[</span><span class="s2">"name"</span><span class="p">]</span><span class="o">.</span><span class="n">rpartition</span><span class="p">(</span><span class="s2">"/"</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span></div> |
| |
| <div class="viewcode-block" id="AutoMLHook.wait_for_operation"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.wait_for_operation">[docs]</a> <span class="k">def</span> <span class="nf">wait_for_operation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operation</span><span class="p">:</span> <span class="n">Operation</span><span class="p">,</span> <span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Waits for long-lasting operation to complete."""</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">operation</span><span class="o">.</span><span class="n">result</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span> |
| <span class="n">error</span> <span class="o">=</span> <span class="n">operation</span><span class="o">.</span><span class="n">exception</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">)</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="n">error</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="AutoMLHook.cancel_auto_ml_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.cancel_auto_ml_job">[docs]</a> <span class="k">def</span> <span class="nf">cancel_auto_ml_job</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="sd">"""Cancel Auto ML Job for training pipeline"""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="o">.</span><span class="n">cancel</span><span class="p">()</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.create_auto_ml_tabular_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_tabular_training_job">[docs]</a> <span class="k">def</span> <span class="nf">create_auto_ml_tabular_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">dataset</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">TabularDataset</span><span class="p">,</span> |
| <span class="n">target_column</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">optimization_prediction_type</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">optimization_objective_recall_value</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">optimization_objective_precision_value</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">predefined_split_column_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">timestamp_split_column_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">weight_column</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">budget_milli_node_hours</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">disable_early_stopping</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items_bigquery_destination_uri</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items_override_destination</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">sync</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Create an AutoML Tabular Training Job.</span> |
| |
| <span class="sd"> :param project_id: Required. Project to run training in.</span> |
| <span class="sd"> :param region: Required. Location to run training in.</span> |
| <span class="sd"> :param display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param dataset: Required. The dataset within the same Project from which data will be used to train</span> |
| <span class="sd"> the Model. The Dataset must use schema compatible with Model being trained, and what is</span> |
| <span class="sd"> compatible should be described in the used TrainingPipeline's [training_task_definition]</span> |
| <span class="sd"> [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular</span> |
| <span class="sd"> Datasets, all their data is exported to training, to pick and choose from.</span> |
| <span class="sd"> :param target_column: Required. The name of the column values of which the Model is to predict.</span> |
| <span class="sd"> :param optimization_prediction_type: The type of prediction the Model is to produce.</span> |
| <span class="sd"> "classification" - Predict one out of multiple target values is picked for each row.</span> |
| <span class="sd"> "regression" - Predict a value based on its relation to other values. This type is available only</span> |
| <span class="sd"> to columns that contain semantically numeric values, i.e. integers or floating point number, even</span> |
| <span class="sd"> if stored as e.g. strings.</span> |
| <span class="sd"> :param optimization_objective: Optional. Objective function the Model is to be optimized towards.</span> |
| <span class="sd"> The training task creates a Model that maximizes/minimizes the value of the objective function</span> |
| <span class="sd"> over the validation set.</span> |
| |
| <span class="sd"> The supported optimization objectives depend on the prediction type, and in the case of</span> |
| <span class="sd"> classification also the number of distinct values in the target column (two distinct values</span> |
| <span class="sd"> -> binary, 3 or more distinct values -> multi class). If the field is not set, the default</span> |
| <span class="sd"> objective function is used.</span> |
| |
| <span class="sd"> Classification (binary):</span> |
| <span class="sd"> "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC)</span> |
| <span class="sd"> curve.</span> |
| <span class="sd"> "minimize-log-loss" - Minimize log loss.</span> |
| <span class="sd"> "maximize-au-prc" - Maximize the area under the precision-recall curve.</span> |
| <span class="sd"> "maximize-precision-at-recall" - Maximize precision for a specified recall value.</span> |
| <span class="sd"> "maximize-recall-at-precision" - Maximize recall for a specified precision value.</span> |
| |
| <span class="sd"> Classification (multi class):</span> |
| <span class="sd"> "minimize-log-loss" (default) - Minimize log loss.</span> |
| |
| <span class="sd"> Regression:</span> |
| <span class="sd"> "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).</span> |
| <span class="sd"> "minimize-mae" - Minimize mean-absolute error (MAE).</span> |
| <span class="sd"> "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).</span> |
| <span class="sd"> :param column_specs: Optional. Alternative to column_transformations where the keys of the dict are</span> |
| <span class="sd"> column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types.</span> |
| <span class="sd"> When creating transformation for BigQuery Struct column, the column should be flattened using "."</span> |
| <span class="sd"> as the delimiter. Only columns with no child should have a transformation. If an input column has</span> |
| <span class="sd"> no transformations on it, such a column is ignored by the training, except for the targetColumn,</span> |
| <span class="sd"> which should have no transformations defined on. Only one of column_transformations or</span> |
| <span class="sd"> column_specs should be passed.</span> |
| <span class="sd"> :param column_transformations: Optional. Transformations to apply to the input columns (i.e. columns</span> |
| <span class="sd"> other than the targetColumn). Each transformation may produce multiple result values from the</span> |
| <span class="sd"> column's value, and all are used for training. When creating transformation for BigQuery Struct</span> |
| <span class="sd"> column, the column should be flattened using "." as the delimiter. Only columns with no child</span> |
| <span class="sd"> should have a transformation. If an input column has no transformations on it, such a column is</span> |
| <span class="sd"> ignored by the training, except for the targetColumn, which should have no transformations</span> |
| <span class="sd"> defined on. Only one of column_transformations or column_specs should be passed. Consider using</span> |
| <span class="sd"> column_specs as column_transformations will be deprecated eventually.</span> |
| <span class="sd"> :param optimization_objective_recall_value: Optional. Required when maximize-precision-at-recall</span> |
| <span class="sd"> optimizationObjective was picked, represents the recall value at which the optimization is done.</span> |
| <span class="sd"> The minimum value is 0 and the maximum is 1.0.</span> |
| <span class="sd"> :param optimization_objective_precision_value: Optional. Required when maximize-recall-at-precision</span> |
| <span class="sd"> optimizationObjective was picked, represents the precision value at which the optimization is</span> |
| <span class="sd"> done.</span> |
| <span class="sd"> The minimum value is 0 and the maximum is 1.0.</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the training pipeline. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be</span> |
| <span class="sd"> in the same region as where the compute resource is created. If set, this TrainingPipeline will</span> |
| <span class="sd"> be secured by this key.</span> |
| <span class="sd"> Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``</span> |
| <span class="sd"> is not set separately.</span> |
