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> Relationship with CVNets
> CoreNet evolved from CVNets, to encompass a broader range of applications beyond computer vision. Its expansion facilitated the training of foundational models, including LLMs.
We can expect it to have grown from here: https://apple.github.io/ml-cvnets/index.html
It looks like a mid-level implementations of training and inference. You can see in their "default_trainer.py"[1] that the engine uses Tensors from torch but implements a it's own training method. It's an interesting (maybe very Apple) choice to build from the ground up instead of partnering with existing frameworks to provide first class support in them.
The MLX examples seem to be inference only at this point. It does look like this might be a landing ground for more MLX specific implementations: e.g. https://github.com/apple/corenet/blob/5b50eca42bc97f6146b812...
It will be interesting to see how it tracks over the next year; especially with their recent acquisitions:
Datakalab https://news.ycombinator.com/item?id=40114350
DarwinAI https://news.ycombinator.com/item?id=39709835
1: https://github.com/apple/corenet/blob/main/corenet/engine/de...
https://github.com/NVIDIA/Megatron-LM
This is probably a good baseline to start thinking about LLM training at scale.
It's interesting that Apple also has https://github.com/apple/axlearn, which is a library on top of Jax. Seems like half the ML teams at Apple use PyTorch, and the other half uses Jax.