pytorch-lightning

Move your model definition into a LightningModule subclass, put the loss computation 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 placement and backward calls.

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

Lightning provides a structured framework with built-in callbacks, checkpointing, and logging, ideal for standardized team workflows. Accelerate makes minimal changes to existing PyTorch loops and offers more flexibility, suiting projects that need custom training logic.

Yes, Lightning includes FSDPStrategy with FULL_SHARD for ZeRO-3 equivalent sharding and DeepSpeedStrategy with stage 3 and CPU offloading. These strategies support models from 7B to 70B+ parameters across multiple GPUs.

Validation only runs when you pass a validation DataLoader to trainer.fit as the second argument or via val_dataloaders. Calling trainer.fit(model, train_loader) without val_loader skips validation entirely.

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