Hugging Face Accelerate

Accelerate targets PyTorch users who want to keep writing their own training loop yet run it on one GPU, many GPUs, several machines, TPUs, or plain CPUs. You wrap the model, optimizer, and dataloaders with a single Accelerator object, call accelerator.backward on the loss, and the library takes care of process groups, device placement, gradient syncing, and mixed precision in fp16, bf16, or fp8 through Transformer Engine or MS-AMP. Running accelerate config records your hardware layout once, and accelerate launch reuses it instead of hand-written torchrun or MPI commands; notebook_launcher covers Colab and Kaggle. Plugins expose DeepSpeed ZeRO, FSDP including FSDP2, and Megatron-LM, and the big-model utilities can shard or offload weights to CPU memory or disk so large checkpoints load on modest hardware. It needs Python 3.10+ and PyTorch 2.0+, installs with pip, and is Apache-2.0. Transformers uses it as its PyTorch training backend, and fastai and InvokeAI build on it, so anyone scaling custom PyTorch code should know it.