Principle:OpenRLHF OpenRLHF DeepSpeed Distributed Setup
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Training_Infrastructure |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
A process that initializes the distributed training backend, establishes inter-process communication, and configures device meshes for data, sequence, and tensor parallelism.
Description
DeepSpeed Distributed Setup handles the critical initialization of multi-GPU and multi-node training. It performs three key operations: (1) sets random seeds for reproducibility, (2) initializes the NCCL distributed backend via DeepSpeed, and (3) creates a device mesh that partitions GPUs across data parallelism, ring attention (sequence parallelism), and tensor parallelism dimensions.
This setup must happen after strategy creation but before any model loading or training operations. The resulting device mesh determines how models are partitioned and how gradients are synchronized.
Usage
Use this principle immediately after creating the strategy object. It is required in all training workflows. The timeout parameter should be increased for large clusters where initialization may be slow.
Theoretical Basis
Distributed initialization creates a communication topology:
- NCCL Backend: GPU-to-GPU communication using NVIDIA Collective Communications Library
- Device Mesh: 3D grid of (data_parallel, sequence_parallel, tensor_parallel) dimensions
- Gradient Accumulation: Computed from global batch size, micro batch size, and world size
Pseudo-code:
# Abstract initialization flow
set_random_seeds(seed)
init_distributed_backend(backend="nccl", timeout=timeout)
device_mesh = create_3d_mesh(dp_size, sp_size, tp_size)
accumulated_gradient = global_batch / micro_batch / world_size