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huggingface-accelerate

Automate distributed PyTorch training setup with Accelerate for DDP, DeepSpeed, FSDP, and Megatron.

Updated May 2, 2026
Search Tags:#pytorch#distributed-training#mixed-precision#fsdp#deepspeed#accelerate#megatron-lm
One-click install
npx skills add https://github.com/AlvaroBiano/hermes-agent --skill huggingface-accelerate-alvarobiano
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/AlvaroBiano/hermes-agent/tree/main/optional-skills/mlops/accelerate
Command: npx skills add https://github.com/AlvaroBiano/hermes-agent --skill huggingface-accelerate-alvarobiano

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Accelerate provides a lightweight, unified API to convert a PyTorch script into a distributed training routine with minimal changes.

Core Features & Use Cases

  • Unified API for multiple backends: DDP, DeepSpeed, FSDP, Megatron, with a single script.
  • Automatic device placement & mixed precision: BF16/FP16/FP8 support without manual moves.
  • Interactive config & single-launch: No per-backend launcher setup; generate launch specs.

Quick Start

Configure Accelerate with accelerate config and start training with accelerate launch train.py.

Frequently Asked Questions about huggingface-accelerate

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

FAQPage Schema
How do I set up distributed training in PyTorch without writing complex launcher scripts?▼

Distributed training in PyTorch can be set up with a unified API that requires only four lines of code changes. You generate an interactive accelerator configuration and start execution using a single launch command.

Does this approach support mixed precision training with BF16 and FP16?▼

Mixed precision training supports BF16, FP16, and FP8. It provides automatic device placement and precision handling without requiring manual tensor moves across your GPUs.

Can I use DeepSpeed and FSDP within the same PyTorch training script?▼

DeepSpeed and FSDP are supported through a unified API. This allows you to run multiple distributed training backends, including DDP and Megatron, within a single script.

What is the easiest way to scale a PyTorch model from a single GPU to a multi-node cluster?▼

Scaling to a multi-node cluster is done by applying minimal code changes to your existing PyTorch script. The unified API automates distributed setup across both single-node multi-GPU and multi-node environments.

Do I need to manually manage device placement when using accelerate for distributed training?▼

Manual device placement is not required. The API automates device placement for your PyTorch models, handling the underlying hardware distribution automatically during training.

What are the limitations of using a unified API for complex distributed training strategies?▼

While the unified API simplifies setup for DDP, DeepSpeed, FSDP, and Megatron, highly customized distributed training strategies may still require direct framework-level modifications beyond the provided four-line code changes.

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