Maybe the act of compilation is an extra step, but I'd much rather have my development be in a high level language that is very suited to experimentation, probing, and testing, and then compile the final result down to something performant.
EDIT: I don't know much about the IOT world, and Tensorflow is likely a bad example as it's not designed to run on edge. So, I could understand that things like llama.cpp, GGML and GGUF are making strides towards easier runtimes. But I still think for dev-time, Python makes sense!
No idea what they're using to run it though. But there's no way I'll stop using Python for working with ML code lol. It just makes life easy.
Sure, and it's only a simple 20 step process that involves building Tensorflow from source. Yeay!
https://medium.com/@hamedmp/exporting-trained-tensorflow-mod...
Let me see what the process for compiling a LLM written in Rust is....
https://github.com/rustformers/llm
cargo install llm-cli
I dunno, maybe we're talking about different things. I'm saying it's better to do model development in a high level language and then export the training or runtime to a lower level framework, multiple of which exist and have existed. It's becoming simpler to use low-level runtimes (llama.cpp vs Tensorflow). Is that the point you're making?