This tutorial explores distributed data parallelism (DDP) in PyTorch, progressing from native DDP to higher-level abstractions like Hugging Face Accelerate and Trainer. Starting with basic PyTorch code for MNIST training, the guide explains how to scale across multiple GPUs.
PyTorch DDP Basics
Native PyTorch DDP uses torch.distributed to set up process groups and DistributedDataParallel to replicate models across GPUs. A typical setup involves creating setup and cleanup functions, then launching with torchrun.
Accelerate for Simplicity
Accelerate simplifies DDP by automating setup and cleanup. With minimal code changes, you can run on single GPU, multi-GPU, or TPUs. Key steps include using accelerator.prepare() for models, optimizers, and dataloaders.
Trainer for Full Abstraction
The Trainer API from Transformers handles all boilerplate, supporting mixed precision, gradient accumulation, and distributed training with zero manual configuration. It integrates seamlessly with Hugging Face models and datasets.
This progression allows practitioners to choose the right level of control versus ease of use.