Deprecate DaggerGPU

Author: jpsamaroo

Project.toml

@@ -1,42 +1,22 @@
 name = "DaggerGPU"
 uuid = "68e73e28-2238-4d5a-bf97-e5d4aa3c4be2"
 authors = ["Julian P Samaroo <[email protected]>"]
-version = "0.2.0"
+version = "0.2.1"
 
 [deps]
-AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Dagger = "d58978e5-989f-55fb-8d15-ea34adc7bf54"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MemPool = "f9f48841-c794-520a-933b-121f7ba6ed94"
-Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
-oneAPI = "8f75cd03-7ff8-4ecb-9b8f-daf728133b1b"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
-[weakdeps]
-AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
-oneAPI = "8f75cd03-7ff8-4ecb-9b8f-daf728133b1b"
-
-[extensions]
-CUDAExt = "CUDA"
-IntelExt = "oneAPI"
-MetalExt = "Metal"
-ROCExt = "AMDGPU"
-
 [compat]
-AMDGPU = "0.9.4"
 Adapt = "1, 2, 3, 4"
-CUDA = "3, 4, 5"
-Dagger = "0.18.12"
+Dagger = "0.18.17"
 KernelAbstractions = "0.9"
 MemPool = "0.3, 0.4"
-Metal = "1.1"
-oneAPI = "1"
 Requires = "1"
 julia = "1.7"

README.md

@@ -2,6 +2,12 @@
 
 **GPU integrations for Dagger.jl**
 
+## Deprecation Notice
+
+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](https://github.com/JuliaParallel/Dagger.jl/issues).
+
+## Original README
+
 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: