Doc theme (#5)

* change docs theme + links + logo

* move mlx intro to landing page

docs/README.md

@@ -7,7 +7,7 @@ for example with `conda`:
 
 ```
 conda install sphinx
-pip install sphinx-rtd-theme
+pip install sphinx-book-theme
 ```
 
 ### Build

docs/src/conf.py

@@ -39,7 +39,17 @@ pygments_style = "sphinx"
 
 # -- Options for HTML output -------------------------------------------------
 
-html_theme = "sphinx_rtd_theme"
+html_theme = "sphinx_book_theme"
+
+html_theme_options = {
+    "show_toc_level": 2,
+    "repository_url": "https://github.com/ml-explore/mlx",
+    "use_repository_button": True,
+    "navigation_with_keys": False,
+}
+
+html_logo = "_static/mlx_logo.png"
+
 
 # -- Options for HTMLHelp output ---------------------------------------------
 

docs/src/index.rst

@@ -1,6 +1,30 @@
 MLX
 ===
 
+MLX is a NumPy-like array framework designed for efficient and flexible
+machine learning on Apple silicon.
+
+The Python API closely follows NumPy with a few exceptions. MLX also has a
+fully featured C++ API which closely follows the Python API.
+
+The main differences between MLX and NumPy are:
+
+ - **Composable function transformations**: MLX has composable function
+   transformations for automatic differentiation, automatic vectorization,
+   and computation graph optimization.
+ - **Lazy computation**: Computations in MLX are lazy. Arrays are only
+   materialized when needed.
+ - **Multi-device**: Operations can run on any of the supported devices (CPU,
+   GPU, ...)
+
+The design of MLX is strongly 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
+memory. Operations on MLX arrays can be performed on any of the supported
+device types without performing data copies. Currently supported device types
+are the CPU and GPU.
+
 .. toctree::
    :caption: Install
    :maxdepth: 1

docs/src/quick_start.rst

@@ -1,28 +1,6 @@
 Quick Start Guide
 =================
 
-MLX is a NumPy-like array framework designed for efficient and flexible
-machine learning on Apple silicon. The Python API closely follows NumPy with
-a few exceptions. MLX also has a fully featured C++ API which closely follows
-the Python API.
-
-The main differences between MLX and NumPy are: 
-
- - **Composable function transformations**: MLX has composable function
-   transformations for automatic differentiation, automatic vectorization,
-   and computation graph optimization.
- - **Lazy computation**: Computations in MLX are lazy. Arrays are only
-   materialized when needed.
- - **Multi-device**: Operations can run on any of the supported devices (CPU,
-   GPU, ...) 
-
-The design of MLX is strongly 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
-memory. Operations on MLX arrays can be performed on any of the supported
-device types without performing data copies. Currently supported device types
-are the CPU and GPU.
 
 Basics
 ------