pytorch-lightning

Scale PyTorch training across multiple GPUs without boilerplate by using Lightning's Trainer to automatically orchestrate devices, manage multi-GPU runs, and apply mixed precision with minimal code changes.

Distributed PyTorch training with FSDP or DeepSpeed is best handled by configuring a Lightning Trainer with the desired strategy, allowing seamless large-scale model training and multi-node orchestration.

PyTorch Lightning supports multi-node setups and automatic mixed precision, providing automatic device handling and zero-boilerplate orchestration for both research environments and production pipelines.

Integrate callbacks and checkpointing into distributed PyTorch training by leveraging Lightning's built-in Trainer features, which manage logging and checkpointing automatically during multi-GPU and multi-node runs.

Use DDP for standard multi-GPU data parallelism and FSDP for large-scale PyTorch training when model size exceeds single-GPU memory, both easily configured through Lightning's strategy settings.