Quickstart to Lightning Fabric

Lightning Fabric is a lightweight framework that integrates into existing PyTorch training loops to simplify distributed training across multiple GPUs and nodes while preserving developers’ control over model logic. Its central Fabric class configures hardware, distributed strategies such as DDP or FSDP, device counts, nodes, precision, logging, plugins, and callbacks, and provides methods for launching distributed processes, preparing data loaders, models, and optimizers, handling backward passes, checkpoints, metric logging, and collective operations. Scaling a standard PyTorch loop requires only a few additions: creating and launching a Fabric instance, applying setup methods to data loaders and model components, and replacing the usual backward call with Fabric’s equivalent. By automatically managing device placement and distributed orchestration, Fabric aims to let researchers and engineers adapt code with minimal restructuring while retaining flexibility for specialized workloads in areas including vision, audio, and language models.

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