dAImond 💎

Deep Learning framework for Ruby, inspired by PyTorch. Written from scratch with love for the Ruby community. Features automatic differentiation, neural networks, and optional Rust backend for 50-100x speedup.

Why Ruby? IDK, i just love this lang. dAImond brings back the happiness to ML.

• 🔥 Automatic Differentiation - Full autograd engine with computational graphs
• 🧠 Neural Networks - Linear layers, activations (ReLU, Sigmoid, Softmax, Tanh)
• 📊 Optimizers - SGD with momentum, learning rate scheduling
• 🎯 Loss Functions - MSE, CrossEntropy
• 💾 Model Serialization - Save/load trained models with Marshal
• 📈 Data Loaders - Batch processing, shuffling, MNIST support
• ⚡ Fast Backend - Numo::NArray for vectorized operations (C-speed)
• 🎨 Beautiful API - Idiomatic Ruby DSL, chainable methods

Add this line to your Gemfile:

gem 'daimond'

Or install manually:

gem install daimond

Dependencies: Ruby 2.7+, numo-narray

require 'daimond'

# Define your model
class NeuralNet < Daimond::NN::Module
  attr_reader :fc1, :fc2
  
  def initialize
    super()
    @fc1 = Daimond::NN::Linear.new(784, 128)
    @fc2 = Daimond::NN::Linear.new(128, 10)
    @parameters = @fc1.parameters + @fc2.parameters
  end
  
  def forward(x)
    x = @fc1.forward(x).relu
    @fc2.forward(x).softmax
  end
end

# Training loop
model = NeuralNet.new
optimizer = Daimond::Optim::SGD.new(model.parameters, lr: 0.1, momentum: 0.9)
criterion = Daimond::Loss::CrossEntropyLoss.new

# Forward → Backward → Update
loss = criterion.call(model.forward(input), target)
optimizer.zero_grad
loss.backward!
optimizer.step

Train a classifier on 60,000 handwritten digits:

ruby examples/mnist.rb

Results:

Epoch 1/5: Loss = 0.2898, Accuracy = 91.35%
Epoch 2/5: Loss = 0.1638, Accuracy = 95.31%
Epoch 3/5: Loss = 0.1389, Accuracy = 96.2%
Epoch 4/5: Loss = 0.1195, Accuracy = 96.68%
Epoch 5/5: Loss = 0.1083, Accuracy = 97.12%

Save your model:

model.save('models/mnist_model.bin')

Load and predict:

model = NeuralNet.new
model.load('models/mnist_model.bin')
prediction = model.forward(test_image)

For 50-100x speedup, compile Rust extensions:

cd ext/daimond_rust
cargo build --release
cd ../..
ruby examples/mnist_conv_rust.rb

• Core autograd engine
• Linear layers & activations
• MNIST 97% accuracy (Adam)
• Conv2D + MaxPool layers
• Rust backend
• Training visualization
• Batch Normalization & Dropout
• GPU support (OpenCL/CUDA via FFI)
• ONNX export/import

I'll be happy to see any contributors! Please read CONTRIBUTING.md for details.

MIT License - see LICENSE file.