burn-core

name	burn-core
description	Burn core framework - Provides tensor operations, automatic differentiation, and neural network building blocks for Rust deep learning.

Burn Core Framework

Overview

The core burn crate provides the fundamental building blocks for deep learning in Rust:

• Tensor Abstraction: Generic tensor types with backend support
• Automatic Differentiation: Type-safe autodiff system
• Neural Network Modules: Built-in layers and utilities
• Optimizers: Adam, SGD, AdamW and more
• Loss Functions: MSE, CrossEntropy, CTC, etc.

Key Features

1. Tensor System

use burn::tensor::Tensor;
use burn_ndarray::NdArrayDevice;

let device = NdArrayDevice::default();
let tensor = Tensor::from_data(&[1.0, 2.0, 3.0], &device);

2. Automatic Differentiation

let x = Tensor::from_data(&[1.0, 2.0], &device).require_grad();
let y = x * 2.0;
let grads = y.backward();

3. Neural Network Module

use burn::nn::{Linear, Module, ReLU};

#[derive(Module)]
struct Model {
    linear: Linear<NdArrayBackend>,
    relu: ReLU,
}

4. Optimizers

use burn::optim::Adam;

let mut optimizer = Adam::new();
optimizer.step(grads);

Core Components

Component	Description
burn::tensor	Tensor operations and autodiff
burn::nn	Neural network layers
burn::optim	Optimization algorithms
burn::loss	Loss functions
burn::data	Data loading utilities

When to Use

• Building custom neural network architectures
• Implementing custom tensor operations
• Creating training pipelines
• Research and experimentation

Related Crates

• burn-ndarray: CPU backend
• burn-wgpu: GPU backend
• burn-onnx: ONNX model import