I know the typical answer is “no because all the libs are in python”… but I am kind of baffled why more porting isn’t going on especially to Go given how Go like Python is stupid easy to learn and yet much faster to run. Truly not trying to start a flame war or anything. I am just a bigger fan of Go than Python and was thinking coming in to 2024 especially with all the huge money in AI now, we’d see a LOT more movement in the much faster runtime of Go while largely as easy if not easier to write/maintain code with. Not sure about Rust… it may run a little faster than Go, but the language is much more difficult to learn/use but it has been growing in popularity so was curious if that is a potential option.
There are some Go libs I’ve found but the few I have seem to be 3, 4 or more years old. I was hoping there would be things like PyTorch and the likes converted to Go.
I was even curious with the power of the GPT4 or DeepSeek Coder or similar, how hard would it be to run conversions between python libraries to go and/or is anyone working on that or is it pretty impossible to do so?
I hear you… that’s what I am trying to understand. I guess going back to when AI dev started, maybe Go wasn’t around much (too early) and Rust as well. But I question why the AI blokes would choose a dynamic slow runtime language for things like training AI, which seems to be the massive bulk of the cpu/gpu work, over using much faster native binary languages like Go/Rust, or even C. But you said something, which maybe is what I am missing. Others have said this too. Python is more or less “glue code” to use underlying C (native) binary libaries. If that is the case, then I get it. I assumed the super crazy ass long training times and expensive GPUs needed was due in part that python is much slower runtime… and that using Go/Rust/C would reduce the large training times by quite a bit if it was used. But I am guessing from all the responses that the python code just pushes the bulk of the work on to the GPU using native binary libs… and thus the code done in python does not have to be super fust runtime. Thus, you pick up the “creative” side of python and benefit from using that in ways that might be harder to do in Go or Rust.
But some have replied they are using Rust for inference, data prep, etc… I’ll have to learn what Inference is… not sure what that part is, and nor do I fully understand what data prep entails. Is it just turning gobs of all sorts of data in various formats in to a specific structure (I gather from some reading a vector database) that the training part understands the structure of that database… so you’re basically gathering data (Scraping the web, reading CSV files, github, etc) and putting that in to a very specific sort of key/value (or similar) structure, that the training bit then uses to train with?