Vertex AI의 Ray 클러스터에서 애플리케이션 개발

import ray

# Necessary even if aiplatform.* symbol is not directly used in your program.
from google.cloud import aiplatform
import vertex_ray

import vertexai
vertexai.init()
# The CLUSTER_RESOURCE_NAME is the one returned from vertex_ray.create_ray_cluster.
CLUSTER_RESOURCE_NAME='projects/{}/locations/{}/persistentResources/{}'.format(PROJECT_ID, LOCATION, CLUSTER_NAME)

ray.init('vertex_ray://{}'.format(CLUSTER_RESOURCE_NAME))

Python version:  3.10.12
Ray version: 2.33
Vertex SDK version: 1.46.0
Dashboard: xxxx-dot-us-central1.aiplatform-training.googleusercontent.com

@ray.remote
def square(x):
  print(x)
  return x * x

# Launch four parallel square tasks.
futures = [square.remote(i) for i in range(4)]

print(ray.get(futures))
# Returns [0, 1, 4, 9]

import ray
import vertex_ray
from ray.job_submission import JobSubmissionClient
from google.cloud import aiplatform  # Necessary even if aiplatform.* symbol is not directly used in your program.

CLUSTER_RESOURCE_NAME='projects/{}/locations/REGION/persistentResources/{}'.format(PROJECT_ID, CLUSTER_NAME)

client = JobSubmissionClient("vertex_ray://{}".format(CLUSTER_RESOURCE_NAME))

job_id = client.submit_job(
  # Entrypoint shell command to execute
  entrypoint="python my_script.py",
  # Path to the local directory that contains the my_script.py file.
  runtime_env={
    "working_dir": "./directory-containing-my-script",
    "pip": ["numpy",
            "setuptools<70.0.0",
            "xgboost",
            "ray==CLUSTER_RAY_VERSION", # pin the Ray version to the same version as the cluster
           ]
  }
)

# Ensure that the Ray job has been created.
print(job_id)

from ray.job_submission import JobSubmissionClient
import vertex_ray

DASHBOARD_ADDRESS=DASHBOARD_ADDRESS

client = JobSubmissionClient(
  "vertex_ray://{}".format(DASHBOARD_ADDRESS),
)

job_id = client.submit_job(
  # Entrypoint shell command to execute
  entrypoint="python my_script.py",
  # Path to the local directory that contains the my_script.py file
  runtime_env={
    "working_dir": "./directory-containing-my-script",
    "pip": ["numpy",
            "setuptools<70.0.0",
            "xgboost",
            "ray==CLUSTER_RAY_VERSION", # pin the Ray version to the same version as the cluster
           ]
  }
)
print(job_id)

$ ray job submit --working-dir ./ --address vertex_ray://{CLUSTER_RESOURCE_NAME} -- python my_script.py