I just discovered

https://github.com/ShigekiKarita/grain

which seems like a very ambitious and active project for making dynamic neural networks run on the GPU using D in front of mir and CUDA.

Are there any long-term goals around this project except for the title?

It would great if someone (author) could write a little background-knowledge (tutorial) around the subject of dynamic neural networks that assists all the details in the examples at

https://github.com/ShigekiKarita/grain/tree/master/example

Further, could parts of grain be refactored out into some generic CUDA-library for use in domains other than dynamic neural networks?

On Tuesday, 12 June 2018 at 11:10:30 UTC, Per Nordlöw wrote:
> I just discovered
>
> https://github.com/ShigekiKarita/grain
>
> which seems like a very ambitious and active project for making dynamic neural networks run on the GPU using D in front of mir and CUDA.
>
> Are there any long-term goals around this project except for the title?
>
> It would great if someone (author) could write a little background-knowledge (tutorial) around the subject of dynamic neural networks that assists all the details in the examples at
>
> https://github.com/ShigekiKarita/grain/tree/master/example
>
> Further, could parts of grain be refactored out into some generic CUDA-library for use in domains other than dynamic neural networks?

Looks interesting, though it seems the author has only just recently tagged the first two releases (3-4 days ago). That doesn't mean that I don't agree with your suggestions (more examples/tutorials, separate GPU & autograd/NN library), just maybe the author has been more focused on basic functionality for now.