So you rely on this, or ONNX Java API?

Probably we will use kinference if it became more mature and support more ops or will contribute to this project, because currently it's focused on subset of NLP models

At kinference they execute everything on the JVM, or I am missing something?

they plan to get into KMP and support GPU on native and JS

currently on JVM without GPU

I'd like yet another library cubool or something like this for boolean algebra on gpu

Interesting solution from the architecture point of view

Unfortunately as of now PyTorch does not support officially importing ONNX modules but some solutions exists. I will support execution of TorchScript modules in kotlin (including training).

@zaleslaw lucky you are to be on holidays 😄 I do now have a prototype for training TorchScript models from kotlin: https://github.com/mipt-npm/kmath/tree/feature/noa/kmath-noa I will add more optimisers (beyond Adam) very soon as well as algorithms for bayesian deep learning as: https://github.com/grinisrit/noa/blob/master/docs/ghmc/bayesian_deep_learning.ipynb

Is it related to the PyTorch wrapper in kmath (for tensor ops) or it is a separate project?

I just believe that people should do as much as possible in python for the model building. But then they should be able to train their python cooked models in Kotlin easily without extra effort including the most experimental ones (and not only perform inference - this is important in reinforcement learning, on-line learning etc., which gets everywhere now). At the same time it's really hard to chase all the architectures in the API (like new layers, various training patterns, different loss functions etc.). Basically, the user shall be able to write his own extensions with raw tensors (including in C++/CUDA) for the python API and being able to use in Kotlin straightaway - that's already possible with the current state of the prototype.

The only place where I will need to keep up with pytorch are the optimisers - but that does not evolve very fast (there are only 7 I think). On top of that I write also my own (in bayesian optimisation). But we need to think more about the API for those with @altavir and other kmath contributors.