Based on Candle by Hugging Face, a minimalist ML framework for Rust with a focus on performance and ease of use, this backend can run on CPU with support for Web Assembly or on Nvidia GPUs using CUDA.

Looking to develop an AI application? Burn enables you to train and deploy your model on mobile devices, browsers, desktops, embedded systems, or large GPU clusters. Engaged in fundamental research? There's no need to master GPU programming to optimize your code; we handle that for you, so you can concentrate on your research.

With Burn successfully installed, you're now ready to start developing your deep learning projects using this powerful framework. While this example is somewhat trivial, the basic workflow section of the Burn Book will walk you through a much more relevant example for …

2024年6月5日 · Burn 是一个全面的动态深度学习框架，使用 Rust 构建，以极高的灵活性、计算效率和可移植性为目标，支持自动内核融合、异步执行和多种硬件后端。 跳到主要内容

Burn emphasizes thread safety by leveraging the ownership system of Rust. With Burn, each module is the owner of its weights. It is therefore possible to send a module to another thread for computing the gradients, then send the gradients to the main thread that can aggregate them, and voilà, you get multi-device training.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. Because we believe the goal of a deep learning framework is to convert computation into useful intelligence, we have made performance a core pillar of Burn.

2025年1月14日 · Burn emphasizes thread safety by leveraging the ownership system of Rust. With Burn, each module is the owner of its weights. It is therefore possible to send a module to another thread for computing the gradients, then send the gradients to the main thread that can aggregate them, and voilà, you get multi-device training.

2022年11月6日 · I’m announcing Burn (https://github.com/burn-rs/burn), a deep learning framework written in Rust supporting multiple backends as plugins using the newly stabilized GAT feature. It’s been around a year that I’ve been thinking about coming up with a deep learning framework in order to fix the frustrations that I have with the alternatives.

Machine learning models can play a vital role in accurately estimating the calories burnt based on various personal and activity-related features. Objective: The goal of this project is to develop a machine learning model that predicts the number of calories burned during physical activities.

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