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huggingface-accelerate

Run distributed PyTorch training across DDP, DeepSpeed, FSDP, and Megatron backends.

Updated Mar 16, 2026
Search Tags:#pytorch#distributed-training#mixed-precision#fsdp#ddp#deepspeed#accelerate
One-click install
npx skills add https://github.com/arsity/scholar-tools --skill huggingface-accelerate-arsity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/08-distributed-training/accelerate
Command: npx skills add https://github.com/arsity/scholar-tools --skill huggingface-accelerate-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires accelerate, torch, transformers, and includes references (resource) components.

What problem does it solve?

Accelerate makes distributed training simple by providing a unified API that abstracts backend setup, device placement, and mixed-precision handling across DDP, DeepSpeed, FSDP, Megatron, and the HF ecosystem.

Core Features & Use Cases

  • Unified API: One interface to orchestrate training across multiple backends (DDP, DeepSpeed, FSDP, Megatron) without rewriting code.
  • Automatic device placement & precision: Hands-free device assignment and mixed-precision support (FP16/BF16/FP8).
  • Interactive configuration: Configure distributed settings with an interactive config flow and a single launch.
  • Ecosystem compatibility: Smooth integration with Transformers, TRL, and other HF libraries to scale experiments.

Quick Start

Install Accelerate and run accelerate config to tailor your environment, then wrap your training script with accelerate.prepare to enable distributed execution.

Frequently Asked Questions about huggingface-accelerate

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

FAQPage Schema
How do I run PyTorch distributed training across multiple GPUs without rewriting my code?▼

You can run PyTorch distributed training across multiple GPUs without rewriting code by wrapping your training script with a unified API. This abstracts backend setup and device placement for DDP, DeepSpeed, and FSDP.

Can I use DeepSpeed and FSDP backends with HuggingFace Transformers?▼

Yes, you can use DeepSpeed and FSDP backends with HuggingFace Transformers. The unified API provides smooth ecosystem integration, allowing you to scale experiments across multiple backends without modifying your core workflow.

How do I configure mixed-precision training for multi-GPU setups?▼

To configure mixed-precision training for multi-GPU setups, run an interactive configuration flow to tailor your environment. The API provides hands-free device assignment and automatic support for FP16, BF16, and FP8 precision.

What is the best way to orchestrate multi-device execution for large-scale model workflows?▼

The best way to orchestrate multi-device execution for large-scale workflows is using a unified API that manages automatic device placement and backend setup. It supports DDP, DeepSpeed, FSDP, and Megatron within the HuggingFace ecosystem.

Do I need to manually handle device placement when scaling PyTorch models?▼

No, you do not need to manually handle device placement when scaling PyTorch models. The API applies automatic device placement and mixed-precision handling, abstracting hardware setup across multiple devices and backends.

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