huggingface-accelerate

Distributed training setup is automated by adding just four lines of code to PyTorch scripts, providing a unified API that handles device placement and mixed precision automatically across multiple backends.

Yes, the same PyTorch script works for both single-node and multi-node GPU workflows. You configure the environment interactively and execute the script using a single launch command to simplify experimentation.

Yes, Accelerate provides a single unified API that works across DeepSpeed, FSDP, Megatron, and DDP, allowing you to switch between these distributed training backends without changing your core PyTorch code.

Mixed precision training with FP16, BF16, or FP8 is handled automatically by the unified API. It manages the precision context and device placement during your training step without requiring manual code adjustments.

The best way to start experimenting is to run an interactive configuration utility, which sets up your environment parameters. You then execute your training script using a single accelerate launch command.

Yes, you need the accelerate package, PyTorch, and Transformers installed. These dependencies are required to run the unified API and execute your scripts across the supported distributed backends.