MLX – Apple’s open source machine learning framework designed for Apple Silicon chips

MLX is an array framework for machine learning launched by Apple’s machine learning research team. The open source framework is designed and optimized for Apple Silicon chips. It draws inspiration from frameworks such as NumPy, PyTorch, Jax, and ArrayFire, providing simple and friendly ways to use it to help developers effectively develop, train and deploy models on Apple’s M-series chips.

The main functions of MLX

• Familiar API: MLX has a Python API that follows NumPy. MLX also has a fully-featured C++ API, which is very similar to the Python API.
• Composable Function Transformation: MLX supports composable function transformations for automatic differentiation, automatic vectorization, and computational graph optimization.
• Lazy calculation: The calculations in MLX are lazy calculations, and the arrays are only concreted when needed.
• Dynamic graph construction: Computational graphs in MLX are dynamically constructed. Changing the shape of function parameters does not trigger slow compilation, and debugging is simple and intuitive.
• Multi-device: Can run on any supported device (CPU and GPU).
• Unified Memory: The main difference between MLX and other frameworks is the unified memory model, array shared memory. Operations on MLX can run on any supported device type without moving data.