加载中
正在获取最新内容，请稍候...
Candle - Minimalist ML Framework in Rust
Candle is a minimalist ML framework for Rust with a focus on performance, including CPU, GPU (CUDA, OpenCL, Metal, WebGPU), and embedded devices support. Designed for inference and lightweight training.
Rust
Added on 2025年6月30日17,505
Stars1,135
ForksRust
LanguageProject Introduction
Summary
Candle is a machine learning framework written in Rust. It aims to be minimalist and provide competitive performance with a focus on inference workflows, supporting a wide range of hardware backends.
Problem Solved
Many existing deep learning frameworks are complex, have large dependencies, or lack first-class support for the Rust programming language. Candle provides a performant, modern, and idiomatic Rust-native alternative for ML workloads, especially inference.
Core Features
Minimalist Design
Focuses on providing core tensor operations and common neural network layers, keeping the API small and understandable.
Multi-platform Backend
Supports execution on various backend hardware, including CPUs, NVIDIA GPUs (CUDA), AMD/Intel GPUs (OpenCL), Apple Silicon (Metal), and browser/WebAssembly via WebGPU.
Inference and Lightweight Training
Includes functionalities for common deep learning tasks like model inference and lightweight model fine-tuning.
Hugging Face Model Compatibility
Provides helpers to easily load and use popular pre-trained models, particularly from the Hugging Face ecosystem.
Tech Stack
Rust
CUDA
cuDNN
OpenCL
Metal
WebGPU
BLAS / LAPACK
使用场景
Candle is suitable for a variety of machine learning applications where Rust is the primary development language, performance is critical, or deployment across diverse hardware is required.
Integrating ML Inference into Rust Applications
Details
Integrate pre-trained neural network models (e.g., from Hugging Face) into backend services or desktop applications written in Rust for tasks like natural language processing, image classification, or recommendation systems.
User Value
Adds ML capabilities to Rust projects with low overhead and high performance.
Deploying ML on Embedded/Edge Devices
Details
Deploy ML models directly on embedded devices or resource-constrained hardware leveraging Candle's efficient backends and Rust's low-level control.
User Value
Enables ML on hardware typically challenging for traditional frameworks.
Client-Side ML with WebGPU
Details
Run ML models within the browser or via WebAssembly using Candle's WebGPU backend, enabling client-side inference.
User Value
Allows performing ML tasks directly in the user's browser, reducing server load.
Recommended Projects
You might be interested in these projects
comfyanonymousComfyUI
ComfyUI is a powerful and modular graphical user interface, API, and backend for stable diffusion models, featuring a flexible graph/nodes interface for constructing complex generation workflows.
Python
807428934
scrapyscrapy
Scrapy is a fast and powerful Python framework designed for efficient web crawling and structured data extraction from websites. It is ideal for scraping, data mining, and automated testing.
Python
5631710890
kestra-iokestra
Kestra is an open-source workflow automation platform designed to orchestrate and schedule code execution across any language, running anywhere. With over 600 plugins, it serves as a powerful alternative to tools like Airflow, n8n, and Zapier, simplifying complex data pipelines and business processes.
Java
194721608