Upgrade to libcuvs=25.04

.github/actions/build_cmake/action.yml

@@ -61,7 +61,7 @@ runs:
           conda install -y -q cuda-toolkit=12.4 -c "nvidia/label/cuda-12.4.0"
         # and CUDA from cuVS channel for cuVS builds
         elif [ "${{ inputs.cuvs }}" = "ON" ]; then
-          conda install -y -q libcuvs=24.12 'cuda-version>=12.0,<=12.5' cuda-toolkit=12.4.1 gxx_linux-64=12.4 -c rapidsai -c conda-forge
+          conda install -y -q libcuvs=25.04 'cuda-version>=12.0,<=12.5' cuda-toolkit=12.4.1 gxx_linux-64=12.4 -c rapidsai -c rapidsai-nightly -c conda-forge
         fi
 
         # install test packages

INSTALL.md

@@ -6,7 +6,7 @@ pre-release nightly builds.
 
 - The CPU-only faiss-cpu conda package is currently available on Linux (x86-64 and aarch64), OSX (arm64 only), and Windows (x86-64)
 - faiss-gpu, containing both CPU and GPU indices, is available on Linux (x86-64 only) for CUDA 11.4 and 12.1
-- faiss-gpu-cuvs [^1] package containing GPU indices provided by [NVIDIA cuVS](https://github.com/rapidsai/cuvs/) version 24.12, is available on Linux (x86-64 only) for CUDA 11.8 and 12.4.
+- faiss-gpu-cuvs package containing GPU indices provided by [NVIDIA cuVS](https://github.com/rapidsai/cuvs/) version 24.12, is available on Linux (x86-64 only) for CUDA 11.8 and 12.4. 
 
 To install the latest stable release:
 
@@ -37,10 +37,10 @@ $ conda install -c pytorch/label/nightly faiss-cpu
 $ conda install -c pytorch/label/nightly -c nvidia faiss-gpu=1.10.0
 
 # GPU(+CPU) version with NVIDIA cuVS (package built with CUDA 12.4)
-conda install -c pytorch -c rapidsai -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version>=12.0,<=12.5'
+conda install -c pytorch -c rapidsai -c rapidsai-nightly -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version>=12.0,<=12.5'
 
 # GPU(+CPU) version with NVIDIA cuVS (package built with CUDA 11.8)
-conda install -c pytorch -c rapidsai -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version>=11.4,<=11.8'
+conda install -c pytorch -c rapidsai -c rapidsai-nightly -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version>=11.4,<=11.8'
 
 # GPU(+CPU) version using AMD ROCm not yet available
 ```
@@ -321,5 +321,3 @@ and you can run
 $ python demos/demo_auto_tune.py
 ```
 to test the GPU code.
-
-[^1]: The vector search and clustering algorithms in NVIDIA RAFT have been formally migrated to [NVIDIA cuVS](https://github.com/rapidsai/cuvs). This package is being renamed to `faiss-gpu-cuvs` in the next stable release, which will use these GPU implementations from the pre-compiled `libcuvs=24.12` binary.

cmake/thirdparty/fetch_rapids.cmake

@@ -15,7 +15,7 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 # =============================================================================
-set(RAPIDS_VERSION "24.12")
+set(RAPIDS_VERSION "25.04")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/FAISS_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake

conda/faiss-gpu-cuvs/meta.yaml

@@ -66,15 +66,15 @@ outputs:
         - _openmp_mutex =4.5=2_kmp_llvm  # [x86_64]
         - mkl =2023  # [x86_64]
         - openblas =0.3 # [not x86_64]
-        - libcuvs =24.12
+        - libcuvs =25.04
         - cuda-version {{ cuda_constraints }}
       run:
         - _openmp_mutex =4.5=2_kmp_llvm  # [x86_64]
         - mkl =2023  # [x86_64]
         - openblas =0.3 # [not x86_64]
         - cuda-cudart {{ cuda_constraints }}
         - libcublas {{ libcublas_constraints }}
-        - libcuvs =24.12
+        - libcuvs =25.04
         - cuda-version {{ cuda_constraints }}
         - libnvjitlink
     test: