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"""
Example Airflow DAG for Google Vertex AI service testing Custom Jobs operations.
"""
from __future__ import annotations
import os
from datetime import datetime
from google.cloud.aiplatform import schema
from google.protobuf.json_format import ParseDict
from google.protobuf.struct_pb2 import Value
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import (
GCSCreateBucketOperator,
GCSDeleteBucketOperator,
GCSSynchronizeBucketsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.custom_job import (
CreateCustomTrainingJobOperator,
DeleteCustomTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
CreateDatasetOperator,
DeleteDatasetOperator,
)
from airflow.providers.google.cloud.transfers.gcs_to_local import GCSToLocalFilesystemOperator
from airflow.utils.trigger_rule import TriggerRule
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]DAG_ID = "example_vertex_ai_custom_job_operations"
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]CUSTOM_DISPLAY_NAME = f"train-housing-custom-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = f"custom-housing-model-{ENV_ID}"
[docs]RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
[docs]CUSTOM_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}".replace("_", "-")
[docs]DATA_SAMPLE_GCS_OBJECT_NAME = "vertex-ai/california_housing_train.csv"
[docs]def TABULAR_DATASET(bucket_name):
return {
"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]MODEL_SERVING_CONTAINER_URI = "gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest"
[docs]LOCAL_TRAINING_SCRIPT_PATH = "california_housing_training_script.py"
with DAG(
f"{DAG_ID}_custom",
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_GCS_BUCKET_NAME,
storage_class="REGIONAL",
location=REGION,
)
move_data_files = GCSSynchronizeBucketsOperator(
task_id="move_files_to_bucket",
source_bucket=RESOURCE_DATA_BUCKET,
source_object="vertex-ai/california-housing-data",
destination_bucket=CUSTOM_GCS_BUCKET_NAME,
destination_object="vertex-ai",
recursive=True,
)
download_training_script_file = GCSToLocalFilesystemOperator(
task_id="download_training_script_file",
object_name="vertex-ai/california_housing_training_script.py",
bucket=CUSTOM_GCS_BUCKET_NAME,
filename=LOCAL_TRAINING_SCRIPT_PATH,
)
create_tabular_dataset = CreateDatasetOperator(
task_id="tabular_dataset",
dataset=TABULAR_DATASET(CUSTOM_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_training_job_operator]
create_custom_training_job = CreateCustomTrainingJobOperator(
task_id="custom_task",
staging_bucket=f"gs://{CUSTOM_GCS_BUCKET_NAME}",
display_name=CUSTOM_DISPLAY_NAME,
script_path=LOCAL_TRAINING_SCRIPT_PATH,
container_uri=CONTAINER_URI,
requirements=["gcsfs==0.7.1"],
model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
# run params
dataset_id=tabular_dataset_id,
replica_count=REPLICA_COUNT,
model_display_name=MODEL_DISPLAY_NAME,
sync=False,
region=REGION,
project_id=PROJECT_ID,
)
model_id_v1 = create_custom_training_job.output["model_id"]
# [END how_to_cloud_vertex_ai_create_custom_training_job_operator]
# [START how_to_cloud_vertex_ai_create_custom_training_job_v2_operator]
create_custom_training_job_v2 = CreateCustomTrainingJobOperator(
task_id="custom_task_v2",
staging_bucket=f"gs://{CUSTOM_GCS_BUCKET_NAME}",
display_name=CUSTOM_DISPLAY_NAME,
script_path=LOCAL_TRAINING_SCRIPT_PATH,
container_uri=CONTAINER_URI,
requirements=["gcsfs==0.7.1"],
model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
parent_model=model_id_v1,
# run params
dataset_id=tabular_dataset_id,
replica_count=REPLICA_COUNT,
model_display_name=MODEL_DISPLAY_NAME,
sync=False,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_custom_training_job_v2_operator]
# [START how_to_cloud_vertex_ai_delete_custom_training_job_operator]
delete_custom_training_job = DeleteCustomTrainingJobOperator(
task_id="delete_custom_training_job",
training_pipeline_id="{{ task_instance.xcom_pull(task_ids='custom_task', key='training_id') }}",
custom_job_id="{{ task_instance.xcom_pull(task_ids='custom_task', key='custom_job_id') }}",
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_vertex_ai_delete_custom_training_job_operator]
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_GCS_BUCKET_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
(
# TEST SETUP
create_bucket
>> move_data_files
>> download_training_script_file
>> create_tabular_dataset
# TEST BODY
>> create_custom_training_job
>> create_custom_training_job_v2
# TEST TEARDOWN
>> delete_custom_training_job
>> delete_tabular_dataset
>> delete_bucket
)
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)