Inspired by heavyweights like PyTorch and Jax, MLX takes it one step further. It promises efficient and flexible machine learning directly on Apple processors with a framework similar to the NumPy array. What’s the purpose? MLX offers a unified memory model by placing arrays in shared memory, allowing operations to be performed on any supported device without the need to copy data.
But here’s the kicker: Apple doesn’t just offer this to developers, it provides them with a universal machine learning environment. The MLX API for Python follows NumPy in familiarity, and a robust C++ API rounds out the toolset. Apple’s goal? We are democratizing machine learning, making MLX “the gift of researchers to researchers.”
Source: Ferra
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