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

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<section id="mlx"> <section id="mlx">
<h1>MLX<a class="headerlink" href="#mlx" title="Permalink to this heading">#</a></h1> <h1>MLX<a class="headerlink" href="#mlx" title="Permalink to this heading">#</a></h1>
<p>MLX is a NumPy-like array framework designed for efficient and flexible <p>MLX is a NumPy-like array framework designed for efficient and flexible machine
machine learning on Apple silicon.</p> learning on Apple silicon, brought to you by Apple machine learning research.</p>
<p>The Python API closely follows NumPy with a few exceptions. MLX also has a <p>The Python API closely follows NumPy with a few exceptions. MLX also has a
fully featured C++ API which closely follows the Python API.</p> fully featured C++ API which closely follows the Python API.</p>
<p>The main differences between MLX and NumPy are:</p> <p>The main differences between MLX and NumPy are:</p>
@ -555,7 +555,7 @@ materialized when needed.</p></li>
GPU, …)</p></li> GPU, …)</p></li>
</ul> </ul>
</div></blockquote> </div></blockquote>
<p>The design of MLX is strongly inspired by frameworks like <a class="reference external" href="https://pytorch.org/">PyTorch</a>, <a class="reference external" href="https://github.com/google/jax">Jax</a>, and <p>The design of MLX is inspired by frameworks like <a class="reference external" href="https://pytorch.org/">PyTorch</a>, <a class="reference external" href="https://github.com/google/jax">Jax</a>, and
<a class="reference external" href="https://arrayfire.org/">ArrayFire</a>. A noteable difference from these <a class="reference external" href="https://arrayfire.org/">ArrayFire</a>. A noteable difference from these
frameworks and MLX is the <em>unified memory model</em>. Arrays in MLX live in shared frameworks and MLX is the <em>unified memory model</em>. Arrays in MLX live in shared
memory. Operations on MLX arrays can be performed on any of the supported memory. Operations on MLX arrays can be performed on any of the supported

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