pytorch-lightning

Scaling PyTorch training across multiple GPUs involves structuring models with LightningModule and running them through the Trainer. This approach enables multi-GPU distributed training using DDP, FSDP, and DeepSpeed while keeping boilerplate minimal.

Removing PyTorch training loop boilerplate is achieved by delegating common training mechanics like logging, checkpointing, and device placement to the Trainer. You define training_step and configure_optimizers to keep model logic clean.

Adding custom behaviors like early stopping to PyTorch training is possible via callback-driven extensibility. You can implement reusable logic for checkpointing, schedulers, and progress reporting without modifying the core module.

PyTorch Lightning supports mixed precision training natively. The Trainer provides high-level abstractions for training loops, automatically handling device placement and distribution strategies to accelerate execution.

Converting a PyTorch model to a LightningModule for research-to-production scenarios requires defining a training_step and configuring optimizers. The Trainer then handles automatic device management and optional distributed training strategies.

Using the same PyTorch code for single GPU and multi-GPU distributed training is fully supported. By structuring models with LightningModule, the Trainer automatically handles distribution strategies like DDP, FSDP, and DeepSpeed.