MLX

Quickstart | Installation |
Documentation |
Examples

MLX is an array framework for machine learning on Apple silicon,
brought to you by Apple machine learning research.

Some key features of MLX include:

Familiar APIs: MLX has a Python API that closely follows NumPy. MLX
also has fully featured C++, C, and
Swift APIs, which closely mirror
the Python API. MLX has higher-level packages like mlx.nn and
mlx.optimizers with APIs that closely follow PyTorch to simplify building
more complex models.
Composable function transformations: MLX supports 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.
Dynamic graph construction: Computation graphs in MLX are constructed
dynamically. Changing the shapes of function arguments does not trigger
slow compilations, and debugging is simple and intuitive.
Multi-device: Operations can run on any of the supported devices
(currently the CPU and the GPU).
Unified memory: A notable difference from MLX and other frameworks
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 transferring data.

MLX is designed by machine learning researchers for machine learning
researchers. The framework is intended to be user-friendly, but still efficient
to train and deploy models. The design of the framework itself is also
conceptually simple. We intend to make it easy for researchers to extend and
improve MLX with the goal of quickly exploring new ideas.

The design of MLX is inspired by frameworks like
NumPy,
PyTorch, Jax, and
ArrayFire.

Examples

The MLX examples repo has a
variety of examples, including:

Quickstart

See the quick start
guide
in the documentation.

Installation

MLX is available on PyPI. To install the Python API, run:

With pip:

pip install mlx

With conda:

conda install -c conda-forge mlx

Checkout the
documentation
for more information on building the C++ and Python APIs from source.

Contributing

Check out the contribution guidelines for more information
on contributing to MLX. See the
docs for more
information on building from source, and running tests.

We are grateful for all of our
contributors. If you contribute
to MLX and wish to be acknowledged, please add your name to the list in your
pull request.

Citing MLX

The MLX software suite was initially developed with equal contribution by Awni
Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
MLX useful in your research and wish to cite it, please use the following
BibTex entry:

@software{mlx2023,
  author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
  title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
  url = {https://github.com/ml-explore},
  version = {0.0},
  year = {2023},
}

Dependency	Optional
nanobind	true
numpy	true
pre-commit	true
setuptools	true
torch	true
typing_extensions	true

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mlx

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