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()

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