allumette

allumette is a toy tensor library built for fun to better understand autodifferentiation.

It is based on minitorch.

Usage

Dataset provides a few ways to create synthetic datasets.

use allumette::{
    backend::backend_type::{Par, Seq},
    data::cpu_tensor_data::CpuTensorData,
    training::{dataset::Dataset, train},
};

let pts = 10;
let dataset = Dataset::simple(pts);
let hidden_layer_size = 3;
let learning_rate = 0.5;
let iterations = 200;
// use Par instead of Seq to leverage rayon's parallel iterators
train::train::<Seq, CpuTensorData>(dataset, learning_rate, iterations, hidden_layer_size);

Build and dependencies

Part of the codebase makes use of the generic_const_exprs experimental feature so it requires nightly.

The set of dependencies is otherwise pretty limited:

• proptest for property-based testing
• rayon for parallel computations
• rand for synthetic data generation

Next up

• parallel backend
• gpu backend
• optimizations
• make encoding tensor dimension as a const generic work