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update install docs and requirements (#2419)

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21
README.md
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@ -11,10 +11,10 @@ brought to you by Apple machine learning research.
Some key features of MLX include: Some key features of MLX include:
- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX - **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
[Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
the Python API. MLX has higher-level packages like `mlx.nn` and the Python API. MLX has higher-level packages like `mlx.nn` and
`mlx.optimizers` with APIs that closely follow PyTorch to simplify building `mlx.optimizers` with APIs that closely follow PyTorch to simplify building
more complex models. more complex models.
@ -68,18 +68,23 @@ in the documentation.
## Installation ## Installation
MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run: MLX is available on [PyPI](https://pypi.org/project/mlx/). To install MLX on
macOS, run:
**With `pip`**: ```bash
```
pip install mlx pip install mlx
``` ```
**With `conda`**: To install the CUDA backend on Linux, run:
```bash
pip install "mlx[cuda]"
``` ```
conda install -c conda-forge mlx
To install a CPU-only Linux package, run:
```bash
pip install "mlx[cpu]"
``` ```
Checkout the Checkout the

22
docs/src/install.rst
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@ -13,7 +13,7 @@ silicon computer is
pip install mlx pip install mlx
To install from PyPI you must meet the following requirements: To install from PyPI your system must meet the following requirements:
- Using an M series chip (Apple silicon) - Using an M series chip (Apple silicon)
- Using a native Python >= 3.9 - Using a native Python >= 3.9
@ -26,13 +26,22 @@ To install from PyPI you must meet the following requirements:
CUDA CUDA
^^^^ ^^^^
MLX has a CUDA backend which you can use on any Linux platform with CUDA 12 MLX has a CUDA backend which you can install with:
and SM 7.0 (Volta) and up. To install MLX with CUDA support, run:
.. code-block:: shell .. code-block:: shell
pip install "mlx[cuda]" pip install "mlx[cuda]"
To install the CUDA package from PyPi your system must meet the following
requirements:
- Nvidia architecture >= SM 7.0 (Volta)
- Nvidia driver >= 550.54.14
- CUDA toolkit >= 12.0
- Linux distribution with glibc >= 2.35
- Python >= 3.9
CPU-only (Linux) CPU-only (Linux)
^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^
@ -42,6 +51,13 @@ For a CPU-only version of MLX that runs on Linux use:
pip install "mlx[cpu]" pip install "mlx[cpu]"
To install the CPU-only package from PyPi your system must meet the following
requirements:
- Linux distribution with glibc >= 2.35
- Python >= 3.9
Troubleshooting Troubleshooting
^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^

1
python/scripts/repair_linux.sh
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@ -2,6 +2,7 @@
auditwheel repair dist/* \ auditwheel repair dist/* \
--plat manylinux_2_35_x86_64 \ --plat manylinux_2_35_x86_64 \
--only-plat \
--exclude libmlx* \ --exclude libmlx* \
-w wheel_tmp -w wheel_tmp