| <span class="sd"> :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the model. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be</span> |
| <span class="sd"> in the same region as where the compute resource is created. If set, the trained Model will be</span> |
| <span class="sd"> secured by this key.</span> |
| <span class="sd"> :param training_fraction_split: Optional. The fraction of the input data that is to be used to train</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param validation_fraction_split: Optional. The fraction of the input data that is to be used to</span> |
| <span class="sd"> validate the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data</span> |
| <span class="sd"> columns. The value of the key (either the label's value or value in the column) must be one of</span> |
| <span class="sd"> {``training``, ``validation``, ``test``}, and it defines to which set the given piece of data is</span> |
| <span class="sd"> assigned. If for a piece of data the key is not present or has an invalid value, that piece is</span> |
| <span class="sd"> ignored by the pipeline. Supported only for tabular and time series Datasets.</span> |
| <span class="sd"> :param timestamp_split_column_name: Optional. The key is a name of one of the Dataset's data columns.</span> |
| <span class="sd"> The value of the key values of the key (the values in the column) must be in RFC 3339 `date-time`</span> |
| <span class="sd"> format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the</span> |
| <span class="sd"> key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only</span> |
| <span class="sd"> for tabular and time series Datasets. This parameter must be used with training_fraction_split,</span> |
| <span class="sd"> validation_fraction_split and test_fraction_split.</span> |
| <span class="sd"> :param weight_column: Optional. Name of the column that should be used as the weight column. Higher</span> |
| <span class="sd"> values in this column give more importance to the row during Model training. The column must have</span> |
| <span class="sd"> numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the</span> |
| <span class="sd"> weight column field is not set, then all rows are assumed to have equal weight of 1.</span> |
| <span class="sd"> :param budget_milli_node_hours (int): Optional. The train budget of creating this Model, expressed in</span> |
| <span class="sd"> milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model</span> |
| <span class="sd"> will not exceed this budget. The final cost will be attempted to be close to the budget, though</span> |
| <span class="sd"> may end up being (even) noticeably smaller - at the backend's discretion. This especially may</span> |
| <span class="sd"> happen when further model training ceases to provide any improvements. If the budget is set to a</span> |
| <span class="sd"> value known to be insufficient to train a Model for the given training set, the training won't be</span> |
| <span class="sd"> attempted and will error. The minimum value is 1000 and the maximum is 72000.</span> |
| <span class="sd"> :param model_display_name: Optional. If the script produces a managed Vertex AI Model. The display</span> |
| <span class="sd"> name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8</span> |
| <span class="sd"> characters. If not provided upon creation, the job's display_name is used.</span> |
| <span class="sd"> :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param disable_early_stopping: Required. If true, the entire budget is used. This disables the early</span> |
| <span class="sd"> stopping feature. By default, the early stopping feature is enabled, which means that training</span> |
| <span class="sd"> might stop before the entire training budget has been used, if further training does no longer</span> |
| <span class="sd"> brings significant improvement to the model.</span> |
| <span class="sd"> :param export_evaluated_data_items: Whether to export the test set predictions to a BigQuery table.</span> |
| <span class="sd"> If False, then the export is not performed.</span> |
| <span class="sd"> :param export_evaluated_data_items_bigquery_destination_uri: Optional. URI of desired destination</span> |
| <span class="sd"> BigQuery table for exported test set predictions.</span> |
| |
| <span class="sd"> Expected format: ``bq://<project_id>:<dataset_id>:<table>``</span> |
| |
| <span class="sd"> If not specified, then results are exported to the following auto-created BigQuery table:</span> |
| <span class="sd"> ``<project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'></span> |
| <span class="sd"> .evaluated_examples``</span> |
| |
| <span class="sd"> Applies only if [export_evaluated_data_items] is True.</span> |
| <span class="sd"> :param export_evaluated_data_items_override_destination: Whether to override the contents of</span> |
| <span class="sd"> [export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test</span> |
| <span class="sd"> set predictions. If False, and the table exists, then the training job will fail. Applies only if</span> |
| <span class="sd"> [export_evaluated_data_items] is True and [export_evaluated_data_items_bigquery_destination_uri]</span> |
| <span class="sd"> is specified.</span> |
| <span class="sd"> :param sync: Whether to execute this method synchronously. If False, this method will be executed in</span> |
| <span class="sd"> concurrent Future and any downstream object will be immediately returned and synced when the</span> |
| <span class="sd"> Future has completed.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_job</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_auto_ml_tabular_training_job</span><span class="p">(</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">optimization_prediction_type</span><span class="o">=</span><span class="n">optimization_prediction_type</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="o">=</span><span class="n">optimization_objective</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="o">=</span><span class="n">column_specs</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="o">=</span><span class="n">column_transformations</span><span class="p">,</span> |
| <span class="n">optimization_objective_recall_value</span><span class="o">=</span><span class="n">optimization_objective_recall_value</span><span class="p">,</span> |
| <span class="n">optimization_objective_precision_value</span><span class="o">=</span><span class="n">optimization_objective_precision_value</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s2">"AutoMLTabularTrainingJob was not created"</span><span class="p">)</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="o">.</span><span class="n">run</span><span class="p">(</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">target_column</span><span class="o">=</span><span class="n">target_column</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="n">training_fraction_split</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="o">=</span><span class="n">validation_fraction_split</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="o">=</span><span class="n">test_fraction_split</span><span class="p">,</span> |
| <span class="n">predefined_split_column_name</span><span class="o">=</span><span class="n">predefined_split_column_name</span><span class="p">,</span> |
| <span class="n">timestamp_split_column_name</span><span class="o">=</span><span class="n">timestamp_split_column_name</span><span class="p">,</span> |
| <span class="n">weight_column</span><span class="o">=</span><span class="n">weight_column</span><span class="p">,</span> |
| <span class="n">budget_milli_node_hours</span><span class="o">=</span><span class="n">budget_milli_node_hours</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="n">model_display_name</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="o">=</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">disable_early_stopping</span><span class="o">=</span><span class="n">disable_early_stopping</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items</span><span class="o">=</span><span class="n">export_evaluated_data_items</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items_bigquery_destination_uri</span><span class="o">=</span><span class="p">(</span> |
| <span class="n">export_evaluated_data_items_bigquery_destination_uri</span> |
| <span class="p">),</span> |
| <span class="n">export_evaluated_data_items_override_destination</span><span class="o">=</span><span class="n">export_evaluated_data_items_override_destination</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="n">sync</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">model</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.create_auto_ml_forecasting_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_forecasting_training_job">[docs]</a> <span class="k">def</span> <span class="nf">create_auto_ml_forecasting_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">dataset</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">TimeSeriesDataset</span><span class="p">,</span> |
