diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 2665ce4205..2c92ad0a99 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -37,7 +37,7 @@ dependencies:
 - openblas
 - pandas
 - pyyaml
-- rmm=23.12.*
+- rmm==23.12.*
 - scikit-build>=0.13.1
 - sysroot_linux-64==2.17
 name: bench_ann_cuda-118_arch-x86_64
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index a3f5eaffaf..bfc9e24730 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -106,10 +106,8 @@ if(RAFT_ANN_BENCH_USE_GGNN)
 endif()
 
 if(RAFT_ANN_BENCH_USE_FAISS)
-  # We need to ensure that faiss has all the conda
-  # information. So we currently use the very ugly
-  # hammer of `link_libraries` to ensure that all
-  # targets in this directory and the faiss directory
+  # We need to ensure that faiss has all the conda information. So we currently use the very ugly
+  # hammer of `link_libraries` to ensure that all targets in this directory and the faiss directory
   # will have the conda includes/link dirs
   link_libraries($<TARGET_NAME_IF_EXISTS:conda_env>)
   include(cmake/thirdparty/get_faiss.cmake)
diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index 9e09e74369..0a398ade1d 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -182,7 +182,7 @@ void bench_search(::benchmark::State& state,
   }
   // algo is static to cache it between close search runs to save time on index loading
   static std::string index_file = "";
-  bool created_new_algo = false;
+  bool created_new_algo         = false;
   if (index.file != index_file) {
     current_algo.reset();
     index_file = index.file;
@@ -194,7 +194,7 @@ void bench_search(::benchmark::State& state,
     if (!current_algo || (algo = dynamic_cast<ANN<T>*>(current_algo.get())) == nullptr) {
       std::cout << "Loading algo" << std::endl;
       created_new_algo = true;
-      auto ualgo = ann::create_algo<T>(
+      auto ualgo       = ann::create_algo<T>(
         index.algo, dataset->distance(), dataset->dim(), index.build_param, index.dev_list);
       algo = ualgo.get();
       algo->load(index_file);
diff --git a/cpp/include/raft/neighbors/cagra_types.hpp b/cpp/include/raft/neighbors/cagra_types.hpp
index ed624d2f8d..ff0eb2b6c6 100644
--- a/cpp/include/raft/neighbors/cagra_types.hpp
+++ b/cpp/include/raft/neighbors/cagra_types.hpp
@@ -266,10 +266,10 @@ struct index : ann::index {
       metric_(metric),
       dataset_(make_device_matrix<T, int64_t>(res, 0, 0)),
       dataset_pinned_(0, resource::get_cuda_stream(res), mr_huge_.get()),
-      //dataset_pinned_(0, resource::get_cuda_stream(res), mr_.get()),
+      // dataset_pinned_(0, resource::get_cuda_stream(res), mr_.get()),
       graph_(make_device_matrix<IdxT, int64_t>(res, 0, 0)),
       graph_pinned_(0, resource::get_cuda_stream(res), mr_huge_.get())
-      //graph_pinned_(0, resource::get_cuda_stream(res), mr_.get())
+  // graph_pinned_(0, resource::get_cuda_stream(res), mr_.get())
   {
     RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
                  "Dataset and knn_graph must have equal number of rows");
diff --git a/cpp/include/raft/neighbors/detail/cagra/cagra_build.cuh b/cpp/include/raft/neighbors/detail/cagra/cagra_build.cuh
index e2365b14c3..b9fc83bc5b 100644
--- a/cpp/include/raft/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/include/raft/neighbors/detail/cagra/cagra_build.cuh
@@ -59,12 +59,13 @@ void build_knn_graph(raft::resources const& res,
                                                             node_degree);
 
   if (!build_params) {
-    build_params          = ivf_pq::index_params{};
-    build_params->n_lists = 16384; //dataset.extent(0) < 4 * 2500 ? 4 : (uint32_t)(dataset.extent(0) / 2500);
-    build_params->pq_dim  = 32; //raft::Pow2<8>::roundUp(dataset.extent(1) / 2);
-    build_params->pq_bits = 8;
+    build_params = ivf_pq::index_params{};
+    build_params->n_lists =
+      16384;  // dataset.extent(0) < 4 * 2500 ? 4 : (uint32_t)(dataset.extent(0) / 2500);
+    build_params->pq_dim                   = 32;  // raft::Pow2<8>::roundUp(dataset.extent(1) / 2);
+    build_params->pq_bits                  = 8;
     build_params->kmeans_trainset_fraction = dataset.extent(0) < 10000 ? 1 : 10;
-    build_params->kmeans_n_iters           = 10; //25;
+    build_params->kmeans_n_iters           = 10;  // 25;
     build_params->add_data_on_build        = true;
   }
 
@@ -94,12 +95,12 @@ void build_knn_graph(raft::resources const& res,
   //
   if (!search_params) {
     search_params            = ivf_pq::search_params{};
-    search_params->n_probes  = 50; //std::min<IdxT>(dataset.extent(1) * 2, build_params->n_lists);
+    search_params->n_probes  = 50;  // std::min<IdxT>(dataset.extent(1) * 2, build_params->n_lists);
     search_params->lut_dtype = CUDA_R_16F;
     search_params->internal_distance_dtype = CUDA_R_16F;
   }
   const auto top_k          = node_degree + 1;
-  uint32_t gpu_top_k        = node_degree * 4; //refine_rate.value_or(2.0f);
+  uint32_t gpu_top_k        = node_degree * 4;  // refine_rate.value_or(2.0f);
   gpu_top_k                 = std::min<IdxT>(std::max(gpu_top_k, top_k), dataset.extent(0));
   const auto num_queries    = dataset.extent(0);
   const auto max_batch_size = 1024;