How many lines of code does Accelerate require to implement?

HuggingFace Accelerate typically requires only four lines of code to convert a standard PyTorch script for distributed training support.

Can I use mixed precision with this skill?

Yes, it handles FP16, BF16, and FP8 mixed precision training automatically, managing gradient scaling and autocasting across different hardware backends.

Does this skill support DeepSpeed integration?

Yes, it provides a unified interface for DeepSpeed ZeRO stages 1, 2, and 3, including optimizer and parameter offloading options.

What hardware is compatible with this skill?

It supports CPU, single-GPU, multi-GPU (DDP), multi-node setups, TPUs, and Apple Silicon (MPS) through a single consistent API.

HuggingFace Accelerate Distributed Training

Name: HuggingFace Accelerate Distributed Training
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Simplifies PyTorch distributed training by providing a unified API for DDP, DeepSpeed, and FSDP with minimal code changes.

About

This skill integrates the HuggingFace Accelerate library into your workflow, allowing you to scale PyTorch training scripts from a single CPU/GPU to multi-GPU, TPU, and multi-node clusters with just four lines of code. It abstracts the complexities of device placement, mixed-precision training (FP16, BF16, FP8), and advanced distributed strategies like DeepSpeed ZeRO and FSDP, providing a consistent interface and interactive configuration tools that eliminate the need for manual launcher setup and low-level boilerplate.

Key Features

Built-in gradient accumulation and distributed checkpointing
Automatic device placement and mixed precision support (FP16/BF16/FP8)
Interactive configuration and single-command launch system
Seamless integration with the HuggingFace ecosystem (Transformers, PEFT, TRL)
384 GitHub stars
Unified API for DDP, DeepSpeed, FSDP, and Megatron-LM

Use Cases

Configuring DeepSpeed ZeRO-3 or FSDP for training large language models
Scaling a single-GPU research script to a multi-node GPU cluster
Implementing mixed-precision training to optimize memory and speed

About

Key Features

Built-in gradient accumulation and distributed checkpointing
Automatic device placement and mixed precision support (FP16/BF16/FP8)
Interactive configuration and single-command launch system
Seamless integration with the HuggingFace ecosystem (Transformers, PEFT, TRL)
384 GitHub stars
Unified API for DDP, DeepSpeed, FSDP, and Megatron-LM

Use Cases

Configuring DeepSpeed ZeRO-3 or FSDP for training large language models
Scaling a single-GPU research script to a multi-node GPU cluster
Implementing mixed-precision training to optimize memory and speed