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| <h1>Source code for airflow.providers.google.cloud.operators.vertex_ai.custom_job</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">"""This module contains Google Vertex AI operators."""</span> |
| |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">TYPE_CHECKING</span><span class="p">,</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.exceptions</span> <span class="kn">import</span> <span class="n">NotFound</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.retry</span> <span class="kn">import</span> <span class="n">Retry</span> |
| <span class="kn">from</span> <span class="nn">google.cloud.aiplatform.models</span> <span class="kn">import</span> <span class="n">Model</span> |
| <span class="kn">from</span> <span class="nn">google.cloud.aiplatform_v1.types.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> |
| <span class="kn">from</span> <span class="nn">google.cloud.aiplatform_v1.types.training_pipeline</span> <span class="kn">import</span> <span class="n">TrainingPipeline</span> |
| |
| <span class="kn">from</span> <span class="nn">airflow.models</span> <span class="kn">import</span> <span class="n">BaseOperator</span> |
| <span class="kn">from</span> <span class="nn">airflow.providers.google.cloud.hooks.vertex_ai.custom_job</span> <span class="kn">import</span> <span class="n">CustomJobHook</span> |
| <span class="kn">from</span> <span class="nn">airflow.providers.google.cloud.links.vertex_ai</span> <span class="kn">import</span> <span class="n">VertexAIModelLink</span><span class="p">,</span> <span class="n">VertexAITrainingPipelinesLink</span> |
| |
| <span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">airflow.utils.context</span> <span class="kn">import</span> <span class="n">Context</span> |
| |
| |
| <div class="viewcode-block" id="CustomTrainingJobBaseOperator"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CustomTrainingJobBaseOperator">[docs]</a><span class="k">class</span> <span class="nc">CustomTrainingJobBaseOperator</span><span class="p">(</span><span class="n">BaseOperator</span><span class="p">):</span> |
| <span class="sd">"""The base class for operators that launch Custom jobs on VertexAI."""</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="o">*</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">container_uri</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">model_serving_container_image_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">model_serving_container_predict_route</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_serving_container_health_route</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_serving_container_command</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="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_serving_container_args</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="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_serving_container_environment_variables</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">model_serving_container_ports</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="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_description</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_instance_schema_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">model_parameters_schema_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">model_prediction_schema_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">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">staging_bucket</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="c1"># RUN</span> |
| <span class="n">dataset_id</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">annotation_schema_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">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">base_output_dir</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">service_account</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">network</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">bigquery_destination</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">args</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">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</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">environment_variables</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">replica_count</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> |
| <span class="n">machine_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"n1-standard-4"</span><span class="p">,</span> |
| <span class="n">accelerator_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"ACCELERATOR_TYPE_UNSPECIFIED"</span><span class="p">,</span> |
| <span class="n">accelerator_count</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">boot_disk_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"pd-ssd"</span><span class="p">,</span> |
| <span class="n">boot_disk_size_gb</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</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">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">tensorboard</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">sync</span><span class="o">=</span><span class="kc">True</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="o">**</span><span class="n">kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">project_id</span> <span class="o">=</span> <span class="n">project_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">display_name</span> <span class="o">=</span> <span class="n">display_name</span> |
| <span class="c1"># START Custom</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">container_uri</span> <span class="o">=</span> <span class="n">container_uri</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_image_uri</span> <span class="o">=</span> <span class="n">model_serving_container_image_uri</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_predict_route</span> <span class="o">=</span> <span class="n">model_serving_container_predict_route</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_health_route</span> <span class="o">=</span> <span class="n">model_serving_container_health_route</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_command</span> <span class="o">=</span> <span class="n">model_serving_container_command</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_args</span> <span class="o">=</span> <span class="n">model_serving_container_args</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_environment_variables</span> <span class="o">=</span> <span class="n">model_serving_container_environment_variables</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_ports</span> <span class="o">=</span> <span class="n">model_serving_container_ports</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_description</span> <span class="o">=</span> <span class="n">model_description</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_instance_schema_uri</span> <span class="o">=</span> <span class="n">model_instance_schema_uri</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_parameters_schema_uri</span> <span class="o">=</span> <span class="n">model_parameters_schema_uri</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_prediction_schema_uri</span> <span class="o">=</span> <span class="n">model_prediction_schema_uri</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span> |
| <span class="bp">self</span><span class="o">.</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="bp">self</span><span class="o">.</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="bp">self</span><span class="o">.</span><span class="n">staging_bucket</span> <span class="o">=</span> <span class="n">staging_bucket</span> |
| <span class="c1"># END Custom</span> |
| <span class="c1"># START Run param</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">dataset_id</span><span class="p">)</span> <span class="k">if</span> <span class="n">dataset_id</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">annotation_schema_uri</span> <span class="o">=</span> <span class="n">annotation_schema_uri</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_display_name</span> <span class="o">=</span> <span class="n">model_display_name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model_labels</span> <span class="o">=</span> <span class="n">model_labels</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">base_output_dir</span> <span class="o">=</span> <span class="n">base_output_dir</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">service_account</span> <span class="o">=</span> <span class="n">service_account</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">network</span> <span class="o">=</span> <span class="n">network</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bigquery_destination</span> <span class="o">=</span> <span class="n">bigquery_destination</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">args</span> <span class="o">=</span> <span class="n">args</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">environment_variables</span> <span class="o">=</span> <span class="n">environment_variables</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">replica_count</span> <span class="o">=</span> <span class="n">replica_count</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">machine_type</span> <span class="o">=</span> <span class="n">machine_type</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">accelerator_type</span> <span class="o">=</span> <span class="n">accelerator_type</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">accelerator_count</span> <span class="o">=</span> <span class="n">accelerator_count</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_type</span> <span class="o">=</span> <span class="n">boot_disk_type</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_size_gb</span> <span class="o">=</span> <span class="n">boot_disk_size_gb</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">training_fraction_split</span> <span class="o">=</span> <span class="n">training_fraction_split</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">validation_fraction_split</span> <span class="o">=</span> <span class="n">validation_fraction_split</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">test_fraction_split</span> <span class="o">=</span> <span class="n">test_fraction_split</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">training_filter_split</span> <span class="o">=</span> <span class="n">training_filter_split</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">validation_filter_split</span> <span class="o">=</span> <span class="n">validation_filter_split</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">test_filter_split</span> <span class="o">=</span> <span class="n">test_filter_split</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">predefined_split_column_name</span> <span class="o">=</span> <span class="n">predefined_split_column_name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">timestamp_split_column_name</span> <span class="o">=</span> <span class="n">timestamp_split_column_name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">tensorboard</span> <span class="o">=</span> <span class="n">tensorboard</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sync</span> <span class="o">=</span> <span class="n">sync</span> |
| <span class="c1"># END Run param</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gcp_conn_id</span> <span class="o">=</span> <span class="n">gcp_conn_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">delegate_to</span> <span class="o">=</span> <span class="n">delegate_to</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span> <span class="o">=</span> <span class="n">impersonation_chain</span></div> |
| |
| |
| <div class="viewcode-block" id="CreateCustomContainerTrainingJobOperator"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator">[docs]</a><span class="k">class</span> <span class="nc">CreateCustomContainerTrainingJobOperator</span><span class="p">(</span><span class="n">CustomTrainingJobBaseOperator</span><span class="p">):</span> |
| <span class="sd">"""Create Custom Container Training job</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 display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param command: The command to be invoked when the container is started.</span> |
