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"""
Example Airflow DAG for Google Vertex AI service testing Ray operations.
"""
from __future__ import annotations
import os
from datetime import datetime
from airflow.providers.common.compat.sdk import AirflowOptionalProviderFeatureException
try:
from google.cloud.aiplatform.vertex_ray.util import resources
except ImportError:
raise AirflowOptionalProviderFeatureException(
"The ray provider is optional and requires the `google-cloud-aiplatform` package to be installed. "
)
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.vertex_ai.ray import (
CreateRayClusterOperator,
DeleteRayClusterOperator,
GetRayClusterOperator,
ListRayClustersOperator,
UpdateRayClusterOperator,
)
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_ray_operations"
LOCATION = "us-central1"
WORKER_NODE_RESOURCES = resources.Resources(
node_count=1,
)
WORKER_NODE_RESOURCES_NEW = resources.Resources(
node_count=2,
)
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "vertex_ai", "ray"],
) as dag:
# [START how_to_cloud_vertex_ai_create_ray_cluster_operator]
create_ray_cluster = CreateRayClusterOperator(
task_id="create_ray_cluster",
project_id=PROJECT_ID,
location=LOCATION,
worker_node_types=[WORKER_NODE_RESOURCES],
python_version="3.10",
ray_version="2.33",
)
# [END how_to_cloud_vertex_ai_create_ray_cluster_operator]
# [START how_to_cloud_vertex_ai_update_ray_cluster_operator]
update_ray_cluster = UpdateRayClusterOperator(
task_id="update_ray_cluster",
project_id=PROJECT_ID,
location=LOCATION,
cluster_id="{{ task_instance.xcom_pull(task_ids='create_ray_cluster', key='cluster_id') }}",
worker_node_types=[WORKER_NODE_RESOURCES_NEW],
)
# [END how_to_cloud_vertex_ai_update_ray_cluster_operator]
# [START how_to_cloud_vertex_ai_get_ray_cluster_operator]
get_ray_cluster = GetRayClusterOperator(
task_id="get_ray_cluster",
project_id=PROJECT_ID,
location=LOCATION,
cluster_id="{{ task_instance.xcom_pull(task_ids='create_ray_cluster', key='cluster_id') }}",
)
# [END how_to_cloud_vertex_ai_get_ray_cluster_operator]
# [START how_to_cloud_vertex_ai_delete_ray_cluster_operator]
delete_ray_cluster = DeleteRayClusterOperator(
task_id="delete_ray_cluster",
project_id=PROJECT_ID,
location=LOCATION,
cluster_id="{{ task_instance.xcom_pull(task_ids='create_ray_cluster', key='cluster_id') }}",
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_vertex_ai_delete_ray_cluster_operator]
# [START how_to_cloud_vertex_ai_list_ray_clusters_operator]
list_ray_clusters = ListRayClustersOperator(
task_id="list_ray_clusters",
project_id=PROJECT_ID,
location=LOCATION,
)
# [END how_to_cloud_vertex_ai_list_ray_clusters_operator]
(
[
create_ray_cluster >> update_ray_cluster >> get_ray_cluster >> delete_ray_cluster,
list_ray_clusters,
]
)
# ### 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)