[Docs] Improve documentation for multi-node service helper script (#20600)

Signed-off-by: Ricardo Decal <rdecal@anyscale.com>
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Ricardo Decal 2025-07-08 19:44:26 -07:00 committed by GitHub
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@ -1,12 +1,35 @@
#!/bin/bash
#
# Helper script to manually start or join a Ray cluster for online serving of vLLM models.
# This script is first executed on the head node, and then on each worker node with the IP address
# of the head node.
#
# Subcommands:
# leader: Launches a Ray head node and blocks until the cluster reaches the expected size (head + workers).
# worker: Starts a worker node that connects to an existing Ray head node.
#
# Example usage:
# On the head node machine, start the Ray head node process and run a vLLM server.
# ./multi-node-serving.sh leader --ray_port=6379 --ray_cluster_size=<SIZE> [<extra ray args>] && \
# python3 -m vllm.entrypoints.openai.api_server --port 8080 --model meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline_parallel_size 2
#
# On each worker node, start the Ray worker node process.
# ./multi-node-serving.sh worker --ray_address=<HEAD_NODE_IP> --ray_port=6379 [<extra ray args>]
#
# About Ray:
# Ray is an open-source distributed execution framework that simplifies
# distributed computing. Learn more:
# https://ray.io/
subcommand=$1
shift
ray_port=6379
ray_init_timeout=300
declare -a start_params
subcommand=$1 # Either "leader" or "worker".
shift # Remove the subcommand from the argument list.
ray_port=6379 # Port used by the Ray head node.
ray_init_timeout=300 # Seconds to wait before timing out.
declare -a start_params # Parameters forwarded to the underlying 'ray start' command.
# Handle the worker subcommand.
case "$subcommand" in
worker)
ray_address=""
@ -32,6 +55,7 @@ case "$subcommand" in
exit 1
fi
# Retry until the worker node connects to the head node or the timeout expires.
for (( i=0; i < $ray_init_timeout; i+=5 )); do
ray start --address=$ray_address:$ray_port --block "${start_params[@]}"
if [ $? -eq 0 ]; then
@ -45,6 +69,7 @@ case "$subcommand" in
exit 1
;;
# Handle the leader subcommand.
leader)
ray_cluster_size=""
while [ $# -gt 0 ]; do
@ -69,10 +94,10 @@ case "$subcommand" in
exit 1
fi
# start the ray daemon
# Start the Ray head node.
ray start --head --port=$ray_port "${start_params[@]}"
# wait until all workers are active
# Poll Ray until every worker node is active.
for (( i=0; i < $ray_init_timeout; i+=5 )); do
active_nodes=`python3 -c 'import ray; ray.init(); print(sum(node["Alive"] for node in ray.nodes()))'`
if [ $active_nodes -eq $ray_cluster_size ]; then