Phase 6: Distributed Training · ~90 min · Python
Data Parallel DDP From Scratch
DistributedDataParallel is a hook on top of allreduce.
Hiring signal: Can build data parallel ddp from scratch end to end
What you will learn
- 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.
Introduction
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.
Type: Build Languages: Python Prerequisites: Phase 19 Track C lessons 42-49 Time: ~90 min
Objective
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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