Update rapidsai references to NVIDIA

.devcontainer/cuda12.9-conda/devcontainer.json

@@ -8,7 +8,7 @@
       "BASE": "rapidsai/devcontainers:26.12-cpp-mambaforge"
     },
     "cacheFrom": [
-      "ghcr.io/rapidsai/cuvs/devcontainer:26.12-cuda12.9-conda"
+      "ghcr.io/nvidia/cuvs/devcontainer:26.12-cuda12.9-conda"
     ]
   },
   "runArgs": [

.devcontainer/cuda12.9-pip/devcontainer.json

@@ -8,7 +8,7 @@
       "BASE": "rapidsai/devcontainers:26.12-cpp-cuda12.9-ucx1.21.0-openmpi5.0.10"
     },
     "cacheFrom": [
-      "ghcr.io/rapidsai/cuvs/devcontainer:26.12-cuda12.9-pip"
+      "ghcr.io/nvidia/cuvs/devcontainer:26.12-cuda12.9-pip"
     ]
   },
   "runArgs": [

.devcontainer/cuda13.3-conda/devcontainer.json

@@ -8,7 +8,7 @@
       "BASE": "rapidsai/devcontainers:26.12-cpp-mambaforge"
     },
     "cacheFrom": [
-      "ghcr.io/rapidsai/cuvs/devcontainer:26.12-cuda13.3-conda"
+      "ghcr.io/nvidia/cuvs/devcontainer:26.12-cuda13.3-conda"
     ]
   },
   "runArgs": [

.devcontainer/cuda13.3-pip/devcontainer.json

@@ -8,7 +8,7 @@
       "BASE": "rapidsai/devcontainers:26.12-cpp-cuda13.3-ucx1.21.0-openmpi5.0.10"
     },
     "cacheFrom": [
-      "ghcr.io/rapidsai/cuvs/devcontainer:26.12-cuda13.3-pip"
+      "ghcr.io/nvidia/cuvs/devcontainer:26.12-cuda13.3-pip"
     ]
   },
   "runArgs": [

.github/CODE_OF_CONDUCT.md

@@ -1 +1 @@
-This project has adopted the [Contributor Covenant Code of Conduct](https://docs.rapids.ai/resources/conduct/).
+This project has adopted the [Contributor Covenant Code of Conduct](https://docs.nvidia.com/datascience/resources/conduct/).

.github/PULL_REQUEST_TEMPLATE.md

@@ -22,7 +22,7 @@ Here are some guidelines to help the review process go smoothly.
    for example when the C/C++ code of a feature is complete but Python bindings
    are still required, then add the label `[HELP-REQ]` so that others can triage
    and assist. The additional changes then can be implemented on top of the
-   same PR. If the assistance is done by members of the rapidsAI team, then no
+   same PR. If the assistance is done by members of the cuVS team, then no
    additional actions are required by the creator of the original PR for this,
    otherwise the original author of the PR needs to give permission to the
    person(s) assisting to commit to their personal fork of the project. If that

.github/labeler.yml

@@ -1,6 +1,6 @@
 # https://github.com/actions/labeler#common-examples
-# Adapted from https://github.com/rapidsai/raft/blob/main/.github/CODEOWNERS
-# Labels culled from https://github.com/rapidsai/raft/labels
+# Adapted from https://github.com/NVIDIA/raft/blob/main/.github/CODEOWNERS
+# Labels culled from https://github.com/NVIDIA/raft/labels
 
 python:
   - changed-files:

.github/release.yml

@@ -1,4 +1,4 @@
-# GitHub Auto-Generated Release Notes Configuration for RAPIDS
+# GitHub Auto-Generated Release Notes Configuration
 # This file configures how GitHub automatically generates release notes
 
 changelog:

Dockerfile

@@ -4,7 +4,7 @@
 # syntax=docker/dockerfile:1.5
 
 # ==============================================================================
-# RAPIDS cuVS Docker Image - Vector Search and Clustering on GPU
+# NVIDIA cuVS Docker Image - Vector Search and Clustering on GPU
 #
 # Usage:
 #   docker build -t cuvs:latest .
@@ -32,9 +32,9 @@ RUN echo "  Building cuVS Docker image with:" && \
     echo "   RAPIDS Version: ${RAPIDS_VER}"
 
