Ferrite: A Deep Learning Library in Rust

A deep learning framework written in pure Rust, inspired by PyTorch. Used this to learn Rust and refine DL concepts.

Installation

Add to your project using:

cargo add ferrite-dl

Or manually add to your Cargo.toml:

[dependencies]
ferrite-dl = "0.1.0"

Features

• Dynamic Computational Graph: Build and modify neural networks on the fly
• Automatic Differentiation: Automatic computation of gradients through the backward() method
• Efficient Tensor Operations: Fast operations with broadcasting support
• Memory Safety: Leveraging Rust's ownership model for safe and efficient memory management
• Rich Tensor API: Comprehensive set of tensor operations including:
  • Element-wise operations (add, multiply, divide)
  • Matrix operations (matmul)
  • Reduction operations (sum, mean, product)
  • Shape manipulation (reshape, transpose, squeeze/unsqueeze)
  • Broadcasting support

Quick Start

use ferrite::*;
use ndarray::array;

fn main() {
    // Create tensors with gradient tracking
    let x = Tensor::from_ndarray(&array![[1,2,3],[4,5,6]], Some(true));
    let y = Tensor::from_ndarray(&array![[1,1,1]], Some(true));

    // Perform operations
    let z = x.mul_tensor(&y);
    let mut f = z.sum();

    // Compute gradients
    f.backward();

    // Access gradients
    println!("grad x: {:?}", x.grad());
    println!("grad y: {:?}", y.grad());
}

Architecture

ferrite is built with a modular architecture:

• TensorStorage: Core tensor storage and operations
• Tensor: High-level tensor interface with autograd support
• Module: Base trait for neural network modules
• Autograd: Automatic differentiation engine
• Parameter: Trainable parameters for neural networks

Implementation Details

• Uses Rc<RefCell<>> for shared ownership and interior mutability
• Implements efficient broadcasting with stride-based computation
• Supports n-dimensional tensors with arbitrary shape
• Provides complete automatic differentiation for supported operations
• Uses traits for clean abstraction of tensor operations

Usage Examples

Creating Tensors

// Create tensor filled with zeros
let zeros = Tensor::zeros(vec![2, 3], Some(true));

// Create tensor from ndarray
let data = Tensor::from_ndarray(&array![[1.0, 2.0], [3.0, 4.0]], Some(true));

Neural Network Module (Will update when finished)

impl SimpleNetwork {
    fn new() -> Self {
        let module = Module::new();
        SimpleNetwork { module }
    }
}

impl Segment for SimpleNetwork {
    fn forward(input: Tensor) -> Tensor {
        // Implement your network logic here
    }
}

Future Plans

• Finish building Neural Network interface
• Add more operations
• Add CUDA support
• Optimize performance for large tensors
• Add more loss functions
• Implement data loading utilities
• Add serialization support

Acknowledgments

• PyTorch (for inspiration)
• Claude (for teaching me Rust)