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accelerate

Scale PyTorch training across multiple GPUs and TPUs with the Accelerator API.

Updated Apr 19, 2026
Search Tags:#pytorch#distributed-training#mixed-precision#training-loop#accelerate#multi-gpu
One-click install
npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill accelerate-chenyiru3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: accelerate
Source: https://github.com/CHENyiru3/AI-Skills-Collections/tree/main/skills-market/ai-ml/training/accelerate
Command: npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill accelerate-chenyiru3

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Hugging Face Accelerate provides a simple API to scale PyTorch training across devices with minimal code changes, solving the challenge of distributed training setup and device management.

Core Features & Use Cases

  • Distributes training across GPUs/TPUs with automatic device placement.
  • Supports mixed precision (fp16/bf16) and gradient accumulation for large models.
  • Integrates with Transformers training workflows and standard PyTorch code.

Quick Start

Install accelerate and wrap your model, optimizer, and dataloader with accelerator.prepare() to enable distributed training and mixed precision across devices.

Frequently Asked Questions about accelerate

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

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

To scale PyTorch training across multiple GPUs, wrap your model, optimizer, and dataloader with the Accelerator API's prepare() function to enable automatic device placement and distributed setups.

Can I use mixed precision and gradient accumulation for large models in distributed training?▼

Mixed precision workflows using fp16 or bf16 and gradient accumulation for large models are fully supported during distributed training via configurable settings through the Accelerator API or optional config files.

What is the best way to manage device placement for PyTorch distributed data-parallel training?▼

The best way to manage device placement for distributed data-parallel training is using the Accelerate API, which automatically handles device allocation across GPUs and TPUs with minimal code modifications.

Does Hugging Face Accelerate work with standard PyTorch code and Transformers training workflows?▼

Hugging Face Accelerate integrates directly with standard PyTorch code and Transformers training workflows, allowing you to scale existing scripts across multiple devices without requiring a complete codebase rewrite.

How do I configure distributed setups for multi-GPU and TPU training?▼

You can configure distributed setups for multi-GPU and TPU training by using optional config files or the Accelerator API to define precision control, device placement, and gradient accumulation parameters.

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