Source code for airflow.providers.google.cloud.example_dags.example_automl_vision_object_detection

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"""
Example Airflow DAG that uses Google AutoML services.
"""
import os
from datetime import datetime

from airflow import models
from airflow.providers.google.cloud.hooks.automl import CloudAutoMLHook
from airflow.providers.google.cloud.operators.automl import (
    AutoMLCreateDatasetOperator,
    AutoMLDeleteDatasetOperator,
    AutoMLDeleteModelOperator,
    AutoMLImportDataOperator,
    AutoMLTrainModelOperator,
)

[docs]GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "your-project-id")
[docs]GCP_AUTOML_LOCATION = os.environ.get("GCP_AUTOML_LOCATION", "us-central1")
[docs]GCP_AUTOML_DETECTION_BUCKET = os.environ.get(
    "GCP_AUTOML_DETECTION_BUCKET", "gs://INVALID BUCKET NAME/img/openimage/csv/salads_ml_use.csv"
)

# Example values
[docs]DATASET_ID = ""

# Example model
[docs]MODEL = {
    "display_name": "auto_model",
    "dataset_id": DATASET_ID,
    "image_object_detection_model_metadata": {},
}

# Example dataset
[docs]DATASET = {
    "display_name": "test_detection_dataset",
    "image_object_detection_dataset_metadata": {},
}

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

[docs]extract_object_id = CloudAutoMLHook.extract_object_id


# Example DAG for AutoML Vision Object Detection
with models.DAG(
    "example_automl_vision_detection",
    schedule_interval=None,  # Override to match your needs
    start_date=datetime(2021, 1, 1),
    catchup=False,
    user_defined_macros={"extract_object_id": extract_object_id},
    tags=['example'],
) as example_dag:
[docs]    create_dataset_task = AutoMLCreateDatasetOperator(
        task_id="create_dataset_task", dataset=DATASET, location=GCP_AUTOML_LOCATION
    )

    dataset_id = create_dataset_task.output["dataset_id"]

    import_dataset_task = AutoMLImportDataOperator(
        task_id="import_dataset_task",
        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 = create_model.output["model_id"]

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

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

    import_dataset_task >> create_model
    delete_model_task >> delete_datasets_task

    # Task dependencies created via `XComArgs`:
    #   create_dataset_task >> import_dataset_task
    #   create_dataset_task >> create_model
    #   create_model >> delete_model_task
    #   create_dataset_task >> delete_datasets_task