AI Engineering/Phase 19/Lesson 77

Data Parallel DDP From Scratch

DistributedDataParallel is a hook on top of allreduce. Wrap a model, broadcast the initial parameters from rank 0 so every rank starts identical, install a backward hook on every parameter that issues an allreduce of the gradient, and the rest is gradient descent. The whole pattern is 200 lines.

Build 1.5 hr Python

Prerequisites: Phase 19 Track C lessons 42-49

#Learning Objectives

Wire a DistributedDataParallel-shaped wrapper that broadcasts initial parameters and allreduces gradients after backward.
Spawn N CPU ranks with torch.multiprocessing.spawn over the gloo backend with file-based rendezvous.
Prove gradient-sync correctness by training the same model on the same data sequentially and showing per-step parameter equivalence.
Defend the use of buckets (gradient fusion) and overlap (comm during backward) as the two changes that turn a working DDP into a production DDP.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.