6.9 The training loop, datasets, dataloaders
You have a training script you reuse for every project.
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The training loop is the instrument: datasets, dataloaders, epoch and validation phases, checkpointing, seeds. Building one good script and reusing it turns every future project into filling in blanks. It follows the PyTorch topic because the loop is PyTorch in motion. The silent failure it teaches you to see is data starvation — a slow dataloader leaves the GPU idle, and without measuring utilisation the training just seems mysteriously slow.
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Dataset and DataLoader, collate functions
The two objects that stand between raw data and a batch, and the function that assembles individual examples into one. Custom assembly is needed more often than beginners expect.
Epoch loop, validation loop, metrics logging
The structure of a training run: passes over the data, a held-out check after each, and a record of what happened. Having one reusable script is worth more than any individual project.
Checkpointing and resuming
Saving progress periodically and being able to continue from it, which matters because long runs are interrupted. Resuming correctly includes the optimiser state, not only the weights.
Reproducibility: seeds, determinism, and its cost
Seeds make a run repeatable, full determinism costs speed, and some operations are not deterministic at all. Deciding how much reproducibility you need is a real trade rather than an ideal.
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