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

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

"""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)

Was this entry helpful?