Getting Started

This guide will help you install and run your first NNLang model.

Installation

From crates.io

cargo install nnlang

From source

git clone https://github.com/gdesouza/nnl
cd nnl
cargo install --path .

Pre-built binaries

Download the latest release from GitHub:

# Linux
curl -L https://github.com/gdesouza/nnl/releases/latest/download/nnc-*-x86_64-unknown-linux-gnu.tar.gz | tar xz
sudo mv nnc /usr/local/bin/

# macOS
curl -L https://github.com/gdesouza/nnl/releases/latest/download/nnc-*-x86_64-apple-darwin.tar.gz | tar xz
sudo mv nnc /usr/local/bin/

Quick Start

1. Create a model file

Save this as model.nnl:

version 0.2;

model my_model {
    config {
        weights: "./weights";
        io: "stdio";
    }

    layer input  = Input(shape: [4]);
    layer fc1   = Dense(units: 3, activation: "relu");
    layer fc2   = Dense(units: 2);
}

2. Create weight files

Create a weights/ directory with:

• weights/fc1.weight.npy — [4, 3] matrix
• weights/fc1.bias.npy — [3] vector
• weights/fc2.weight.npy — [3, 2] matrix
• weights/fc2.bias.npy — [2] vector

3. Compile

nnc compile model.nnl --emit exe -o model

4. Run inference

# Input: 4 floats
echo -n -e '\x00\x00\x80\x3f\x00\x00\x00@\x00\x00@@\x00\x00\x80@' > input.bin
./model < input.bin > output.bin

Or test with known input/output:

nnc test model.nnl --input test_input.npy --expected expected_output.npy

Next Steps

• Language Reference — full language syntax
• CLI Reference — all commands
• Examples — complete model examples