| <span class="n">target_column</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">time_column</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">time_series_identifier_column</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">unavailable_at_forecast_columns</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> |
| <span class="n">available_at_forecast_columns</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> |
| <span class="n">forecast_horizon</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="n">data_granularity_unit</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">data_granularity_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">predefined_split_column_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">weight_column</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">time_series_attribute_columns</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">context_window</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items_bigquery_destination_uri</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items_override_destination</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">quantiles</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">validation_options</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">budget_milli_node_hours</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">sync</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Create an AutoML Forecasting Training Job.</span> |
| |
| <span class="sd"> :param project_id: Required. Project to run training in.</span> |
| <span class="sd"> :param region: Required. Location to run training in.</span> |
| <span class="sd"> :param display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param dataset: Required. The dataset within the same Project from which data will be used to train</span> |
| <span class="sd"> the Model. The Dataset must use schema compatible with Model being trained, and what is</span> |
| <span class="sd"> compatible should be described in the used TrainingPipeline's [training_task_definition]</span> |
| <span class="sd"> [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For time series</span> |
| <span class="sd"> Datasets, all their data is exported to training, to pick and choose from.</span> |
| <span class="sd"> :param target_column: Required. Name of the column that the Model is to predict values for.</span> |
| <span class="sd"> :param time_column: Required. Name of the column that identifies time order in the time series.</span> |
| <span class="sd"> :param time_series_identifier_column: Required. Name of the column that identifies the time series.</span> |
| <span class="sd"> :param unavailable_at_forecast_columns: Required. Column names of columns that are unavailable at</span> |
| <span class="sd"> forecast. Each column contains information for the given entity (identified by the</span> |
| <span class="sd"> [time_series_identifier_column]) that is unknown before the forecast (e.g. population of a city</span> |
| <span class="sd"> in a given year, or weather on a given day).</span> |
| <span class="sd"> :param available_at_forecast_columns: Required. Column names of columns that are available at</span> |
| <span class="sd"> forecast. Each column contains information for the given entity (identified by the</span> |
| <span class="sd"> [time_series_identifier_column]) that is known at forecast.</span> |
| <span class="sd"> :param forecast_horizon: Required. The amount of time into the future for which forecasted values for</span> |
| <span class="sd"> the target are returned. Expressed in number of units defined by the [data_granularity_unit] and</span> |
| <span class="sd"> [data_granularity_count] field. Inclusive.</span> |
| <span class="sd"> :param data_granularity_unit: Required. The data granularity unit. Accepted values are ``minute``,</span> |
| <span class="sd"> ``hour``, ``day``, ``week``, ``month``, ``year``.</span> |
| <span class="sd"> :param data_granularity_count: Required. The number of data granularity units between data points in</span> |
| <span class="sd"> the training data. If [data_granularity_unit] is `minute`, can be 1, 5, 10, 15, or 30. For all</span> |
| <span class="sd"> other values of [data_granularity_unit], must be 1.</span> |
| <span class="sd"> :param optimization_objective: Optional. Objective function the model is to be optimized towards. The</span> |
| <span class="sd"> training process creates a Model that optimizes the value of the objective function over the</span> |
| <span class="sd"> validation set. The supported optimization objectives:</span> |
| <span class="sd"> "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).</span> |
| <span class="sd"> "minimize-mae" - Minimize mean-absolute error (MAE).</span> |
| <span class="sd"> "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).</span> |
| <span class="sd"> "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).</span> |
| <span class="sd"> "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and</span> |
| <span class="sd"> mean-absolute-error (MAE).</span> |
| <span class="sd"> "minimize-quantile-loss" - Minimize the quantile loss at the defined quantiles. (Set this</span> |
| <span class="sd"> objective to build quantile forecasts.)</span> |
| <span class="sd"> :param column_specs: Optional. Alternative to column_transformations where the keys of the dict are</span> |
| <span class="sd"> column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types.</span> |
| <span class="sd"> When creating transformation for BigQuery Struct column, the column should be flattened using "."</span> |
| <span class="sd"> as the delimiter. Only columns with no child should have a transformation. If an input column has</span> |
| <span class="sd"> no transformations on it, such a column is ignored by the training, except for the targetColumn,</span> |
| <span class="sd"> which should have no transformations defined on. Only one of column_transformations or</span> |
| <span class="sd"> column_specs should be passed.</span> |
| <span class="sd"> :param column_transformations: Optional. Transformations to apply to the input columns (i.e. columns</span> |
| <span class="sd"> other than the targetColumn). Each transformation may produce multiple result values from the</span> |
| <span class="sd"> column's value, and all are used for training. When creating transformation for BigQuery Struct</span> |
| <span class="sd"> column, the column should be flattened using "." as the delimiter. Only columns with no child</span> |
| <span class="sd"> should have a transformation. If an input column has no transformations on it, such a column is</span> |
| <span class="sd"> ignored by the training, except for the targetColumn, which should have no transformations</span> |
| <span class="sd"> defined on. Only one of column_transformations or column_specs should be passed. Consider using</span> |
| <span class="sd"> column_specs as column_transformations will be deprecated eventually.</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the training pipeline. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be</span> |
| <span class="sd"> in the same region as where the compute resource is created. If set, this TrainingPipeline will</span> |
| <span class="sd"> be secured by this key.</span> |
| <span class="sd"> Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``</span> |
| <span class="sd"> is not set separately.</span> |
| <span class="sd"> :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the model. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be</span> |
| <span class="sd"> in the same region as where the compute resource is created.</span> |
| <span class="sd"> If set, the trained Model will be secured by this key.</span> |
| <span class="sd"> :param training_fraction_split: Optional. The fraction of the input data that is to be used to train</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param validation_fraction_split: Optional. The fraction of the input data that is to be used to</span> |
| <span class="sd"> validate the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data</span> |
| <span class="sd"> columns. The value of the key (either the label's value or value in the column) must be one of</span> |
| <span class="sd"> {``TRAIN``, ``VALIDATE``, ``TEST``}, and it defines to which set the given piece of data is</span> |
| <span class="sd"> assigned. If for a piece of data the key is not present or has an invalid value, that piece is</span> |
| <span class="sd"> ignored by the pipeline.</span> |
| <span class="sd"> Supported only for tabular and time series Datasets.</span> |
| <span class="sd"> :param weight_column: Optional. Name of the column that should be used as the weight column. Higher</span> |
| <span class="sd"> values in this column give more importance to the row during Model training. The column must have</span> |
| <span class="sd"> numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the</span> |
| <span class="sd"> weight column field is not set, then all rows are assumed to have equal weight of 1.</span> |
