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"""
Example Airflow DAG for Google Vertex AI service testing Batch Prediction 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 (
CreateAutoMLForecastingTrainingJobOperator,
DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job import (
CreateBatchPredictionJobOperator,
DeleteBatchPredictionJobOperator,
ListBatchPredictionJobsOperator,
)
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_batch_prediction_operations"
[docs]FORECAST_DISPLAY_NAME = f"auto-ml-forecasting-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = f"auto-ml-forecasting-model-{ENV_ID}"
[docs]JOB_DISPLAY_NAME = f"batch_prediction_job_test_{ENV_ID}"
[docs]RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
[docs]DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}".replace("_", "-")
[docs]DATA_SAMPLE_GCS_OBJECT_NAME = "vertex-ai/forecast-dataset.csv"
[docs]FORECAST_DATASET = {
"display_name": f"forecast-dataset-{ENV_ID}",
"metadata_schema_uri": schema.dataset.metadata.time_series,
"metadata": ParseDict(
{
"input_config": {
"gcs_source": {"uri": [f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/{DATA_SAMPLE_GCS_OBJECT_NAME}"]}
}
},
Value(),
),
}
[docs]TEST_TIME_COLUMN = "date"
[docs]TEST_TIME_SERIES_IDENTIFIER_COLUMN = "store_name"
[docs]TEST_TARGET_COLUMN = "sale_dollars"
[docs]COLUMN_SPECS = {
TEST_TIME_COLUMN: "timestamp",
TEST_TARGET_COLUMN: "numeric",
"city": "categorical",
"zip_code": "categorical",
"county": "categorical",
}
[docs]BIGQUERY_SOURCE = f"bq://{PROJECT_ID}.test_iowa_liquor_sales_forecasting_us.2021_sales_predict"
[docs]GCS_DESTINATION_PREFIX = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/output"
[docs]MODEL_PARAMETERS: dict[str, str] = {}
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
render_template_as_native_obj=True,
tags=["example", "vertex_ai", "batch_prediction_job"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=DATA_SAMPLE_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=DATA_SAMPLE_GCS_BUCKET_NAME,
destination_object="vertex-ai",
recursive=True,
)
create_forecast_dataset = CreateDatasetOperator(
task_id="forecast_dataset",
dataset=FORECAST_DATASET,
region=REGION,
project_id=PROJECT_ID,
)
create_auto_ml_forecasting_training_job = CreateAutoMLForecastingTrainingJobOperator(
task_id="auto_ml_forecasting_task",
display_name=FORECAST_DISPLAY_NAME,
optimization_objective="minimize-rmse",
column_specs=COLUMN_SPECS,
# run params
dataset_id=create_forecast_dataset.output["dataset_id"],
target_column=TEST_TARGET_COLUMN,
time_column=TEST_TIME_COLUMN,
time_series_identifier_column=TEST_TIME_SERIES_IDENTIFIER_COLUMN,
available_at_forecast_columns=[TEST_TIME_COLUMN],
unavailable_at_forecast_columns=[TEST_TARGET_COLUMN],
time_series_attribute_columns=["city", "zip_code", "county"],
forecast_horizon=30,
context_window=30,
data_granularity_unit="day",
data_granularity_count=1,
weight_column=None,
budget_milli_node_hours=1000,
model_display_name=MODEL_DISPLAY_NAME,
predefined_split_column_name=None,
region=REGION,
project_id=PROJECT_ID,
)
# [START how_to_cloud_vertex_ai_create_batch_prediction_job_operator]
create_batch_prediction_job = CreateBatchPredictionJobOperator(
task_id="create_batch_prediction_job",
job_display_name=JOB_DISPLAY_NAME,
model_name="{{ti.xcom_pull('auto_ml_forecasting_task')['name']}}",
predictions_format="csv",
bigquery_source=BIGQUERY_SOURCE,
gcs_destination_prefix=GCS_DESTINATION_PREFIX,
model_parameters=MODEL_PARAMETERS,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_batch_prediction_job_operator]
# [START how_to_cloud_vertex_ai_create_batch_prediction_job_operator_def]
create_batch_prediction_job_def = CreateBatchPredictionJobOperator(
task_id="create_batch_prediction_job_def",
job_display_name=JOB_DISPLAY_NAME,
model_name="{{ti.xcom_pull('auto_ml_forecasting_task')['name']}}",
predictions_format="csv",
bigquery_source=BIGQUERY_SOURCE,
gcs_destination_prefix=GCS_DESTINATION_PREFIX,
model_parameters=MODEL_PARAMETERS,
region=REGION,
project_id=PROJECT_ID,
deferrable=True,
)
# [END how_to_cloud_vertex_ai_create_batch_prediction_job_operator_def]
# [START how_to_cloud_vertex_ai_list_batch_prediction_job_operator]
list_batch_prediction_job = ListBatchPredictionJobsOperator(
task_id="list_batch_prediction_jobs",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_batch_prediction_job_operator]
# [START how_to_cloud_vertex_ai_delete_batch_prediction_job_operator]
delete_batch_prediction_job = DeleteBatchPredictionJobOperator(
task_id="delete_batch_prediction_job",
batch_prediction_job_id=create_batch_prediction_job.output["batch_prediction_job_id"],
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_vertex_ai_delete_batch_prediction_job_operator]
delete_batch_prediction_job_def = DeleteBatchPredictionJobOperator(
task_id="delete_batch_prediction_job_def",
batch_prediction_job_id=create_batch_prediction_job_def.output["batch_prediction_job_id"],
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_auto_ml_forecasting_training_job = DeleteAutoMLTrainingJobOperator(
task_id="delete_auto_ml_forecasting_training_job",
training_pipeline_id="{{ task_instance.xcom_pull(task_ids='auto_ml_forecasting_task', "
"key='training_id') }}",
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_forecast_dataset = DeleteDatasetOperator(
task_id="delete_forecast_dataset",
dataset_id=create_forecast_dataset.output["dataset_id"],
region=REGION,
project_id=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_forecast_dataset
>> create_auto_ml_forecasting_training_job
# TEST BODY
>> [create_batch_prediction_job, create_batch_prediction_job_def]
>> list_batch_prediction_job
# TEST TEARDOWN
>> [delete_batch_prediction_job, delete_batch_prediction_job_def]
>> delete_auto_ml_forecasting_training_job
>> delete_forecast_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)