I want to learn something new and challenge myself. I'm completely new to Rust and want to learn it as a second side project next to maintaining adapters.
First I will complete a couple of tutorials to get comfortable with writing code in Rust. The longterm goal will be to build an inference server for LLM deployment from scratch. As I spend more time with adapters, I am becoming more and more interested in the full engineering cycle of building and also SERVING machine learning models.
Therefore I think that by learning Rust I can start building up another view on the problem machine learning from a systems programming and performance optimization standpoint, which hopefully will complement my Python/PyTorch standpoint.
This repository will document my journey from Rust beginner to building a complete LLM inference server.
If you should stumble upon this repo and have constructive feedback on anything I am more than happy to include it! So I encourage you to open a new issue and explain to me what I am doing wrong! :D
For now I use this repo as a Rust workspace (as explained here). The Rust By Example guides start with rbe_xx; chapters of the book are marked by b_xx.