MLX

Introduction

MLX is an array framework for machine learning optimized for Apple Silicon. It provides NumPy-like Python APIs and also offers C++, C and Swift bindings. MLX supports composable function transformations, lazy computation, dynamic graph construction, and multi-device execution.

Key features

• Familiar NumPy-style Python API with higher-level packages like mlx.nn and mlx.optimizers.
• Composable function transforms for autodiff, vectorization, and graph optimization.
• Lazy computation and a unified memory model to minimize device data transfers.

Use cases

• Research and prototyping on Apple Silicon (M-series) with efficient array operations.
• Multi-language projects requiring C++/Python/Swift interoperability.
• Applications that benefit from lazy execution and unified memory for performance.

Technical details

• Core implemented in C++ with Python package and extensive examples.
• Supports installation via PyPI and source builds; detailed docs on ReadTheDocs.
• Example repositories (mlx-examples) demonstrate transformer LM, Stable Diffusion, Whisper, and LoRA finetuning.