PyTorch Lightning for Production Training: Best Practices and Advanced Features

PyTorch Lightning organizes PyTorch code for reproducibility and scalability. Core structure: LightningModule with training_step, validation_step, configure_optimizers methods. LightningDataModule for data loading. Trainer handles device management, mixed precision, distributed training. Mixed precision: Trainer(precision="16-mixed") reduces memory 50%, speeds up training 1.5-2x on modern GPUs with minimal accuracy loss. Multi-GPU training: Trainer(devices=4, strategy="ddp") for 4 GPU training with DistributedDataParallel. No code changes required beyond Trainer configuration. Gradient accumulation: effective batch size scaling without memory increase. Trainer(accumulate_grad_batches=4) accumulates 4 mini-batches before optimizer step. Callbacks: ModelCheckpoint (save best models), EarlyStopping (prevent overfitting), LearningRateMonitor (track lr during training), RichProgressBar (beautiful CLI output). Logging: built-in integrations with TensorBoard, Weights & Biases, MLflow. Trainer(logger=WandbLogger(project="my-project")). Profiling: Trainer(profiler="advanced") identifies training bottlenecks. Common issues: num_workers=0 causes CPU bottleneck - set to 4-8. pin_memory=True speeds up CPU-GPU transfer. persistent_workers=True avoids worker restart overhead between epochs. Hyperparameter optimization: LightningCLI with YAML configs enables clean hyperparameter management and optimization with Optuna.