Accelerate

Accelerate — explained in detail

Accelerate is a Hugging Face library that aims to let the same PyTorch training code scale from a single GPU to multi-GPU and multi-node clusters without major rewrites. At its core is the Accelerator class, which wraps the usual training loop and automatically handles device placement, distributed synchronization and mixed precision. Which distributed strategy actually runs — classic DDP, FSDP (Fully Sharded Data Parallel) or DeepSpeed — is decided by the configuration, not by the application code.

In practice only a few lines are needed: model, optimizer and dataloader are passed through accelerator.prepare(...), and the backward pass runs via accelerator.backward(loss). You set up the desired environment interactively with accelerate config and start training with accelerate launch. Accelerate supports automatic mixed precision including FP8, as well as sharding of optimizer states, gradients and parameters via FSDP to fit large models and batch sizes into memory.

Example / Practical use

A research team first develops a fine-tuning script on a single GPU. For the large run on eight GPUs it changes not a single line of model code — it runs accelerate config, selects FSDP with mixed precision and launches via accelerate launch train.py. The same code would run just as well with DeepSpeed ZeRO if chosen in the configuration. Accelerate thus serves as a common abstraction layer over otherwise incompatible distributed backends.

Distinction from related terms

Transformers uses Accelerate internally (for example for device_map and the Trainer), but it is the model library, not the training infrastructure. PEFT and LoRA reduce training cost through parameter-efficient methods — orthogonal to Accelerate, which manages distribution and precision. DeepSpeed and PyTorch FSDP are the underlying engines that Accelerate bundles under a single interface rather than replacing them.

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