Everyone who trains models in PyTorch writes the same loop. Iterate the dataloader, move the batch to the device, forward pass, compute loss, zero the gradients, backward, step the optimiser, accumulate metrics, and somewhere in there handle validation, checkpointing and logging. It is not difficult code and it is the same code every time, and every hand written version contains a slightly different set of small mistakes. Forgetting to zero gradients.
Leaving the model in training mode during evaluation. Accumulating a loss tensor that keeps the graph alive and quietly eats memory. This framework exists to write that loop once, correctly, so you stop writing it, and the documentation is at its best explaining exactly that. The restructuring is the core idea and it is well presented.
Your model, the training step, the validation step and the optimiser configuration live in one class. A separate object handles running it. The documentation walks through converting an ordinary PyTorch script into that shape, and the conversion page is the best thing on the site because it shows the before and after directly and the deletion of boilerplate speaks for itself. Where the framework really pays is on the operational features.
Training across multiple GPUs becomes an argument rather than a rewrite. Mixed precision becomes an argument. Gradient accumulation, gradient clipping, checkpointing every n steps, early stopping, resuming from a checkpoint, logging to whichever tracker you use, all of it configuration rather than code you maintain. Each of those is something you could implement yourself, and each has an obvious naive implementation that is subtly wrong, and getting them off your plate is the actual value proposition.
The reference documentation for the trainer is thorough. There are a lot of arguments, they are all documented, and the behaviour of the awkward ones is generally described accurately. Now the problems, and the first is the standard complaint about every framework of this kind. It is superb while your work fits the shape it expects and becomes an obstacle the moment it does not.
Multiple optimisers with an unusual alternation. A training step that needs to peek at the next batch. Custom behaviour partway through the backward pass. Anything adversarial or otherwise structurally odd.
At that point you are no longer writing PyTorch, you are working out which hook fires at which moment and how to smuggle your logic into it, and the documentation for that path is much weaker than the documentation for the happy path. Version churn has been real. The library has been through significant reorganisations, including a rename and a restructuring of the package layout, and code written against older versions does not straightforwardly run on newer ones. The consequence for a learner is that a large amount of the tutorial content and community discussion you will find is written against a version you are not using, and reconciling that is your problem.
The current documentation is fine, the ecosystem around it is a minefield of stale examples. Hooks and callbacks are powerful and the ordering is hard to hold in your head. When exactly does this fire relative to that, what state is available when it does, and what happens if two callbacks want the same moment. The reference lists the hooks and does less to build an intuition for the sequence, and debugging a callback that fires at the wrong time is an unpleasant afternoon.
The commercial pull is constant. This is maintained by a company that sells cloud training infrastructure and the documentation lives on their platform, with the platform never far from view. The open source framework is genuinely open source and genuinely useful without any of it, and the funnel is always there. My real reservation is about learning order.
If you have never written a training loop by hand, do not start here. The whole point of the framework is to hide mechanics you should understand first, and someone who learns this without that grounding will be unable to debug anything that goes wrong underneath, because they never saw what it is doing. Write the loop yourself half a dozen times, get bored, get bitten by the classic mistakes, and then adopt this. In that order the tool is a clear win.
My three point five is for a genuinely useful framework with solid reference documentation and an excellent migration story, marked down for an abstraction that leaks badly at the edges, for version history that poisons the surrounding ecosystem of examples, and for hook mechanics that the docs never quite make intuitive.