zhangyiss/mlx
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx.git synced 2025-06-28 20:41:15 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

docs

Browse Source
This commit is contained in:
Awni Hannun 2023-12-05 14:18:20 -08:00 committed by CircleCI Docs
parent a60a600c6a
commit d3d0ad9564
3 changed files with 7 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
docs/build/html/_sources/index.rst vendored
View File

@ -1,8 +1,8 @@
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

6
docs/build/html/index.html vendored
View File

@ -539,8 +539,8 @@ document.write(`
<section id="mlx">
<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
machine learning on Apple silicon.</p>
<p>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.</p>
<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>
<p>The main differences between MLX and NumPy are:</p>
@ -555,7 +555,7 @@ materialized when needed.</p></li>
GPU, …)</p></li>
</ul>
</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
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

2
docs/build/html/searchindex.js vendored
View File

File diff suppressed because one or more lines are too long