| <span class="sd"> It overrides the entrypoint instruction in Dockerfile when provided</span> |
| <span class="sd"> :param container_uri: Required: Uri of the training container image in the GCR.</span> |
| <span class="sd"> :param model_serving_container_image_uri: If the training produces a managed Vertex AI Model, the URI</span> |
| <span class="sd"> of the Model serving container suitable for serving the model produced by the</span> |
| <span class="sd"> training script.</span> |
| <span class="sd"> :param model_serving_container_predict_route: If the training produces a managed Vertex AI Model, An</span> |
| <span class="sd"> HTTP path to send prediction requests to the container, and which must be supported</span> |
| <span class="sd"> by it. If not specified a default HTTP path will be used by Vertex AI.</span> |
| <span class="sd"> :param model_serving_container_health_route: If the training produces a managed Vertex AI Model, an</span> |
| <span class="sd"> HTTP path to send health check requests to the container, and which must be supported</span> |
| <span class="sd"> by it. If not specified a standard HTTP path will be used by AI Platform.</span> |
| <span class="sd"> :param model_serving_container_command: The command with which the container is run. Not executed</span> |
| <span class="sd"> within a shell. The Docker image's ENTRYPOINT is used if this is not provided.</span> |
| <span class="sd"> Variable references $(VAR_NAME) are expanded using the container's</span> |
| <span class="sd"> environment. If a variable cannot be resolved, the reference in the</span> |
| <span class="sd"> input string will be unchanged. The $(VAR_NAME) syntax can be escaped</span> |
| <span class="sd"> with a double $$, ie: $$(VAR_NAME). Escaped references will never be</span> |
| <span class="sd"> expanded, regardless of whether the variable exists or not.</span> |
| <span class="sd"> :param model_serving_container_args: The arguments to the command. The Docker image's CMD is used if</span> |
| <span class="sd"> this is not provided. Variable references $(VAR_NAME) are expanded using the</span> |
| <span class="sd"> container's environment. If a variable cannot be resolved, the reference</span> |
| <span class="sd"> in the input string will be unchanged. The $(VAR_NAME) syntax can be</span> |
| <span class="sd"> escaped with a double $$, ie: $$(VAR_NAME). Escaped references will</span> |
| <span class="sd"> never be expanded, regardless of whether the variable exists or not.</span> |
| <span class="sd"> :param model_serving_container_environment_variables: The environment variables that are to be</span> |
| <span class="sd"> present in the container. Should be a dictionary where keys are environment variable names</span> |
| <span class="sd"> and values are environment variable values for those names.</span> |
| <span class="sd"> :param model_serving_container_ports: Declaration of ports that are exposed by the container. This</span> |
| <span class="sd"> field is primarily informational, it gives Vertex AI information about the</span> |
| <span class="sd"> network connections the container uses. Listing or not a port here has</span> |
| <span class="sd"> no impact on whether the port is actually exposed, any port listening on</span> |
| <span class="sd"> the default "0.0.0.0" address inside a container will be accessible from</span> |
| <span class="sd"> the network.</span> |
| <span class="sd"> :param model_description: The description of the Model.</span> |
| <span class="sd"> :param model_instance_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the format of a single instance, which</span> |
| <span class="sd"> are used in</span> |
| <span class="sd"> ``PredictRequest.instances``,</span> |
| <span class="sd"> ``ExplainRequest.instances``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.input_config``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform. Note: The URI given on output will be immutable</span> |
| <span class="sd"> and probably different, including the URI scheme, than the</span> |
| <span class="sd"> one given on input. The output URI will point to a location</span> |
| <span class="sd"> where the user only has a read access.</span> |
| <span class="sd"> :param model_parameters_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the parameters of prediction and</span> |
| <span class="sd"> explanation via</span> |
| <span class="sd"> ``PredictRequest.parameters``,</span> |
| <span class="sd"> ``ExplainRequest.parameters``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.model_parameters``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform, if no parameters are supported it is set to an</span> |
| <span class="sd"> empty string. Note: The URI given on output will be</span> |
| <span class="sd"> immutable and probably different, including the URI scheme,</span> |
| <span class="sd"> than the one given on input. The output URI will point to a</span> |
| <span class="sd"> location where the user only has a read access.</span> |
| <span class="sd"> :param model_prediction_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the format of a single prediction</span> |
| <span class="sd"> produced by this Model, which are returned via</span> |
| <span class="sd"> ``PredictResponse.predictions``,</span> |
| <span class="sd"> ``ExplainResponse.explanations``,</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.output_config``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform. Note: The URI given on output will be immutable</span> |
| <span class="sd"> and probably different, including the URI scheme, than the</span> |
| <span class="sd"> one given on input. The output URI will point to a location</span> |
| <span class="sd"> where the user only has a read access.</span> |
| <span class="sd"> :param project_id: Project to run training in.</span> |
| <span class="sd"> :param region: Location to run training in.</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to</span> |
| <span class="sd"> organize TrainingPipelines.</span> |
| <span class="sd"> Label keys and values can be no longer than 64</span> |
| <span class="sd"> characters, can only</span> |
| <span class="sd"> contain lowercase letters, numeric characters,</span> |
| <span class="sd"> underscores and dashes. International characters</span> |
| <span class="sd"> are allowed.</span> |
| <span class="sd"> See https://goo.gl/xmQnxf for more information</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> by this key if ``model_to_upload`` 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> resource is created.</span> |
| |
| <span class="sd"> If set, the trained Model will be secured by this key.</span> |
| <span class="sd"> :param staging_bucket: Bucket used to stage source and training artifacts.</span> |
| <span class="sd"> :param dataset: Vertex AI to fit this training against.</span> |
| <span class="sd"> :param annotation_schema_uri: Google Cloud Storage URI points to a YAML file describing</span> |
| <span class="sd"> annotation schema. The schema is defined as an OpenAPI 3.0.2</span> |
| <span class="sd"> [Schema Object]</span> |
| <span class="sd"> (https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schema-object)</span> |
| |
| <span class="sd"> Only Annotations that both match this schema and belong to</span> |
| <span class="sd"> DataItems not ignored by the split method are used in</span> |
| <span class="sd"> respectively training, validation or test role, depending on</span> |
| <span class="sd"> the role of the DataItem they are on.</span> |
| |
| <span class="sd"> When used in conjunction with</span> |
| <span class="sd"> ``annotations_filter``,</span> |
| <span class="sd"> the Annotations used for training are filtered by both</span> |
| <span class="sd"> ``annotations_filter``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``annotation_schema_uri``.</span> |
| <span class="sd"> :param model_display_name: If the script produces a managed Vertex AI Model. The display name of</span> |
| <span class="sd"> the Model. The name can be up to 128 characters long and can be consist</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> organize your Models.</span> |
| <span class="sd"> Label keys and values can be no longer than 64</span> |
| <span class="sd"> characters, can only</span> |
| <span class="sd"> contain lowercase letters, numeric characters,</span> |
| <span class="sd"> underscores and dashes. International characters</span> |
| <span class="sd"> are allowed.</span> |
| <span class="sd"> See https://goo.gl/xmQnxf for more information</span> |
| <span class="sd"> and examples of labels.</span> |
| <span class="sd"> :param base_output_dir: GCS output directory of job. If not provided a timestamped directory in the</span> |
| <span class="sd"> staging directory will be used.</span> |
| |
| <span class="sd"> Vertex AI sets the following environment variables when it runs your training code:</span> |
| |
| <span class="sd"> - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts,</span> |
| <span class="sd"> i.e. <base_output_dir>/model/</span> |
| <span class="sd"> - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints,</span> |
| <span class="sd"> i.e. <base_output_dir>/checkpoints/</span> |
| <span class="sd"> - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard</span> |
| <span class="sd"> logs, i.e. <base_output_dir>/logs/</span> |
| <span class="sd"> :param service_account: Specifies the service account for workload run-as account.</span> |
| <span class="sd"> Users submitting jobs must have act-as permission on this run-as account.</span> |
| <span class="sd"> :param network: The full name of the Compute Engine network to which the job</span> |
| <span class="sd"> should be peered.</span> |
| <span class="sd"> Private services access must already be configured for the network.</span> |
| <span class="sd"> If left unspecified, the job is not peered with any network.</span> |
| <span class="sd"> :param bigquery_destination: Provide this field if `dataset` is a BiqQuery dataset.</span> |
| <span class="sd"> The BigQuery project location where the training data is to</span> |
| <span class="sd"> be written to. In the given project a new dataset is created</span> |
| <span class="sd"> with name</span> |
| <span class="sd"> ``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>``</span> |
| <span class="sd"> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All</span> |
| <span class="sd"> training input data will be written into that dataset. In</span> |
| <span class="sd"> the dataset three tables will be created, ``training``,</span> |
| <span class="sd"> ``validation`` and ``test``.</span> |
| |
| <span class="sd"> - AIP_DATA_FORMAT = "bigquery".</span> |
| <span class="sd"> - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training"</span> |
| <span class="sd"> - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation"</span> |
| <span class="sd"> - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"</span> |
| <span class="sd"> :param args: Command line arguments to be passed to the Python script.</span> |
| <span class="sd"> :param environment_variables: Environment variables to be passed to the container.</span> |
| <span class="sd"> Should be a dictionary where keys are environment variable names</span> |
| <span class="sd"> and values are environment variable values for those names.</span> |
| <span class="sd"> At most 10 environment variables can be specified.</span> |
| <span class="sd"> The Name of the environment variable must be unique.</span> |
| <span class="sd"> :param replica_count: The number of worker replicas. If replica count = 1 then one chief</span> |
| <span class="sd"> replica will be provisioned. If replica_count > 1 the remainder will be</span> |
| <span class="sd"> provisioned as a worker replica pool.</span> |
| <span class="sd"> :param machine_type: The type of machine to use for training.</span> |
| <span class="sd"> :param accelerator_type: Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED,</span> |
| <span class="sd"> NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4,</span> |
| <span class="sd"> NVIDIA_TESLA_T4</span> |
