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huggingface-accelerate

Simplify distributed training of PyTorch models across multiple GPUs and machines.

Updated Jun 26, 2026
Search Tags:#PyTorch#DeepSpeed#distributed training#FSDP#Megatron#DDP
One-click install
npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill huggingface-accelerate-nitish-gitbit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/optional-skills/mlops/accelerate
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill huggingface-accelerate-nitish-gitbit

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexity and overhead of distributed training by providing a unified API that simplifies the process and enables efficient scaling of machine learning models across multiple GPUs and machines.

Core Features & Use Cases

  • Unified API: Supports DeepSpeed, FSDP, Megatron, and DDP, with a single API for all.
  • Automatic Device Placement: Automatically assigns model components to GPUs and optimizes for mixed precision training.
  • Mixed Precision: Supports FP16, BF16, and FP8 for efficient training on H100 GPUs.
  • Interactive Configuration: Allows interactive configuration of training settings and hardware.
  • Use Case: Accelerate your PyTorch script for multi-GPU training by adding just a few lines of code.

Quick Start

Install the 'accelerate' package and add the following lines to your script:

from accelerate import Accelerator
accelerator = Accelerator()

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 without changing my codebase?▼

Simplify distributed training across multiple GPUs by adding a unified API with just a few lines of code. Initialize the Accelerator in your script to enable automatic device placement and scale efficiently.

Can I use DeepSpeed and FSDP for multi-GPU training with a single API?▼

Yes, you can use a single API to run multi-GPU training with DeepSpeed, FSDP, Megatron, and DDP. This unified approach eliminates the need to maintain different scripts for various distributed training frameworks.

Does PyTorch mixed precision training support FP16 and BF16 for H100 GPUs?▼

PyTorch mixed precision training supports FP16, BF16, and FP8 to optimize efficiency on H100 GPUs. The unified API automatically handles precision settings during the training process.

What do I need to set up before starting multi-GPU training with PyTorch?▼

You need PyTorch, Python, and the accelerate package installed in your environment. After installing dependencies, import the Accelerator class and initialize it to configure hardware and training settings interactively.

How does automatic device placement work for machine learning models?▼

Automatic device placement assigns model components to available GPUs and optimizes them for mixed precision training. This mechanism abstracts manual hardware management, enabling efficient scaling across machines.

When should I not use a unified API for distributed training?▼

Avoid a unified API for distributed training if your project requires highly customized hardware communication patterns outside standard DeepSpeed or FSDP configurations. It is designed to simplify standard scaling rather than handle niche manual optimizations.

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