 # Container metadata
-LABEL maintainer="RAPIDS cuVS Team"
-LABEL description="RAPIDS cuVS - Vector Search and Clustering on GPU"
-LABEL org.opencontainers.image.source="https://github.com/nvidia/cuvs"
+LABEL maintainer="NVIDIA cuVS Team"
+LABEL description="NVIDIA cuVS - Vector Search and Clustering on GPU"
+LABEL org.opencontainers.image.source="https://github.com/NVIDIA/cuvs"
 LABEL org.opencontainers.image.usage="docker run --gpus all -it <image>"
 
 # Environment setup

README.md

@@ -1,4 +1,4 @@
-# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuVS: Vector Search and Clustering on the GPU</div>
+# cuVS: Vector Search and Clustering on the GPU
 
 
 ## Contents
@@ -15,11 +15,10 @@
 - [Documentation](https://docs.nvidia.com/cuvs): Library documentation.
 - [Build and Install Guide](https://docs.nvidia.com/cuvs/installation): Instructions for installing and building cuVS.
 - [Getting Started Guide](https://docs.nvidia.com/cuvs/getting-started): Guide to getting started with cuVS.
-- [Code Examples](https://github.com/nvidia/cuvs/tree/HEAD/examples): Self-contained Code Examples.
+- [Code Examples](https://github.com/NVIDIA/cuvs/tree/HEAD/examples): Self-contained Code Examples.
 - [API Reference Documentation](https://docs.nvidia.com/cuvs/api_reference): API Documentation.
-- [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
-- [GitHub repository](https://github.com/nvidia/cuvs): Download the cuVS source code.
-- [Issue tracker](https://github.com/nvidia/cuvs/issues): Report issues or request features.
+- [GitHub repository](https://github.com/NVIDIA/cuvs): Download the cuVS source code.
+- [Issue tracker](https://github.com/NVIDIA/cuvs/issues): Report issues or request features.
 
 ## What is cuVS?
 
@@ -69,7 +68,7 @@ In addition to the items above, cuVS shoulders the burden of keeping non-trivial
 
 ## cuVS Technology Stack
 
-cuVS is built on top of the RAPIDS RAFT library of high performance machine learning primitives and provides all the necessary routines for vector search and clustering on the GPU.
+cuVS is built on top of the NVIDIA RAFT library of high performance machine learning primitives and provides all the necessary routines for vector search and clustering on the GPU.
 
 ![cuVS is built on top of low-level CUDA libraries and provides many important routines that enable vector search and clustering on the GPU](img/tech_stack.png "cuVS Technology Stack")
 
@@ -82,7 +81,7 @@ cuVS comes with pre-built packages that can be installed through [conda](https:/
 > [!NOTE]
 > If compiled binary size is a concern, please note that the cuVS builds for CUDA 13 are roughly half the size of CUDA 12 builds. This is a result of improved compression rates in the newer supported CUDA drivers. We will be adopting the newer drivers for CUDA 12 builds in Spring of 2026, which will ultimately bring them down to roughly the size of the CUDA 13 builds. In the meantime, the NVIDIA cuVS team is continuing to shave down the binary sizes for all supported CUDA versions. If binary size is an issue for you, please consider linking to cuVS statically either by building from source or using pre-built `libcuvs-static` conda package.
 
-Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
+Please see the [Build and Install Guide](https://docs.nvidia.com/cuvs/installation) for more information on installing the available cuVS packages and building from source.
 
 ### Standalone C library (Docker)
 
@@ -118,7 +117,7 @@ cagra::index_params index_params;
 auto index = cagra::build(res, index_params, dataset);
 ```
 
-For more code examples of the C++ APIs, including drop-in Cmake project templates, please refer to the [C++ examples](https://github.com/nvidia/cuvs/tree/HEAD/examples) directory in the codebase.
+For more code examples of the C++ APIs, including drop-in Cmake project templates, please refer to the [C++ examples](https://github.com/NVIDIA/cuvs/tree/HEAD/examples) directory in the codebase.
 
 ### C API
 
@@ -147,7 +146,7 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
-For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/nvidia/cuvs/tree/main/examples/c)
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/NVIDIA/cuvs/tree/main/examples/c)
 
 ### Rust API
 
@@ -194,7 +193,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
 }
 ```
 
-For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/nvidia/cuvs/tree/main/examples/rust).
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/NVIDIA/cuvs/tree/main/examples/rust).
 
