pytorch-lightning

Scale PyTorch training across multiple GPUs by structuring your model as a LightningModule and using the Trainer to automatically apply distributed strategies like DDP and FSDP with minimal code changes. This eliminates manual boilerplate for multi-GPU scaling.

Eliminate PyTorch boilerplate by using a high-level API with a Trainer and LightningModule. This organizes your code to maintain flexibility while enabling rapid prototyping and scalable training across devices from laptops to production-grade clusters.

Yes, PyTorch Lightning supports DeepSpeed for multi-node distributed training. The Trainer includes automatic distributed strategies, allowing you to port workflows from single-node CPU or GPU environments to multi-node clusters using DeepSpeed, DDP, or FSDP.

Yes, you can use flexible callbacks to customize your PyTorch training loop. The framework provides a high-level API where callbacks act as optional components alongside the LightningModule and Trainer to inject custom logic without cluttering the core training code.

Yes, a LightningModule is required to run training with the Trainer. This structure organizes your PyTorch code, eliminates boilerplate, and enables automatic distributed strategies across CPU, single-GPU, multi-GPU, or multi-node clusters.