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122 lines
4.5 KiB
Markdown
122 lines
4.5 KiB
Markdown
# MLX
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[**Quickstart**](#quickstart) | [**Installation**](#installation) |
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[**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |
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[**Examples**](#examples)
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[](https://circleci.com/gh/ml-explore/mlx)
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MLX is an array framework for machine learning on Apple silicon,
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brought to you by Apple machine learning research.
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Some key features of MLX include:
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- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
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also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
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[Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
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the Python API. MLX has higher-level packages like `mlx.nn` and
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`mlx.optimizers` with APIs that closely follow PyTorch to simplify building
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more complex models.
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- **Composable function transformations**: MLX supports composable function
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transformations for automatic differentiation, automatic vectorization,
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and computation graph optimization.
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- **Lazy computation**: Computations in MLX are lazy. Arrays are only
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materialized when needed.
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- **Dynamic graph construction**: Computation graphs in MLX are constructed
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dynamically. Changing the shapes of function arguments does not trigger
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slow compilations, and debugging is simple and intuitive.
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- **Multi-device**: Operations can run on any of the supported devices
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(currently the CPU and the GPU).
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- **Unified memory**: A notable difference from MLX and other frameworks
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is the *unified memory model*. Arrays in MLX live in shared memory.
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Operations on MLX arrays can be performed on any of the supported
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device types without transferring data.
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MLX is designed by machine learning researchers for machine learning
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researchers. The framework is intended to be user-friendly, but still efficient
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to train and deploy models. The design of the framework itself is also
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conceptually simple. We intend to make it easy for researchers to extend and
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improve MLX with the goal of quickly exploring new ideas.
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The design of MLX is inspired by frameworks like
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[NumPy](https://numpy.org/doc/stable/index.html),
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[PyTorch](https://pytorch.org/), [Jax](https://github.com/google/jax), and
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[ArrayFire](https://arrayfire.org/).
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## Examples
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The [MLX examples repo](https://github.com/ml-explore/mlx-examples) has a
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variety of examples, including:
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- [Transformer language model](https://github.com/ml-explore/mlx-examples/tree/main/transformer_lm) training.
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- Large-scale text generation with
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[LLaMA](https://github.com/ml-explore/mlx-examples/tree/main/llms/llama) and
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finetuning with [LoRA](https://github.com/ml-explore/mlx-examples/tree/main/lora).
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- Generating images with [Stable Diffusion](https://github.com/ml-explore/mlx-examples/tree/main/stable_diffusion).
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- Speech recognition with [OpenAI's Whisper](https://github.com/ml-explore/mlx-examples/tree/main/whisper).
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## Quickstart
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See the [quick start
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guide](https://ml-explore.github.io/mlx/build/html/usage/quick_start.html)
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in the documentation.
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## Installation
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MLX is available on [PyPI](https://pypi.org/project/mlx/). To install MLX on
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macOS, run:
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```bash
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pip install mlx
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```
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To install the CUDA backend on Linux, run:
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```bash
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pip install mlx[cuda]
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```
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To install a CPU-only Linux package, run:
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```bash
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pip install mlx[cpu]
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```
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Checkout the
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[documentation](https://ml-explore.github.io/mlx/build/html/install.html#)
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for more information on building the C++ and Python APIs from source.
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## Contributing
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Check out the [contribution guidelines](https://github.com/ml-explore/mlx/tree/main/CONTRIBUTING.md) for more information
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on contributing to MLX. See the
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[docs](https://ml-explore.github.io/mlx/build/html/install.html) for more
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information on building from source, and running tests.
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We are grateful for all of [our
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contributors](https://github.com/ml-explore/mlx/tree/main/ACKNOWLEDGMENTS.md#Individual-Contributors). If you contribute
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to MLX and wish to be acknowledged, please add your name to the list in your
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pull request.
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## Citing MLX
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The MLX software suite was initially developed with equal contribution by Awni
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Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
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MLX useful in your research and wish to cite it, please use the following
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BibTex entry:
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```text
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@software{mlx2023,
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author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
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title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
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url = {https://github.com/ml-explore},
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version = {0.0},
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year = {2023},
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}
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```
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