| <span class="sd"> :param accelerator_count: The number of accelerators to attach to a worker replica.</span> |
| <span class="sd"> :param boot_disk_type: Type of the boot disk, default is `pd-ssd`.</span> |
| <span class="sd"> Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or</span> |
| <span class="sd"> `pd-standard` (Persistent Disk Hard Disk Drive).</span> |
| <span class="sd"> :param boot_disk_size_gb: Size in GB of the boot disk, default is 100GB.</span> |
| <span class="sd"> boot disk size must be within the range of [100, 64000].</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</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> this filter are used to test the Model. A filter with same syntax</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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 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</span> |
| <span class="sd"> value in the column) must be one of {``training``,</span> |
| <span class="sd"> ``validation``, ``test``}, and it defines to which set the</span> |
| <span class="sd"> given piece of data is assigned. If for a piece of data the</span> |
| <span class="sd"> 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 timestamp_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 values of the key (the values in</span> |
| <span class="sd"> the column) must be in RFC 3339 `date-time` format, where</span> |
| <span class="sd"> `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a</span> |
| <span class="sd"> piece of data the key is not present or has an invalid value,</span> |
| <span class="sd"> that piece is ignored by the pipeline.</span> |
| |
| <span class="sd"> Supported only for tabular and time series Datasets.</span> |
| <span class="sd"> :param tensorboard: Optional. The name of a Vertex AI resource to which this CustomJob will upload</span> |
| <span class="sd"> logs. Format:</span> |
| <span class="sd"> ``projects/{project}/locations/{location}/tensorboards/{tensorboard}``</span> |
| <span class="sd"> For more information on configuring your service account please visit:</span> |
| <span class="sd"> https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training</span> |
| <span class="sd"> :param sync: Whether to execute the AI Platform job synchronously. If False, this method</span> |
| <span class="sd"> will be executed in concurrent Future and any downstream object will</span> |
| <span class="sd"> be immediately returned and synced when the Future has completed.</span> |
| <span class="sd"> :param gcp_conn_id: The connection ID to use connecting to Google Cloud.</span> |
| <span class="sd"> :param delegate_to: The account to impersonate using domain-wide delegation of authority,</span> |
| <span class="sd"> if any. For this to work, the service account making the request must have</span> |
| <span class="sd"> domain-wide delegation enabled.</span> |
| <span class="sd"> :param impersonation_chain: Optional service account to impersonate using short-term</span> |
| <span class="sd"> credentials, or chained list of accounts required to get the access_token</span> |
| <span class="sd"> of the last account in the list, which will be impersonated in the request.</span> |
| <span class="sd"> If set as a string, the account must grant the originating account</span> |
| <span class="sd"> the Service Account Token Creator IAM role.</span> |
| <span class="sd"> If set as a sequence, the identities from the list must grant</span> |
| <span class="sd"> Service Account Token Creator IAM role to the directly preceding identity, with first</span> |
| <span class="sd"> account from the list granting this role to the originating account (templated).</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="CreateCustomContainerTrainingJobOperator.template_fields"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator.template_fields">[docs]</a> <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s1">'region'</span><span class="p">,</span> |
| <span class="s1">'command'</span><span class="p">,</span> |
| <span class="s1">'impersonation_chain'</span><span class="p">,</span></div> |
| <span class="p">]</span> |
| <div class="viewcode-block" id="CreateCustomContainerTrainingJobOperator.operator_extra_links"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator.operator_extra_links">[docs]</a> <span class="n">operator_extra_links</span> <span class="o">=</span> <span class="p">(</span><span class="n">VertexAIModelLink</span><span class="p">(),)</span></div> |
| |
| <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="o">*</span><span class="p">,</span> |
| <span class="n">command</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="p">[],</span> |
| <span class="o">**</span><span class="n">kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">command</span> <span class="o">=</span> <span class="n">command</span> |
| |
| <div class="viewcode-block" id="CreateCustomContainerTrainingJobOperator.execute"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator.execute">[docs]</a> <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="s2">"Context"</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hook</span> <span class="o">=</span> <span class="n">CustomJobHook</span><span class="p">(</span> |
| <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span><span class="p">,</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">hook</span><span class="o">.</span><span class="n">create_custom_container_training_job</span><span class="p">(</span> |
| <span class="n">project_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">region</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">container_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">container_uri</span><span class="p">,</span> |
| <span class="n">command</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">command</span><span class="p">,</span> |
| <span class="n">model_serving_container_image_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_image_uri</span><span class="p">,</span> |
| <span class="n">model_serving_container_predict_route</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_predict_route</span><span class="p">,</span> |
| <span class="n">model_serving_container_health_route</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_health_route</span><span class="p">,</span> |
| <span class="n">model_serving_container_command</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_command</span><span class="p">,</span> |
| <span class="n">model_serving_container_args</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_args</span><span class="p">,</span> |
| <span class="n">model_serving_container_environment_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_environment_variables</span><span class="p">,</span> |
| <span class="n">model_serving_container_ports</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_ports</span><span class="p">,</span> |
| <span class="n">model_description</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_description</span><span class="p">,</span> |
| <span class="n">model_instance_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_instance_schema_uri</span><span class="p">,</span> |
| <span class="n">model_parameters_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_parameters_schema_uri</span><span class="p">,</span> |
| <span class="n">model_prediction_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_prediction_schema_uri</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">staging_bucket</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">staging_bucket</span><span class="p">,</span> |
| <span class="c1"># RUN</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">annotation_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">annotation_schema_uri</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">base_output_dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">base_output_dir</span><span class="p">,</span> |
| <span class="n">service_account</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">service_account</span><span class="p">,</span> |
| <span class="n">network</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> |
| <span class="n">bigquery_destination</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bigquery_destination</span><span class="p">,</span> |
| <span class="n">args</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> |
| <span class="n">environment_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">environment_variables</span><span class="p">,</span> |
| <span class="n">replica_count</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">replica_count</span><span class="p">,</span> |
| <span class="n">machine_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">machine_type</span><span class="p">,</span> |
| <span class="n">accelerator_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">accelerator_type</span><span class="p">,</span> |
| <span class="n">accelerator_count</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">accelerator_count</span><span class="p">,</span> |
| <span class="n">boot_disk_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_type</span><span class="p">,</span> |
| <span class="n">boot_disk_size_gb</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_size_gb</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</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="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">test_filter_split</span><span class="p">,</span> |
| <span class="n">predefined_split_column_name</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">timestamp_split_column_name</span><span class="p">,</span> |
| <span class="n">tensorboard</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tensorboard</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="n">result</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> |
| <span class="n">model_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="o">.</span><span class="n">extract_model_id</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> |
| <span class="n">VertexAIModelLink</span><span class="o">.</span><span class="n">persist</span><span class="p">(</span><span class="n">context</span><span class="o">=</span><span class="n">context</span><span class="p">,</span> <span class="n">task_instance</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_id</span><span class="o">=</span><span class="n">model_id</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span></div> |
| |
| <div class="viewcode-block" id="CreateCustomContainerTrainingJobOperator.on_kill"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator.on_kill">[docs]</a> <span class="k">def</span> <span class="nf">on_kill</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">"""</span> |
| <span class="sd"> Callback called when the operator is killed.</span> |
| <span class="sd"> Cancel any running job.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="o">.</span><span class="n">cancel_job</span><span class="p">()</span></div></div> |
| |
| |
| <div class="viewcode-block" id="CreateCustomPythonPackageTrainingJobOperator"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator">[docs]</a><span class="k">class</span> <span class="nc">CreateCustomPythonPackageTrainingJobOperator</span><span class="p">(</span><span class="n">CustomTrainingJobBaseOperator</span><span class="p">):</span> |
| <span class="sd">"""Create Custom Python Package Training job</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 display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param python_package_gcs_uri: Required: GCS location of the training python package.</span> |
| <span class="sd"> :param python_module_name: Required: The module name of the training python package.</span> |
| <span class="sd"> :param container_uri: Required: Uri of the training container image in the GCR.</span> |
| <span class="sd"> :param model_serving_container_image_uri: If the training produces a managed Vertex AI Model, the URI</span> |
| <span class="sd"> of the Model serving container suitable for serving the model produced by the</span> |
| <span class="sd"> training script.</span> |
| <span class="sd"> :param model_serving_container_predict_route: If the training produces a managed Vertex AI Model, An</span> |
