6 4 Experiment Tracking And Checkpointing At Scale – Training Deep Networks | Deep Learning

Introduction

Keeping large deep learning projects under control: logging every run with tools like TensorBoard and Weights & Biases, comparing ideas fairly over several seeds, and checkpointing long training jobs so they survive crashes.

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Introduction

A deep learning project quickly turns into dozens or hundreds of runs: different learning rates, architectures, augmentations, data versions, sometimes each taking days on several GPUs. A week later, someone asks "which settings gave that 91.3% result?" and nobody remembers. You rerun the "same" experiment and get 89.7%. A cloud machine is reclaimed halfway through a three-day run and everything is lost.

Three habits prevent this:

log every run automatically, with its full configuration;
compare ideas fairly, over several random seeds, not single lucky runs;
checkpoint long jobs so they can resume exactly where they stopped.

Think of a scientist's lab notebook: every experiment is written down with its date, ingredients, conditions and results, so anyone can repeat it and trust it. In deep learning, tools do most of this note-taking for you.

Introduction

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