개요
MLX는 Apple 기계 학습 연구 팀이 개발한 배열 프레임워크로, Apple 칩 전용으로 설계되었으며 NumPy 및 PyTorch와 유사한 API를 제공합니다. 구성 가능한 함수 변환, 지연 계산, 동적 그래프 구축 및 CPU와 GPU의 통합 메모리를 지원합니다. 기계 학습 모델을 효율적으로 훈련하고 배포하는 것을 목표로 합니다.
README 미리보기
# MLX\n\n[**Quickstart**](#quickstart) | [**Installation**](#installation) |\n[**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |\n[**Examples**](#examples)\n\n[](https://circleci.com/gh/ml-explore/mlx)\n\nMLX is an array framework for machine learning on Apple silicon,\nbrought to you by Apple machine learning research.\n\nSome key features of MLX include:\n\n- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX\n also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and\n [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror\n the Python API. MLX has higher-level packages like `mlx.nn` and\n `mlx.optimizers` with APIs that closely follow PyTorch to simplify building\n more complex models.\n\n- **Composable function transformations**: MLX supports composable function\n transformations for automatic differentiation, automatic vectorization,\n and computation graph optimization.\n\n- **Lazy computation**: Computations in MLX are lazy. Arrays are only\n materialized when needed.\n\n- **Dynamic graph construction**: Computation graphs in MLX are constructed\n dynamically. Changing the shapes of function arguments does not trigger\n slow compilations, and debugging is simple and intuitive.\n\n- **Multi-device**: Operations can run on any of the supported devices\n (currently the CPU and the GPU).\n\n- **Unified memory**: A notable difference from MLX and other frameworks\n is the *unified memory model*. Arrays in MLX live in shared memory.\n Operations on MLX arrays can be performed on any of the supported\n device types without transferring data.\n\nMLX is designed by machine learning researchers for machine learning\nresearchers. The framework is intended to be user-friendly, but still efficient\nto train and deploy models. The design of the framework itself is also\nconceptually simple. We intend to make it easy for researchers to extend and\nimprove MLX with the