I am excited to share some new development in the Julia ML stack. We are announcing Torch.jl which wraps the existing PyTorch and its CUDA kernels and makes them available through Julia. Torch.jl also exposes the kernels through integration with Flux.jl and Zygote.jl (for differentiation), so it requires minimum handling on the user side. Most operations can just be handled like we would any other function calls in Julia. It operates similarly to how a model is moved to the GPU with
We have also written a blog post about it which goes in more depth and talks about its intended use and development.
We are excited to see folks use it and see more development in the area! Please file issues for cases that may not be covered yet in the issue tracker, and we would love to hear about all the features that would be nice to see in here!