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

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

from __future__ import annotations

import os
from datetime import datetime
from typing import cast

# The storage module cannot be imported yet https://github.com/googleapis/python-storage/issues/393
from google.cloud import storage  # type: ignore[attr-defined]

from airflow.decorators import task
from airflow.models.dag import DAG
from airflow.models.xcom_arg import XComArg
from airflow.providers.google.cloud.operators.automl import (
    AutoMLCreateDatasetOperator,
    AutoMLDeleteDatasetOperator,
    AutoMLDeleteModelOperator,
    AutoMLImportDataOperator,
    AutoMLTrainModelOperator,
)
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.transfers.gcs_to_gcs import GCSToGCSOperator
from airflow.utils.trigger_rule import TriggerRule

[docs]DAG_ID = "automl_translate"
[docs]GCP_PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "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 = "translate_test_model"
[docs]MODEL = {
    "display_name": MODEL_NAME,
    "translation_model_metadata": {},
}

[docs]DATASET_NAME = f"ds_{DAG_ID}_{ENV_ID}".replace("-", "_")
[docs]DATASET = {
    "display_name": DATASET_NAME,
    "translation_dataset_metadata": {
        "source_language_code": "en",
        "target_language_code": "es",
    },
}

[docs]CSV_FILE_NAME = "en-es.csv"
[docs]TSV_FILE_NAME = "en-es.tsv"
[docs]GCS_FILE_PATH = f"automl/datasets/translate/{CSV_FILE_NAME}"
[docs]AUTOML_DATASET_BUCKET = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/automl/{CSV_FILE_NAME}"
[docs]IMPORT_INPUT_CONFIG = {"gcs_source": {"input_uris": [AUTOML_DATASET_BUCKET]}}


# Example DAG for AutoML Translation
with DAG(
    DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "automl", "translate"],
) as dag:
[docs]    create_bucket = GCSCreateBucketOperator(
        task_id="create_bucket",
        bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
        storage_class="REGIONAL",
        location=GCP_AUTOML_LOCATION,
    )

    @task
    def upload_csv_file_to_gcs():
        # download file into memory
        storage_client = storage.Client()
        bucket = storage_client.bucket(RESOURCE_DATA_BUCKET)
        blob = bucket.blob(GCS_FILE_PATH)
        contents = blob.download_as_string().decode()

        # update memory content
        updated_contents = contents.replace("template-bucket", DATA_SAMPLE_GCS_BUCKET_NAME)

        # upload updated content to bucket
        destination_bucket = storage_client.bucket(DATA_SAMPLE_GCS_BUCKET_NAME)
        destination_blob = destination_bucket.blob(f"automl/{CSV_FILE_NAME}")
        destination_blob.upload_from_string(updated_contents)

    upload_csv_file_to_gcs_task = upload_csv_file_to_gcs()

    copy_dataset_file = GCSToGCSOperator(
        task_id="copy_dataset_file",
        source_bucket=RESOURCE_DATA_BUCKET,
        source_object=f"automl/datasets/translate/{TSV_FILE_NAME}",
        destination_bucket=DATA_SAMPLE_GCS_BUCKET_NAME,
        destination_object=f"automl/{TSV_FILE_NAME}",
    )

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

    dataset_id = cast(str, XComArg(create_dataset, key="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"))

    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 >> upload_csv_file_to_gcs_task >> copy_dataset_file]
        # TEST BODY
        >> create_dataset
        >> import_dataset
        >> create_model
        # TEST TEARDOWN
        >> delete_dataset
        >> delete_model
        >> 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)