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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]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-container/california_housing_train.csv"
[docs]RESOURCES_PATH = Path(__file__).parent / "resources"
[docs]CSV_ZIP_FILE_LOCAL_PATH = str(RESOURCES_PATH / "California-housing-custom-container.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]MACHINE_TYPE = "n1-standard-4"
[docs]ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
[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-container/ && "
f"unzip {CSV_ZIP_FILE_LOCAL_PATH} -d /custom-job-container/",
)
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-container/*",
)
(
# 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)
