docs-archive/apache-airflow-providers-google/8.0.0/_sources/_api/airflow/providers/google/cloud/example_dags/example_vertex_ai/index.rst.txt

:py:mod:`airflow.providers.google.cloud.example_dags.example_vertex_ai`
=======================================================================

.. py:module:: airflow.providers.google.cloud.example_dags.example_vertex_ai

.. autoapi-nested-parse::

   Example Airflow DAG that demonstrates operators for the Google Vertex AI service in the Google
   Cloud Platform.

   This DAG relies on the following OS environment variables:

   * GCP_VERTEX_AI_BUCKET - Google Cloud Storage bucket where the model will be saved
     after training process was finished.
   * CUSTOM_CONTAINER_URI - path to container with model.
   * PYTHON_PACKAGE_GSC_URI - path to test model in archive.
   * LOCAL_TRAINING_SCRIPT_PATH - path to local training script.
   * DATASET_ID - ID of dataset which will be used in training process.
   * MODEL_ID - ID of model which will be used in predict process.
   * MODEL_ARTIFACT_URI - The artifact_uri should be the path to a GCS directory containing saved model
     artifacts.



Module Contents
---------------

.. py:data:: PROJECT_ID
   

   

.. py:data:: REGION
   

   

.. py:data:: BUCKET
   

   

.. py:data:: STAGING_BUCKET
   

   

.. py:data:: DISPLAY_NAME
   

   

.. py:data:: CONTAINER_URI
   :annotation: = gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest

   

.. py:data:: CUSTOM_CONTAINER_URI
   

   

.. py:data:: MODEL_SERVING_CONTAINER_URI
   :annotation: = gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest

   

.. py:data:: REPLICA_COUNT
   :annotation: = 1

   

.. py:data:: MACHINE_TYPE
   :annotation: = n1-standard-4

   

.. py:data:: ACCELERATOR_TYPE
   :annotation: = ACCELERATOR_TYPE_UNSPECIFIED

   

.. py:data:: ACCELERATOR_COUNT
   :annotation: = 0

   

.. py:data:: TRAINING_FRACTION_SPLIT
   :annotation: = 0.7

   

.. py:data:: TEST_FRACTION_SPLIT
   :annotation: = 0.15

   

.. py:data:: VALIDATION_FRACTION_SPLIT
   :annotation: = 0.15

   

.. py:data:: PYTHON_PACKAGE_GCS_URI
   

   

.. py:data:: PYTHON_MODULE_NAME
   :annotation: = aiplatform_custom_trainer_script.task

   

.. py:data:: LOCAL_TRAINING_SCRIPT_PATH
   

   

.. py:data:: TRAINING_PIPELINE_ID
   :annotation: = test-training-pipeline-id

   

.. py:data:: CUSTOM_JOB_ID
   :annotation: = test-custom-job-id

   

.. py:data:: IMAGE_DATASET
   

   

.. py:data:: TABULAR_DATASET
   

   

.. py:data:: TEXT_DATASET
   

   

.. py:data:: VIDEO_DATASET
   

   

.. py:data:: TIME_SERIES_DATASET
   

   

.. py:data:: DATASET_ID
   

   

.. py:data:: TEST_EXPORT_CONFIG
   

   

.. py:data:: TEST_IMPORT_CONFIG
   

   

.. py:data:: DATASET_TO_UPDATE
   

   

.. py:data:: TEST_UPDATE_MASK
   

   

.. py:data:: TEST_TIME_COLUMN
   :annotation: = date

   

.. py:data:: TEST_TIME_SERIES_IDENTIFIER_COLUMN
   :annotation: = store_name

   

.. py:data:: TEST_TARGET_COLUMN
   :annotation: = sale_dollars

   

.. py:data:: COLUMN_SPECS
   

   

.. py:data:: COLUMN_TRANSFORMATIONS
   

   

.. py:data:: MODEL_ID
   

   

.. py:data:: MODEL_ARTIFACT_URI
   

   

.. py:data:: MODEL_NAME
   

   

.. py:data:: JOB_DISPLAY_NAME
   

   

.. py:data:: BIGQUERY_SOURCE
   

   

.. py:data:: GCS_DESTINATION_PREFIX
   :annotation: = gs://test-vertex-ai-bucket-us/output

   

.. py:data:: MODEL_PARAMETERS
   

   

.. py:data:: ENDPOINT_CONF
   

   

.. py:data:: DEPLOYED_MODEL
   

   

.. py:data:: MODEL_OUTPUT_CONFIG
   

   

.. py:data:: MODEL_OBJ
   

   

.. py:data:: create_custom_container_training_job
   

   

.. py:data:: create_image_dataset_job
   

   

.. py:data:: create_auto_ml_forecasting_training_job
   

   

.. py:data:: create_batch_prediction_job
   

   

.. py:data:: create_endpoint
   

   

.. py:data:: create_hyperparameter_tuning_job
   

   

.. py:data:: upload_model