batched-fn VS tch-rs

Some research: - Found a crate for exactly what I want, called batched_fn which seems to do exactly what I want with a catch that I cannot run async tasks (download, preprocess, etc) within the batch handler, ie, it's specifically for inference. I've opened an issue about it. - What I plan to do, is : - The response handlers pass their id's to a batching mechanism, and have a reciver for the output channel(details below) - to have a batching mechanism that batches up image id's on high load. - Pass it to another thread that downloads, preprocesses it and infers form it - This thread passes it to the result channel that every response handler has a reciever for. Every response handler checks if the message that it's reciving is for itself, and accordingly returns a JSON API response

I wanted to do something like this but then I would miss on proper CUDA acceleration and lose performance compared to using torchlib.

I wrote a forgettable llama implementation for https://github.com/LaurentMazare/tch-rs (pytorch's torchlib rust binding).

I first encountered OCaml's PyTorch bindings because apparently they generate a C wrapper around PyTorch's C++ API, and Rust's PyTorch bindings use OCaml's C wrapper. See: https://github.com/LaurentMazare/tch-rs

You could try looking at the min-GPT example of tch-rs. I'd also strongly suggest watching Karpathy's video to understand what's going on.

If you pefer to see it in code there's a succint gpt implementation here https://github.com/LaurentMazare/tch-rs/blob/main/examples/m...

Rust is fully compatible w/ C bindings, so even Python libraries written in C can be easily set up to work in Rust (and have been). For example, see PyTorch Rust bindings, which actually works faster than in Python because all of the glue code around the C++ API is in Rust instead of Python.

Pytorch

I have looked into this a bit and found some crates which help in loading torchscript models called tch-rs