Burn Framework (Part 3): Training & Deployment

After constructing neural network modules in Burn ↗, the next step is establishing a robust training loop and deploying models into production.

Managing Training Loops with `LearnerBuilder`#

Burn provides LearnerBuilder to manage checkpoint saving, metric logging, tensorboard integration, and optimizer state tracking out of the box.

src/train.rs

use burn::train::LearnerBuilder;
use burn::optim::AdamConfig;
use burn::backend::Autodiff;
use burn::backend::Wgpu;

type MyAutodiffBackend = Autodiff<Wgpu>;

pub fn run_training() {
    let device = Default::default();
    let optimizer = AdamConfig::new().init();

    // [!code focus]
    // Automate training pipeline with LearnerBuilder
    let learner = LearnerBuilder::new("./artifacts")
        .metric_train_numeric("loss")
        .with_file_checkpointer(1)
        .devices(vec![device])
        .num_epochs(10)
        .build(Model::<MyAutodiffBackend>::new(784, 128, 10, &device), optimizer, 1e-3);
}

rust

Cross-Platform Production Deployment#

Burn offers a seamless production experience across targets:

Weight Exporting: Export and import model weights using NamedMappable or ONNX.
WebAssembly (WASM): Run client-side model inference inside browsers without backend APIs.
Embedded & No-STD: Deploy models on edge devices and microcontrollers without standard library requirements.

# Building for WebAssembly target
cargo build --target wasm32-unknown-unknown --release

bash

Summary#

Burn ↗ marks a massive milestone for the Rust AI ecosystem. Combining Rust speed, memory safety, and flexible backend abstractions, Burn is an exceptional choice for modern production deep learning systems.

Managing Training Loops with LearnerBuilder#

Cross-Platform Production Deployment#

Summary#

References#

Managing Training Loops with `LearnerBuilder`#