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"""
Example Airflow DAG for Google Vertex AI service testing Auto ML operations.
"""
from __future__ import annotations
import os
from datetime import datetime
from google.cloud.aiplatform import schema
from google.protobuf.json_format import ParseDict
from google.protobuf.struct_pb2 import Value
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import (
GCSCreateBucketOperator,
GCSDeleteBucketOperator,
GCSSynchronizeBucketsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
CreateAutoMLTabularTrainingJobOperator,
DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
CreateDatasetOperator,
DeleteDatasetOperator,
)
from airflow.utils.trigger_rule import TriggerRule
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]DAG_ID = "example_vertex_ai_auto_ml_operations"
[docs]TABULAR_DISPLAY_NAME = f"auto-ml-tabular-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = "adopted-prediction-model"
[docs]RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
[docs]TABULAR_GCS_BUCKET_NAME = f"bucket_tabular_{DAG_ID}_{ENV_ID}".replace("_", "-")
[docs]TABULAR_DATASET = {
"display_name": f"tabular-dataset-{ENV_ID}",
"metadata_schema_uri": schema.dataset.metadata.tabular,
"metadata": ParseDict(
{
"input_config": {
"gcs_source": {"uri": [f"gs://{TABULAR_GCS_BUCKET_NAME}/vertex-ai/tabular-dataset.csv"]}
}
},
Value(),
),
}
with DAG(
f"{DAG_ID}_tabular_training_job",
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "vertex_ai", "auto_ml"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=TABULAR_GCS_BUCKET_NAME,
storage_class="REGIONAL",
location=REGION,
)
move_dataset_file = GCSSynchronizeBucketsOperator(
task_id="move_dataset_to_bucket",
source_bucket=RESOURCE_DATA_BUCKET,
source_object="vertex-ai/datasets",
destination_bucket=TABULAR_GCS_BUCKET_NAME,
destination_object="vertex-ai",
recursive=True,
)
create_tabular_dataset = CreateDatasetOperator(
task_id="tabular_dataset",
dataset=TABULAR_DATASET,
region=REGION,
project_id=PROJECT_ID,
)
tabular_dataset_id = create_tabular_dataset.output["dataset_id"]
# [START how_to_cloud_vertex_ai_create_auto_ml_tabular_training_job_operator]
create_auto_ml_tabular_training_job = CreateAutoMLTabularTrainingJobOperator(
task_id="auto_ml_tabular_task",
display_name=TABULAR_DISPLAY_NAME,
optimization_prediction_type="classification",
column_transformations=COLUMN_TRANSFORMATIONS,
dataset_id=tabular_dataset_id,
target_column="Adopted",
training_fraction_split=0.8,
validation_fraction_split=0.1,
test_fraction_split=0.1,
model_display_name=MODEL_DISPLAY_NAME,
disable_early_stopping=False,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_auto_ml_tabular_training_job_operator]
delete_auto_ml_tabular_training_job = DeleteAutoMLTrainingJobOperator(
task_id="delete_auto_ml_training_job",
training_pipeline_id="{{ task_instance.xcom_pull(task_ids='auto_ml_tabular_task', "
"key='training_id') }}",
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_tabular_dataset = DeleteDatasetOperator(
task_id="delete_tabular_dataset",
dataset_id=tabular_dataset_id,
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_bucket = GCSDeleteBucketOperator(
task_id="delete_bucket",
bucket_name=TABULAR_GCS_BUCKET_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
(
# TEST SETUP
create_bucket
>> move_dataset_file
>> create_tabular_dataset
# TEST BODY
>> create_auto_ml_tabular_training_job
# TEST TEARDOWN
>> delete_auto_ml_tabular_training_job
>> delete_tabular_dataset
>> delete_bucket
)
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)