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Using the interactive Ray Cluster for vLLM

  • This example demonstrates how to connect to the Practicus AI Ray cluster we created, and execute vLLM + 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()

from vllm import LLM, SamplingParams

prompts = [
    "Mexico is famous for ",
    "The largest country in the world is ",
]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="facebook/opt-125m")
responses = llm.generate(prompts, sampling_params)

for response in responses:
    print(response.outputs[0].text)

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)

# 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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