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

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"""
Example Airflow DAG that uses Google AutoML services.
"""
from __future__ import annotations

import os
from datetime import datetime
from typing import cast

from airflow.models.dag import DAG
from airflow.models.xcom_arg import XComArg
from airflow.providers.google.cloud.hooks.automl import CloudAutoMLHook
from airflow.providers.google.cloud.operators.automl import (
    AutoMLCreateDatasetOperator,
    AutoMLDeleteDatasetOperator,
    AutoMLDeleteModelOperator,
    AutoMLDeployModelOperator,
    AutoMLImportDataOperator,
    AutoMLTrainModelOperator,
)
from airflow.providers.google.cloud.operators.gcs import (
    GCSCreateBucketOperator,
    GCSDeleteBucketOperator,
    GCSSynchronizeBucketsOperator,
)
from airflow.utils.trigger_rule import TriggerRule


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


[docs]DAG_ID = "example_automl_vision_obj_detect"


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


[docs]GCP_AUTOML_LOCATION = "us-central1"


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


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



[docs]MODEL_NAME = "vision_detect_test_model"


[docs]MODEL = {
    "display_name": MODEL_NAME,
    "image_object_detection_model_metadata": {},
}



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


[docs]DATASET = {
    "display_name": DATASET_NAME,
    "image_object_detection_dataset_metadata": {},
}



[docs]AUTOML_DATASET_BUCKET = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/automl/object_detection.csv"


[docs]IMPORT_INPUT_CONFIG = {"gcs_source": {"input_uris": [AUTOML_DATASET_BUCKET]}}


[docs]extract_object_id = CloudAutoMLHook.extract_object_id



# Example DAG for AutoML Vision Object Detection
with DAG(
    DAG_ID,
    schedule="@once",  # Override to match your needs
    start_date=datetime(2021, 1, 1),
    catchup=False,
    user_defined_macros={"extract_object_id": extract_object_id},
    tags=["example", "automl", "object-detection"],
) as dag:

[docs]    create_bucket = GCSCreateBucketOperator(
        task_id="create_bucket",
        bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
        storage_class="REGIONAL",
        location=GCP_AUTOML_LOCATION,
    )


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

    create_dataset = AutoMLCreateDatasetOperator(
        task_id="create_dataset_task", dataset=DATASET, location=GCP_AUTOML_LOCATION
    )

    dataset_id = cast(str, XComArg(create_dataset, key="dataset_id"))
    MODEL["dataset_id"] = dataset_id

    import_dataset = AutoMLImportDataOperator(
        task_id="import_dataset",
        dataset_id=dataset_id,
        location=GCP_AUTOML_LOCATION,
        input_config=IMPORT_INPUT_CONFIG,
    )

    MODEL["dataset_id"] = dataset_id

    create_model = AutoMLTrainModelOperator(task_id="create_model", model=MODEL, location=GCP_AUTOML_LOCATION)
    model_id = cast(str, XComArg(create_model, key="model_id"))

    deploy_model = AutoMLDeployModelOperator(
        task_id="deploy_model",
        model_id=model_id,
        location=GCP_AUTOML_LOCATION,
        project_id=GCP_PROJECT_ID,
    )

    delete_model = AutoMLDeleteModelOperator(
        task_id="delete_model",
        model_id=model_id,
        location=GCP_AUTOML_LOCATION,
        project_id=GCP_PROJECT_ID,
    )

    delete_dataset = AutoMLDeleteDatasetOperator(
        task_id="delete_dataset",
        dataset_id=dataset_id,
        location=GCP_AUTOML_LOCATION,
        project_id=GCP_PROJECT_ID,
    )

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

    (
        # TEST SETUP
        [create_bucket >> move_dataset_file, create_dataset]
        # TEST BODY
        >> import_dataset
        >> create_model
        >> deploy_model
        # TEST TEARDOWN
        >> delete_model
        >> delete_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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