 ## Contributing
 

ci/check_c_abi/pyproject.toml

@@ -16,7 +16,7 @@ dependencies = ["libclang", "msgspec", "termcolor"]
 check-c-abi = "check_c_abi.main:main_cli"
 
 [project.urls]
-Homepage = "https://github.com/nvidia/cuvs"
+Homepage = "https://github.com/NVIDIA/cuvs"
 
 [tool.setuptools.dynamic]
 version = { file = "VERSION" }

ci/release/update-version.sh

@@ -161,7 +161,7 @@ sed_runner "s/version = \".*\"/version = \"${NEXT_FULL_RUST_TAG}\"/g" rust/Cargo
 # .devcontainer files
 find .devcontainer/ -type f -name devcontainer.json -print0 | while IFS= read -r -d '' filename; do
     sed_runner "s@rapidsai/devcontainers:[0-9.]*@rapidsai/devcontainers:${NEXT_SHORT_TAG}@g" "${filename}"
-    sed_runner "s@ghcr.io/rapidsai/cuvs/devcontainer:[0-9.]*@ghcr.io/rapidsai/cuvs/devcontainer:${NEXT_SHORT_TAG}@g" "${filename}"
+    sed_runner "s@ghcr.io/nvidia/cuvs/devcontainer:[0-9.]*@ghcr.io/nvidia/cuvs/devcontainer:${NEXT_SHORT_TAG}@g" "${filename}"
     sed_runner "s@rapidsai/devcontainers/features/ucx:[0-9.]*@rapidsai/devcontainers/features/ucx:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
     sed_runner "s@rapidsai/devcontainers/features/cuda:[0-9.]*@rapidsai/devcontainers/features/cuda:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
     sed_runner "s@rapidsai/devcontainers/features/rapids-build-utils:[0-9.]*@rapidsai/devcontainers/features/rapids-build-utils:${NEXT_SHORT_TAG_PEP440}@" "${filename}"

cpp/bench/ann/src/common/cuda_huge_page_resource.hpp

@@ -25,7 +25,7 @@ namespace raft::mr {
  * works on GH systems.
  *
  * TODO(tfeher): consider improving or removing this helper once we made progress with
- * https://github.com/rapidsai/raft/issues/1819
+ * https://github.com/NVIDIA/raft/issues/1819
  */
 class cuda_huge_page_resource {
  public:

cpp/bench/ann/src/diskann/diskann_wrapper.h

@@ -35,7 +35,7 @@ diskann::Metric parse_metric_to_diskann(cuvs::bench::Metric metric)
   }
 }
 
-// TODO (tarangj): Remaining features are tracked at https://github.com/nvidia/cuvs/issues/656
+// TODO (tarangj): Remaining features are tracked at https://github.com/NVIDIA/cuvs/issues/656
 template <typename T>
 class diskann_memory : public algo<T> {
  public:

cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 #pragma once
@@ -268,7 +268,7 @@ class faiss_cpu_ivfrabitq : public faiss_cpu<T> {
 };
 
 // TODO(snanditale): Enable this in cmake
-//  ref: https://github.com/rapidsai/raft/issues/1876
+//  ref: https://github.com/NVIDIA/raft/issues/1876
 template <typename T>
 class faiss_cpu_ivfsq : public faiss_cpu<T> {
  public:

cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 #pragma once
@@ -417,7 +417,7 @@ class faiss_gpu_ivfpq : public faiss_gpu<T> {
 };
 
 // TODO(snanditale): Enable this in cmake
-//  ref: https://github.com/rapidsai/raft/issues/1876
+//  ref: https://github.com/NVIDIA/raft/issues/1876
 template <typename T>
 class faiss_gpu_ivfsq : public faiss_gpu<T> {
  public:

cpp/cmake/modules/compute_matrix_product.py

@@ -6,7 +6,7 @@
 # generation. We *could* write this in CMake with `string(JSON)`, but writing
 # it in Python is much easier. Once we have a version of CMake that has
 # https://gitlab.kitware.com/cmake/cmake/-/merge_requests/11516, we may be able
-# to port the algorithm to CMake script and use it in other RAPIDS projects.
+# to port the algorithm to CMake script and use it in other projects.
 
 import argparse
 import json

cpp/cmake/thirdparty/get_cuvs.cmake

@@ -1,12 +1,12 @@
 # =============================================================================
 # cmake-format: off
-# SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 # cmake-format: on
 