| <span class="sd"> :param time_series_attribute_columns: Optional. Column names that should be used as attribute</span> |
| <span class="sd"> columns. Each column is constant within a time series.</span> |
| <span class="sd"> :param context_window: Optional. The amount of time into the past training and prediction data is</span> |
| <span class="sd"> used for model training and prediction respectively. Expressed in number of units defined by the</span> |
| <span class="sd"> [data_granularity_unit] and [data_granularity_count] fields. When not provided uses the default</span> |
| <span class="sd"> value of 0 which means the model sets each series context window to be 0 (also known as "cold</span> |
| <span class="sd"> start"). Inclusive.</span> |
| <span class="sd"> :param export_evaluated_data_items: Whether to export the test set predictions to a BigQuery table.</span> |
| <span class="sd"> If False, then the export is not performed.</span> |
| <span class="sd"> :param export_evaluated_data_items_bigquery_destination_uri: Optional. URI of desired destination</span> |
| <span class="sd"> BigQuery table for exported test set predictions. Expected format:</span> |
| <span class="sd"> ``bq://<project_id>:<dataset_id>:<table>``</span> |
| <span class="sd"> If not specified, then results are exported to the following auto-created BigQuery table:</span> |
| <span class="sd"> ``<project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'></span> |
| <span class="sd"> .evaluated_examples``</span> |
| <span class="sd"> Applies only if [export_evaluated_data_items] is True.</span> |
| <span class="sd"> :param export_evaluated_data_items_override_destination: Whether to override the contents of</span> |
| <span class="sd"> [export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test</span> |
| <span class="sd"> set predictions. If False, and the table exists, then the training job will fail.</span> |
| <span class="sd"> Applies only if [export_evaluated_data_items] is True and</span> |
| <span class="sd"> [export_evaluated_data_items_bigquery_destination_uri] is specified.</span> |
| <span class="sd"> :param quantiles: Quantiles to use for the `minizmize-quantile-loss`</span> |
| <span class="sd"> [AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in this case.</span> |
| <span class="sd"> Accepts up to 5 quantiles in the form of a double from 0 to 1, exclusive. Each quantile must be</span> |
| <span class="sd"> unique.</span> |
| <span class="sd"> :param validation_options: Validation options for the data validation component. The available</span> |
| <span class="sd"> options are: "fail-pipeline" - (default), will validate against the validation and fail the</span> |
| <span class="sd"> pipeline if it fails. "ignore-validation" - ignore the results of the validation and continue the</span> |
| <span class="sd"> pipeline</span> |
| <span class="sd"> :param budget_milli_node_hours: Optional. The train budget of creating this Model, expressed in milli</span> |
| <span class="sd"> node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will</span> |
| <span class="sd"> not exceed this budget. The final cost will be attempted to be close to the budget, though may</span> |
| <span class="sd"> end up being (even) noticeably smaller - at the backend's discretion. This especially may happen</span> |
| <span class="sd"> when further model training ceases to provide any improvements. If the budget is set to a value</span> |
| <span class="sd"> known to be insufficient to train a Model for the given training set, the training won't be</span> |
| <span class="sd"> attempted and will error. The minimum value is 1000 and the maximum is 72000.</span> |
| <span class="sd"> :param model_display_name: Optional. If the script produces a managed Vertex AI Model. The display</span> |
| <span class="sd"> name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8</span> |
| <span class="sd"> characters. If not provided upon creation, the job's display_name is used.</span> |
| <span class="sd"> :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param sync: Whether to execute this method synchronously. If False, this method will be executed in</span> |
| <span class="sd"> concurrent Future and any downstream object will be immediately returned and synced when the</span> |
| <span class="sd"> Future has completed.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_job</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_auto_ml_forecasting_training_job</span><span class="p">(</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">optimization_objective</span><span class="o">=</span><span class="n">optimization_objective</span><span class="p">,</span> |
| <span class="n">column_specs</span><span class="o">=</span><span class="n">column_specs</span><span class="p">,</span> |
| <span class="n">column_transformations</span><span class="o">=</span><span class="n">column_transformations</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s2">"AutoMLForecastingTrainingJob was not created"</span><span class="p">)</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="o">.</span><span class="n">run</span><span class="p">(</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">target_column</span><span class="o">=</span><span class="n">target_column</span><span class="p">,</span> |
| <span class="n">time_column</span><span class="o">=</span><span class="n">time_column</span><span class="p">,</span> |
| <span class="n">time_series_identifier_column</span><span class="o">=</span><span class="n">time_series_identifier_column</span><span class="p">,</span> |
| <span class="n">unavailable_at_forecast_columns</span><span class="o">=</span><span class="n">unavailable_at_forecast_columns</span><span class="p">,</span> |
| <span class="n">available_at_forecast_columns</span><span class="o">=</span><span class="n">available_at_forecast_columns</span><span class="p">,</span> |
| <span class="n">forecast_horizon</span><span class="o">=</span><span class="n">forecast_horizon</span><span class="p">,</span> |
| <span class="n">data_granularity_unit</span><span class="o">=</span><span class="n">data_granularity_unit</span><span class="p">,</span> |
| <span class="n">data_granularity_count</span><span class="o">=</span><span class="n">data_granularity_count</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="n">training_fraction_split</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="o">=</span><span class="n">validation_fraction_split</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="o">=</span><span class="n">test_fraction_split</span><span class="p">,</span> |
| <span class="n">predefined_split_column_name</span><span class="o">=</span><span class="n">predefined_split_column_name</span><span class="p">,</span> |
| <span class="n">weight_column</span><span class="o">=</span><span class="n">weight_column</span><span class="p">,</span> |
| <span class="n">time_series_attribute_columns</span><span class="o">=</span><span class="n">time_series_attribute_columns</span><span class="p">,</span> |
| <span class="n">context_window</span><span class="o">=</span><span class="n">context_window</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items</span><span class="o">=</span><span class="n">export_evaluated_data_items</span><span class="p">,</span> |
| <span class="n">export_evaluated_data_items_bigquery_destination_uri</span><span class="o">=</span><span class="p">(</span> |
| <span class="n">export_evaluated_data_items_bigquery_destination_uri</span> |
| <span class="p">),</span> |
| <span class="n">export_evaluated_data_items_override_destination</span><span class="o">=</span><span class="n">export_evaluated_data_items_override_destination</span><span class="p">,</span> |
| <span class="n">quantiles</span><span class="o">=</span><span class="n">quantiles</span><span class="p">,</span> |
| <span class="n">validation_options</span><span class="o">=</span><span class="n">validation_options</span><span class="p">,</span> |
| <span class="n">budget_milli_node_hours</span><span class="o">=</span><span class="n">budget_milli_node_hours</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="n">model_display_name</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="o">=</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="n">sync</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">model</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.create_auto_ml_image_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_image_training_job">[docs]</a> <span class="k">def</span> <span class="nf">create_auto_ml_image_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">dataset</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageDataset</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"classification"</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"CLOUD"</span><span class="p">,</span> |
