briny_ai

A minimal, dependency-free deep learning core for scalar and tensor autograd, written in Rust.

This library provides low-level primitives for defining and training differentiable models on top of multi-dimensional arrays (Tensor<T>), supporting:

Elementwise operations with autograd
Matrix multiplication with gradient tracking
Loss functions (MSE)
Stochastic gradient descent (SGD)
JSON and binary-based tensor serialization
Compile-time tensor creation macros

Features

Pure Rust, no dependencies
Compact and fast .bpat binary model format
Forward + backward computation via closures
Extensible tensor structure with runtime shape checking

Usage

To use briny_ai, add the following to your Cargo.toml:

[dependencies]

briny_ai = "0.1.0"

Example

use briny_ai::tensors::{Tensor, WithGrad};
use briny_ai::backprop::{relu, matmul, mse_loss, sgd};

fn main() {
    let x = WithGrad::from(Tensor::new(vec![1, 2], vec![1.0, 2.0]));
    let w = WithGrad::from(Tensor::new(vec![2, 1], vec![0.5, -1.0]));

    let (y, back1) = matmul(&x, &w);
    let (a, back2) = relu(&WithGrad::from(y));
    let target = Tensor::new(vec![1, 1], vec![0.0]);
    let (loss, back3) = mse_loss(&WithGrad::from(a.clone()), &target);

    let grad_a = back3(1.0);
    let grad_y = back2(&grad_a);
    let (grad_x, grad_w) = back1(&grad_y);

    println!("Loss: {:.4}", loss);
    println!("Gradients: {:?}", grad_w);
}

Saving & Loading

use briny_ai::modelio::{save_model, load_model};

let tensor = Tensor::new(vec![2, 2], vec![1.0, 2.0, 3.0, 4.0]);
save_model("model.bpat", &[tensor.clone()]).unwrap();

let tensors = load_model("model.bpat").unwrap();
assert_eq!(tensors[0], tensor);

Limitations

Only f64 tensors are supported
No broadcasting or shape inference yet
No support for convolution or GPU acceleration
Autograd is manual via backward closures

Roadmap

Broadcasting + batched ops
Drop-in replacements for BLAS (SIMD)
CUDA / WebGPU backend support
Graph-based autograd (reuse, optimization)
Custom layers & high-level model struct

Contributing

PRs welcome. This project is early-stage but cleanly structured and easy to extend. Open issues or ideas any time! It's MIT Licensed, too.

briny_ai 0.1.0