As @haolu7 mentioned, you could take a pre-trained model and use AITemplate to do model inference. All you need to do is to re-write the model using AITemplate frontend and map PyTorch params to AITemplate params. Besides, AITemplate has a limited operator coverage compared to mature frameworks like PyTorch so you may need to implement your own kernels if necessary (though it already supports Bert, VIT, StableDiffusion, ResNet, Detectron, and general recommendation models).
AITemplate delivers much better perf (1.9x ~ 12.8x) compared to PyTorch eager on SOTA models, including Bert, ResNet, VIT and StableDiffusion.
AITemplate also delivers high perf numbers using AMD GPUs (MI-250). With AITemplate, MI-250 achieves 80% ~ 96% A100 perf on various ResNet / Bert / VIT models.
AITemplate uses sophisticated fusion techniques to optimize perf, including vertical, horizontal, and memory fusions.
btw, I'm one of the authors of AITemplate, happy to answer any questions.
1. It contains more columns than only "FrontPage", such as "Jobs" and "Comments";
2. The UI seems more concise. Although there are not indent between comments, the text is much clearer.
However, neither of these apps can be used to add comments. So I can only come back to my computer and add this comment on the web page.