Apple issued another statement: You can now use Apple device @exolabs_to run your personal AI cluster
Use MLX to connect your iPhone, iPad, and Mac locally to form a large GPU! Nvidia makes super GPUs in the data center. Apple says I have many Edge Devices. It’s okay to use this cheap way to serialize GPUs.
MLX is an array framework for machine learning research on Apple chips, provided to you by the Apple Machine Learning Research Team.
Some of the main features of MLX include:
Familiar APIs: MLX has a Python API that closely follows NumPy. MLX also has full-featured C++, C and Swift APIs that are very similar to the Python APIs. MLX has higher-level packages such as mlx.nn and mlx.optimizers, and its API closely follows PyTorch to simplify building more complex models.
Composable function conversions: MLX supports composable function conversions for automatic differentiation, automatic vectorization, and calculation graph optimization.
Lazy calculations: Calculations in MLX are lazy. Arrays are concretized only when needed.
Dynamic graph construction: Calculation graphs in MLX are dynamically constructed. Changing the shape of function parameters does not trigger slow compilation, and debugging is simple and intuitive.
Multi-device: Operations can run on any supported device (currently CPU and GPU).
Unified memory: A significant difference from MLX and other frameworks is the unified memory model. Arrays in MLX are located in shared memory. Operations on MLX arrays can be performed on any supported device type without transferring data.
MLX was designed by machine learning researchers for machine learning researchers. The framework is designed to be user friendly, but still allows for effective training and deployment of models. The design of the framework itself is also conceptually simple. We intend to make it easy for researchers to extend and improve MLX with the goal of quickly exploring new ideas.
MLX is inspired by frameworks such as NumPy, PyTorch, Jax and ArrayFire.
If you want to learn more, you can click on the link below the video.
Thank you for watching this video. If you like it, please subscribe and like it. thank
Project address:http://github.com/ml-explore/mlx
Oil tubing: