huggingface-accelerate

Configure distributed training for PyTorch and Transformers with DDP, DeepSpeed, and FSDP.

Updated Aug 27, 2026

Search Tags:#PyTorch #Transformer #DeepSpeed #distributed training #mixed precision #Accelerate #FSDP

Ready to run

npx skills add https://github.com/valentinuuiuiu/vikarma --skill huggingface-accelerate-valentinuuiuiu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/valentinuuiuiu/vikarma/tree/main/hermes_agent/optional-skills/mlops/accelerate
Command: npx skills add https://github.com/valentinuuiuiu/vikarma --skill huggingface-accelerate-valentinuuiuiu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a simple and unified API for distributed training across various frameworks and hardware setups, making it easier to scale AI models efficiently.

Core Features & Use Cases

Unified API: Simplifies code changes for distributed training with minimal modifications.
DDP/DeepSpeed/FSDP Support: Supports DistributedDataParallel, DeepSpeed, and Fully Sharded Data Parallel for flexibility.
Mixed Precision: Supports automatic mixed precision (FP16/BF16/FP8) for faster training.
Interactive Config: Offers an interactive configuration interface for different training scenarios.
Use Case: Ideal for training complex models on multiple GPUs or across different hardware setups, with reduced code complexity.

Quick Start

Install Accelerate and convert your PyTorch script by adding 4 lines of code.

Frequently Asked Questions about huggingface-accelerate

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

FAQPage Schema

How do I simplify distributed training across multiple GPUs with PyTorch?▼

Distributed training is simplified by adding just four lines of code to your PyTorch script, providing a unified API that handles DistributedDataParallel, DeepSpeed, and FSDP configurations automatically.

What is the easiest way to run mixed precision training with HuggingFace Transformers?▼

Mixed precision training is supported automatically through an interactive configuration interface, enabling FP16, BF16, or FP8 optimizations for faster training across CPU, GPU, and TPU hardware.

Does FSDP work with DeepSpeed for distributed training?▼

Yes, both Fully Sharded Data Parallel (FSDP) and DeepSpeed are supported natively, offering flexible options for scaling complex models across different hardware setups with minimal code modifications.

How do I convert an existing PyTorch script for multi-GPU training?▼

You can convert your PyTorch script by adding four lines of code using the unified API, which abstracts away the complexity of multi-GPU and hardware-specific logic without major refactoring.

Do I need PyTorch and Transformers installed to use Accelerate?▼

Yes, you need the PyTorch and Transformers libraries installed, as the unified API builds upon these frameworks to simplify distributed training and mixed precision configurations.