Oom
Oom in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
What the GPU actually executes
GPU performance comes from feeding a few substantial, well-shaped kernels—not merely from placing tensors on a CUDA device.
GPU-aware trainingKeep it fed: loaders and async copies
A fast training step needs an equally deliberate supply chain from storage to pinned memory to the device.
GPU-aware trainingMixed precision without NaNs
Mixed precision is trustworthy only when dtype choices, loss scaling, skipped steps, and quality parity are all observable.
GPU-aware trainingActivations, accumulation, and checkpointing
Checkpointing trades compute for activation memory; accumulation trades update frequency for batch memory—measure and validate each trade separately.
GPU-aware trainingProfile from timeline to cost per sample
Profile a controlled workload, explain the timeline, and judge improvements by cost to an unchanged quality target.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.