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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
# 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.hooks.automl import CloudAutoMLHook
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 = "example_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_translate_{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}"
# Example DAG for AutoML Translation
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
user_defined_macros={"extract_object_id": extract_object_id},
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)
