pytorch-lightning

Scale PyTorch training by configuring a LightningModule and Trainer to handle device placement automatically. This approach supports DDP, FSDP, and DeepSpeed strategies for multi-GPU distributed training while separating research logic from engineering code.

Manage mixed precision and checkpointing by utilizing built-in callbacks and automatic precision management within the Lightning Trainer. The framework natively handles FP16 and BF16 precision options alongside robust checkpointing to streamline experimentation across distributed hardware.

PyTorch Lightning supports DeepSpeed and FSDP for multi-node training out of the box. It provides built-in distributed strategies that automatically handle device placement and precision management across multi-GPU and multi-node environments.

You can integrate common ML tooling with PyTorch Lightning training workflows. The framework satisfies requirements for integration with standard ML tooling and provides rich logging capabilities alongside callbacks for production deployment.

Use FSDP or DDP instead of standard PyTorch when your model training requires scaling across multiple GPUs or nodes. These distributed strategies provide automatic device handling and precision management that standard PyTorch lacks natively.

Separate research and engineering concerns in PyTorch code to accelerate training and ship reliable models faster. PyTorch Lightning enforces a clean structure that handles boilerplate like device placement and distributed strategies automatically.