NABLA

Nabla is a Python library that provides three key features:

• Multidimensional Array computation (like NumPy) with strong GPU acceleration
• Composable Function Transformations: vmap, grad, jit, and other Automatic Differentiation tools
• Deep integration with MAX and (custom) Mojo kernels

For tutorials and API reference, visit: nablaml.com

Installation

Now available on PyPI!

pip install nabla-ml

Quick Start

import nabla as nb

# Example function using Nabla's array operations
def foo(input):
    return nb.sum(input * input, axes=0)

# Vectorize, differentiate, accelerate
foo_grads = nb.jit(nb.vmap(nb.grad(foo)))
gradients = foo_grads(nb.randn((10, 5)))

Development Setup and Reproducibility

This guide is for contributors or for reproducing the validation and benchmark results presented in the thesis.

1. Initial Setup

First, clone the repository and set up a virtual environment with all necessary dependencies.

# Clone the repository
git clone https://github.com/nabla-ml/nabla.git
cd nabla

# Create and activate a virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate

# Install all core and development dependencies
pip install -r requirements-dev.txt

2. Run the Correctness Validation Suite

This runs the full test suite to verify Nabla's correctness against JAX.

# Navigate to the unit test directory from the project root
cd nabla/tests/unit

# Execute the unified test script
python unified.py all -all-configs

3. Run the Performance Benchmarks

This script reproduces the performance benchmarks for Nabla, JAX, and PyTorch.

# Navigate to the benchmark directory
cd nabla/tests/benchmarks

# Run the benchmark script
python benchmark.py

Repository Structure

nabla/
├── nabla/                     # Core Python library
│   ├── core/                  # Array class and MAX compiler integration
│   ├── nn/                    # Neural network modules and models
│   ├── ops/                   # Mathematical operations (binary, unary, linalg, etc.)
│   ├── transforms/            # Function transformations (vmap, grad, jit, etc.)
│   └── utils/                 # Utilities (formatting, types, MAX-interop, etc.)
├── tests/                     # Comprehensive test suite
├── tutorials/                 # Notebooks on Nabla usage for ML tasks
├── examples/                  # Example scripts for common use cases
└── experimental/              # Core (pure) Mojo library (WIP!)

Contributing

Contributions welcome! Discuss significant changes in Issues first. Submit PRs for bugs, docs, and smaller features.

License

Nabla is licensed under the Apache-2.0 license.

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Thank you for checking out Nabla!