Learn/Course 12
Training systems · weeks 15

GPU-aware training

Training repeatedly OOMs and misses its nightly completion window.

Opening production brief

A measurable problem, before a model.

Users

Operators, product owners, and people affected by the gpu training decision.

Data

kernels, HBM bandwidth, arithmetic intensity, pinned memory with version, owner, event time, and availability contracts.

Constraints

Training repeatedly OOMs and misses its nightly completion window. Every solution must state latency, cost, capacity, and fallback limits.

Success

Offline evidence plus a deployable resolution: A profiled, mixed-precision, memory-budgeted training loop.

CHAPTER 1

Frame and baseline

Turn the production problem into explicit data, metric, and baseline contracts.

  1. 01

    What the GPU actually executes

    Use what the gpu actually executes to move the gpu training production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. 02

    Keep it fed: loaders and async copies

    Use keep it fed: loaders and async copies to move the gpu training production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
CHAPTER 2

Build and stress

Formalize the machinery, test counterexamples, and expose system limits.

  1. 03

    Mixed precision without NaNs

    Use mixed precision without nans to move the gpu training production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. 04

    Activations, accumulation, and checkpointing

    Use activations, accumulation, and checkpointing to move the gpu training production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
CHAPTER 3

Resolve and operate

Join model behavior to architecture, observability, rollout, and rollback.

  1. 05

    Profile from timeline to cost per sample

    Use profile from timeline to cost per sample to move the gpu training production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. HW

    Single-GPU optimization plan

    Profile and optimize a correctness-locked trainer without sacrificing model quality.

    Rubric · staged hints · reference resolution
Prerequisite guidance

1 concepts make this faster.

Prerequisites are guidance, never hard gates.

C09Neural networksA small network built from forward pass through backpropagation and optimization.
Ready to work the problem?

What the GPU actually executes

Start lesson 01