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

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"""Example Airflow DAG for Google AutoML service testing model operations."""

from __future__ import annotations

import os
from datetime import datetime

from google.protobuf.struct_pb2 import Value

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.automl import (
    AutoMLBatchPredictOperator,
    AutoMLCreateDatasetOperator,
    AutoMLDeleteDatasetOperator,
    AutoMLDeleteModelOperator,
    AutoMLGetModelOperator,
    AutoMLImportDataOperator,
    AutoMLPredictOperator,
    AutoMLTrainModelOperator,
)
from airflow.providers.google.cloud.operators.gcs import (
    GCSCreateBucketOperator,
    GCSDeleteBucketOperator,
    GCSSynchronizeBucketsOperator,
)
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]DAG_ID = "automl_model"
[docs]GCP_PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "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]DATASET_NAME = f"ds_{DAG_ID}_{ENV_ID}".replace("-", "_")
[docs]DATASET = { "display_name": DATASET_NAME, "tables_dataset_metadata": {"target_column_spec_id": ""}, }
[docs]AUTOML_DATASET_BUCKET = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/automl/bank-marketing-split.csv"
[docs]IMPORT_INPUT_CONFIG = {"gcs_source": {"input_uris": [AUTOML_DATASET_BUCKET]}}
[docs]IMPORT_OUTPUT_CONFIG = { "gcs_destination": {"output_uri_prefix": f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/automl"} }
# change the name here
[docs]MODEL_NAME = f"md_{DAG_ID}_{ENV_ID}".replace("-", "_")
[docs]MODEL = { "display_name": MODEL_NAME, "tables_model_metadata": {"train_budget_milli_node_hours": 1000}, }
[docs]PREDICT_VALUES = [ Value(string_value="TRAINING"), Value(string_value="51"), Value(string_value="blue-collar"), Value(string_value="married"), Value(string_value="primary"), Value(string_value="no"), Value(string_value="620"), Value(string_value="yes"), Value(string_value="yes"), Value(string_value="cellular"), Value(string_value="29"), Value(string_value="jul"), Value(string_value="88"), Value(string_value="10"), Value(string_value="-1"), Value(string_value="0"), Value(string_value="unknown"), ]
with DAG( dag_id=DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "automl", "model"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator( task_id="create_bucket", bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME, storage_class="REGIONAL", location=GCP_AUTOML_LOCATION, )
move_dataset_file = GCSSynchronizeBucketsOperator( task_id="move_data_to_bucket", source_bucket=RESOURCE_DATA_BUCKET, source_object="automl/datasets/model", destination_bucket=DATA_SAMPLE_GCS_BUCKET_NAME, destination_object="automl", recursive=True, ) create_dataset = AutoMLCreateDatasetOperator( task_id="create_dataset", dataset=DATASET, location=GCP_AUTOML_LOCATION, project_id=GCP_PROJECT_ID, ) dataset_id = create_dataset.output["dataset_id"] MODEL["dataset_id"] = dataset_id import_dataset = AutoMLImportDataOperator( task_id="import_dataset", dataset_id=dataset_id, location=GCP_AUTOML_LOCATION, input_config=IMPORT_INPUT_CONFIG, ) # [START howto_operator_automl_create_model] create_model = AutoMLTrainModelOperator( task_id="create_model", model=MODEL, location=GCP_AUTOML_LOCATION, project_id=GCP_PROJECT_ID, ) model_id = create_model.output["model_id"] # [END howto_operator_automl_create_model] # [START howto_operator_get_model] get_model = AutoMLGetModelOperator( task_id="get_model", model_id=model_id, location=GCP_AUTOML_LOCATION, project_id=GCP_PROJECT_ID, ) # [END howto_operator_get_model] # [START howto_operator_prediction] predict_task = AutoMLPredictOperator( task_id="predict_task", model_id=model_id, payload={ "row": { "values": PREDICT_VALUES, } }, location=GCP_AUTOML_LOCATION, project_id=GCP_PROJECT_ID, ) # [END howto_operator_prediction] # [START howto_operator_batch_prediction] batch_predict_task = AutoMLBatchPredictOperator( task_id="batch_predict_task", model_id=model_id, input_config=IMPORT_INPUT_CONFIG, output_config=IMPORT_OUTPUT_CONFIG, location=GCP_AUTOML_LOCATION, project_id=GCP_PROJECT_ID, ) # [END howto_operator_batch_prediction] # [START howto_operator_automl_delete_model] delete_model = AutoMLDeleteModelOperator( task_id="delete_model", model_id=model_id, location=GCP_AUTOML_LOCATION, project_id=GCP_PROJECT_ID, ) # [END howto_operator_automl_delete_model] delete_dataset = AutoMLDeleteDatasetOperator( task_id="delete_dataset", dataset_id=dataset_id, location=GCP_AUTOML_LOCATION, project_id=GCP_PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE, ) delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE, ) ( # TEST SETUP [create_bucket >> move_dataset_file, create_dataset] >> import_dataset # TEST BODY >> create_model >> get_model >> predict_task >> batch_predict_task # TEST TEARDOWN >> delete_model >> delete_dataset >> 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)

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