Scaling training workloads beyond a single machine introduces distinct challenges related to network latency and bandwidth. While intra-node communication often utilizes high-speed interconnects like NVLink, multi-node setups depend on the network fabric, which can become a primary bottleneck. This chapter focuses on configuring PyTorch FSDP to operate efficiently in these distributed cluster environments.

You will learn how to initialize multi-node process groups and configure the underlying NCCL backend for stability and speed. We will break down the collective communication primitives, specifically AllGather and ReduceScatter, that drive FSDP, analyzing how they synchronize shards across the cluster. The text also covers backward prefetching techniques designed to hide communication latency by overlapping it with computation.

Finally, we will implement Hybrid Sharded Data Parallel (HSDP). This method mixes full sharding within a node with data replication across nodes, offering a mechanical way to balance memory savings against network communication costs. By the end of this chapter, you will be able to configure a cluster environment that maintains high throughput even as node counts increase.