| <span class="n">base_model</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">validation_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">budget_milli_node_hours</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">disable_early_stopping</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">sync</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Create an AutoML Image Training Job.</span> |
| |
| <span class="sd"> :param project_id: Required. Project to run training in.</span> |
| <span class="sd"> :param region: Required. Location to run training in.</span> |
| <span class="sd"> :param display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param dataset: Required. The dataset within the same Project from which data will be used to train</span> |
| <span class="sd"> the Model. The Dataset must use schema compatible with Model being trained, and what is</span> |
| <span class="sd"> compatible should be described in the used TrainingPipeline's [training_task_definition]</span> |
| <span class="sd"> [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular</span> |
| <span class="sd"> Datasets, all their data is exported to training, to pick and choose from.</span> |
| <span class="sd"> :param prediction_type: The type of prediction the Model is to produce, one of:</span> |
| <span class="sd"> "classification" - Predict one out of multiple target values is picked for each row.</span> |
| <span class="sd"> "object_detection" - Predict a value based on its relation to other values. This type is</span> |
| <span class="sd"> available only to columns that contain semantically numeric values, i.e. integers or floating</span> |
| <span class="sd"> point number, even if stored as e.g. strings.</span> |
| <span class="sd"> :param multi_label: Required. Default is False. If false, a single-label (multi-class) Model will be</span> |
| <span class="sd"> trained (i.e. assuming that for each image just up to one annotation may be applicable). If true,</span> |
| <span class="sd"> a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may</span> |
| <span class="sd"> be applicable).</span> |
| <span class="sd"> This is only applicable for the "classification" prediction_type and will be ignored otherwise.</span> |
| <span class="sd"> :param model_type: Required. One of the following:</span> |
| <span class="sd"> "CLOUD" - Default for Image Classification. A Model best tailored to be used within Google Cloud,</span> |
| <span class="sd"> and which cannot be exported.</span> |
| <span class="sd"> "CLOUD_HIGH_ACCURACY_1" - Default for Image Object Detection. A model best tailored to be used</span> |
| <span class="sd"> within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should</span> |
| <span class="sd"> also have a higher prediction quality than other cloud models.</span> |
| <span class="sd"> "CLOUD_LOW_LATENCY_1" - A model best tailored to be used within Google Cloud, and which cannot be</span> |
| <span class="sd"> exported. Expected to have a low latency, but may have lower prediction quality than other cloud</span> |
| <span class="sd"> models.</span> |
| <span class="sd"> "MOBILE_TF_LOW_LATENCY_1" - A model that, in addition to being available within Google Cloud, can</span> |
| <span class="sd"> also be exported as TensorFlow or Core ML model and used on a mobile or edge device afterwards.</span> |
| <span class="sd"> Expected to have low latency, but may have lower prediction quality than other mobile models.</span> |
| <span class="sd"> "MOBILE_TF_VERSATILE_1" - A model that, in addition to being available within Google Cloud, can</span> |
| <span class="sd"> also be exported as TensorFlow or Core ML model and used on a mobile or edge device with</span> |
| <span class="sd"> afterwards.</span> |
| <span class="sd"> "MOBILE_TF_HIGH_ACCURACY_1" - A model that, in addition to being available within Google Cloud,</span> |
| <span class="sd"> can also be exported as TensorFlow or Core ML model and used on a mobile or edge device</span> |
| <span class="sd"> afterwards. Expected to have a higher latency, but should also have a higher prediction quality</span> |
| <span class="sd"> than other mobile models.</span> |
| <span class="sd"> :param base_model: Optional. Only permitted for Image Classification models. If it is specified, the</span> |
| <span class="sd"> new model will be trained based on the `base` model. Otherwise, the new model will be trained</span> |
| <span class="sd"> from scratch. The `base` model must be in the same Project and Location as the new Model to</span> |
| <span class="sd"> train, and have the same model_type.</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the training pipeline. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be</span> |
| <span class="sd"> in the same region as where the compute resource is created. If set, this TrainingPipeline will</span> |
| <span class="sd"> be secured by this key.</span> |
| <span class="sd"> Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``</span> |
| <span class="sd"> is not set separately.</span> |
| <span class="sd"> :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the model. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.</span> |
| <span class="sd"> The key needs to be in the same region as where the compute resource is created.</span> |
| <span class="sd"> If set, the trained Model will be secured by this key.</span> |
| <span class="sd"> :param training_fraction_split: Optional. The fraction of the input data that is to be used to train</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param validation_fraction_split: Optional. The fraction of the input data that is to be used to</span> |
| <span class="sd"> validate the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match</span> |
| <span class="sd"> this filter are used to train the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param validation_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match</span> |
| <span class="sd"> this filter are used to validate the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this</span> |
| <span class="sd"> filter are used to test the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param budget_milli_node_hours: Optional. The train budget of creating this Model, expressed in milli</span> |
| <span class="sd"> node hours i.e. 1,000 value in this field means 1 node hour.</span> |
| <span class="sd"> Defaults by `prediction_type`:</span> |
| <span class="sd"> `classification` - For Cloud models the budget must be: 8,000 - 800,000 milli node hours</span> |
| <span class="sd"> (inclusive). The default value is 192,000 which represents one day in wall time, assuming 8 nodes</span> |
| <span class="sd"> are used.</span> |
| <span class="sd"> `object_detection` - For Cloud models the budget must be: 20,000 - 900,000 milli node hours</span> |
| <span class="sd"> (inclusive). The default value is 216,000 which represents one day in wall time, assuming 9 nodes</span> |
| <span class="sd"> are used.</span> |
| <span class="sd"> The training cost of the model will not exceed this budget. The final cost will be attempted to</span> |
| <span class="sd"> be close to the budget, though may end up being (even) noticeably smaller - at the backend's</span> |
| <span class="sd"> discretion. This especially may happen when further model training ceases to provide any</span> |
| <span class="sd"> improvements. If the budget is set to a value known to be insufficient to train a Model for the</span> |
| <span class="sd"> given training set, the training won't be attempted and will error.</span> |
| <span class="sd"> :param model_display_name: Optional. The display name of the managed Vertex AI Model. The name can be</span> |
| <span class="sd"> up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon</span> |
| <span class="sd"> creation, the job's display_name is used.</span> |
| <span class="sd"> :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param disable_early_stopping: Required. If true, the entire budget is used. This disables the early</span> |
| <span class="sd"> stopping feature. By default, the early stopping feature is enabled, which means that training</span> |
| <span class="sd"> might stop before the entire training budget has been used, if further training does no longer</span> |
| <span class="sd"> brings significant improvement to the model.</span> |
| <span class="sd"> :param sync: Whether to execute this method synchronously. If False, this method will be executed in</span> |
| <span class="sd"> concurrent Future and any downstream object will be immediately returned and synced when the</span> |
| <span class="sd"> Future has completed.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_job</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_auto_ml_image_training_job</span><span class="p">(</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="o">=</span><span class="n">prediction_type</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="o">=</span><span class="n">multi_label</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span> |
