Nabla is a scientific computing library in Python, featuring:
- Multidimensional array computation with strong GPU acceleration
- JAX-like composable function transformations:
grad,vmap,jit, and other automatic differentiation tools - Deep integration with the MAX compiler and custom Mojo 🔥 kernels
For tutorials and API reference, visit: nablaml.com
Installation
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=-1)
# Differentiate, vectorize, accelerate
foo_grads = nb.jit(nb.vmap(nb.grad(foo)))
gradients = foo_grads(nb.randn((10, 5)).to(nb.accelerator()))
For Developers
- Clone the repository
- Create a virtual environment (recommended)
- Install dependencies
git clone https://github.com/nabla-ml/nabla.git
cd nabla
python3 -m venv venv
source venv/bin/activate
pip install -r requirements-dev.txt
pip install -e ".[dev]"
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
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.
Thank you for checking out Nabla!
1 maintainer
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