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

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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 import models
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,
    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_TRANSLATION_BUCKET = os.environ.get(
    "GCP_AUTOML_TRANSLATION_BUCKET", "gs://INVALID BUCKET NAME/file"
)

# Example model
[docs]MODEL = {
    "display_name": "auto_model_1",
    "translation_model_metadata": {},
}

# Example dataset
[docs]DATASET = {
    "display_name": "test_translation_dataset",
    "translation_dataset_metadata": {
        "source_language_code": "en",
        "target_language_code": "es",
    },
}


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

[docs]extract_object_id = CloudAutoMLHook.extract_object_id


# Example DAG for AutoML Translation
with models.DAG(
    "example_automl_translation",
    start_date=datetime(2021, 1, 1),
    schedule="@once",
    catchup=False,
    user_defined_macros={"extract_object_id": extract_object_id},
    tags=["example"],
) as dag:
[docs]    create_dataset_task = AutoMLCreateDatasetOperator(
        task_id="create_dataset_task", dataset=DATASET, location=GCP_AUTOML_LOCATION
    )

    dataset_id = cast(str, XComArg(create_dataset_task, key="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 = cast(str, XComArg(create_model, key="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,
    )

    # TEST BODY
    import_dataset_task >> create_model
    # TEST TEARDOWN
    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

    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)