| <span class="n">base_model</span><span class="o">=</span><span class="n">base_model</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s2">"AutoMLImageTrainingJob was not created"</span><span class="p">)</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="o">.</span><span class="n">run</span><span class="p">(</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="n">training_fraction_split</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="o">=</span><span class="n">validation_fraction_split</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="o">=</span><span class="n">test_fraction_split</span><span class="p">,</span> |
| <span class="n">training_filter_split</span><span class="o">=</span><span class="n">training_filter_split</span><span class="p">,</span> |
| <span class="n">validation_filter_split</span><span class="o">=</span><span class="n">validation_filter_split</span><span class="p">,</span> |
| <span class="n">test_filter_split</span><span class="o">=</span><span class="n">test_filter_split</span><span class="p">,</span> |
| <span class="n">budget_milli_node_hours</span><span class="o">=</span><span class="n">budget_milli_node_hours</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="n">model_display_name</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="o">=</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">disable_early_stopping</span><span class="o">=</span><span class="n">disable_early_stopping</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="n">sync</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">model</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.create_auto_ml_text_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_text_training_job">[docs]</a> <span class="k">def</span> <span class="nf">create_auto_ml_text_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">dataset</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">TextDataset</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">sentiment_max</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">validation_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">sync</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Create an AutoML Text Training Job.</span> |
| |
| <span class="sd"> :param project_id: Required. Project to run training in.</span> |
| <span class="sd"> :param region: Required. Location to run training in.</span> |
| <span class="sd"> :param display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param dataset: Required. The dataset within the same Project from which data will be used to train</span> |
| <span class="sd"> the Model. The Dataset must use schema compatible with Model being trained, and what is</span> |
| <span class="sd"> compatible should be described in the used TrainingPipeline's [training_task_definition]</span> |
| <span class="sd"> [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].</span> |
| <span class="sd"> :param prediction_type: The type of prediction the Model is to produce, one of:</span> |
| <span class="sd"> "classification" - A classification model analyzes text data and returns a list of categories</span> |
| <span class="sd"> that apply to the text found in the data. Vertex AI offers both single-label and multi-label text</span> |
| <span class="sd"> classification models.</span> |
| <span class="sd"> "extraction" - An entity extraction model inspects text data for known entities referenced in the</span> |
| <span class="sd"> data and labels those entities in the text.</span> |
| <span class="sd"> "sentiment" - A sentiment analysis model inspects text data and identifies the prevailing</span> |
| <span class="sd"> emotional opinion within it, especially to determine a writer's attitude as positive, negative,</span> |
| <span class="sd"> or neutral.</span> |
| <span class="sd"> :param multi_label: Required and only applicable for text classification task. If false, a</span> |
| <span class="sd"> single-label (multi-class) Model will be trained (i.e. assuming that for each text snippet just</span> |
| <span class="sd"> up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e.</span> |
| <span class="sd"> assuming that for each text snippet multiple annotations may be applicable).</span> |
| <span class="sd"> :param sentiment_max: Required and only applicable for sentiment task. A sentiment is expressed as an</span> |
| <span class="sd"> integer ordinal, where higher value means a more positive sentiment. The range of sentiments that</span> |
| <span class="sd"> will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the</span> |
| <span class="sd"> range must be represented in the dataset before a model can be created. Only the Annotations with</span> |
| <span class="sd"> this sentimentMax will be used for training. sentimentMax value must be between 1 and 10</span> |
| <span class="sd"> (inclusive).</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the training pipeline. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.</span> |
| <span class="sd"> The key needs to be in the same region as where the compute resource is created.</span> |
| <span class="sd"> If set, this TrainingPipeline will be secured by this key.</span> |
| <span class="sd"> Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``</span> |
| <span class="sd"> is not set separately.</span> |
| <span class="sd"> :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the model. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.</span> |
| <span class="sd"> The key needs to be in the same region as where the compute resource is created.</span> |
| <span class="sd"> If set, the trained Model will be secured by this key.</span> |
| <span class="sd"> :param training_fraction_split: Optional. The fraction of the input data that is to be used to train</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param validation_fraction_split: Optional. The fraction of the input data that is to be used to</span> |
| <span class="sd"> validate the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match</span> |
| <span class="sd"> this filter are used to train the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param validation_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match</span> |
| <span class="sd"> this filter are used to validate the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this</span> |
| <span class="sd"> filter are used to test the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param model_display_name: Optional. The display name of the managed Vertex AI Model. The name can be</span> |
| <span class="sd"> up to 128 characters long and can consist of any UTF-8 characters.</span> |
| <span class="sd"> If not provided upon creation, the job's display_name is used.</span> |
| <span class="sd"> :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param sync: Whether to execute this method synchronously. If False, this method will be executed in</span> |
| <span class="sd"> concurrent Future and any downstream object will be immediately returned and synced when the</span> |
| <span class="sd"> Future has completed.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_job</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_auto_ml_text_training_job</span><span class="p">(</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="o">=</span><span class="n">prediction_type</span><span class="p">,</span> |
| <span class="n">multi_label</span><span class="o">=</span><span class="n">multi_label</span><span class="p">,</span> |
| <span class="n">sentiment_max</span><span class="o">=</span><span class="n">sentiment_max</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s2">"AutoMLTextTrainingJob was not created"</span><span class="p">)</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="o">.</span><span class="n">run</span><span class="p">(</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="n">training_fraction_split</span><span class="p">,</span> |
| <span class="n">validation_fraction_split</span><span class="o">=</span><span class="n">validation_fraction_split</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="o">=</span><span class="n">test_fraction_split</span><span class="p">,</span> |
| <span class="n">training_filter_split</span><span class="o">=</span><span class="n">training_filter_split</span><span class="p">,</span> |
| <span class="n">validation_filter_split</span><span class="o">=</span><span class="n">validation_filter_split</span><span class="p">,</span> |
| <span class="n">test_filter_split</span><span class="o">=</span><span class="n">test_filter_split</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="n">model_display_name</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="o">=</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="n">sync</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">model</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.create_auto_ml_video_training_job"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.create_auto_ml_video_training_job">[docs]</a> <span class="k">def</span> <span class="nf">create_auto_ml_video_training_job</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">dataset</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">VideoDataset</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"classification"</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"CLOUD"</span><span class="p">,</span> |
