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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 import CloudAutoMLHook
from import (
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "example_automl_text"
[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_TEXT_BUCKET = os.environ.get( "GCP_AUTOML_TEXT_BUCKET", "gs://INVALID BUCKET NAME/NL-entity/dataset.csv"
) # Example values
[docs]DATASET_ID = ""
# Example model
[docs]MODEL = { "display_name": "auto_model_1", "dataset_id": DATASET_ID, "text_extraction_model_metadata": {},
} # Example dataset
[docs]DATASET = {"display_name": "test_text_dataset", "text_extraction_dataset_metadata": {}}
[docs]IMPORT_INPUT_CONFIG = {"gcs_source": {"input_uris": [GCP_AUTOML_TEXT_BUCKET]}}
[docs]extract_object_id = CloudAutoMLHook.extract_object_id
# Example DAG for AutoML Natural Language Entities Extraction with models.DAG( DAG_ID, schedule="@once", # Override to match your needs start_date=datetime(2021, 1, 1), catchup=False, user_defined_macros={"extract_object_id": extract_object_id}, tags=["example", "automl"], ) 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, trigger_rule=TriggerRule.ALL_DONE, ) ( # TEST SETUP create_dataset_task # TEST BODY >> import_dataset_task >> create_model >> delete_model_task # TEST TEARDOWN >> 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/
[docs]test_run = get_test_run(dag)

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