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