| <span class="sd"> HTTP path to send prediction requests to the container, and which must be supported</span> |
| <span class="sd"> by it. If not specified a default HTTP path will be used by Vertex AI.</span> |
| <span class="sd"> :param model_serving_container_health_route: If the training produces a managed Vertex AI Model, an</span> |
| <span class="sd"> HTTP path to send health check requests to the container, and which must be supported</span> |
| <span class="sd"> by it. If not specified a standard HTTP path will be used by AI Platform.</span> |
| <span class="sd"> :param model_serving_container_command: The command with which the container is run. Not executed</span> |
| <span class="sd"> within a shell. The Docker image's ENTRYPOINT is used if this is not provided.</span> |
| <span class="sd"> Variable references $(VAR_NAME) are expanded using the container's</span> |
| <span class="sd"> environment. If a variable cannot be resolved, the reference in the</span> |
| <span class="sd"> input string will be unchanged. The $(VAR_NAME) syntax can be escaped</span> |
| <span class="sd"> with a double $$, ie: $$(VAR_NAME). Escaped references will never be</span> |
| <span class="sd"> expanded, regardless of whether the variable exists or not.</span> |
| <span class="sd"> :param model_serving_container_args: The arguments to the command. The Docker image's CMD is used if</span> |
| <span class="sd"> this is not provided. Variable references $(VAR_NAME) are expanded using the</span> |
| <span class="sd"> container's environment. If a variable cannot be resolved, the reference</span> |
| <span class="sd"> in the input string will be unchanged. The $(VAR_NAME) syntax can be</span> |
| <span class="sd"> escaped with a double $$, ie: $$(VAR_NAME). Escaped references will</span> |
| <span class="sd"> never be expanded, regardless of whether the variable exists or not.</span> |
| <span class="sd"> :param model_serving_container_environment_variables: The environment variables that are to be</span> |
| <span class="sd"> present in the container. Should be a dictionary where keys are environment variable names</span> |
| <span class="sd"> and values are environment variable values for those names.</span> |
| <span class="sd"> :param model_serving_container_ports: Declaration of ports that are exposed by the container. This</span> |
| <span class="sd"> field is primarily informational, it gives Vertex AI information about the</span> |
| <span class="sd"> network connections the container uses. Listing or not a port here has</span> |
| <span class="sd"> no impact on whether the port is actually exposed, any port listening on</span> |
| <span class="sd"> the default "0.0.0.0" address inside a container will be accessible from</span> |
| <span class="sd"> the network.</span> |
| <span class="sd"> :param model_description: The description of the Model.</span> |
| <span class="sd"> :param model_instance_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the format of a single instance, which</span> |
| <span class="sd"> are used in</span> |
| <span class="sd"> ``PredictRequest.instances``,</span> |
| <span class="sd"> ``ExplainRequest.instances``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.input_config``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform. Note: The URI given on output will be immutable</span> |
| <span class="sd"> and probably different, including the URI scheme, than the</span> |
| <span class="sd"> one given on input. The output URI will point to a location</span> |
| <span class="sd"> where the user only has a read access.</span> |
| <span class="sd"> :param model_parameters_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the parameters of prediction and</span> |
| <span class="sd"> explanation via</span> |
| <span class="sd"> ``PredictRequest.parameters``,</span> |
| <span class="sd"> ``ExplainRequest.parameters``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.model_parameters``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform, if no parameters are supported it is set to an</span> |
| <span class="sd"> empty string. Note: The URI given on output will be</span> |
| <span class="sd"> immutable and probably different, including the URI scheme,</span> |
| <span class="sd"> than the one given on input. The output URI will point to a</span> |
| <span class="sd"> location where the user only has a read access.</span> |
| <span class="sd"> :param model_prediction_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the format of a single prediction</span> |
| <span class="sd"> produced by this Model, which are returned via</span> |
| <span class="sd"> ``PredictResponse.predictions``,</span> |
| <span class="sd"> ``ExplainResponse.explanations``,</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.output_config``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform. Note: The URI given on output will be immutable</span> |
| <span class="sd"> and probably different, including the URI scheme, than the</span> |
| <span class="sd"> one given on input. The output URI will point to a location</span> |
| <span class="sd"> where the user only has a read access.</span> |
| <span class="sd"> :param project_id: Project to run training in.</span> |
| <span class="sd"> :param region: Location to run training in.</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to</span> |
| <span class="sd"> organize TrainingPipelines.</span> |
| <span class="sd"> Label keys and values can be no longer than 64</span> |
| <span class="sd"> characters, can only</span> |
| <span class="sd"> contain lowercase letters, numeric characters,</span> |
| <span class="sd"> underscores and dashes. International characters</span> |
| <span class="sd"> are allowed.</span> |
| <span class="sd"> See https://goo.gl/xmQnxf for more information</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> by this key if ``model_to_upload`` 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> resource is created.</span> |
| |
| <span class="sd"> If set, the trained Model will be secured by this key.</span> |
| <span class="sd"> :param staging_bucket: Bucket used to stage source and training artifacts.</span> |
| <span class="sd"> :param dataset: Vertex AI to fit this training against.</span> |
| <span class="sd"> :param annotation_schema_uri: Google Cloud Storage URI points to a YAML file describing</span> |
| <span class="sd"> annotation schema. The schema is defined as an OpenAPI 3.0.2</span> |
| <span class="sd"> [Schema Object]</span> |
| <span class="sd"> (https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schema-object)</span> |
| |
| <span class="sd"> Only Annotations that both match this schema and belong to</span> |
| <span class="sd"> DataItems not ignored by the split method are used in</span> |
| <span class="sd"> respectively training, validation or test role, depending on</span> |
| <span class="sd"> the role of the DataItem they are on.</span> |
| |
| <span class="sd"> When used in conjunction with</span> |
| <span class="sd"> ``annotations_filter``,</span> |
| <span class="sd"> the Annotations used for training are filtered by both</span> |
| <span class="sd"> ``annotations_filter``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``annotation_schema_uri``.</span> |
| <span class="sd"> :param model_display_name: If the script produces a managed Vertex AI Model. The display name of</span> |
| <span class="sd"> the Model. The name can be up to 128 characters long and can be consist</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> organize your Models.</span> |
| <span class="sd"> Label keys and values can be no longer than 64</span> |
| <span class="sd"> characters, can only</span> |
| <span class="sd"> contain lowercase letters, numeric characters,</span> |
| <span class="sd"> underscores and dashes. International characters</span> |
| <span class="sd"> are allowed.</span> |
| <span class="sd"> See https://goo.gl/xmQnxf for more information</span> |
| <span class="sd"> and examples of labels.</span> |
| <span class="sd"> :param base_output_dir: GCS output directory of job. If not provided a timestamped directory in the</span> |
| <span class="sd"> staging directory will be used.</span> |
| |
| <span class="sd"> Vertex AI sets the following environment variables when it runs your training code:</span> |
| |
| <span class="sd"> - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts,</span> |
| <span class="sd"> i.e. <base_output_dir>/model/</span> |
| <span class="sd"> - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints,</span> |
| <span class="sd"> i.e. <base_output_dir>/checkpoints/</span> |
| <span class="sd"> - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard</span> |
| <span class="sd"> logs, i.e. <base_output_dir>/logs/</span> |
| <span class="sd"> :param service_account: Specifies the service account for workload run-as account.</span> |
| <span class="sd"> Users submitting jobs must have act-as permission on this run-as account.</span> |
| <span class="sd"> :param network: The full name of the Compute Engine network to which the job</span> |
| <span class="sd"> should be peered.</span> |
| <span class="sd"> Private services access must already be configured for the network.</span> |
| <span class="sd"> If left unspecified, the job is not peered with any network.</span> |
| <span class="sd"> :param bigquery_destination: Provide this field if `dataset` is a BiqQuery dataset.</span> |
| <span class="sd"> The BigQuery project location where the training data is to</span> |
| <span class="sd"> be written to. In the given project a new dataset is created</span> |
| <span class="sd"> with name</span> |
| <span class="sd"> ``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>``</span> |
| <span class="sd"> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All</span> |
| <span class="sd"> training input data will be written into that dataset. In</span> |
| <span class="sd"> the dataset three tables will be created, ``training``,</span> |
| <span class="sd"> ``validation`` and ``test``.</span> |
| |
| <span class="sd"> - AIP_DATA_FORMAT = "bigquery".</span> |
| <span class="sd"> - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training"</span> |
| <span class="sd"> - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation"</span> |
| <span class="sd"> - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"</span> |
| <span class="sd"> :param args: Command line arguments to be passed to the Python script.</span> |
| <span class="sd"> :param environment_variables: Environment variables to be passed to the container.</span> |
| <span class="sd"> Should be a dictionary where keys are environment variable names</span> |
| <span class="sd"> and values are environment variable values for those names.</span> |
| <span class="sd"> At most 10 environment variables can be specified.</span> |
| <span class="sd"> The Name of the environment variable must be unique.</span> |
| <span class="sd"> :param replica_count: The number of worker replicas. If replica count = 1 then one chief</span> |
| <span class="sd"> replica will be provisioned. If replica_count > 1 the remainder will be</span> |
| <span class="sd"> provisioned as a worker replica pool.</span> |
| <span class="sd"> :param machine_type: The type of machine to use for training.</span> |
| <span class="sd"> :param accelerator_type: Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED,</span> |
| <span class="sd"> NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4,</span> |
| <span class="sd"> NVIDIA_TESLA_T4</span> |
| <span class="sd"> :param accelerator_count: The number of accelerators to attach to a worker replica.</span> |
| <span class="sd"> :param boot_disk_type: Type of the boot disk, default is `pd-ssd`.</span> |
| <span class="sd"> Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or</span> |
| <span class="sd"> `pd-standard` (Persistent Disk Hard Disk Drive).</span> |
| <span class="sd"> :param boot_disk_size_gb: Size in GB of the boot disk, default is 100GB.</span> |
| <span class="sd"> boot disk size must be within the range of [100, 64000].</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</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> this filter are used to test the Model. A filter with same syntax</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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 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</span> |
