Source code for tests.system.providers.google.cloud.automl.example_automl_vision_classification

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"""
Example Airflow DAG that uses Google AutoML services.
"""

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.gcs import (
    GCSCreateBucketOperator,
    GCSDeleteBucketOperator,
    GCSSynchronizeBucketsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
    CreateAutoMLImageTrainingJobOperator,
    DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
    ImportDataOperator,
)
from airflow.utils.trigger_rule import TriggerRule


[docs]DAG_ID = "example_automl_vision_clss"


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


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


[docs]REGION = "us-central1"


[docs]IMAGE_DISPLAY_NAME = f"automl-vision-clss-{ENV_ID}"


[docs]MODEL_DISPLAY_NAME = f"automl-vision-clss-model-{ENV_ID}"



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


[docs]IMAGE_GCS_BUCKET_NAME = f"bucket_image_clss_{ENV_ID}".replace("_", "-")



[docs]IMAGE_DATASET = {
    "display_name": f"automl-vision-clss-dataset-{ENV_ID}",
    "metadata_schema_uri": schema.dataset.metadata.image,
    "metadata": Value(string_value="image-dataset"),
}


[docs]IMAGE_DATA_CONFIG = [
    {
        "import_schema_uri": schema.dataset.ioformat.image.single_label_classification,
        "gcs_source": {"uris": [f"gs://{IMAGE_GCS_BUCKET_NAME}/automl/image-dataset-classification.csv"]},
    },
]


# Example DAG for AutoML Vision Classification
with DAG(
    DAG_ID,
    schedule="@once",  # Override to match your needs
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "automl", "vision", "classification"],
) as dag:

[docs]    create_bucket = GCSCreateBucketOperator(
        task_id="create_bucket",
        bucket_name=IMAGE_GCS_BUCKET_NAME,
        storage_class="REGIONAL",
        location=REGION,
    )


    move_dataset_file = GCSSynchronizeBucketsOperator(
        task_id="move_dataset_to_bucket",
        source_bucket=RESOURCE_DATA_BUCKET,
        source_object="automl/datasets/vision",
        destination_bucket=IMAGE_GCS_BUCKET_NAME,
        destination_object="automl",
        recursive=True,
    )

    create_image_dataset = CreateDatasetOperator(
        task_id="image_dataset",
        dataset=IMAGE_DATASET,
        region=REGION,
        project_id=PROJECT_ID,
    )
    image_dataset_id = create_image_dataset.output["dataset_id"]

    import_image_dataset = ImportDataOperator(
        task_id="import_image_data",
        dataset_id=image_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        import_configs=IMAGE_DATA_CONFIG,
    )

    # [START howto_cloud_create_image_classification_training_job_operator]
    create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
        task_id="auto_ml_image_task",
        display_name=IMAGE_DISPLAY_NAME,
        dataset_id=image_dataset_id,
        prediction_type="classification",
        multi_label=False,
        model_type="CLOUD",
        training_fraction_split=0.6,
        validation_fraction_split=0.2,
        test_fraction_split=0.2,
        budget_milli_node_hours=8000,
        model_display_name=MODEL_DISPLAY_NAME,
        disable_early_stopping=False,
        region=REGION,
        project_id=PROJECT_ID,
    )
    # [END howto_cloud_create_image_classification_training_job_operator]

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

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

    delete_bucket = GCSDeleteBucketOperator(
        task_id="delete_bucket",
        bucket_name=IMAGE_GCS_BUCKET_NAME,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    (
        # TEST SETUP
        [
            create_bucket >> move_dataset_file,
            create_image_dataset,
        ]
        >> import_image_dataset
        # TEST BODY
        >> create_auto_ml_image_training_job
        # TEST TEARDOWN
        >> delete_auto_ml_image_training_job
        >> delete_image_dataset
        >> delete_bucket
    )

    from tests.system.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.system.utils 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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