Apple launches MLX deep learning framework: optimized for Apple Silicon, which can achieve "combination of software and hardware"

Thanks to CTOnews.com netizen DGGKMY for the clue delivery! CTOnews.com, December 6 (Xinhua)-- Apple today unveiled a MLX deep learning framework optimized for Apple Silicon chips, which claims to simplify the process of designing and deploying models for researchers on Mac, iPad and iPhone platforms.

CTOnews.com learned that Apple has released the project in GitHub. Interested friends can click here to visit. The specific framework features are as follows:

Familiar API:Python and C++ API have familiar frameworks such as NumPy and PyTorch, making it easy for experienced researchers to learn.

Easy and efficient: MLX uses combinable functional transformations to optimize Apple Silicon performance.

Delayed calculation: can prevent unnecessary calculation and improve resource efficiency.

Dynamic design: it can adapt to the change of input shape and simplify the debugging and testing process.

Combination of software and hardware: MLX seamlessly uses the CPU and GPU of Apple devices to ensure that users can make full use of the hardware.

Unified memory advantage: MLX uses Apple's unified memory to further enhance data movement speed

Researcher-friendly: MLX is designed for researchers.