![]() ![]() Lazy computation: Computations in MLX are lazy.Composable function transformations: MLX has composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization.MLX has higher-level packages like mlx.nn and mlx.optimizers with APIs that closely follow PyTorch to simplify building more complex models. MLX also has a fully featured C++ API, which closely mirrors the Python API. Familiar APIs: MLX has a Python API that closely follows NumPy.They also highlight some key MLX features: We intend to make it easy for researchers to extend and improve MLX with the goal of quickly exploring new ideas. The design of the framework itself is also conceptually simple. The framework is intended to be user-friendly, but still efficient to train and deploy models. MLX is designed by machine learning researchers for machine learning researchers. Here’s a description from the documentation: Request a FREE account today and discover how you can put your Apple fleet on auto-pilot at a price point that is hard to believe. Over 38,000 organizations leverage Mosyle solutions to automate the deployment, management, and security of millions of Apple devices daily. ![]() Mosyle is the only solution that fully integrates five different applications on a single Apple-only platform, allowing businesses and schools to easily and automatically deploy, manage, and protect all their Apple devices. This story is supported by Mosyle, the only Apple Unified Platform.
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