Burn Next generation of deep learning framework 深度学习框架需要具备一些基本功能,如模型构建、训练、推演;辅助功能如数据集预处理与后处理、模型序列化存储与加载;对加速硬件的支持如NVIDIA CUDA、AMD ROCm或者华为昇腾等。当这些基本功能完备后,开发者就可以将其作为基础平台,在其上构建各种创新的模型实验与应用。
Rust Burn is a new deep learning framework written entirely in the Rust programming language. The motivation behind creating a new framework rather than using existing ones like PyTorch or TensorFlow is to build a versatile framework that caters well to various users including researchers, machine le...
: https://pixijs.com/blog/pixi-v8-launches[4]Burn - a new comprehensive dynamic Deep Learning Framework built using Rust: https://github.com/tracel-ai/burn[5]Leptos - Build fast web applications with Rust: https://github.com/leptos-rs/leptos[6]护照博物馆: https://fuun.fun/go/1099[7...
1、Burn - a new comprehensive dynamic Deep Learning Framework built using Rust Burn 是一个使用 Ru...
1、Burn - a new comprehensive dynamic Deep Learning Framework built using Rust Burn 是一个使用 Rust 语言开发的深度学习框架,旨在结合 Rust 的安全性和高性能特点,提供一个灵活且高效的平台,适用于多样化的深度学习应用。 2、Leptos - Build fast web applications with Rust ...
tracel-ai/burn: Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. spearow/juice: The Hacker's Machine Learning Engine rust-ml/linfa: A Rust machine learning framework. ...
blackportal-ai/delta - Δ An Open-Source Machine Learning Framework in Rust. blackportal-ai/nebula - A Package Manager for Machine Learning Datasets and Models. burn - A Flexible and Comprehensive Deep Learning Framework. coreylowman/dfdx - CUDA accelerated machine learning framework that leverag...
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Tch-rs, short for "Torch-rs" or "Torch Rust," is a set of Rust bindings for PyTorch, a popular deep-learning framework. These bindings provide Rust developers with access to PyTorch's rich set of functionalities for building and training neural networks. Tch-rs acts as a bridge, allowing...
One of the main roles of a deep learning framework is to reduce the amount of memory necessary to run models. The naive way of handling memory is that each tensor has its own memory space, which is allocated when the tensor is created then deallocated as the tensor gets out of scope. ...