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"""
Example Airflow DAG for Google Vertex AI service testing Endpoint Service operations.
"""
from __future__ import annotations
import os
from datetime import datetime
from google.cloud.aiplatform import schema
from google.protobuf.struct_pb2 import Value
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
CreateAutoMLImageTrainingJobOperator,
DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
CreateDatasetOperator,
DeleteDatasetOperator,
ImportDataOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.endpoint_service import (
CreateEndpointOperator,
DeleteEndpointOperator,
DeployModelOperator,
ListEndpointsOperator,
UndeployModelOperator,
)
try:
from airflow.sdk import TriggerRule
except ImportError:
# Compatibility for Airflow < 3.1
from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
DAG_ID = "vertex_ai_endpoint_service_operations"
REGION = "us-central1"
IMAGE_DISPLAY_NAME = f"auto-ml-image-{ENV_ID}"
MODEL_DISPLAY_NAME = f"auto-ml-image-model-{ENV_ID}"
RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
IMAGE_DATASET = {
"display_name": f"image-dataset-{ENV_ID}",
"metadata_schema_uri": schema.dataset.metadata.image,
"metadata": Value(string_value="image-dataset"),
}
IMAGE_DATA_CONFIG = [
{
"import_schema_uri": schema.dataset.ioformat.image.single_label_classification,
"gcs_source": {"uris": [f"gs://{RESOURCE_DATA_BUCKET}/vertex-ai/datasets/flowers-dataset.csv"]},
},
]
ENDPOINT_CONF = {
"display_name": f"endpoint_test_{ENV_ID}",
}
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2025, 1, 1),
catchup=False,
render_template_as_native_obj=True,
tags=["example", "vertex_ai", "auto_ml", "image_classification", "endpoint_service"],
) as dag:
create_image_dataset = CreateDatasetOperator(
task_id="image_dataset",
dataset=IMAGE_DATASET,
region=REGION,
project_id=PROJECT_ID,
)
image_dataset_id = create_image_dataset.output["dataset_id"]
import_image_dataset = ImportDataOperator(
task_id="import_image_data",
dataset_id=image_dataset_id,
region=REGION,
project_id=PROJECT_ID,
import_configs=IMAGE_DATA_CONFIG,
)
# [START how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator]
create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
task_id="auto_ml_image_task",
display_name=IMAGE_DISPLAY_NAME,
dataset_id=image_dataset_id,
prediction_type="classification",
multi_label=False,
model_type="CLOUD",
training_fraction_split=0.6,
validation_fraction_split=0.2,
test_fraction_split=0.2,
budget_milli_node_hours=8000,
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_image_training_job_operator]
DEPLOYED_MODEL = {
# format: 'projects/{project}/locations/{location}/models/{model}'
"model": "{{ti.xcom_pull('auto_ml_image_task')['name']}}",
"display_name": f"temp_endpoint_test_{ENV_ID}",
"automatic_resources": {
"min_replica_count": 1,
"max_replica_count": 1,
},
}
# [START how_to_cloud_vertex_ai_create_endpoint_operator]
create_endpoint = CreateEndpointOperator(
task_id="create_endpoint",
endpoint=ENDPOINT_CONF,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_endpoint_operator]
# [START how_to_cloud_vertex_ai_delete_endpoint_operator]
delete_endpoint = DeleteEndpointOperator(
task_id="delete_endpoint",
endpoint_id=create_endpoint.output["endpoint_id"],
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_delete_endpoint_operator]
# [START how_to_cloud_vertex_ai_list_endpoints_operator]
list_endpoints = ListEndpointsOperator(
task_id="list_endpoints",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_endpoints_operator]
# [START how_to_cloud_vertex_ai_deploy_model_operator]
deploy_model = DeployModelOperator(
task_id="deploy_model",
endpoint_id=create_endpoint.output["endpoint_id"],
deployed_model=DEPLOYED_MODEL,
traffic_split={"0": 100},
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_deploy_model_operator]
# [START how_to_cloud_vertex_ai_undeploy_model_operator]
undeploy_model = UndeployModelOperator(
task_id="undeploy_model",
endpoint_id=create_endpoint.output["endpoint_id"],
deployed_model_id=deploy_model.output["deployed_model_id"],
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_undeploy_model_operator]
delete_auto_ml_image_training_job = DeleteAutoMLTrainingJobOperator(
task_id="delete_auto_ml_training_job",
training_pipeline_id="{{ task_instance.xcom_pull(task_ids='auto_ml_image_task', "
"key='training_id') }}",
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_image_dataset = DeleteDatasetOperator(
task_id="delete_image_dataset",
dataset_id=image_dataset_id,
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
(
# TEST SETUP
create_image_dataset
>> import_image_dataset
>> create_auto_ml_image_training_job
# TEST BODY
>> create_endpoint
>> deploy_model
>> undeploy_model
>> delete_endpoint
>> list_endpoints
# TEST TEARDOWN
>> delete_auto_ml_image_training_job
>> delete_image_dataset
)
# ### Everything below this line is not part of example ###
# ### Just for system tests purpose ###
from tests_common.test_utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests_common.test_utils.system_tests import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)