The only mentioned alternative so far is `ggml`, which I have heard mentions of but am not familiar with. What are other alternatives & their corresponding pros and cons? Now, I am familiar with:
- Tensorflow (complex API right?)
- Keras (simple API?)
I think Keras more or less died when the TF 2.0 transition was botched so badly. Many moved to Torch and never looked back.
FWIW I actually prefer JAX but my work is atypical compared to the usual "predict churn" or whatever application.
It seems like this rewrite cleans up the meandering mess of Keras's middle period, or at least I'd be willing to give it a try.
Edit: Seems like `run_eagerly` solves a lot of the pain of debugging Keras code.
Mojo is another ML specific python dialect.
I think most people including me associate Keras with being very simple and high level, which isn't really correct now based on the article.
ggml is somewhat specialized, I was giving it as an example of the kind of framework I think we'll see more of, that is specialized, dependency free, written in a compiled language and directly optimized for speed. As use cases consolidate around specific architecture like LLMs, a faster and simpler low lever framework becomes preferable to a swiss army knife with abstraction upon abstraction like pytorch.