GPU integrations for Dagger.jl
DaggerGPU's logic and extensions have been merged upstream into Dagger.jl, making DaggerGPU no longer necessary. You can now load CUDA logic with using Dagger, CUDA, and similarly for all other backends. Please report all issues to the Dagger issue tracker.
DaggerGPU.jl makes use of the Dagger.Processor infrastructure to dispatch Dagger kernels to NVIDIA, AMD, and Apple GPUs, via CUDA.jl, AMDGPU.jl, and Metal.jl respectively. Usage is simple: add or dev DaggerGPU.jl and CUDA.jl/AMDGPU.jl/Metal.jl appropriately, load it with using DaggerGPU, and add DaggerGPU.CuArrayDeviceProc/DaggerGPU.ROCArrayProc/DaggerGPU.MtlArrayDeviceProc to your scheduler or thunk options (see Dagger.jl documentation for details on how to do this).
DaggerGPU.jl is still experimental, but we welcome GPU-owning users to try it out and report back on any issues or sharp edges that they encounter. When filing an issue about DaggerGPU.jl, please provide:
- The complete error message and backtrace
- Julia version
- GPU vendor and model
- CUDA/AMDGPU version(s)