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huggingface-accelerate

Simplifies PyTorch distributed training via unified API for DKP, DeepSpeed, and more.

3|Updated Feb 21, 2026
Search Tags:#deep learning#gpu#scaling#pytorch#distributed training#huggingface#accelerate
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill huggingface-accelerate-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/08-distributed-training/accelerate
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill huggingface-accelerate-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill drastically simplifies the process of adding distributed training capabilities to PyTorch scripts, enabling users to leverage multi-GPU and multi-node setups with minimal code changes.

Core Features & Use Cases

  • Unified API: Seamlessly switch between DDP, DeepSpeed, FSDP, and Megatron with the same code.
  • Automatic Configuration: Handles device placement, mixed precision (FP16/BF16/FP8), and sharding automatically.
  • Quick Prototyping: Convert existing single-GPU scripts to distributed training in just 4 lines of code.
  • Use Case: You have a PyTorch training script that runs on a single GPU. With this Skill, you can adapt it to run on a cluster of 8 GPUs with DeepSpeed ZeRO-3 optimization by adding only a few lines and running a single accelerate launch command.

Quick Start

Use the huggingface-accelerate skill to convert your PyTorch script 'train.py' for distributed training.

Frequently Asked Questions about huggingface-accelerate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert a single-GPU PyTorch script to distributed training across multiple GPUs?▼

You can convert single-GPU PyTorch scripts to distributed training by adding just four lines of code to handle device placement and running the execution via a single launch command.

Can I use the same PyTorch code to switch between DeepSpeed, FSDP, and DDP?▼

Yes, a unified API allows you to seamlessly switch between DeepSpeed, FSDP, DDP, and Megatron-LM using the exact same PyTorch codebase without manual refactoring.

How does distributed training handle mixed precision and sharding configurations automatically?▼

Distributed training automates mixed precision scaling (FP16/BF16/FP8) and sharding configurations, managing device placement and optimization states without requiring explicit manual code.

What is the easiest way to scale a PyTorch model to a multi-node environment?▼

Scaling PyTorch models to multi-node environments is simplified through automated device placement and unified sharding APIs, enabling efficient scaling with minimal code modifications.

Does distributed training support DeepSpeed ZeRO-3 optimization for existing scripts?▼

Yes, existing scripts can be adapted to run on an 8-GPU cluster with DeepSpeed ZeRO-3 optimization by adding a few lines and executing a single launch command.

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