Home › Model Libraries & Training › Hugging Face Accelerate

Hugging Face Accelerate - AI Model Libraries & Training Tool

Overview

Hugging Face Accelerate is a lightweight library to launch, train, and run PyTorch models on a wide range of devices. It provides simplified distributed configuration, automatic mixed precision (including fp8), and integrations for FSDP and DeepSpeed.

Key Features

  • Launch, train, and run PyTorch models across almost any device
  • Simplified distributed configuration for single-node and multi-node training
  • Automatic mixed precision support, including fp8
  • Easy integration with FSDP and DeepSpeed for large-model parallelism

Ideal Use Cases

  • Run distributed training across multiple GPUs or nodes
  • Reduce memory and compute using automatic mixed precision
  • Train very large models with FSDP or DeepSpeed
  • Develop and scale PyTorch workflows from local to distributed environments

Getting Started

  • Open the GitHub repository to review documentation and examples
  • Install the package, e.g., pip install accelerate
  • Run 'accelerate config' to specify devices and distributed settings
  • Use 'accelerate launch' to run your training or inference script
  • Enable AMP, FSDP, or DeepSpeed in the config when required

Pricing

No pricing information provided; repository and code are available on GitHub.

Limitations

  • Designed for PyTorch; requires familiarity with PyTorch APIs
  • Primarily a developer library; requires coding and command-line usage
  • Not a hosted managed training service; does not provide built-in cloud hosting

Key Information

  • Category: Model Libraries & Training
  • Type: AI Model Libraries & Training Tool
Visit Official Website

Related Tools

Diffusers

A library implementing state-of-the-art diffusion models for image, video, and audio generation, supporting both PyTorch and FLAX frameworks.
Hugging Face Transformers

A comprehensive library of pretrained models for text, vision, audio, video, and multimodal tasks, enabling fine-tuning and inference across many generative AI use cases.
Unsloth AI

Unsloth AI is an enterprise platform that accelerates fine-tuning of large language models and vision models by leveraging innovative quantization techniques. It enables faster performance (up to 2.2x faster) and uses significantly less VRAM, making model deployment and training more efficient. The organization also offers open-source tools and models, and is integrated with Hugging Face, with additional details available on its website.
AutoTrain

Hugging Face AutoTrain is an automated machine learning (AutoML) tool that allows users to train, evaluate, and deploy state-of-the-art ML models without writing code. It supports a range of tasks including text classification, image classification, token classification, summarization, question answering, translation, tabular data tasks, and LLM finetuning, with seamless integration into the Hugging Face ecosystem.
DeepScaleR

DeepScaleR is an open-source project that democratizes reinforcement learning (RL) for large language models (LLMs). The repository provides training scripts, model checkpoints, detailed hyperparameter configurations, datasets, and evaluation logs to reproduce and scale RL techniques on LLMs, aimed at reproducibility and research in advanced AI training.
TRL

TRL is a comprehensive open-source library that enables post-training of transformer language models using reinforcement learning techniques such as Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO). It integrates with Hugging Face’s Transformers ecosystem and supports efficient scaling with tools like Accelerate and PEFT.