pytorch-lightning

Move your model definition into a LightningModule subclass, put the forward and loss logic in training_step, and return the optimizer from configure_optimizers. Then create a Trainer and call trainer.fit with your DataLoader, removing all manual device and loop code.

Set accelerator='gpu', devices to the GPU count, and strategy='ddp' in the Trainer. Lightning automatically handles process spawning, data distribution, and gradient synchronization with no code changes to your LightningModule.

Lightning provides a structured framework with built-in callbacks, logging, and checkpointing for organized training code. Accelerate makes minimal changes to existing PyTorch loops and offers more flexibility, suiting projects that want to keep custom loop logic.

Yes, Lightning includes FSDPStrategy with FULL_SHARD for models in the 7-70B range and DeepSpeedStrategy with ZeRO-3 and CPU offload for models above 70B parameters. Both are enabled by passing the strategy object to the Trainer.

Validation only runs when you pass a validation DataLoader to trainer.fit as the second argument. Calling trainer.fit(model, train_loader) without val_loader skips validation entirely, so monitored callbacks like EarlyStopping will not trigger.

Reduce batch size, enable gradient accumulation with accumulate_grad_batches, or switch to mixed precision with precision='bf16' or 'fp16' to cut memory usage roughly in half. For very large models, use FSDP with cpu_offload enabled.