Principle:Facebookresearch Habitat lab Distributed Process Setup
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Reinforcement_Learning |
| Last Updated | 2026-02-15 02:00 GMT |
Overview
Initialization of distributed data-parallel processes across multiple GPUs and nodes for decentralized PPO training with gradient synchronization.
Description
Distributed Process Setup establishes the communication infrastructure for Decentralized Distributed PPO (DD-PPO). Unlike centralized approaches, DD-PPO runs independent environment instances on each GPU worker and synchronizes only gradients via allreduce operations. This requires:
- Discovering the cluster topology (number of nodes, GPUs per node, rank assignment)
- Initializing PyTorch's distributed process group with the NCCL backend
- Wrapping the policy network in DistributedDataParallel (DDP)
The setup supports both SLURM-managed clusters (reading environment variables) and single-machine multi-GPU configurations.
Usage
Use this principle when training with DD-PPO on multiple GPUs. Required for multi-node training on HPC clusters. Single-GPU training skips this step entirely.
Theoretical Basis
DD-PPO achieves near-linear scaling by:
- Each worker collects rollouts independently (no centralized experience)
- After rollout collection, workers compute local gradients
- Gradients are averaged across workers via allreduce (NCCL backend)
- All workers apply the same averaged update, maintaining synchronized parameters
Pseudo-code:
# Abstract distributed setup
local_rank = discover_rank_from_environment()
init_process_group(backend="nccl", rank=local_rank)
policy = DistributedDataParallel(policy, device_ids=[local_rank])
# Training proceeds with synchronized gradient updates