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

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

"""
Example Airflow DAG for Google Vertex AI service testing Custom Jobs 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.custom_job import (
    CreateCustomContainerTrainingJobOperator,
    DeleteCustomTrainingJobOperator,
)
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_custom_job_operations"
[docs]REGION = "us-central1"
[docs]CONTAINER_DISPLAY_NAME = f"train-housing-container-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = f"container-housing-model-{ENV_ID}"
[docs]CUSTOM_CONTAINER_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]DATA_SAMPLE_GCS_OBJECT_NAME = "vertex-ai/california_housing_train.csv"
[docs]CSV_FILE_LOCAL_PATH = "/custom-job/california_housing_train.csv"
[docs]RESOURCES_PATH = Path(__file__).parent / "resources"
[docs]CSV_ZIP_FILE_LOCAL_PATH = str(RESOURCES_PATH / "California-housing.zip")
[docs]TABULAR_DATASET = lambda bucket_name: { "display_name": f"tabular-dataset-{ENV_ID}", "metadata_schema_uri": schema.dataset.metadata.tabular, "metadata": ParseDict( {"input_config": {"gcs_source": {"uri": [f"gs://{bucket_name}/{DATA_SAMPLE_GCS_OBJECT_NAME}"]}}}, Value(),
), }
[docs]CONTAINER_URI = "gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest"
[docs]CUSTOM_CONTAINER_URI = f"us-central1-docker.pkg.dev/{PROJECT_ID}/system-tests/housing:latest"
[docs]MODEL_SERVING_CONTAINER_URI = "gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest"
[docs]REPLICA_COUNT = 1
[docs]MACHINE_TYPE = "n1-standard-4"
[docs]ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
[docs]ACCELERATOR_COUNT = 0
[docs]TRAINING_FRACTION_SPLIT = 0.7
[docs]TEST_FRACTION_SPLIT = 0.15
[docs]VALIDATION_FRACTION_SPLIT = 0.15
with models.DAG( f"{DAG_ID}_custom_container", schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "vertex_ai", "custom_job"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator( task_id="create_bucket", bucket_name=CUSTOM_CONTAINER_GCS_BUCKET_NAME, storage_class="REGIONAL", location=REGION,
) unzip_file = BashOperator( task_id="unzip_csv_data_file", bash_command=f"mkdir -p /custom-job/ && unzip {CSV_ZIP_FILE_LOCAL_PATH} -d /custom-job/", ) upload_files = LocalFilesystemToGCSOperator( task_id="upload_file_to_bucket", src=CSV_FILE_LOCAL_PATH, dst=DATA_SAMPLE_GCS_OBJECT_NAME, bucket=CUSTOM_CONTAINER_GCS_BUCKET_NAME, ) create_tabular_dataset = CreateDatasetOperator( task_id="tabular_dataset", dataset=TABULAR_DATASET(CUSTOM_CONTAINER_GCS_BUCKET_NAME), region=REGION, project_id=PROJECT_ID, ) tabular_dataset_id = create_tabular_dataset.output["dataset_id"] # [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator] create_custom_container_training_job = CreateCustomContainerTrainingJobOperator( task_id="custom_container_task", staging_bucket=f"gs://{CUSTOM_CONTAINER_GCS_BUCKET_NAME}", display_name=CONTAINER_DISPLAY_NAME, container_uri=CUSTOM_CONTAINER_URI, model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI, # run params dataset_id=tabular_dataset_id, command=["python3", "task.py"], model_display_name=MODEL_DISPLAY_NAME, replica_count=REPLICA_COUNT, machine_type=MACHINE_TYPE, accelerator_type=ACCELERATOR_TYPE, accelerator_count=ACCELERATOR_COUNT, training_fraction_split=TRAINING_FRACTION_SPLIT, validation_fraction_split=VALIDATION_FRACTION_SPLIT, test_fraction_split=TEST_FRACTION_SPLIT, region=REGION, project_id=PROJECT_ID, ) # [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator] delete_custom_training_job = DeleteCustomTrainingJobOperator( task_id="delete_custom_training_job", training_pipeline_id=create_custom_container_training_job.output["training_id"], custom_job_id=create_custom_container_training_job.output["custom_job_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=CUSTOM_CONTAINER_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE, ) clear_folder = BashOperator( task_id="clear_folder", bash_command="rm -r /custom-job/*", ) ( # TEST SETUP create_bucket >> unzip_file >> upload_files >> create_tabular_dataset # TEST BODY >> create_custom_container_training_job # TEST TEARDOWN >> delete_custom_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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