菜单
Burn NdArray backend - CPU-based tensor operations using Rust's ndarray crate. Ideal for development, testing, and lightweight inference.
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基于 SOC 职业分类
Convert Markdown with LaTeX math, images, and tables into a polished PDF. Two rendering engines: (1) weasyprint — pure Python, no browser needed; (2) chromium — best for matrices/bmatrix/pmatrix/vmatrix and complex formulas. Supports Chinese/Japanese fonts. Can chain after ocr-md-polish for a complete OCR→clean→PDF workflow.
Burn core framework - Provides tensor operations, automatic differentiation, and neural network building blocks for Rust deep learning.
Burn CUDA backend - High-performance NVIDIA GPU acceleration. Provides optimal performance for NVIDIA hardware.
Burn Metal backend - Apple GPU acceleration for macOS and iOS. Optimized for Apple Silicon and Intel-based Macs.
Burn ONNX support - Import ONNX models into Burn for inference on any backend. Supports compile-time code generation and runtime loading.
Provides guidance on using Burn deep learning framework for Rust projects. Invoke when working with tensor operations, model inference, or ONNX model integration in Rust.
| name | burn-ndarray |
| description | Burn NdArray backend - CPU-based tensor operations using Rust's ndarray crate. Ideal for development, testing, and lightweight inference. |
burn-ndarray provides a CPU backend for Burn using the Rust ndarray crate. It's the default backend for development and testing.
use burn::tensor::Tensor;
use burn_ndarray::NdArrayDevice;
// Create device
let device = NdArrayDevice::default();
// Create tensor
let tensor = Tensor::from_data(&[1.0, 2.0, 3.0], &device);
// Perform operations
let result = tensor + 2.0;
use burn_ndarray::NdArrayBackend;
type Backend = NdArrayBackend;
let model: Model<Backend> = Model::default();
[model_exports]
default_backend = "ndarray"
default_device = "Cpu"
[ndarray]
burn-onnx for CPU inferenceburn-wgpu or burn-cuda for GPU accelerationburn: Core frameworkburn-onnx: ONNX model import for CPU inference