| <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">training_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">test_filter_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">sync</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">models</span><span class="o">.</span><span class="n">Model</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Create an AutoML Video Training Job.</span> |
| |
| <span class="sd"> :param project_id: Required. Project to run training in.</span> |
| <span class="sd"> :param region: Required. Location to run training in.</span> |
| <span class="sd"> :param display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param dataset: Required. The dataset within the same Project from which data will be used to train</span> |
| <span class="sd"> the Model. The Dataset must use schema compatible with Model being trained, and what is</span> |
| <span class="sd"> compatible should be described in the used TrainingPipeline's [training_task_definition]</span> |
| <span class="sd"> [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular</span> |
| <span class="sd"> Datasets, all their data is exported to training, to pick and choose from.</span> |
| <span class="sd"> :param prediction_type: The type of prediction the Model is to produce, one of:</span> |
| <span class="sd"> "classification" - A video classification model classifies shots and segments in your videos</span> |
| <span class="sd"> according to your own defined labels.</span> |
| <span class="sd"> "object_tracking" - A video object tracking model detects and tracks multiple objects in shots</span> |
| <span class="sd"> and segments. You can use these models to track objects in your videos according to your own</span> |
| <span class="sd"> pre-defined, custom labels.</span> |
| <span class="sd"> "action_recognition" - A video action recognition model pinpoints the location of actions with</span> |
| <span class="sd"> short temporal durations (~1 second).</span> |
| <span class="sd"> :param model_type: Required. One of the following:</span> |
| <span class="sd"> "CLOUD" - available for "classification", "object_tracking" and "action_recognition" A Model best</span> |
| <span class="sd"> tailored to be used within Google Cloud, and which cannot be exported.</span> |
| <span class="sd"> "MOBILE_VERSATILE_1" - available for "classification", "object_tracking" and "action_recognition"</span> |
| <span class="sd"> A model that, in addition to being available within Google Cloud, can also be exported (see</span> |
| <span class="sd"> ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge</span> |
| <span class="sd"> device with afterwards.</span> |
| <span class="sd"> "MOBILE_CORAL_VERSATILE_1" - available only for "object_tracking" A versatile model that is meant</span> |
| <span class="sd"> to be exported (see ModelService.ExportModel) and used on a Google Coral device.</span> |
| <span class="sd"> "MOBILE_CORAL_LOW_LATENCY_1" - available only for "object_tracking" A model that trades off</span> |
| <span class="sd"> quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral</span> |
| <span class="sd"> device.</span> |
| <span class="sd"> "MOBILE_JETSON_VERSATILE_1" - available only for "object_tracking" A versatile model that is</span> |
| <span class="sd"> meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.</span> |
| <span class="sd"> "MOBILE_JETSON_LOW_LATENCY_1" - available only for "object_tracking" A model that trades off</span> |
| <span class="sd"> quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA</span> |
| <span class="sd"> Jetson device.</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the training pipeline. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.</span> |
| <span class="sd"> The key needs to be in the same region as where the compute resource is created.</span> |
| <span class="sd"> If set, this TrainingPipeline will be secured by this key.</span> |
| <span class="sd"> Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``</span> |
| <span class="sd"> is not set separately.</span> |
| <span class="sd"> :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer</span> |
| <span class="sd"> managed encryption key used to protect the model. Has the form:</span> |
| <span class="sd"> ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.</span> |
| <span class="sd"> The key needs to be in the same region as where the compute resource is created.</span> |
| <span class="sd"> If set, the trained Model will be secured by this key.</span> |
| <span class="sd"> :param training_fraction_split: Optional. The fraction of the input data that is to be used to train</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate</span> |
| <span class="sd"> the Model. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match</span> |
| <span class="sd"> this filter are used to train the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this</span> |
| <span class="sd"> filter are used to test the Model. A filter with same syntax as the one used in</span> |
| <span class="sd"> DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the</span> |
| <span class="sd"> FilterSplit filters, then it is assigned to the first set that applies to it in the training,</span> |
| <span class="sd"> validation, test order. This is ignored if Dataset is not provided.</span> |
| <span class="sd"> :param model_display_name: Optional. The display name of the managed Vertex AI Model. The name can be</span> |
| <span class="sd"> up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon</span> |
| <span class="sd"> creation, the job's display_name is used.</span> |
| <span class="sd"> :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label</span> |
| <span class="sd"> keys and values can be no longer than 64 characters (Unicode codepoints), can only contain</span> |
| <span class="sd"> lowercase letters, numeric characters, underscores and dashes. International characters are</span> |
| <span class="sd"> allowed. See https://goo.gl/xmQnxf for more information and examples of labels.</span> |
| <span class="sd"> :param sync: Whether to execute this method synchronously. If False, this method will be executed in</span> |
| <span class="sd"> concurrent Future and any downstream object will be immediately returned and synced when the</span> |
| <span class="sd"> Future has completed.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_job</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_auto_ml_video_training_job</span><span class="p">(</span> |
| <span class="n">project</span><span class="o">=</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">display_name</span><span class="o">=</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">prediction_type</span><span class="o">=</span><span class="n">prediction_type</span><span class="p">,</span> |
| <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> |
| <span class="n">training_encryption_spec_key_name</span><span class="o">=</span><span class="n">training_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">model_encryption_spec_key_name</span><span class="o">=</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s2">"AutoMLVideoTrainingJob was not created"</span><span class="p">)</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job</span><span class="o">.</span><span class="n">run</span><span class="p">(</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="n">training_fraction_split</span><span class="p">,</span> |
| <span class="n">test_fraction_split</span><span class="o">=</span><span class="n">test_fraction_split</span><span class="p">,</span> |
| <span class="n">training_filter_split</span><span class="o">=</span><span class="n">training_filter_split</span><span class="p">,</span> |
| <span class="n">test_filter_split</span><span class="o">=</span><span class="n">test_filter_split</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="n">model_display_name</span><span class="p">,</span> |
| <span class="n">model_labels</span><span class="o">=</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="n">sync</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">model</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.delete_training_pipeline"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.delete_training_pipeline">[docs]</a> <span class="k">def</span> <span class="nf">delete_training_pipeline</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">training_pipeline</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">retry</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Retry</span><span class="p">,</span> <span class="n">_MethodDefault</span><span class="p">]</span> <span class="o">=</span> <span class="n">DEFAULT</span><span class="p">,</span> |
| <span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">metadata</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(),</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">Operation</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Deletes a TrainingPipeline.</span> |
