Executing batch jobs in Ray Cluster

In this example we will: - Create a Ray cluster - Submit a job python file - Terminate the cluster after job is completed.

Before you begin

• Create "ray" under your "~/my" folder
• And copy job.py under this folder

worker_size = None
worker_count = None
log_level = "DEBUG"
worker_image = "practicus-ray"
create_ray_folder = False

assert worker_size, "Please enter your worker_size."
assert worker_count, "Please enter your worker_count."
assert log_level, "Please enter your log_level."
assert worker_image, "Please enter your worker_image."
assert create_ray_folder, "Please enter your create_ray_folder."

import os
import shutil

if create_ray_folder:
    # Define source and destination
    source_dir = "samples/notebooks/05_distributed_computing/05_ray/02_batch_job"
    source_file = f"{source_dir}/job.py"

    # Move it to '~/my', which is your persistent home directory.
    job_dir = os.path.expanduser("~/my/ray")
    dest_file = f"{job_dir}/job.py"

    # Copy the job file to the shared location
    if not os.path.exists(job_dir):
        os.makedirs(job_dir)

    print(f"Copying job file from '{source_file}' to '{dest_file}'...")
    try:
        shutil.copy(source_file, dest_file)
        print("✅ Copy successful.")
    except FileNotFoundError:
        print(f"❌ Error: Could not find source file at {source_file}")
else:
    job_dir = "~/my/ray"

import practicuscore as prt

distributed_config = prt.DistJobConfig(
    job_type=prt.DistJobType.ray,
    job_dir=job_dir,
    py_file="job.py",
    worker_count=worker_count,
)

worker_config = prt.WorkerConfig(
    # Please note that Ray requires a specific worker image
    worker_image=worker_image,
    worker_size=worker_size,
    distributed_config=distributed_config,
    log_level=log_level,
)

coordinator_worker = prt.create_worker(
    worker_config=worker_config,
)

prt.distributed.live_view(
    job_dir=job_dir,
    job_id=coordinator_worker.job_id,
)

# You can view the logs during or after the job is completed
# To view coordinator (master) set rank = 0
rank = 0
# To view other workers set rank = 1,2, ..

prt.distributed.view_log(job_dir=job_dir, job_id=coordinator_worker.job_id, rank=rank)

Wrapping up

• Once the job is completed, you can view the results in ~/my/ray/result.csv/
• Please note that result.csv is a folder that can contain parts of the processed file by each worker (Ray executors)
• Also note that you do not need to terminate the cluster since it has a 'py_file' to execute, which defaults terminate_on_completion parameter to True.
• You can change terminate_on_completion to False to keep the cluster running after the job is completed to troubleshoot issues.
• You can view other prt.DistJobConfig properties to customize the cluster

Supplementary Files

job.py

import practicuscore as prt

ray = prt.distributed.get_client()


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


def calculate():
    numbers = [i for i in range(10)]
    futures = [square.remote(i) for i in numbers]
    results = ray.get(futures)
    print("Distributed square results of", numbers, "is", results)


if __name__ == "__main__":
    calculate()
    ray.shutdown()

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