Apple publishes MLX – an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research.
Some key features of MLX include:
- Familiar APIs: MLX has a Python API that closely follows NumPy. MLX also has a fully featured C++ API, which closely mirrors the Python API. MLX has higher-level packages like
mlx.optimizerswith APIs that closely follow PyTorch to simplify building more complex models.
- Composable function transformations: MLX has composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization.
- Lazy computation: Computations in MLX are lazy. Arrays are only materialized when needed.
- Dynamic graph construction: Computation graphs in MLX are built dynamically. Changing the shapes of function arguments does not trigger slow compilations, and debugging is simple and intuitive.
- Multi-device: Operations can run on any of the supported devices (currently, the CPU and GPU).
- Unified memory: A notable difference from MLX and other frameworks is the unified memory model. Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without moving data.
Framework is available under MIT license and comes with examples on various machine learning tasks.