Principle:Allenai Open instruct Ray Cluster Setup
| Knowledge Sources | |
|---|---|
| Domains | Distributed Computing Reinforcement Learning |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Ray cluster setup is the process of initializing a distributed computing cluster using the Ray framework to coordinate multi-node reinforcement learning training across heterogeneous GPU resources.
Description
Modern RL training pipelines such as GRPO require multiple distinct workloads running concurrently: policy training on DeepSpeed learners, text generation on vLLM inference engines, reward computation via verifiers, and data preparation. Ray provides an actor-based distributed computing framework that orchestrates these heterogeneous workloads across a cluster of machines.
A Ray cluster consists of a single head node and zero or more worker nodes. The head node runs the Ray GCS (Global Control Store), the scheduler, and the dashboard. Worker nodes connect to the head node and make their resources (GPUs, CPUs, memory) available for task and actor scheduling. The head node is identified by its IP address and a designated port.
In the context of GRPO training, the cluster setup must:
- Establish the head node on the first replica (rank 0).
- Have all subsequent replicas join as worker nodes by connecting to the head node address.
- Handle lifecycle management so that worker nodes exit gracefully when the head node terminates.
- Configure environment variables for NCCL and other performance-critical settings.
Usage
Ray cluster setup is required whenever GRPO training spans multiple machines. It should be the first step in any multi-node RL training pipeline, executed before any Ray actors (vLLM engines, policy trainers, data preparation actors) are created. In single-node configurations, Ray can still be used but starts in local mode without explicit cluster initialization.
Theoretical Basis
The Ray actor model is based on the actor paradigm from concurrent computing, where each actor is an independent unit of computation with its own state. Communication between actors occurs via asynchronous message passing (remote procedure calls in Ray's API). The cluster scheduler uses a bottom-up distributed scheduler that enables tasks to be scheduled at each node, with spillover to the global scheduler for load balancing.
Key properties of Ray's cluster architecture:
- Fault tolerance: Workers can detect head node failure and shut down gracefully.
- Resource isolation: Each actor requests specific resources (GPUs, CPUs), and the scheduler ensures non-overlapping allocation.
- Heterogeneous scheduling: Different actor types can request different resource bundles (e.g., vLLM engines request multiple GPUs for tensor parallelism, learners request single GPUs).
The initialization sequence follows this pseudocode:
if replica_rank == 0:
ray start --head --port=PORT
# Head node is ready; launch training script
else:
ray start --address=HEAD_IP:PORT
# Worker enters monitoring loop
while head_is_reachable():
sleep(5)
# Head gone => cleanup and exit 0
This pattern ensures that worker processes do not linger after training completes, preventing resource leaks in managed cluster environments such as Beaker.