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Awni Hannun
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MLX
===
MLX is a NumPy-like array framework designed for efficient and flexible
machine learning on Apple silicon.
MLX is a NumPy-like array framework designed for efficient and flexible machine
learning on Apple silicon, brought to you by Apple machine learning research.
The Python API closely follows NumPy with a few exceptions. MLX also has a
fully featured C++ API which closely follows the Python API.
@@ -17,7 +17,7 @@ The main differences between MLX and NumPy are:
- **Multi-device**: Operations can run on any of the supported devices (CPU,
GPU, ...)
The design of MLX is strongly inspired by frameworks like `PyTorch
The design of MLX is inspired by frameworks like `PyTorch
<https://pytorch.org/>`_, `Jax <https://github.com/google/jax>`_, and
`ArrayFire <https://arrayfire.org/>`_. A noteable difference from these
frameworks and MLX is the *unified memory model*. Arrays in MLX live in shared