多节点服务

来源 examples/online_serving/multi-node-serving.sh。

#!/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  # 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=""
    while [ $# -gt 0 ]; do
      case "$1" in
        --ray_address=*)
          ray_address="${1#*=}"
          ;;
        --ray_port=*)
          ray_port="${1#*=}"
          ;;
        --ray_init_timeout=*)
          ray_init_timeout="${1#*=}"
          ;;
        *)
          start_params+=("$1")
      esac
      shift
    done

    if [ -z "$ray_address" ]; then
      echo "Error: Missing argument --ray_address"
      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
        echo "Worker: Ray runtime started with head address $ray_address:$ray_port"
        exit 0
      fi
      echo "Waiting until the ray worker is active..."
      sleep 5s;
    done
    echo "Ray worker starts timeout, head address: $ray_address:$ray_port"
    exit 1
    ;;

  # Handle the leader subcommand.
  leader)
    ray_cluster_size=""
    while [ $# -gt 0 ]; do
          case "$1" in
            --ray_port=*)
              ray_port="${1#*=}"
              ;;
            --ray_cluster_size=*)
              ray_cluster_size="${1#*=}"
              ;;
            --ray_init_timeout=*)
              ray_init_timeout="${1#*=}"
              ;;
            *)
              start_params+=("$1")
          esac
          shift
    done

    if [ -z "$ray_cluster_size" ]; then
      echo "Error: Missing argument --ray_cluster_size"
      exit 1
    fi

    # Start the Ray head node.
    ray start --head --port=$ray_port "${start_params[@]}"

    # 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
          echo "All ray workers are active and the ray cluster is initialized successfully."
          exit 0
        fi
        echo "Wait for all ray workers to be active. $active_nodes/$ray_cluster_size is active"
        sleep 5s;
    done

    echo "Waiting for all ray workers to be active timed out."
    exit 1
    ;;

  *)
    echo "unknown subcommand: $subcommand"
    exit 1
    ;;
esac