burn-ndarray

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

Overview

burn-ndarray provides a CPU backend for Burn using the Rust ndarray crate. It's the default backend for development and testing.

Key Features

• CPU Computing: All tensor operations run on CPU
• Easy Setup: No GPU dependencies required
• Cross-platform: Works on Linux, Windows, macOS
• WASM Support: Can compile to WebAssembly

Usage

Basic Setup

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;

Backend Type

use burn_ndarray::NdArrayBackend;

type Backend = NdArrayBackend;
let model: Model<Backend> = Model::default();

burn.toml Configuration

[model_exports]
default_backend = "ndarray"
default_device = "Cpu"

[ndarray]

Best Practices

1. Use for development and testing
2. Good for small to medium models
3. Ideal for WASM deployments
4. Use with burn-onnx for CPU inference

Performance Considerations

• Single-threaded by default
• Good for prototyping
• Not recommended for large-scale training
• Use burn-wgpu or burn-cuda for GPU acceleration

When to Use

• Development and debugging
• CI/CD pipelines
• WASM applications
• Lightweight inference
• Embedded systems without GPU

Related Crates

• burn: Core framework
• burn-onnx: ONNX model import for CPU inference