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

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# 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 (
    CreateAutoMLTabularTrainingJobOperator,
    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]TABULAR_DISPLAY_NAME = f"auto-ml-tabular-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = "adopted-prediction-model"
[docs]TABULAR_GCS_BUCKET_NAME = f"bucket_tabular_{DAG_ID}_{ENV_ID}"
[docs]RESOURCES_PATH = Path(__file__).parent / "resources"
[docs]TABULAR_ZIP_CSV_FILE_LOCAL_PATH = str(RESOURCES_PATH / "tabular-dataset.csv.zip")
[docs]TABULAR_GCS_OBJECT_NAME = "vertex-ai/tabular-dataset.csv"
[docs]TABULAR_CSV_FILE_LOCAL_PATH = "/tabular/tabular-dataset.csv"
[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(),
), }
[docs]COLUMN_TRANSFORMATIONS = [ {"categorical": {"column_name": "Type"}}, {"numeric": {"column_name": "Age"}}, {"categorical": {"column_name": "Breed1"}}, {"categorical": {"column_name": "Color1"}}, {"categorical": {"column_name": "Color2"}}, {"categorical": {"column_name": "MaturitySize"}}, {"categorical": {"column_name": "FurLength"}}, {"categorical": {"column_name": "Vaccinated"}}, {"categorical": {"column_name": "Sterilized"}}, {"categorical": {"column_name": "Health"}}, {"numeric": {"column_name": "Fee"}}, {"numeric": {"column_name": "PhotoAmt"}},
] with models.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,
) unzip_file = BashOperator( task_id="unzip_csv_data_file", bash_command=f"unzip {TABULAR_ZIP_CSV_FILE_LOCAL_PATH} -d /tabular/", ) upload_files = LocalFilesystemToGCSOperator( task_id="upload_file_to_bucket", src=TABULAR_CSV_FILE_LOCAL_PATH, dst=TABULAR_GCS_OBJECT_NAME, bucket=TABULAR_GCS_BUCKET_NAME, ) 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=create_auto_ml_tabular_training_job.output["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, ) clear_folder = BashOperator( task_id="clear_folder", bash_command="rm -r /tabular/*", ) ( # TEST SETUP create_bucket >> unzip_file >> upload_files >> create_tabular_dataset # TEST BODY >> create_auto_ml_tabular_training_job # TEST TEARDOWN >> delete_auto_ml_tabular_training_job >> delete_tabular_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)

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