Source code for tests.system.google.cloud.vertex_ai.example_vertex_ai_auto_ml_video_training

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"""
Example Airflow DAG for Google Vertex AI service testing Auto ML operations.
"""

from __future__ import annotations

import os
from datetime import datetime

from google.cloud.aiplatform import schema
from google.protobuf.struct_pb2 import Value

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
    CreateAutoMLVideoTrainingJobOperator,
    DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
    ImportDataOperator,
)
from airflow.utils.trigger_rule import TriggerRule



[docs]
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")




[docs]
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")




[docs]
DAG_ID = "vertex_ai_auto_ml_operations"




[docs]
REGION = "us-central1"




[docs]
VIDEO_DISPLAY_NAME = f"auto-ml-video-{ENV_ID}"




[docs]
MODEL_DISPLAY_NAME = f"auto-ml-video-model-{ENV_ID}"





[docs]
RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"




[docs]
VIDEO_GCS_BUCKET_NAME = f"bucket_video_{DAG_ID}_{ENV_ID}".replace("_", "-")





[docs]
VIDEO_DATASET = {
    "display_name": f"video-dataset-{ENV_ID}",
    "metadata_schema_uri": schema.dataset.metadata.video,
    "metadata": Value(string_value="video-dataset"),
}




[docs]
VIDEO_DATA_CONFIG = [
    {
        "import_schema_uri": schema.dataset.ioformat.video.classification,
        "gcs_source": {"uris": [f"gs://{RESOURCE_DATA_BUCKET}/automl/datasets/video/classification.csv"]},
    },
]



with DAG(
    f"{DAG_ID}_video_training_job",
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "vertex_ai", "auto_ml"],
) as dag:


[docs]
    create_video_dataset = CreateDatasetOperator(
        task_id="video_dataset",
        dataset=VIDEO_DATASET,
        region=REGION,
        project_id=PROJECT_ID,
    )


    video_dataset_id = create_video_dataset.output["dataset_id"]

    import_video_dataset = ImportDataOperator(
        task_id="import_video_data",
        dataset_id=video_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        import_configs=VIDEO_DATA_CONFIG,
    )

    # [START how_to_cloud_vertex_ai_create_auto_ml_video_training_job_operator]
    create_auto_ml_video_training_job = CreateAutoMLVideoTrainingJobOperator(
        task_id="auto_ml_video_task",
        display_name=VIDEO_DISPLAY_NAME,
        prediction_type="classification",
        model_type="CLOUD",
        dataset_id=video_dataset_id,
        model_display_name=MODEL_DISPLAY_NAME,
        region=REGION,
        project_id=PROJECT_ID,
    )
    model_id_v1 = create_auto_ml_video_training_job.output["model_id"]
    # [END how_to_cloud_vertex_ai_create_auto_ml_video_training_job_operator]

    # [START how_to_cloud_vertex_ai_create_auto_ml_video_training_job_v2_operator]
    create_auto_ml_video_training_job_v2 = CreateAutoMLVideoTrainingJobOperator(
        task_id="auto_ml_video_v2_task",
        display_name=VIDEO_DISPLAY_NAME,
        prediction_type="classification",
        model_type="CLOUD",
        dataset_id=video_dataset_id,
        model_display_name=MODEL_DISPLAY_NAME,
        parent_model=model_id_v1,
        region=REGION,
        project_id=PROJECT_ID,
    )
    # [END how_to_cloud_vertex_ai_create_auto_ml_video_training_job_v2_operator]

    delete_auto_ml_video_training_job = DeleteAutoMLTrainingJobOperator(
        task_id="delete_auto_ml_video_training_job",
        training_pipeline_id="{{ task_instance.xcom_pull(task_ids='auto_ml_video_task', "
        "key='training_id') }}",
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    delete_video_dataset = DeleteDatasetOperator(
        task_id="delete_video_dataset",
        dataset_id=video_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    (
        # TEST SETUP
        create_video_dataset
        >> import_video_dataset
        # TEST BODY
        >> create_auto_ml_video_training_job
        >> create_auto_ml_video_training_job_v2
        # TEST TEARDOWN
        >> delete_auto_ml_video_training_job
        >> delete_video_dataset
    )

    # ### Everything below this line is not part of example ###
    # ### Just for system tests purpose ###
    from tests_common.test_utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()

from tests_common.test_utils.system_tests import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)


[docs]
test_run = get_test_run(dag)

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