Post
You gotta love what Aapple’s mlx team cooked:
- A unified memory model that literally does compute-magic: parallel operations with automatic dependency insertions.
- Supports off-the-shelf use of all the fun stuff in composable func transformations (differentiation, vectorization, computation graph optimization).
- Houses simplified forms of all the APIs we love and in the languages we adore (python, C++, C) sorry Swift :)
- mlx.nn is a stallion 🔥 simple to use.
- Open-source friendly (who would have thought lol).
- Dynamic graph construction👍🏼
- Supports both CPU and GPU🤖
- Beginner Friendly 👌🏼
- Great examples (clean code💯)
- Good documentation
Well done Awni Hannun et al 👏🏼
Could this be The Transformer of ml frameworks? Well at least for us mac users 😂
Repo: https://github.com/ml-explore/mlx
Examples: https://github.com/ml-explore/mlx-examples
Documentation: https://ml-explore.github.io/mlx/build/html/python/nn.html
- A unified memory model that literally does compute-magic: parallel operations with automatic dependency insertions.
- Supports off-the-shelf use of all the fun stuff in composable func transformations (differentiation, vectorization, computation graph optimization).
- Houses simplified forms of all the APIs we love and in the languages we adore (python, C++, C) sorry Swift :)
- mlx.nn is a stallion 🔥 simple to use.
- Open-source friendly (who would have thought lol).
- Dynamic graph construction👍🏼
- Supports both CPU and GPU🤖
- Beginner Friendly 👌🏼
- Great examples (clean code💯)
- Good documentation
Well done Awni Hannun et al 👏🏼
Could this be The Transformer of ml frameworks? Well at least for us mac users 😂
Repo: https://github.com/ml-explore/mlx
Examples: https://github.com/ml-explore/mlx-examples
Documentation: https://ml-explore.github.io/mlx/build/html/python/nn.html
