huggingface-accelerate

Distributed training across multiple GPUs requires minimal code changes when you prepare your model with a unified Accelerator API, enabling single-script portability across single-node or multi-node setups without rewriting your training loop.

FSDP and DeepSpeed can be used in the same PyTorch training script through a unified API that supports both backends, allowing you to switch between distributed training strategies via configuration rather than modifying core training logic.

Migrating single-GPU PyTorch code to a multi-GPU setup is best handled by applying automatic device placement and mixed precision through an Accelerator object, preserving your original script structure while enabling multi-node execution.

Distributed training with Megatron-LM works across CPU, GPU, and TPU environments by leveraging a high-level API that abstracts hardware backends, ensuring your training script maintains portability across different accelerator configurations.

Mixed precision and device placement for multi-node training are configured interactively using a quick config command, which automatically handles accelerator setup and applies the specified optimizations during model preparation.