| <span class="sd"> value in the column) must be one of {``training``,</span> |
| <span class="sd"> ``validation``, ``test``}, and it defines to which set the</span> |
| <span class="sd"> given piece of data is assigned. If for a piece of data the</span> |
| <span class="sd"> 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 timestamp_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 values of the key (the values in</span> |
| <span class="sd"> the column) must be in RFC 3339 `date-time` format, where</span> |
| <span class="sd"> `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a</span> |
| <span class="sd"> piece of data the key is not present or has an invalid value,</span> |
| <span class="sd"> that piece is ignored by the pipeline.</span> |
| |
| <span class="sd"> Supported only for tabular and time series Datasets.</span> |
| <span class="sd"> :param tensorboard: Optional. The name of a Vertex AI resource to which this CustomJob will upload</span> |
| <span class="sd"> logs. Format:</span> |
| <span class="sd"> ``projects/{project}/locations/{location}/tensorboards/{tensorboard}``</span> |
| <span class="sd"> For more information on configuring your service account please visit:</span> |
| <span class="sd"> https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training</span> |
| <span class="sd"> :param sync: Whether to execute the AI Platform job synchronously. If False, this method</span> |
| <span class="sd"> will be executed in concurrent Future and any downstream object will</span> |
| <span class="sd"> be immediately returned and synced when the Future has completed.</span> |
| <span class="sd"> :param gcp_conn_id: The connection ID to use connecting to Google Cloud.</span> |
| <span class="sd"> :param delegate_to: The account to impersonate using domain-wide delegation of authority,</span> |
| <span class="sd"> if any. For this to work, the service account making the request must have</span> |
| <span class="sd"> domain-wide delegation enabled.</span> |
| <span class="sd"> :param impersonation_chain: Optional service account to impersonate using short-term</span> |
| <span class="sd"> credentials, or chained list of accounts required to get the access_token</span> |
| <span class="sd"> of the last account in the list, which will be impersonated in the request.</span> |
| <span class="sd"> If set as a string, the account must grant the originating account</span> |
| <span class="sd"> the Service Account Token Creator IAM role.</span> |
| <span class="sd"> If set as a sequence, the identities from the list must grant</span> |
| <span class="sd"> Service Account Token Creator IAM role to the directly preceding identity, with first</span> |
| <span class="sd"> account from the list granting this role to the originating account (templated).</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="CreateCustomPythonPackageTrainingJobOperator.template_fields"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator.template_fields">[docs]</a> <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s1">'region'</span><span class="p">,</span> |
| <span class="s1">'impersonation_chain'</span><span class="p">,</span></div> |
| <span class="p">]</span> |
| <div class="viewcode-block" id="CreateCustomPythonPackageTrainingJobOperator.operator_extra_links"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator.operator_extra_links">[docs]</a> <span class="n">operator_extra_links</span> <span class="o">=</span> <span class="p">(</span><span class="n">VertexAIModelLink</span><span class="p">(),)</span></div> |
| |
| <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="o">*</span><span class="p">,</span> |
| <span class="n">python_package_gcs_uri</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">python_module_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">python_package_gcs_uri</span> <span class="o">=</span> <span class="n">python_package_gcs_uri</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">python_module_name</span> <span class="o">=</span> <span class="n">python_module_name</span> |
| |
| <div class="viewcode-block" id="CreateCustomPythonPackageTrainingJobOperator.execute"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator.execute">[docs]</a> <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="s2">"Context"</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hook</span> <span class="o">=</span> <span class="n">CustomJobHook</span><span class="p">(</span> |
| <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span><span class="p">,</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">hook</span><span class="o">.</span><span class="n">create_custom_python_package_training_job</span><span class="p">(</span> |
| <span class="n">project_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">region</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">python_package_gcs_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">python_package_gcs_uri</span><span class="p">,</span> |
| <span class="n">python_module_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">python_module_name</span><span class="p">,</span> |
| <span class="n">container_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">container_uri</span><span class="p">,</span> |
| <span class="n">model_serving_container_image_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_image_uri</span><span class="p">,</span> |
| <span class="n">model_serving_container_predict_route</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_predict_route</span><span class="p">,</span> |
| <span class="n">model_serving_container_health_route</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_health_route</span><span class="p">,</span> |
| <span class="n">model_serving_container_command</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_command</span><span class="p">,</span> |
| <span class="n">model_serving_container_args</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_args</span><span class="p">,</span> |
| <span class="n">model_serving_container_environment_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_environment_variables</span><span class="p">,</span> |
| <span class="n">model_serving_container_ports</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_ports</span><span class="p">,</span> |
| <span class="n">model_description</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_description</span><span class="p">,</span> |
| <span class="n">model_instance_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_instance_schema_uri</span><span class="p">,</span> |
| <span class="n">model_parameters_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_parameters_schema_uri</span><span class="p">,</span> |
| <span class="n">model_prediction_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_prediction_schema_uri</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">staging_bucket</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">staging_bucket</span><span class="p">,</span> |
| <span class="c1"># RUN</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">annotation_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">annotation_schema_uri</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">base_output_dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">base_output_dir</span><span class="p">,</span> |
| <span class="n">service_account</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">service_account</span><span class="p">,</span> |
| <span class="n">network</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> |
| <span class="n">bigquery_destination</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bigquery_destination</span><span class="p">,</span> |
| <span class="n">args</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> |
| <span class="n">environment_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">environment_variables</span><span class="p">,</span> |
| <span class="n">replica_count</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">replica_count</span><span class="p">,</span> |
| <span class="n">machine_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">machine_type</span><span class="p">,</span> |
| <span class="n">accelerator_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">accelerator_type</span><span class="p">,</span> |
| <span class="n">accelerator_count</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">accelerator_count</span><span class="p">,</span> |
| <span class="n">boot_disk_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_type</span><span class="p">,</span> |
| <span class="n">boot_disk_size_gb</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_size_gb</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</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="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">test_filter_split</span><span class="p">,</span> |
| <span class="n">predefined_split_column_name</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">timestamp_split_column_name</span><span class="p">,</span> |
| <span class="n">tensorboard</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tensorboard</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="n">result</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> |
| <span class="n">model_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="o">.</span><span class="n">extract_model_id</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> |
| <span class="n">VertexAIModelLink</span><span class="o">.</span><span class="n">persist</span><span class="p">(</span><span class="n">context</span><span class="o">=</span><span class="n">context</span><span class="p">,</span> <span class="n">task_instance</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_id</span><span class="o">=</span><span class="n">model_id</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span></div> |
| |
| <div class="viewcode-block" id="CreateCustomPythonPackageTrainingJobOperator.on_kill"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator.on_kill">[docs]</a> <span class="k">def</span> <span class="nf">on_kill</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">"""</span> |
| <span class="sd"> Callback called when the operator is killed.</span> |
| <span class="sd"> Cancel any running job.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="o">.</span><span class="n">cancel_job</span><span class="p">()</span></div></div> |
| |
| |
| <div class="viewcode-block" id="CreateCustomTrainingJobOperator"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomTrainingJobOperator">[docs]</a><span class="k">class</span> <span class="nc">CreateCustomTrainingJobOperator</span><span class="p">(</span><span class="n">CustomTrainingJobBaseOperator</span><span class="p">):</span> |
| <span class="sd">"""Create Custom Training job</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 display_name: Required. The user-defined name of this TrainingPipeline.</span> |
| <span class="sd"> :param script_path: Required. Local path to training script.</span> |
| <span class="sd"> :param container_uri: Required: Uri of the training container image in the GCR.</span> |
| <span class="sd"> :param requirements: List of python packages dependencies of script.</span> |
| <span class="sd"> :param model_serving_container_image_uri: If the training produces a managed Vertex AI Model, the URI</span> |
| <span class="sd"> of the Model serving container suitable for serving the model produced by the</span> |
| <span class="sd"> training script.</span> |
| <span class="sd"> :param model_serving_container_predict_route: If the training produces a managed Vertex AI Model, An</span> |
| <span class="sd"> HTTP path to send prediction requests to the container, and which must be supported</span> |
| <span class="sd"> by it. If not specified a default HTTP path will be used by Vertex AI.</span> |
| <span class="sd"> :param model_serving_container_health_route: If the training produces a managed Vertex AI Model, an</span> |
| <span class="sd"> HTTP path to send health check requests to the container, and which must be supported</span> |
| <span class="sd"> by it. If not specified a standard HTTP path will be used by AI Platform.</span> |
