pytorch-lightning

To reduce PyTorch boilerplate, map your scripts into a high-level Lightning workflow using LightningModule and Trainer. This structure abstracts the training loop, ensuring consistent, scalable execution across single and multi-GPU setups.

The best way to run distributed training is using Lightning's unified Trainer with built-in strategies like DDP, FSDP, and DeepSpeed. This enables effortless multi-GPU scaling and automatic precision management without writing complex distributed logic.

Yes, Lightning supports deterministic training across CPUs, GPUs, and TPUs. By defining a LightningModule and utilizing the Trainer, you achieve reproducible research workflows with automatic precision handling and consistent logging across different hardware environments.

Yes, you can use custom callbacks and loggers with the Lightning Trainer. The Lightning workflow explicitly supports optional callbacks and loggers alongside the core LightningModule, allowing you to monitor and modify training behavior dynamically.

Yes, installing transformers is required alongside lightning and torch. These three dependencies form the core environment needed to map your PyTorch training code into the high-level Lightning workflow for distributed and deterministic execution.

FSDP integration in Lightning works through the unified Trainer API, abstracting the complex boilerplate required in standard PyTorch. You simply select the FSDP distributed strategy to achieve fully sharded data parallel training with effortless reproducibility.