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You gotta love what Aappleโs 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 ๐ฅ simple to use.
- Open-source friendly (who would have thought lol).
- Dynamic graph construction๐๐ผ
- Supports both CPU and GPU๐ค
- Beginner Friendly ๐๐ผ
- Great examples (clean code๐ฏ)
- Good documentation
Well done Awni Hannun et al ๐๐ผ
Could this be The Transformer of ml frameworks? Well at least for us mac users ๐
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 ๐ฅ simple to use.
- Open-source friendly (who would have thought lol).
- Dynamic graph construction๐๐ผ
- Supports both CPU and GPU๐ค
- Beginner Friendly ๐๐ผ
- Great examples (clean code๐ฏ)
- Good documentation
Well done Awni Hannun et al ๐๐ผ
Could this be The Transformer of ml frameworks? Well at least for us mac users ๐
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
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