What problem does it solve? Writing raw PyTorch training loops requires repetitive boilerplate for device management, distributed synchronization, mixed precision, checkpointing, and logging, which introduces bugs and slows experimentation. ## Core Features & Use Cases - Structured Training Loops: Organize model code into LightningModule with training_step, validation_step, and configure_optimizers while the Trainer handles devices, precision, and logging. - Distributed Training: Switch between DDP, FSDP, and DeepSpeed strategies across multi-GPU and multi-node clusters with a single parameter change. - Callbacks and Tuning: Use ModelCheckpoint, EarlyStopping, and LearningRateMonitor callbacks, plus integrations with Ray Tune, Optuna, and WandB sweeps for hyperparameter search. - Use Case: Convert an existing PyTorch MNIST classifier into a LightningModule, then scale it from one GPU to eight GPUs with DDP and automatic best-model checkpointing without rewriting the loop. ## Quick Start Convert my PyTorch training script into a PyTorch Lightning module and train it on two GPUs with early stopping and checkpointing.