| |
| <span class="sd"> :param project_id: Required. The ID of the Google Cloud project that the service belongs to.</span> |
| <span class="sd"> :param region: Required. The ID of the Google Cloud region that the service belongs to.</span> |
| <span class="sd"> :param training_pipeline: Required. The name of the TrainingPipeline resource to be deleted.</span> |
| <span class="sd"> :param retry: Designation of what errors, if any, should be retried.</span> |
| <span class="sd"> :param timeout: The timeout for this request.</span> |
| <span class="sd"> :param metadata: Strings which should be sent along with the request as metadata.</span> |
| <span class="sd"> """</span> |
| <span class="n">client</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_pipeline_service_client</span><span class="p">(</span><span class="n">region</span><span class="p">)</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">training_pipeline_path</span><span class="p">(</span><span class="n">project_id</span><span class="p">,</span> <span class="n">region</span><span class="p">,</span> <span class="n">training_pipeline</span><span class="p">)</span> |
| |
| <span class="n">result</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">delete_training_pipeline</span><span class="p">(</span> |
| <span class="n">request</span><span class="o">=</span><span class="p">{</span> |
| <span class="s1">'name'</span><span class="p">:</span> <span class="n">name</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">retry</span><span class="o">=</span><span class="n">retry</span><span class="p">,</span> |
| <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span> |
| <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.get_training_pipeline"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.get_training_pipeline">[docs]</a> <span class="k">def</span> <span class="nf">get_training_pipeline</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">training_pipeline</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">retry</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Retry</span><span class="p">,</span> <span class="n">_MethodDefault</span><span class="p">]</span> <span class="o">=</span> <span class="n">DEFAULT</span><span class="p">,</span> |
| <span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">metadata</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(),</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">TrainingPipeline</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Gets a TrainingPipeline.</span> |
| |
| <span class="sd"> :param project_id: Required. The ID of the Google Cloud project that the service belongs to.</span> |
| <span class="sd"> :param region: Required. The ID of the Google Cloud region that the service belongs to.</span> |
| <span class="sd"> :param training_pipeline: Required. The name of the TrainingPipeline resource.</span> |
| <span class="sd"> :param retry: Designation of what errors, if any, should be retried.</span> |
| <span class="sd"> :param timeout: The timeout for this request.</span> |
| <span class="sd"> :param metadata: Strings which should be sent along with the request as metadata.</span> |
| <span class="sd"> """</span> |
| <span class="n">client</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_pipeline_service_client</span><span class="p">(</span><span class="n">region</span><span class="p">)</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">training_pipeline_path</span><span class="p">(</span><span class="n">project_id</span><span class="p">,</span> <span class="n">region</span><span class="p">,</span> <span class="n">training_pipeline</span><span class="p">)</span> |
| |
| <span class="n">result</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get_training_pipeline</span><span class="p">(</span> |
| <span class="n">request</span><span class="o">=</span><span class="p">{</span> |
| <span class="s1">'name'</span><span class="p">:</span> <span class="n">name</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">retry</span><span class="o">=</span><span class="n">retry</span><span class="p">,</span> |
| <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span> |
| <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span></div> |
| |
| <span class="nd">@GoogleBaseHook</span><span class="o">.</span><span class="n">fallback_to_default_project_id</span> |
| <div class="viewcode-block" id="AutoMLHook.list_training_pipelines"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/hooks/vertex_ai/auto_ml/index.html#airflow.providers.google.cloud.hooks.vertex_ai.auto_ml.AutoMLHook.list_training_pipelines">[docs]</a> <span class="k">def</span> <span class="nf">list_training_pipelines</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">page_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">page_token</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="nb">filter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">read_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">retry</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Retry</span><span class="p">,</span> <span class="n">_MethodDefault</span><span class="p">]</span> <span class="o">=</span> <span class="n">DEFAULT</span><span class="p">,</span> |
| <span class="n">timeout</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">metadata</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(),</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">ListTrainingPipelinesPager</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Lists TrainingPipelines in a Location.</span> |
| |
| <span class="sd"> :param project_id: Required. The ID of the Google Cloud project that the service belongs to.</span> |
| <span class="sd"> :param region: Required. The ID of the Google Cloud region that the service belongs to.</span> |
| <span class="sd"> :param filter: Optional. The standard list filter. Supported fields:</span> |
| |
| <span class="sd"> - ``display_name`` supports = and !=.</span> |
| |
| <span class="sd"> - ``state`` supports = and !=.</span> |
| |
| <span class="sd"> Some examples of using the filter are:</span> |
| |
| <span class="sd"> - ``state="PIPELINE_STATE_SUCCEEDED" AND display_name="my_pipeline"``</span> |
| |
| <span class="sd"> - ``state="PIPELINE_STATE_RUNNING" OR display_name="my_pipeline"``</span> |
| |
| <span class="sd"> - ``NOT display_name="my_pipeline"``</span> |
| |
| <span class="sd"> - ``state="PIPELINE_STATE_FAILED"``</span> |
| <span class="sd"> :param page_size: Optional. The standard list page size.</span> |
| <span class="sd"> :param page_token: Optional. The standard list page token. Typically obtained via</span> |
| <span class="sd"> [ListTrainingPipelinesResponse.next_page_token][google.cloud.aiplatform.v1.ListTrainingPipelinesResponse.next_page_token]</span> |
| <span class="sd"> of the previous</span> |
| <span class="sd"> [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines]</span> |
| <span class="sd"> call.</span> |
| <span class="sd"> :param read_mask: Optional. Mask specifying which fields to read.</span> |
| <span class="sd"> :param retry: Designation of what errors, if any, should be retried.</span> |
| <span class="sd"> :param timeout: The timeout for this request.</span> |
| <span class="sd"> :param metadata: Strings which should be sent along with the request as metadata.</span> |
| <span class="sd"> """</span> |
| <span class="n">client</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_pipeline_service_client</span><span class="p">(</span><span class="n">region</span><span class="p">)</span> |
| <span class="n">parent</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">common_location_path</span><span class="p">(</span><span class="n">project_id</span><span class="p">,</span> <span class="n">region</span><span class="p">)</span> |
| |
| <span class="n">result</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">list_training_pipelines</span><span class="p">(</span> |
| <span class="n">request</span><span class="o">=</span><span class="p">{</span> |
| <span class="s1">'parent'</span><span class="p">:</span> <span class="n">parent</span><span class="p">,</span> |
| <span class="s1">'page_size'</span><span class="p">:</span> <span class="n">page_size</span><span class="p">,</span> |
| <span class="s1">'page_token'</span><span class="p">:</span> <span class="n">page_token</span><span class="p">,</span> |
| <span class="s1">'filter'</span><span class="p">:</span> <span class="nb">filter</span><span class="p">,</span> |
| <span class="s1">'read_mask'</span><span class="p">:</span> <span class="n">read_mask</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">retry</span><span class="o">=</span><span class="n">retry</span><span class="p">,</span> |
| <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span> |
| <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span></div></div> |
| </pre></div> |
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