| <span class="sd"> :param model_serving_container_command: The command with which the container is run. Not executed</span> |
| <span class="sd"> within a shell. The Docker image's ENTRYPOINT is used if this is not provided.</span> |
| <span class="sd"> Variable references $(VAR_NAME) are expanded using the container's</span> |
| <span class="sd"> environment. If a variable cannot be resolved, the reference in the</span> |
| <span class="sd"> input string will be unchanged. The $(VAR_NAME) syntax can be escaped</span> |
| <span class="sd"> with a double $$, ie: $$(VAR_NAME). Escaped references will never be</span> |
| <span class="sd"> expanded, regardless of whether the variable exists or not.</span> |
| <span class="sd"> :param model_serving_container_args: The arguments to the command. The Docker image's CMD is used if</span> |
| <span class="sd"> this is not provided. Variable references $(VAR_NAME) are expanded using the</span> |
| <span class="sd"> container's environment. If a variable cannot be resolved, the reference</span> |
| <span class="sd"> in the input string will be unchanged. The $(VAR_NAME) syntax can be</span> |
| <span class="sd"> escaped with a double $$, ie: $$(VAR_NAME). Escaped references will</span> |
| <span class="sd"> never be expanded, regardless of whether the variable exists or not.</span> |
| <span class="sd"> :param model_serving_container_environment_variables: The environment variables that are to be</span> |
| <span class="sd"> present in the container. Should be a dictionary where keys are environment variable names</span> |
| <span class="sd"> and values are environment variable values for those names.</span> |
| <span class="sd"> :param model_serving_container_ports: Declaration of ports that are exposed by the container. This</span> |
| <span class="sd"> field is primarily informational, it gives Vertex AI information about the</span> |
| <span class="sd"> network connections the container uses. Listing or not a port here has</span> |
| <span class="sd"> no impact on whether the port is actually exposed, any port listening on</span> |
| <span class="sd"> the default "0.0.0.0" address inside a container will be accessible from</span> |
| <span class="sd"> the network.</span> |
| <span class="sd"> :param model_description: The description of the Model.</span> |
| <span class="sd"> :param model_instance_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the format of a single instance, which</span> |
| <span class="sd"> are used in</span> |
| <span class="sd"> ``PredictRequest.instances``,</span> |
| <span class="sd"> ``ExplainRequest.instances``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.input_config``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform. Note: The URI given on output will be immutable</span> |
| <span class="sd"> and probably different, including the URI scheme, than the</span> |
| <span class="sd"> one given on input. The output URI will point to a location</span> |
| <span class="sd"> where the user only has a read access.</span> |
| <span class="sd"> :param model_parameters_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the parameters of prediction and</span> |
| <span class="sd"> explanation via</span> |
| <span class="sd"> ``PredictRequest.parameters``,</span> |
| <span class="sd"> ``ExplainRequest.parameters``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.model_parameters``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform, if no parameters are supported it is set to an</span> |
| <span class="sd"> empty string. Note: The URI given on output will be</span> |
| <span class="sd"> immutable and probably different, including the URI scheme,</span> |
| <span class="sd"> than the one given on input. The output URI will point to a</span> |
| <span class="sd"> location where the user only has a read access.</span> |
| <span class="sd"> :param model_prediction_schema_uri: Optional. Points to a YAML file stored on Google Cloud</span> |
| <span class="sd"> Storage describing the format of a single prediction</span> |
| <span class="sd"> produced by this Model, which are returned via</span> |
| <span class="sd"> ``PredictResponse.predictions``,</span> |
| <span class="sd"> ``ExplainResponse.explanations``,</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``BatchPredictionJob.output_config``.</span> |
| <span class="sd"> The schema is defined as an OpenAPI 3.0.2 `Schema</span> |
| <span class="sd"> Object <https://tinyurl.com/y538mdwt#schema-object>`__.</span> |
| <span class="sd"> AutoML Models always have this field populated by AI</span> |
| <span class="sd"> Platform. Note: The URI given on output will be immutable</span> |
| <span class="sd"> and probably different, including the URI scheme, than the</span> |
| <span class="sd"> one given on input. The output URI will point to a location</span> |
| <span class="sd"> where the user only has a read access.</span> |
| <span class="sd"> :param project_id: Project to run training in.</span> |
| <span class="sd"> :param region: Location to run training in.</span> |
| <span class="sd"> :param labels: Optional. The labels with user-defined metadata to</span> |
| <span class="sd"> organize TrainingPipelines.</span> |
| <span class="sd"> Label keys and values can be no longer than 64</span> |
| <span class="sd"> characters, can only</span> |
| <span class="sd"> contain lowercase letters, numeric characters,</span> |
| <span class="sd"> underscores and dashes. International characters</span> |
| <span class="sd"> are allowed.</span> |
| <span class="sd"> See https://goo.gl/xmQnxf for more information</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> by this key if ``model_to_upload`` 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</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> resource is created.</span> |
| |
| <span class="sd"> If set, the trained Model will be secured by this key.</span> |
| <span class="sd"> :param staging_bucket: Bucket used to stage source and training artifacts.</span> |
| <span class="sd"> :param dataset: Vertex AI to fit this training against.</span> |
| <span class="sd"> :param annotation_schema_uri: Google Cloud Storage URI points to a YAML file describing</span> |
| <span class="sd"> annotation schema. The schema is defined as an OpenAPI 3.0.2</span> |
| <span class="sd"> [Schema Object]</span> |
| <span class="sd"> (https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schema-object)</span> |
| |
| <span class="sd"> Only Annotations that both match this schema and belong to</span> |
| <span class="sd"> DataItems not ignored by the split method are used in</span> |
| <span class="sd"> respectively training, validation or test role, depending on</span> |
| <span class="sd"> the role of the DataItem they are on.</span> |
| |
| <span class="sd"> When used in conjunction with</span> |
| <span class="sd"> ``annotations_filter``,</span> |
| <span class="sd"> the Annotations used for training are filtered by both</span> |
| <span class="sd"> ``annotations_filter``</span> |
| <span class="sd"> and</span> |
| <span class="sd"> ``annotation_schema_uri``.</span> |
| <span class="sd"> :param model_display_name: If the script produces a managed Vertex AI Model. The display name of</span> |
| <span class="sd"> the Model. The name can be up to 128 characters long and can be consist</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> organize your Models.</span> |
| <span class="sd"> Label keys and values can be no longer than 64</span> |
| <span class="sd"> characters, can only</span> |
| <span class="sd"> contain lowercase letters, numeric characters,</span> |
| <span class="sd"> underscores and dashes. International characters</span> |
| <span class="sd"> are allowed.</span> |
| <span class="sd"> See https://goo.gl/xmQnxf for more information</span> |
| <span class="sd"> and examples of labels.</span> |
| <span class="sd"> :param base_output_dir: GCS output directory of job. If not provided a timestamped directory in the</span> |
| <span class="sd"> staging directory will be used.</span> |
| |
| <span class="sd"> Vertex AI sets the following environment variables when it runs your training code:</span> |
| |
| <span class="sd"> - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts,</span> |
| <span class="sd"> i.e. <base_output_dir>/model/</span> |
| <span class="sd"> - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints,</span> |
| <span class="sd"> i.e. <base_output_dir>/checkpoints/</span> |
| <span class="sd"> - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard</span> |
| <span class="sd"> logs, i.e. <base_output_dir>/logs/</span> |
| <span class="sd"> :param service_account: Specifies the service account for workload run-as account.</span> |
| <span class="sd"> Users submitting jobs must have act-as permission on this run-as account.</span> |
| <span class="sd"> :param network: The full name of the Compute Engine network to which the job</span> |
| <span class="sd"> should be peered.</span> |
| <span class="sd"> Private services access must already be configured for the network.</span> |
| <span class="sd"> If left unspecified, the job is not peered with any network.</span> |
| <span class="sd"> :param bigquery_destination: Provide this field if `dataset` is a BiqQuery dataset.</span> |
| <span class="sd"> The BigQuery project location where the training data is to</span> |
| <span class="sd"> be written to. In the given project a new dataset is created</span> |
| <span class="sd"> with name</span> |
| <span class="sd"> ``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>``</span> |
| <span class="sd"> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All</span> |
| <span class="sd"> training input data will be written into that dataset. In</span> |
| <span class="sd"> the dataset three tables will be created, ``training``,</span> |
| <span class="sd"> ``validation`` and ``test``.</span> |
| |
| <span class="sd"> - AIP_DATA_FORMAT = "bigquery".</span> |
| <span class="sd"> - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training"</span> |
| <span class="sd"> - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation"</span> |
| <span class="sd"> - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"</span> |
| <span class="sd"> :param args: Command line arguments to be passed to the Python script.</span> |
| <span class="sd"> :param environment_variables: Environment variables to be passed to the container.</span> |
| <span class="sd"> Should be a dictionary where keys are environment variable names</span> |
| <span class="sd"> and values are environment variable values for those names.</span> |
| <span class="sd"> At most 10 environment variables can be specified.</span> |
| <span class="sd"> The Name of the environment variable must be unique.</span> |
| <span class="sd"> :param replica_count: The number of worker replicas. If replica count = 1 then one chief</span> |
| <span class="sd"> replica will be provisioned. If replica_count > 1 the remainder will be</span> |
| <span class="sd"> provisioned as a worker replica pool.</span> |
| <span class="sd"> :param machine_type: The type of machine to use for training.</span> |
| <span class="sd"> :param accelerator_type: Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED,</span> |
| <span class="sd"> NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4,</span> |
| <span class="sd"> NVIDIA_TESLA_T4</span> |
| <span class="sd"> :param accelerator_count: The number of accelerators to attach to a worker replica.</span> |
| <span class="sd"> :param boot_disk_type: Type of the boot disk, default is `pd-ssd`.</span> |
| <span class="sd"> Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or</span> |
| <span class="sd"> `pd-standard` (Persistent Disk Hard Disk Drive).</span> |
| <span class="sd"> :param boot_disk_size_gb: Size in GB of the boot disk, default is 100GB.</span> |
| <span class="sd"> boot disk size must be within the range of [100, 64000].</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</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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</span> |
| <span class="sd"> this filter are used to test the Model. A filter with same syntax</span> |
| <span class="sd"> as the one used in DatasetService.ListDataItems may be used. If a</span> |
