pytorch-lightning

Move your model 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 to gpu, devices to the GPU count, and strategy to ddp in the Trainer, then run your script normally. Lightning handles process spawning, data distribution, and gradient synchronization automatically, with multi-node support via num_nodes.

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

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

Out-of-memory errors usually come from batch size or precision settings. Reduce batch size, set accumulate_grad_batches to simulate larger batches, or switch precision to bf16 or fp16 to cut memory usage roughly in half.

Use the built-in Tuner for automatic learning rate and batch size finding, or integrate Ray Tune, Optuna, or WandB sweeps. Each framework provides callbacks that report validation metrics and support early pruning of poor trials.