burn

people to play. I support real time games with a short time to play and longer games that take place over days. If you're interested in AI help with that would also be appreciated. I'm currently thinking I will not work on it more until [burn](https://github.com/tracel-ai/burn) gets support for reinforcement learning. Any suggestions or comments are welcome, thanks!

ess than a week. I also think they are trying to solve for getting a practitioner to just get up and going right away, whereas I was trying to build a modular configuration on top of the crate instead of a one-and-done type script. But there were countless generic types, several traits to define and implement in order to make some generic parameter fit those bounds, and the crate has more proc_macro derives than I'd like (my target number is 0) such as `#[derive(Module, Config, new)]` because they obfuscate the code that I actually have to write and don't teach me anything. TL;DR the crate felt super powerful but also very foreign. It didn't quite click to the point where I thought it was intuitive or I felt very fluent with it. But then again, I spent like 5 days with it. One other minor annoying thing was that I couldn't download exactly what I wanted out of HuggingFace directly. I ended up having to use `HuggingfaceDatasetLoader::new("carlosejimenez/wikitext__wikitext-2-raw-v1")` instead of `HuggingfaceDatasetLoader::new("Salesforce/wikitext")` because the latter would get an auth error, but this may also be my ignorance about how HF is supposed to work... Eventually, I got the whole thing to work quite neatly and was able to tweak hyperparameters and get my model to increasingly better perplexity. With more tweaks, a better tokenizer, possibly better data source, and an NVIDIA GPU rather than Apple Silicon, I could have squeezed even more out of it. My original goal was to try to slap an iced GUI on the project so that I could tweak the hyperparameters there, compare models, plot the training and inference, etc. with a GUI instead of code. Sort of a no-code approach to training models. I think it's an area worth exploring more, but I have a main quest I need to finish first so I just wrote down my findings in an unpublished "paper" and tabled it for now. ________ [0]: https://github.com/tracel-ai/burn [1]: https://github.com/iced-rs/iced

tegration with autotune. Additionally, kernels created on-the-fly can now be executed in-place for reduced memory usage. We now support running multiple optimization streams independantly, which helps when metric updates and training run on the same device, but different threads. This feature isn't enabled by default yet, but you can enable it with a backend decorator. Future releases will add more optimizations to the compiler, and we will probably ship it by default. We also have plans to add other compilation targets in addition to WebGPU, namely Vulkan and CUDA. One of the major quality of life improvements is the addition of the new PyTorch recorder that allows loading PyTorch weights into Burn modules. We also support specifying regex to dynamically map the weights to your Burn model if the structure isn't the same as the PyTorch implementation. With this new release, we spent a lot of time solidifying our infrastructure, testing our framework on additional OS (Windows and MacOS). Overall, our CI is more mature and allows us to more easily ensure the quality and correctness of every code change across backends and operating systems. Release Notes: https://github.com/tracel-ai/burn/releases/tag/v0.12.0 Burn Book: https://burn.dev/book/