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# mypy ignore arg types (for templated fields)
# type: ignore[arg-type]
"""
Example Airflow DAG for Google Vertex AI service testing Model Service 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 (
CreateCustomTrainingJobOperator,
DeleteCustomTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
CreateDatasetOperator,
DeleteDatasetOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.model_service import (
DeleteModelOperator,
ExportModelOperator,
ListModelsOperator,
UploadModelOperator,
)
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_model_service_operations"
[docs]TRAIN_DISPLAY_NAME = f"train-housing-custom-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = f"custom-housing-model-{ENV_ID}"
[docs]DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]STAGING_BUCKET = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}"
[docs]DATA_SAMPLE_GCS_OBJECT_NAME = "vertex-ai/california_housing_train.csv"
[docs]CSV_FILE_LOCAL_PATH = "/model_service/california_housing_train.csv"
[docs]RESOURCES_PATH = Path(__file__).parent / "resources"
[docs]CSV_ZIP_FILE_LOCAL_PATH = str(RESOURCES_PATH / "California-housing-ai-model.zip")
[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://{DATA_SAMPLE_GCS_BUCKET_NAME}/{DATA_SAMPLE_GCS_OBJECT_NAME}"]}
}
},
Value(),
),
}
[docs]CONTAINER_URI = "gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest"
[docs]LOCAL_TRAINING_SCRIPT_PATH = "/model_service/california_housing_training_script.py"
[docs]MODEL_OUTPUT_CONFIG = {
"artifact_destination": {
"output_uri_prefix": STAGING_BUCKET,
},
"export_format_id": "custom-trained",
}
[docs]MODEL_SERVING_CONTAINER_URI = "gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest"
[docs]MODEL_OBJ = {
"display_name": f"model-{ENV_ID}",
"artifact_uri": "{{ti.xcom_pull('custom_task')['artifactUri']}}",
"container_spec": {
"image_uri": MODEL_SERVING_CONTAINER_URI,
"command": [],
"args": [],
"env": [],
"ports": [],
"predict_route": "",
"health_route": "",
},
}
with models.DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
render_template_as_native_obj=True,
tags=["example", "vertex_ai", "model_service"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
storage_class="REGIONAL",
location=REGION,
)
unzip_file = BashOperator(
task_id="unzip_csv_data_file",
bash_command=f"mkdir -p /model_service && unzip {CSV_ZIP_FILE_LOCAL_PATH} -d /model_service/",
)
upload_files = LocalFilesystemToGCSOperator(
task_id="upload_file_to_bucket",
src=CSV_FILE_LOCAL_PATH,
dst=DATA_SAMPLE_GCS_OBJECT_NAME,
bucket=DATA_SAMPLE_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"]
create_custom_training_job = CreateCustomTrainingJobOperator(
task_id="custom_task",
staging_bucket=f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}",
display_name=TRAIN_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=1,
model_display_name=MODEL_DISPLAY_NAME,
sync=False,
region=REGION,
project_id=PROJECT_ID,
)
# [START how_to_cloud_vertex_ai_upload_model_operator]
upload_model = UploadModelOperator(
task_id="upload_model",
region=REGION,
project_id=PROJECT_ID,
model=MODEL_OBJ,
)
# [END how_to_cloud_vertex_ai_upload_model_operator]
# [START how_to_cloud_vertex_ai_export_model_operator]
export_model = ExportModelOperator(
task_id="export_model",
project_id=PROJECT_ID,
region=REGION,
model_id=upload_model.output["model_id"],
output_config=MODEL_OUTPUT_CONFIG,
)
# [END how_to_cloud_vertex_ai_export_model_operator]
# [START how_to_cloud_vertex_ai_delete_model_operator]
delete_model = DeleteModelOperator(
task_id="delete_model",
project_id=PROJECT_ID,
region=REGION,
model_id=upload_model.output["model_id"],
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_vertex_ai_delete_model_operator]
# [START how_to_cloud_vertex_ai_list_models_operator]
list_models = ListModelsOperator(
task_id="list_models",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_models_operator]
delete_custom_training_job = DeleteCustomTrainingJobOperator(
task_id="delete_custom_training_job",
training_pipeline_id=create_custom_training_job.output["training_id"],
custom_job_id=create_custom_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=DATA_SAMPLE_GCS_BUCKET_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
clear_folder = BashOperator(
task_id="clear_folder",
bash_command="rm -r /model_service/*",
)
(
# TEST SETUP
create_bucket
>> unzip_file
>> upload_files
>> create_tabular_dataset
>> create_custom_training_job
# TEST BODY
>> upload_model
>> export_model
>> delete_model
>> list_models
# 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)