Distributed AI Systems

3

Distributed Training with PyTorch DDP

Scaling training across multiple GPUs with DistributedDataParallel

 

The search for the ultimate algorithm is a search for scale.

Richard Sutton

How DDP works internally

In Chapter 2, we explored the hardware foundation and parallelism strategies that make distributed training possible. Now we turn to the practical implementation: DistributedDataParallel (DDP), PyTorch's standard approach for data-parallel distributed training. DDP is how you actually scale training across multiple GPUs—whether they're in a single machine or spread across a cluster.

The core idea is elegant: replicate your model on every GPU, shard your data across GPUs, and synchronize gradients after each backward pass. Each GPU processes ...