MLX is an array framework designed for machine learning on Apple silicon, targeting ML researchers and developers. It offers a user-friendly yet efficient platform for training and deploying models, simplifying the exploration of new ideas with a conceptually simple and extensible design.

How It Works

MLX leverages a unified memory model, allowing arrays to reside in shared memory accessible by both CPU and GPU without explicit data transfers. Computations are lazy and use a dynamically constructed graph, enabling efficient execution and straightforward debugging, even with changing input shapes. Its design is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire, incorporating composable function transformations for automatic differentiation, vectorization, and graph optimization.

Quick Start & Requirements

• Installation: pip install mlx or conda install -c conda-forge mlx.
• Prerequisites: Apple silicon hardware.
• Documentation: MLX Quickstart, Installation, Documentation, Examples.

Highlighted Details

• Familiar Python API mirroring NumPy, with C++, C, and Swift APIs.
• Higher-level packages (mlx.nn, mlx.optimizers) mimic PyTorch APIs.
• Supports multi-device operations (CPU and GPU) via unified memory.
• Examples include Transformer training, LLaMA fine-tuning, Stable Diffusion, and Whisper.

Maintenance & Community

MLX is developed by Apple machine learning research. Contributions are welcomed.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README text.

Limitations & Caveats

The framework is primarily designed for Apple silicon, limiting its use on other hardware architectures. The BibTex entry lists the version as 0.0, suggesting it may be in early development stages.