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"""
Example Airflow DAG for Jobs on Ray operations.
"""
from __future__ import annotations
import os
from datetime import datetime
from airflow.exceptions import AirflowOptionalProviderFeatureException
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.ray import (
RayDeleteJobOperator,
RayGetJobInfoOperator,
RayListJobsOperator,
RayStopJobOperator,
RaySubmitJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.ray import (
CreateRayClusterOperator,
DeleteRayClusterOperator,
GetRayClusterOperator,
)
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. "
)
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 = "ray_job_operations"
LOCATION = "us-central1"
JOB_ID = f"{DAG_ID}_{ENV_ID}".replace("-", "_")
WORKER_NODE_RESOURCES = resources.Resources(
node_count=1,
)
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
render_template_as_native_obj=True,
tags=["example", "job", "ray"],
) as dag:
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",
)
get_ray_cluster = GetRayClusterOperator(
task_id="get_ray_cluster",
project_id=PROJECT_ID,
location=LOCATION,
cluster_id=create_ray_cluster.output["cluster_id"],
)
# [START how_to_ray_submit_job]
submit_ray_job = RaySubmitJobOperator(
task_id="submit_ray_job",
cluster_address="{{ task_instance.xcom_pull(task_ids='get_ray_cluster')['dashboard_address'] }}",
entrypoint="echo hi && sleep 105 && echo hi2",
runtime_env={
"pip": [
"ray==2.33.0",
],
},
get_job_logs=False,
wait_for_job_done=False,
submission_id=JOB_ID,
)
# [END how_to_ray_submit_job]
# [START how_to_ray_get_job_info]
info_ray_job = RayGetJobInfoOperator(
task_id="info_ray_job",
cluster_address="{{ task_instance.xcom_pull(task_ids='get_ray_cluster')['dashboard_address'] }}",
job_id=JOB_ID,
)
# [END how_to_ray_get_job_info]
# [START how_to_ray_list_jobs]
list_ray_job = RayListJobsOperator(
task_id="list_ray_job",
cluster_address="{{ task_instance.xcom_pull(task_ids='get_ray_cluster')['dashboard_address'] }}",
)
# [END how_to_ray_list_jobs]
# [START how_to_ray_stop_job]
stop_ray_job = RayStopJobOperator(
task_id="stop_ray_job",
job_id=JOB_ID,
cluster_address="{{ task_instance.xcom_pull(task_ids='get_ray_cluster')['dashboard_address'] }}",
)
# [END how_to_ray_stop_job]
# [START how_to_ray_delete_job]
delete_ray_job = RayDeleteJobOperator(
task_id="delete_ray_job",
cluster_address="{{ task_instance.xcom_pull(task_ids='get_ray_cluster')['dashboard_address'] }}",
job_id=JOB_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_ray_delete_job]
delete_ray_cluster = DeleteRayClusterOperator(
task_id="delete_ray_cluster",
project_id=PROJECT_ID,
location=LOCATION,
cluster_id=create_ray_cluster.output["cluster_id"],
trigger_rule=TriggerRule.ALL_DONE,
)
(
create_ray_cluster
>> get_ray_cluster
>> submit_ray_job
>> info_ray_job
>> stop_ray_job
>> list_ray_job
>> delete_ray_job
>> delete_ray_cluster
)
# ### 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)