Data pipelines and checkpoints: keep GPUs fed and work recoverable

Picture it

A high-performance engine is useless if the fuel pump stutters. GPUs process data so quickly that decoding text or fetching files can become a bottleneck. Furthermore, saving checkpoints is like taking a snapshot of a long road trip; if the engine fails, you want to resume exactly where you left off, with the same momentum and direction, without waiting hours for the film to develop.

What to understand

• Shuffling, packing, tokenization, data provenance, and input stalls
• Distributed checkpoints, consistency, storage bandwidth, and resume validation

Follow the references

1. 1 · Course + videos

   Stanford · CS336 Language Modeling from Scratch (2025) ↗

   Use data processing and systems lectures to locate CPU/storage bottlenecks.

2. 2 · Tutorial

   PyTorch · Getting started with distributed checkpoint ↗

   Read save/load semantics and distributed state handling.

Make it concrete

Design a checkpoint manifest and recovery drill. Measure save pause, bytes written, restart time, and whether the data stream and optimizer resume as intended.