Summary
MLX is a NumPy-like array framework for machine learning on Apple silicon, with features such as composable function transformations, lazy computation, and a unified memory model.
Abstract
MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon, with a Python API that closely follows NumPy. It has composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization, and supports lazy computation and multi-device operations. The design of MLX is inspired by frameworks like PyTorch, Jax, and ArrayFire, with a notable difference being the unified memory model where arrays live in shared memory and can be operated on without data copies. The documentation covers installation, usage, and reference for both Python and C++ APIs.
Bullet Points
- •NumPy-like array framework for machine learning on Apple silicon
- •Composable function transformations for automatic differentiation, vectorization, and optimization
- •Lazy computation, only materializing arrays when needed
- •Multi-device operations on CPU and GPU
- •Unified memory model for arrays, allowing operations without data copies
- •Features inspired by PyTorch, Jax, and ArrayFire
- •Coverage of installation, usage, and reference for Python and C++ APIs
- •Additional resources for developers and Metal Debugger.