Principle:OpenRLHF OpenRLHF Distributed Strategy Configuration
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Training_Infrastructure |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
A configuration pattern that initializes distributed training infrastructure by selecting and parameterizing a parallelism strategy based on command-line arguments.
Description
Distributed Strategy Configuration is the first step in any OpenRLHF training pipeline. It creates a DeepspeedStrategy object that encapsulates all distributed training settings: ZeRO optimization stage, batch sizes, gradient accumulation, random seeds, and gradient clipping norms. The strategy object serves as the central coordinator for all subsequent training operations including model preparation, optimizer creation, data loading, and checkpointing.
The pattern decouples strategy selection from training logic, allowing the same training scripts to run on single-GPU or multi-node setups by changing only CLI arguments.
Usage
Use this principle at the very start of any training workflow. It must be invoked before model loading, dataset preparation, or optimizer creation, since all those operations depend on the initialized strategy.
Theoretical Basis
Distributed training strategies (data parallelism, ZeRO-1/2/3, tensor parallelism) partition model state across devices to reduce per-device memory requirements:
- ZeRO Stage 1: Partitions optimizer states across GPUs
- ZeRO Stage 2: Partitions optimizer states + gradients
- ZeRO Stage 3: Partitions optimizer states + gradients + parameters
The strategy factory pattern selects the appropriate configuration based on a zero_stage parameter, then initializes the distributed backend (NCCL) and device mesh.