 # Use RAPIDS_VERSION_MAJOR_MINOR from cmake/rapids_config.cmake
 set(CUVS_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
-set(CUVS_FORK "rapidsai")
+set(CUVS_FORK "NVIDIA")
 set(CUVS_PINNED_TAG "${rapids-cmake-checkout-tag}")
 
 function(find_and_configure_cuvs)

cpp/cmake/thirdparty/get_raft.cmake

@@ -6,7 +6,7 @@
 
 # Use RAPIDS_VERSION_MAJOR_MINOR from rapids_config.cmake
 set(RAFT_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
-set(RAFT_FORK "rapidsai")
+set(RAFT_FORK "NVIDIA")
 set(RAFT_PINNED_TAG "${rapids-cmake-checkout-tag}")
 set(LIBRAFT_LOGGING_LEVEL "INFO"
     CACHE STRING "Choose the RAFT logging level compiled into cuVS."

cpp/include/cuvs/cluster/kmeans.hpp

@@ -240,7 +240,7 @@ enum class kmeans_type { KMeans = 0, KMeansBalanced = 1 };
  * (`raft::resource::comms_initialized(handle)`), and on a single GPU otherwise.
  *
  * TODO: Evaluate replacing the extent type with int64_t. Reference issue:
- * https://github.com/nvidia/cuvs/issues/1961
+ * https://github.com/NVIDIA/cuvs/issues/1961
  *
  * This overload supports out-of-core computation where the dataset resides
  * on the host. Data is processed in batches, streaming from host to

cpp/include/cuvs/cluster/spectral.hpp

@@ -49,7 +49,7 @@ struct params {
  * @{
  */
 
-// TODO: int64_t nnz support (see https://github.com/nvidia/cuvs/issues/1484)
+// TODO: int64_t nnz support (see https://github.com/NVIDIA/cuvs/issues/1484)
 
 /**
  * @brief Perform spectral clustering on a connectivity graph

cpp/include/cuvs/neighbors/common.hpp

@@ -1735,7 +1735,7 @@ constexpr static IdxT kInvalidRecord =
  * @tparam IdxT The index type for source indices
  * @tparam SizeT The size type
  *
- * TODO: Make this struct internal (tracking issue: https://github.com/nvidia/cuvs/issues/1726)
+ * TODO: Make this struct internal (tracking issue: https://github.com/NVIDIA/cuvs/issues/1726)
  */
 template <typename ValueT, typename IdxT, typename SizeT = uint32_t>
 struct list_base {

cpp/include/cuvs/neighbors/hnsw.hpp

@@ -896,7 +896,7 @@ struct search_params : cuvs::neighbors::search_params {
  * @}
  */
 
-// TODO: Filtered Search APIs: https://github.com/nvidia/cuvs/issues/363
+// TODO: Filtered Search APIs: https://github.com/NVIDIA/cuvs/issues/363
 
 /**
  * @defgroup hnsw_cpp_index_search Search hnswlib index

cpp/include/cuvs/neighbors/ivf_flat.hpp

@@ -266,7 +266,7 @@ struct index : cuvs::neighbors::index {
  private:
   /**
    * TODO: in theory, we can lift this to the template parameter and keep it at hardware maximum
-   * possible value by padding the `dim` of the data https://github.com/rapidsai/raft/issues/711
+   * possible value by padding the `dim` of the data https://github.com/NVIDIA/raft/issues/711
    */
   uint32_t veclen_;
   cuvs::distance::DistanceType metric_;
@@ -285,7 +285,7 @@ struct index : cuvs::neighbors::index {
   static auto calculate_veclen(uint32_t dim) -> uint32_t
   {
     // TODO: consider padding the dimensions and fixing veclen to its maximum possible value as a
-    // template parameter (https://github.com/rapidsai/raft/issues/711)
+    // template parameter (https://github.com/NVIDIA/raft/issues/711)
 
     // NOTE: keep this consistent with the select_interleaved_scan_kernel logic
     // in detail/ivf_flat_interleaved_scan-inl.cuh.

cpp/scripts/run-cmake-format.sh

@@ -1,17 +1,17 @@
 #!/bin/bash
 
-# SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
 # This script is a wrapper for cmakelang that may be used with pre-commit. The
-# wrapping is necessary because RAPIDS libraries split configuration for
-# cmakelang linters between a local config file and a second config file that's
-# shared across all of RAPIDS via rapids-cmake. In order to keep it up to date
-# this file is only maintained in one place (the rapids-cmake repo) and
-# pulled down during builds. We need a way to invoke CMake linting commands
-# without causing pre-commit failures (which could block local commits or CI),
-# while also being sufficiently flexible to allow users to maintain the config
-# file independently of a build directory.
+# wrapping is necessary because libraries split configuration for cmakelang
+# linters between a local config file and a second config file that's shared
+# via rapids-cmake. In order to keep it up to date this file is only maintained
+# in one place (the rapids-cmake repo) and pulled down during builds. We need a
+# way to invoke CMake linting commands without causing pre-commit failures
+# (which could block local commits or CI), while also being sufficiently
+# flexible to allow users to maintain the config file independently of a build
+# directory.
 #
 # This script provides the minimal functionality to enable those use cases. It
 # searches in a number of predefined locations for the rapids-cmake config file

cpp/src/cluster/detail/connectivities.cuh

@@ -96,7 +96,7 @@ struct distance_graph_impl<Linkage::KNN_GRAPH, value_idx, value_t> {
 
