#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""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.baseoperator import chain
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]
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]
DAG_ID = "vertex_ai_custom_job_operations"
[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 = "us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-2:latest"
[docs]
MODEL_SERVING_CONTAINER_URI = "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-2:latest"
# LOCAL_TRAINING_SCRIPT_PATH should be set for Airflow which is running on distributed system.
# For example in Composer the correct path is `gcs/data/california_housing_training_script.py`.
# Because `gcs/data/` is shared folder for Airflow's workers.
[docs]
IS_COMPOSER = bool(os.environ.get("COMPOSER_ENVIRONMENT", ""))
[docs]
LOCAL_TRAINING_SCRIPT_PATH = "gcs/data/california_housing_training_script.py" if IS_COMPOSER else ""
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,
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_operator_deferrable]
create_custom_training_job_deferrable = CreateCustomTrainingJobOperator(
task_id="custom_task_deferrable",
staging_bucket=f"gs://{CUSTOM_GCS_BUCKET_NAME}",
display_name=f"{CUSTOM_DISPLAY_NAME}-def",
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=f"{MODEL_DISPLAY_NAME}-def",
region=REGION,
project_id=PROJECT_ID,
deferrable=True,
)
model_id_deferrable_v1 = create_custom_training_job_deferrable.output["model_id"]
# [END how_to_cloud_vertex_ai_create_custom_training_job_operator_deferrable]
# [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,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_custom_training_job_v2_operator]
# [START how_to_cloud_vertex_ai_create_custom_training_job_v2_deferrable_operator]
create_custom_training_job_deferrable_v2 = CreateCustomTrainingJobOperator(
task_id="custom_task_deferrable_v2",
staging_bucket=f"gs://{CUSTOM_GCS_BUCKET_NAME}",
display_name=f"{CUSTOM_DISPLAY_NAME}-def",
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_deferrable_v1,
# run params
dataset_id=tabular_dataset_id,
replica_count=REPLICA_COUNT,
model_display_name=f"{MODEL_DISPLAY_NAME}-def",
region=REGION,
project_id=PROJECT_ID,
deferrable=True,
)
# [END how_to_cloud_vertex_ai_create_custom_training_job_v2_deferrable_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_custom_training_job_deferrable = DeleteCustomTrainingJobOperator(
task_id="delete_custom_training_job_deferrable",
training_pipeline_id="{{ task_instance.xcom_pull(task_ids='custom_task_deferrable', key='training_id') }}",
custom_job_id="{{ task_instance.xcom_pull(task_ids='custom_task_deferrable', key='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_GCS_BUCKET_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
(
chain(
# TEST SETUP
create_bucket,
move_data_files,
download_training_script_file,
create_tabular_dataset,
# TEST BODY
[create_custom_training_job, create_custom_training_job_deferrable],
[create_custom_training_job_v2, create_custom_training_job_deferrable_v2],
# TEST TEARDOWN
[delete_custom_training_job, delete_custom_training_job_deferrable],
delete_tabular_dataset,
delete_bucket,
)
)
# ### 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: tests/system/README.md#run_via_pytest)
[docs]
test_run = get_test_run(dag)