| <span class="sd"> single DataItem is matched by more than one of the FilterSplit filters,</span> |
| <span class="sd"> 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 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</span> |
| <span class="sd"> value in the column) must be one of {``training``,</span> |
| <span class="sd"> ``validation``, ``test``}, and it defines to which set the</span> |
| <span class="sd"> given piece of data is assigned. If for a piece of data the</span> |
| <span class="sd"> 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 timestamp_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 values of the key (the values in</span> |
| <span class="sd"> the column) must be in RFC 3339 `date-time` format, where</span> |
| <span class="sd"> `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a</span> |
| <span class="sd"> piece of data the key is not present or has an invalid value,</span> |
| <span class="sd"> that piece is ignored by the pipeline.</span> |
| |
| <span class="sd"> Supported only for tabular and time series Datasets.</span> |
| <span class="sd"> :param tensorboard: Optional. The name of a Vertex AI resource to which this CustomJob will upload</span> |
| <span class="sd"> logs. Format:</span> |
| <span class="sd"> ``projects/{project}/locations/{location}/tensorboards/{tensorboard}``</span> |
| <span class="sd"> For more information on configuring your service account please visit:</span> |
| <span class="sd"> https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training</span> |
| <span class="sd"> :param sync: Whether to execute the AI Platform job synchronously. If False, this method</span> |
| <span class="sd"> will be executed in concurrent Future and any downstream object will</span> |
| <span class="sd"> be immediately returned and synced when the Future has completed.</span> |
| <span class="sd"> :param gcp_conn_id: The connection ID to use connecting to Google Cloud.</span> |
| <span class="sd"> :param delegate_to: The account to impersonate using domain-wide delegation of authority,</span> |
| <span class="sd"> if any. For this to work, the service account making the request must have</span> |
| <span class="sd"> domain-wide delegation enabled.</span> |
| <span class="sd"> :param impersonation_chain: Optional service account to impersonate using short-term</span> |
| <span class="sd"> credentials, or chained list of accounts required to get the access_token</span> |
| <span class="sd"> of the last account in the list, which will be impersonated in the request.</span> |
| <span class="sd"> If set as a string, the account must grant the originating account</span> |
| <span class="sd"> the Service Account Token Creator IAM role.</span> |
| <span class="sd"> If set as a sequence, the identities from the list must grant</span> |
| <span class="sd"> Service Account Token Creator IAM role to the directly preceding identity, with first</span> |
| <span class="sd"> account from the list granting this role to the originating account (templated).</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="CreateCustomTrainingJobOperator.template_fields"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomTrainingJobOperator.template_fields">[docs]</a> <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s1">'region'</span><span class="p">,</span> |
| <span class="s1">'script_path'</span><span class="p">,</span> |
| <span class="s1">'requirements'</span><span class="p">,</span> |
| <span class="s1">'impersonation_chain'</span><span class="p">,</span></div> |
| <span class="p">]</span> |
| <div class="viewcode-block" id="CreateCustomTrainingJobOperator.operator_extra_links"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomTrainingJobOperator.operator_extra_links">[docs]</a> <span class="n">operator_extra_links</span> <span class="o">=</span> <span class="p">(</span><span class="n">VertexAIModelLink</span><span class="p">(),)</span></div> |
| |
| <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="o">*</span><span class="p">,</span> |
| <span class="n">script_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">requirements</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="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">requirements</span> <span class="o">=</span> <span class="n">requirements</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">script_path</span> <span class="o">=</span> <span class="n">script_path</span> |
| |
| <div class="viewcode-block" id="CreateCustomTrainingJobOperator.execute"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomTrainingJobOperator.execute">[docs]</a> <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="s2">"Context"</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hook</span> <span class="o">=</span> <span class="n">CustomJobHook</span><span class="p">(</span> |
| <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span><span class="p">,</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">hook</span><span class="o">.</span><span class="n">create_custom_training_job</span><span class="p">(</span> |
| <span class="n">project_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">region</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">display_name</span><span class="p">,</span> |
| <span class="n">script_path</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">script_path</span><span class="p">,</span> |
| <span class="n">container_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">container_uri</span><span class="p">,</span> |
| <span class="n">requirements</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">requirements</span><span class="p">,</span> |
| <span class="n">model_serving_container_image_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_image_uri</span><span class="p">,</span> |
| <span class="n">model_serving_container_predict_route</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_predict_route</span><span class="p">,</span> |
| <span class="n">model_serving_container_health_route</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_health_route</span><span class="p">,</span> |
| <span class="n">model_serving_container_command</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_command</span><span class="p">,</span> |
| <span class="n">model_serving_container_args</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_args</span><span class="p">,</span> |
| <span class="n">model_serving_container_environment_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_environment_variables</span><span class="p">,</span> |
| <span class="n">model_serving_container_ports</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_serving_container_ports</span><span class="p">,</span> |
| <span class="n">model_description</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_description</span><span class="p">,</span> |
| <span class="n">model_instance_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_instance_schema_uri</span><span class="p">,</span> |
| <span class="n">model_parameters_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_parameters_schema_uri</span><span class="p">,</span> |
| <span class="n">model_prediction_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_prediction_schema_uri</span><span class="p">,</span> |
| <span class="n">labels</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">model_encryption_spec_key_name</span><span class="p">,</span> |
| <span class="n">staging_bucket</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">staging_bucket</span><span class="p">,</span> |
| <span class="c1"># RUN</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">annotation_schema_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">annotation_schema_uri</span><span class="p">,</span> |
| <span class="n">model_display_name</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">model_labels</span><span class="p">,</span> |
| <span class="n">base_output_dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">base_output_dir</span><span class="p">,</span> |
| <span class="n">service_account</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">service_account</span><span class="p">,</span> |
| <span class="n">network</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> |
| <span class="n">bigquery_destination</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bigquery_destination</span><span class="p">,</span> |
| <span class="n">args</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> |
| <span class="n">environment_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">environment_variables</span><span class="p">,</span> |
| <span class="n">replica_count</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">replica_count</span><span class="p">,</span> |
| <span class="n">machine_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">machine_type</span><span class="p">,</span> |
| <span class="n">accelerator_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">accelerator_type</span><span class="p">,</span> |
| <span class="n">accelerator_count</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">accelerator_count</span><span class="p">,</span> |
| <span class="n">boot_disk_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_type</span><span class="p">,</span> |
| <span class="n">boot_disk_size_gb</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boot_disk_size_gb</span><span class="p">,</span> |
| <span class="n">training_fraction_split</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</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="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">test_filter_split</span><span class="p">,</span> |
| <span class="n">predefined_split_column_name</span><span class="o">=</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">timestamp_split_column_name</span><span class="p">,</span> |
| <span class="n">tensorboard</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tensorboard</span><span class="p">,</span> |
| <span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="n">result</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> |
| <span class="n">model_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="o">.</span><span class="n">extract_model_id</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> |
| <span class="n">VertexAIModelLink</span><span class="o">.</span><span class="n">persist</span><span class="p">(</span><span class="n">context</span><span class="o">=</span><span class="n">context</span><span class="p">,</span> <span class="n">task_instance</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_id</span><span class="o">=</span><span class="n">model_id</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span></div> |
| |
| <div class="viewcode-block" id="CreateCustomTrainingJobOperator.on_kill"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomTrainingJobOperator.on_kill">[docs]</a> <span class="k">def</span> <span class="nf">on_kill</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">"""</span> |
| <span class="sd"> Callback called when the operator is killed.</span> |
| <span class="sd"> Cancel any running job.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hook</span><span class="o">.</span><span class="n">cancel_job</span><span class="p">()</span></div></div> |
| |
| |
| <div class="viewcode-block" id="DeleteCustomTrainingJobOperator"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.DeleteCustomTrainingJobOperator">[docs]</a><span class="k">class</span> <span class="nc">DeleteCustomTrainingJobOperator</span><span class="p">(</span><span class="n">BaseOperator</span><span class="p">):</span> |
| <span class="sd">"""Deletes a CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob.</span> |
| |
| <span class="sd"> :param training_pipeline_id: Required. The name of the TrainingPipeline resource to be deleted.</span> |
| <span class="sd"> :param custom_job_id: Required. The name of the CustomJob to delete.</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 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"> :param gcp_conn_id: The connection ID to use connecting to Google Cloud.</span> |
| <span class="sd"> :param delegate_to: The account to impersonate using domain-wide delegation of authority,</span> |