     // TODO: Wouldn't need to copy here if we could compute knn
     // graph directly on the device uvectors
-    // ref: https://github.com/rapidsai/raft/issues/227
+    // ref: https://github.com/NVIDIA/raft/issues/227
     raft::copy_async(indices.data(), knn_graph_coo.cols(), knn_graph_coo.nnz, stream);
     raft::copy_async(data.data(), knn_graph_coo.vals(), knn_graph_coo.nnz, stream);
   }

cpp/src/distance/detail/kernels/gram_matrix.cu

@@ -351,7 +351,7 @@ void GramMatrixBase<math_t>::linear(raft::resources const& handle,
   math_t beta  = 0.0;
   if (is_row_major) {
     // #TODO: Use mdspan-based API when stride-capable
-    // https://github.com/rapidsai/raft/issues/875
+    // https://github.com/NVIDIA/raft/issues/875
     raft::linalg::gemm(handle,
                        true,
                        false,
@@ -369,7 +369,7 @@ void GramMatrixBase<math_t>::linear(raft::resources const& handle,
                        raft::resource::get_cuda_stream(handle).get());
   } else {
     // #TODO: Use mdspan-based API when stride-capable
-    // https://github.com/rapidsai/raft/issues/875
+    // https://github.com/NVIDIA/raft/issues/875
     raft::linalg::gemm(handle,
                        false,
                        true,

cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh

@@ -120,7 +120,7 @@ RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
         // to each other after sorting by distances. Thus, for now we sweep a neighboring window of
         // size 4 or sweep the entire row to check for duplicates, and keep the first occurrence
         // only.
-        // related issue: https://github.com/nvidia/cuvs/issues/1056
+        // related issue: https://github.com/NVIDIA/cuvs/issues/1056
         // uniqueMask[colId] = static_cast<int16_t>(blockValues[colId] != blockValues[colId - 1]);
 
         int is_unique = 1;

cpp/src/neighbors/detail/cagra/cagra_ace.cuh

@@ -187,7 +187,7 @@ void ace_get_partition_labels(
 
   // Sampling vectors from dataset. Uses float conversion on host instead of
   // raft::matrix::sample_rows to minimize GPU memory usage.
-  // TODO(julianmi): Switch to sample_rows when https://github.com/nvidia/cuvs/issues/1461 is
+  // TODO(julianmi): Switch to sample_rows when https://github.com/NVIDIA/cuvs/issues/1461 is
   // addressed.
   size_t n_samples         = dataset_size * sampling_rate;
   const size_t min_samples = 100 * n_partitions;

cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh

@@ -526,7 +526,7 @@ struct search
                       // Bound random seed selection to the graph size, not the dataset size.
                       // During iterative / CAGRA-Q build the graph is smaller than the dataset,
                       // so using dataset_size here selects seeds that index past the graph end
-                      // (out-of-bounds access). See https://github.com/rapidsai/cuvs/pull/1780.
+                      // (out-of-bounds access). See https://github.com/NVIDIA/cuvs/pull/1780.
                       static_cast<IndexT>(graph.extent(0)));
 
     std::shared_ptr<rtcx::algorithm_launcher> compute_distance_to_child_nodes_launcher =

cpp/src/neighbors/detail/hnsw.hpp

@@ -2095,7 +2095,7 @@ from_cagra(raft::resources const& res,
     // gather points from dataset to form query set on host
     auto host_query_set = raft::make_host_matrix<T, int64_t>(num_pts, dim);
     // TODO: Use `raft::matrix::gather` when available as a public API
-    // Issue: https://github.com/rapidsai/raft/issues/2572
+    // Issue: https://github.com/NVIDIA/raft/issues/2572
 #pragma omp parallel for num_threads(num_threads)
     for (auto i = start_idx; i < end_idx; i++) {
       auto pt_id = order[i];