Source code for tests.system.providers.google.cloud.vertex_ai.example_vertex_ai_auto_ml_forecasting_training

#
# 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.

# mypy ignore arg types (for templated fields)
# type: ignore[arg-type]

"""
Example Airflow DAG for Google Vertex AI service testing Auto ML operations.
"""
from __future__ import annotations

import os
from datetime import datetime
from pathlib import Path

from google.cloud.aiplatform import schema
from google.protobuf.json_format import ParseDict
from google.protobuf.struct_pb2 import Value

from airflow import models
from airflow.operators.bash import BashOperator
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
    CreateAutoMLForecastingTrainingJobOperator,
    DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
)
from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]DAG_ID = "vertex_ai_auto_ml_operations"
[docs]REGION = "us-central1"
[docs]FORECASTING_DISPLAY_NAME = f"auto-ml-forecasting-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = f"auto-ml-forecasting-model-{ENV_ID}"
[docs]FORECAST_GCS_BUCKET_NAME = f"bucket_forecast_{DAG_ID}_{ENV_ID}"
[docs]RESOURCES_PATH = Path(__file__).parent / "resources"
[docs]FORECAST_ZIP_CSV_FILE_LOCAL_PATH = str(RESOURCES_PATH / "forecast-dataset.csv.zip")
[docs]FORECAST_GCS_OBJECT_NAME = "vertex-ai/forecast-dataset.csv"
[docs]FORECAST_CSV_FILE_LOCAL_PATH = "/forecast/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://{FORECAST_GCS_BUCKET_NAME}/vertex-ai/forecast-dataset.csv"]} } }, 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",
} with models.DAG( f"{DAG_ID}_forecasting_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=FORECAST_GCS_BUCKET_NAME, storage_class="REGIONAL", location=REGION,
) unzip_file = BashOperator( task_id="unzip_csv_data_file", bash_command=f"unzip {FORECAST_ZIP_CSV_FILE_LOCAL_PATH} -d /forecast/", ) upload_files = LocalFilesystemToGCSOperator( task_id="upload_file_to_bucket", src=FORECAST_CSV_FILE_LOCAL_PATH, dst=FORECAST_GCS_OBJECT_NAME, bucket=FORECAST_GCS_BUCKET_NAME, ) create_forecast_dataset = CreateDatasetOperator( task_id="forecast_dataset", dataset=FORECAST_DATASET, region=REGION, project_id=PROJECT_ID, ) forecast_dataset_id = create_forecast_dataset.output["dataset_id"] # [START how_to_cloud_vertex_ai_create_auto_ml_forecasting_training_job_operator] create_auto_ml_forecasting_training_job = CreateAutoMLForecastingTrainingJobOperator( task_id="auto_ml_forecasting_task", display_name=FORECASTING_DISPLAY_NAME, optimization_objective="minimize-rmse", column_specs=COLUMN_SPECS, # run params dataset_id=forecast_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, ) # [END how_to_cloud_vertex_ai_create_auto_ml_forecasting_training_job_operator] # [START how_to_cloud_vertex_ai_delete_auto_ml_training_job_operator] delete_auto_ml_forecasting_training_job = DeleteAutoMLTrainingJobOperator( task_id="delete_auto_ml_forecasting_training_job", training_pipeline_id=create_auto_ml_forecasting_training_job.output["training_id"], region=REGION, project_id=PROJECT_ID, ) # [END how_to_cloud_vertex_ai_delete_auto_ml_training_job_operator] delete_forecast_dataset = DeleteDatasetOperator( task_id="delete_forecast_dataset", dataset_id=forecast_dataset_id, region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE, ) delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=FORECAST_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE ) clear_folder = BashOperator( task_id="clear_folder", bash_command="rm -r /forecast/*", ) ( # TEST SETUP create_bucket >> unzip_file >> upload_files >> create_forecast_dataset # TEST BODY >> create_auto_ml_forecasting_training_job # TEST TEARDOWN >> delete_auto_ml_forecasting_training_job >> delete_forecast_dataset >> delete_bucket >> clear_folder ) 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?