huggingface-accelerate

Name: huggingface-accelerate
Availability: InStock
Author: mesferali

Simplify distributed training for PyTorch scripts with a unified API.

Updated Jan 12, 2026

Search Tags:#deep learning #gpu #scaling #pytorch #distributed training #fsdp #deepspeed

Ready to run

npx skills add https://github.com/MesferAli/XCircle --skill huggingface-accelerate-mesferali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/accelerate
Command: npx skills add https://github.com/MesferAli/XCircle --skill huggingface-accelerate-mesferali

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the process of adding distributed training support to PyTorch scripts, making it easier to scale models across multiple GPUs or machines.

Core Features & Use Cases

Unified API: Integrates seamlessly with DeepSpeed, FSDP, Megatron, and DDP.
Automatic Configuration: Handles device placement, mixed precision (FP16/BF16/FP8), and distributed setup with minimal code changes.
Use Case: You have a PyTorch script for training a large language model on a single GPU. With Accelerate, you can adapt it to run on a cluster of 8 GPUs with just a few lines of code and a single launch command.

Quick Start

Use the accelerate skill to launch your training script 'train.py' on multiple GPUs.

Frequently Asked Questions about huggingface-accelerate

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

FAQPage Schema

How do I scale PyTorch distributed training across multiple GPUs with minimal code changes?▼

You can scale PyTorch distributed training across multiple GPUs with minimal code changes by using a unified API that automates device placement, mixed precision, and multi-GPU configuration with just a few lines of code and a single launch command.

What is the best way to run a single-GPU PyTorch script on a multi-GPU cluster?▼

The best way to run a single-GPU PyTorch script on a multi-GPU cluster is to adapt it using a unified API that automatically handles distributed setup, enabling rapid scaling to 8 GPUs or more with minimal modifications.

Does this distributed training approach support DeepSpeed and FSDP backends?▼

Yes, this distributed training approach supports DeepSpeed and FSDP backends, integrating seamlessly with them along with Megatron and DDP through a single unified API to simplify multi-node and multi-GPU setups.

How does mixed precision training work for deep learning models on multiple GPUs?▼

Mixed precision training for deep learning models on multiple GPUs works by automatically handling FP16, BF16, and FP8 configurations, streamlining the distributed setup process so you can focus on model logic rather than manual device placement.

Do I need to rewrite my PyTorch script to use FSDP for multi-node training?▼

You do not need to rewrite your PyTorch script to use FSDP for multi-node training; you can adapt your existing script to run on a cluster of 8 GPUs with just a few lines of code and a single launch command.