| <span class="sd"> if any. For this to work, the service account making the request must have</span> |
| <span class="sd"> domain-wide delegation enabled.</span> |
| <span class="sd"> :param impersonation_chain: Optional service account to impersonate using short-term</span> |
| <span class="sd"> credentials, or chained list of accounts required to get the access_token</span> |
| <span class="sd"> of the last account in the list, which will be impersonated in the request.</span> |
| <span class="sd"> If set as a string, the account must grant the originating account</span> |
| <span class="sd"> the Service Account Token Creator IAM role.</span> |
| <span class="sd"> If set as a sequence, the identities from the list must grant</span> |
| <span class="sd"> Service Account Token Creator IAM role to the directly preceding identity, with first</span> |
| <span class="sd"> account from the list granting this role to the originating account (templated).</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="DeleteCustomTrainingJobOperator.template_fields"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.DeleteCustomTrainingJobOperator.template_fields">[docs]</a> <span class="n">template_fields</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"region"</span><span class="p">,</span> <span class="s2">"project_id"</span><span class="p">,</span> <span class="s2">"impersonation_chain"</span><span class="p">)</span></div> |
| |
| <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="o">*</span><span class="p">,</span> |
| <span class="n">training_pipeline_id</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">custom_job_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">project_id</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="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="o">**</span><span class="n">kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">training_pipeline</span> <span class="o">=</span> <span class="n">training_pipeline_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">custom_job</span> <span class="o">=</span> <span class="n">custom_job_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">project_id</span> <span class="o">=</span> <span class="n">project_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">retry</span> <span class="o">=</span> <span class="n">retry</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">timeout</span> <span class="o">=</span> <span class="n">timeout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span> <span class="o">=</span> <span class="n">metadata</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gcp_conn_id</span> <span class="o">=</span> <span class="n">gcp_conn_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">delegate_to</span> <span class="o">=</span> <span class="n">delegate_to</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span> <span class="o">=</span> <span class="n">impersonation_chain</span> |
| |
| <div class="viewcode-block" id="DeleteCustomTrainingJobOperator.execute"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.DeleteCustomTrainingJobOperator.execute">[docs]</a> <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="s2">"Context"</span><span class="p">):</span> |
| <span class="n">hook</span> <span class="o">=</span> <span class="n">CustomJobHook</span><span class="p">(</span> |
| <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Deleting custom training pipeline: </span><span class="si">%s</span><span class="s2">"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">training_pipeline</span><span class="p">)</span> |
| <span class="n">training_pipeline_operation</span> <span class="o">=</span> <span class="n">hook</span><span class="o">.</span><span class="n">delete_training_pipeline</span><span class="p">(</span> |
| <span class="n">training_pipeline</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">training_pipeline</span><span class="p">,</span> |
| <span class="n">region</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">retry</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">hook</span><span class="o">.</span><span class="n">wait_for_operation</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">,</span> <span class="n">operation</span><span class="o">=</span><span class="n">training_pipeline_operation</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Training pipeline was deleted."</span><span class="p">)</span> |
| <span class="k">except</span> <span class="n">NotFound</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"The Training Pipeline ID </span><span class="si">%s</span><span class="s2"> does not exist."</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">training_pipeline</span><span class="p">)</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Deleting custom job: </span><span class="si">%s</span><span class="s2">"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">custom_job</span><span class="p">)</span> |
| <span class="n">custom_job_operation</span> <span class="o">=</span> <span class="n">hook</span><span class="o">.</span><span class="n">delete_custom_job</span><span class="p">(</span> |
| <span class="n">custom_job</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">custom_job</span><span class="p">,</span> |
| <span class="n">region</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">retry</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">hook</span><span class="o">.</span><span class="n">wait_for_operation</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">,</span> <span class="n">operation</span><span class="o">=</span><span class="n">custom_job_operation</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Custom job was deleted."</span><span class="p">)</span> |
| <span class="k">except</span> <span class="n">NotFound</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"The Custom Job ID </span><span class="si">%s</span><span class="s2"> does not exist."</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">custom_job</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="ListCustomTrainingJobOperator"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.ListCustomTrainingJobOperator">[docs]</a><span class="k">class</span> <span class="nc">ListCustomTrainingJobOperator</span><span class="p">(</span><span class="n">BaseOperator</span><span class="p">):</span> |
| <span class="sd">"""Lists CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob 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"> :param gcp_conn_id: The connection ID to use connecting to Google Cloud.</span> |
| <span class="sd"> :param delegate_to: The account to impersonate using domain-wide delegation of authority,</span> |
| <span class="sd"> if any. For this to work, the service account making the request must have</span> |
| <span class="sd"> domain-wide delegation enabled.</span> |
| <span class="sd"> :param impersonation_chain: Optional service account to impersonate using short-term</span> |
| <span class="sd"> credentials, or chained list of accounts required to get the access_token</span> |
| <span class="sd"> of the last account in the list, which will be impersonated in the request.</span> |
| <span class="sd"> If set as a string, the account must grant the originating account</span> |
| <span class="sd"> the Service Account Token Creator IAM role.</span> |
| <span class="sd"> If set as a sequence, the identities from the list must grant</span> |
| <span class="sd"> Service Account Token Creator IAM role to the directly preceding identity, with first</span> |
| <span class="sd"> account from the list granting this role to the originating account (templated).</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="ListCustomTrainingJobOperator.template_fields"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.ListCustomTrainingJobOperator.template_fields">[docs]</a> <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s2">"region"</span><span class="p">,</span> |
| <span class="s2">"project_id"</span><span class="p">,</span> |
| <span class="s2">"impersonation_chain"</span><span class="p">,</span></div> |
| <span class="p">]</span> |
| <div class="viewcode-block" id="ListCustomTrainingJobOperator.operator_extra_links"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.ListCustomTrainingJobOperator.operator_extra_links">[docs]</a> <span class="n">operator_extra_links</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="n">VertexAITrainingPipelinesLink</span><span class="p">(),</span></div> |
| <span class="p">]</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="o">*</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">project_id</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="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="o">**</span><span class="n">kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">project_id</span> <span class="o">=</span> <span class="n">project_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">page_size</span> <span class="o">=</span> <span class="n">page_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">page_token</span> <span class="o">=</span> <span class="n">page_token</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">filter</span> <span class="o">=</span> <span class="nb">filter</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">read_mask</span> <span class="o">=</span> <span class="n">read_mask</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">retry</span> <span class="o">=</span> <span class="n">retry</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">timeout</span> <span class="o">=</span> <span class="n">timeout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span> <span class="o">=</span> <span class="n">metadata</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gcp_conn_id</span> <span class="o">=</span> <span class="n">gcp_conn_id</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">delegate_to</span> <span class="o">=</span> <span class="n">delegate_to</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span> <span class="o">=</span> <span class="n">impersonation_chain</span> |
| |
| <div class="viewcode-block" id="ListCustomTrainingJobOperator.execute"><a class="viewcode-back" href="../../../../../../../_api/airflow/providers/google/cloud/operators/vertex_ai/custom_job/index.html#airflow.providers.google.cloud.operators.vertex_ai.custom_job.ListCustomTrainingJobOperator.execute">[docs]</a> <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="s2">"Context"</span><span class="p">):</span> |
| <span class="n">hook</span> <span class="o">=</span> <span class="n">CustomJobHook</span><span class="p">(</span> |
| <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">impersonation_chain</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">results</span> <span class="o">=</span> <span class="n">hook</span><span class="o">.</span><span class="n">list_training_pipelines</span><span class="p">(</span> |
| <span class="n">region</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">page_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">page_size</span><span class="p">,</span> |
| <span class="n">page_token</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">page_token</span><span class="p">,</span> |
| <span class="nb">filter</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">filter</span><span class="p">,</span> |
| <span class="n">read_mask</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">read_mask</span><span class="p">,</span> |
| <span class="n">retry</span><span class="o">=</span><span class="bp">self</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="bp">self</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="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">VertexAITrainingPipelinesLink</span><span class="o">.</span><span class="n">persist</span><span class="p">(</span><span class="n">context</span><span class="o">=</span><span class="n">context</span><span class="p">,</span> <span class="n">task_instance</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">TrainingPipeline</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> <span class="k">for</span> <span class="n">result</span> <span class="ow">in</span> <span class="n">results</span><span class="p">]</span></div></div> |
| </pre></div> |
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