aurane 0.1.0

A modern domain-specific language for machine learning that compiles to PyTorch

Aurane

A Modern Domain-Specific Language for Machine Learning

Overview

Aurane is a domain-specific language designed for machine learning development. It enables developers to write expressive, clean .aur files that compile into production-ready, idiomatic PyTorch code. The language abstracts away boilerplate while maintaining full control over model architecture and training configuration.

Installation

# Clone repository
git clone https://github.com/desenyon/aurane.git
cd aurane

# Install with all features
pip install -e ".[all]"

Requirements

• Python 3.10+
• PyTorch 2.0+ (for ML features)
• Rich 13.0+ (for CLI interface)

Quick Start

Example Model Definition

Create mnist.aur:

use torch
use torchvision

experiment MnistBaseline:
    seed = 42
    device = "auto"

dataset mnist_train:
    from torchvision.datasets.MNIST
    root = "./data"
    train = True
    batch = 128

model MnistNet:
    input_shape = (1, 28, 28)
    def forward(x):
        x -> conv2d(32, kernel=3).relu
          -> maxpool(2)
          -> conv2d(64, kernel=3).relu
          -> maxpool(2)
          -> flatten()
          -> dense(128).relu
          -> dropout(0.5)
          -> dense(10)

train MnistNet on mnist_train:
    loss = cross_entropy
    optimizer = adam(lr=1e-3)
    epochs = 5

Compilation

# Basic compilation
aurane compile mnist.aur mnist.py

# With analysis and visualization
aurane compile mnist.aur mnist.py --analyze --show-ast

# Auto-recompile on changes
aurane watch mnist.aur mnist.py

Generated Output

Clean, idiomatic PyTorch code:

class MnistNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv2d1 = nn.Conv2d(1, 32, 3)
        self.conv2d2 = nn.Conv2d(32, 64, 3)
        self.dense1 = nn.Linear(1600, 128)
        self.dropout1 = nn.Dropout(0.5)
        self.dense2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv2d1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2d2(x))
        x = F.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        x = F.relu(self.dense1(x))
        x = self.dropout1(x)
        x = self.dense2(x)
        return x

Features

CLI

Aurane includes a feature-rich command-line interface with:

# Compile with beautiful progress bars
aurane compile model.aur output.py --analyze

# Inspect model architecture
aurane inspect model.aur --verbose --stats

# Live development with auto-reload
aurane watch model.aur output.py

# Interactive REPL for experimentation
aurane interactive

# Format your Aurane code
aurane format examples/

# Lint for potential issues
aurane lint model.aur

# Benchmark compilation performance
aurane benchmark model.aur

Model Inspection

Obtain detailed insights into model architecture:

$ aurane inspect examples/resnet.aur --verbose

## Quick Start

Watch Mode

Automatic recompilation on file changes:

$ aurane watch model.aur output.py
[watching] model.aur
[success] Compiled successfully (1.2s)

[changed] File modified, recompiling...
[success] Compiled successfully (0.8s)

Interactive REPL

Live coding environment for rapid prototyping:

$ aurane interactive

aurane> model SimpleNet:
....... input_shape = (1, 28, 28)
....... def forward(x):
.......     x -> conv2d(32).relu -> flatten() -> dense(10)

aurane> .compile
[success] Compilation successful

Advanced Examples

ResNet-Style Architecture

model ResNetClassifier:
    input_shape = (3, 224, 224)
    def forward(x):
        x -> conv2d(64, kernel=7, stride=2, padding=3).relu
          -> maxpool(3, stride=2)
          -> conv2d(64, kernel=3, padding=1).relu
          -> conv2d(64, kernel=3, padding=1).relu
          -> conv2d(128, kernel=3, stride=2, padding=1).relu
          -> avgpool(7)
          -> flatten()
          -> dense(1000)

train ResNetClassifier on imagenet:
    loss = cross_entropy
    optimizer = adam(lr=1e-3, weight_decay=1e-4)
    scheduler = cosine_annealing(T_max=50)
    epochs = 50

Transformer Model

model LanguageModel:
    input_shape = (128,)
    vocab_size = 50000
  
    def forward(x):
        x -> embedding(vocab_size, 512)
          -> positional_encoding(max_len=128)
          -> multihead_attention(heads=8)
          -> layer_norm()
          -> dense(2048).gelu
          -> dense(512)
          -> dropout(0.1)
          -> dense(vocab_size)

GAN Architecture

model Generator:
    input_shape = (100,)
    def forward(z):
        z -> dense(256).relu
          -> batch_norm()
          -> dense(512).relu
          -> dense(784).tanh
          -> reshape(1, 28, 28)

model Discriminator:
    input_shape = (1, 28, 28)
    def forward(x):
        x -> flatten()
          -> dense(512).leaky_relu(0.2)
          -> dropout(0.3)
          -> dense(1).sigmoid

More examples in the examples/ directory.

CLI Commands

Core Commands

Command	Description
compile	Compile .aur file to Python
inspect	Analyze model architecture
watch	Auto-recompile on changes
interactive	Start REPL mode
format	Format Aurane source files
lint	Check for style and errors
benchmark	Measure compilation performance
run	Compile and execute

See CLI Reference for detailed usage.

Language Reference

Supported Layers

Convolution: conv1d, conv2d, conv3d Pooling: maxpool, avgpool, adaptive_avgpool Linear: dense/linear, embedding Normalization: batch_norm, layer_norm, group_norm Activation: .relu, .gelu, .leaky_relu, .tanh, .sigmoid, .softmax Regularization: dropout Reshaping: flatten, reshape

Configuration Blocks

# Experiments
experiment MyExperiment:
    seed = 42
    device = "cuda"
    mixed_precision = true

# Datasets
dataset training_data:
    from torchvision.datasets.CIFAR10
    root = "./data"
    train = True
    batch = 256

# Training
train MyModel on training_data:
    validate_on = validation_data
    loss = cross_entropy
    optimizer = adam(lr=1e-3)
    scheduler = cosine_annealing(T_max=50)
    epochs = 100
    early_stopping = true

See Language Reference for complete syntax.

Shape Inference

Aurane automatically infers tensor shapes through your network:

$ aurane inspect model.aur --verbose

Layer              Output Shape    Parameters
--------------------------------------------
Conv2D(32)         (32, 26, 26)        320
MaxPool(2)         (32, 13, 13)          0
Conv2D(64)         (64, 11, 11)     18,496
Flatten()          (7,744)               0
Dense(128)         (128)           991,360
Dense(10)          (10)              1,290
--------------------------------------------
Total Parameters: 1,011,466

Roadmap

v0.1.0 (Current)

• Complete PyTorch backend
• Enhanced CLI with 8+ commands
• Model inspection and visualization
• Interactive REPL
• Format and lint tools

v0.1.5 (Planned)

• TensorFlow/Keras backend
• Custom layer definitions
• Model composition
• VS Code extension

v0.2.0 (Future)

• JAX/Flax backend
• Distributed training
• Hyperparameter search
• Model optimization

Documentation

Comprehensive documentation is available in the docs/ directory:

• Getting Started
• Language Reference
• CLI Commands
• Examples

License

This project is licensed under the MIT License. See LICENSE for details.