pytorch-lightning

Move your model into a LightningModule subclass, implement training_step and configure_optimizers, then call Trainer.fit with your DataLoader. The Trainer handles device placement, backward passes, and optimizer steps automatically.

Set accelerator='gpu', devices to the GPU count, and strategy='ddp' in the Trainer. Lightning automatically spawns processes, distributes data, and synchronizes gradients without code changes.

Lightning suits teams wanting a standardized structure with built-in callbacks, logging, and checkpointing. Accelerate fits when you want minimal changes to an existing raw PyTorch loop and more manual control.

Yes, Lightning provides FSDPStrategy with FULL_SHARD for ZeRO-3-equivalent sharding and DeepSpeedStrategy with stage 3 and CPU offloading. These target models from 7B to 70B+ parameters.

Validation only runs when you pass a validation DataLoader to trainer.fit as the second argument. Calling trainer.fit(model, train_loader) alone skips validation entirely.

Reduce batch size, set accumulate_grad_batches to simulate larger batches, or use precision='bf16' to halve memory usage. For very large models, switch to FSDP with cpu_offload enabled.