micrograd VS tracing

I recently decided to turbo-teach myself basic cuda with a proper project. I really enjoyed Karpathy’s micrograd (https://github.com/karpathy/micrograd), so I extended it with cuda kernels and 2D tensor logic. It’s a bit longer than the original project, but it’s still very readable for anyone wanting to quickly learn about gpu acceleration in practice.

FOr inference, less than 1KLOC of pure, dependency-free C is enough (if you include the tokenizer and command line parsing)[1]. This was a non-obvious fact for me, in principle, you could run a modern LLM 20 years ago with just 1000 lines of code, assuming you're fine with things potentially taking days to run of course.

Training wouldn't be that much harder, Micrograd[2] is 200LOC of pure Python, 1000 lines would probably be enough for training an (extremely slow) LLM. By "extremely slow", I mean that a training run that normally takes hours could probably take dozens of years, but the results would, in principle, be the same.

If you were writing in C instead of Python and used something like Llama CPP's optimization tricks, you could probably get somewhat acceptable training performance in 2 or 3 KLOC. You'd still be off by one or two orders of magnitude when compared to a GPU cluster, but a lot better than naive, loopy Python.

[1] https://github.com/karpathy/llama2.c

[2] https://github.com/karpathy/micrograd

Perhaps they were thinking of https://github.com/karpathy/micrograd

I was (for fun) trying to recreate micrograd in Haskell. The ideia is simple:

Check out this repo I found a few years back when I was looking into understanding pytorch better. It's basically a super tiny autodiff library that only works on scalars. The whole repo is under 200 lines of code, so you can pull up pycharm or whatever and step through the code and see how it all comes together. Or... you know. Just read it, it's not super complicated.

I'm doing an ML apprenticeship [1] these weeks and Karpathy's videos are part of it. We've been deep down into them. I found them excellent. All concepts he illustrates are crystal clear in his mind (even though they are complicated concepts themselves) and that shows in his explanations.

Also, the way he builds up everything is magnificent. Starting from basic python classes, to derivatives and gradient descent, to micrograd [2] and then from a bigram counting model [3] to makemore [4] and nanoGPT [5]

[1]: https://www.foundersandcoders.com/ml

[2]: https://github.com/karpathy/micrograd

[3]: https://github.com/karpathy/randomfun/blob/master/lectures/m...

[4]: https://github.com/karpathy/makemore

[5]: https://github.com/karpathy/nanoGPT

Just published my first crate, rustygrad, a Rust implementation of Andrej Karpathy's micrograd!

I've been trying to reimplement Karpathy's micrograd library in rust as a fun side project.

The video description is as follows:

In this stream, we peel back the crust on the tracing crate — https://github.com/tokio-rs/tracing/ — and explore its interface, structure, and mechanisms. We talk about spans, events, their attributes and fields, and how to think about them in async code. We also dig into what subscribers are, how they pick up events, and how you can construct your own subscribers through the layer abstraction. For more details about tracing, see https://docs.rs/tracing/latest/tracing/.

> What's been your biggest issues around ergonomics/amenities for OpenTelemetry?

I can't speak generally, but in the Rust ecosystem the various crates don't play well together. Here's one example: <https://github.com/tokio-rs/tracing/issues/2648> There are four crates involved (tracing-attributes, tracing-opentelemetry, opentelemetry, and opentelemetry-datadog) and none of them fit properly into any of the others.

The tracing (logging) mechanism in an asynchronous codebase (tracing).

Tracing is Tokio's alternative for async code.

At a technical level, in Rust, both [tracing]https://crates.io/crates/tracing) and log are entire ecosystems (though for the latter at least there's also third party logging frameworks), and there's at least a bridge from log to tracing.

I am using https://github.com/tokio-rs/tracing for logging purposes in my application. I would like to develop a feature wherein logs should be saved to a database table (via sea-orm). Something similar is this, but it does not solve my needs fully.

I've used the tracing infrastructure with tracing_flame to profile some hot paths in async code: https://github.com/tokio-rs/tracing/tree/master/tracing-flame

Oh nice! IIRC when I checked, it was the Unicode tables that smashed the code size. I recently hit the same issue with the tracing crate, where a crate feature (for env var filtering) pulled in regex and my binary was suddenly 1MB bigger.

I know about tools such as tracing, jaeger or tracy. While having a complete tracing could be a potential solution, these tools don't work with no_std.

tracing by itself only outputs log data, you need to consume them in a subscriber, the tracing-subscriber crate exists for this. (example)