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Using the interactive Ray Cluster Client

  • This example demonstrates how to connect to the Practicus AI Ray cluster we created, and execute simple Ray operations.
  • Please run this example on the Ray Coordinator (master).
import practicuscore as prt

# Let's get a Ray session.
# this is similar to running `import ray` and then `ray.init()`
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)


calculate()

Ray Dashboard

Practicus AI Ray offers an interactive dashboard where you can view execution details. Let's open the dashboard.

dashboard_url = prt.distributed.open_dashboard()

print("Page did not open? You can open this url manually:", dashboard_url)

@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)


calculate()

Now you should see in real-time the execution details in a view similar to the below. You can click the Job tab for useful information.

Ray Dashboard

# Let's close the session
ray.shutdown()

Terminating the cluster

  • You can go back to the other worker where you created the cluster to run:

coordinator_worker.terminate()
- Or, terminate "self" and children workers with the below:

prt.get_local_worker().terminate()

Troubleshooting

If you’re experiencing issues with an interactive cluster that doesn’t run job/train.py, please follow these steps:

  1. Agent Count Mismatch: If the number of distributed agents shown by prt.distributed.get_client() is less than what you expected, wait a moment and then run get_client() again. This is usually because the agents have not yet joined the cluster. Note: Batch jobs automatically wait for agents to join.

  2. Viewing Logs: To view logs, navigate to the ~/my/.distributed folder.

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