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’m studying AI on Rust with dfdx.
The cool side of it is that you can have a model where every tensor (shape dimensions and lengths) are checked at compile-time, and that includes layer parameters, allowing you to basically avoid runtime shape errors.
I’ve no clue what that means… I’ll take your word for it. :). Just started on this AI journey and so far trying to learn how to run a model locally, figure out how to maximize what little hardware I have, but interested in the whole shabang… how you gather data, what sort of data, what it looks like, how you format it (is that inference?) to then be ingested during the training step. What code is used for training… what does training do, how does training result in a model, and what running the model does… is the model a binary (code) that you just pass input and get output. So much to learn.