Depend on cuda-core with CUDA version specific extras

dependencies.yaml

@@ -448,9 +448,28 @@ dependencies:
           - *scikit_learn
   depends_on_cuda_core:
     common:
-      - output_types: [conda, requirements, constraints, pyproject]
+      - output_types: [conda, constraints]
         packages:
           - cuda-core>=1.0.0,<2.0.0
+    # cuda-core enforces a minimum cuda-bindings version at import time but only
+    # declares it in the cu12/cu13 extras, so wheels must request the extra.
+    specific:
+      - output_types: [requirements, pyproject]
+        matrices:
+          # if use_cuda_wheels=false is provided, do not add the cu12/cu13 extras
+          # because they pull in CUDA wheels
+          - matrix:
+              use_cuda_wheels: "false"
+            packages:
+              - cuda-core>=1.0.0,<2.0.0
+          - matrix:
+              cuda: "12.*"
+            packages:
+              - cuda-core[cu12]>=1.0.0,<2.0.0
+          # fallback to CUDA 13 versions if 'cuda' is '13.*' or not provided
+          - matrix:
+            packages:
+              - cuda-core[cu13]>=1.0.0,<2.0.0
   depends_on_cuda_python:
     specific:
       - output_types: [conda, requirements, constraints, pyproject]

python/nvforest/pyproject.toml

@@ -28,7 +28,7 @@ license = "Apache-2.0"
 requires-python = ">=3.12"
 dependencies = [
     "cuda-bindings>=13.0.2,<14.0",
-    "cuda-core>=1.0.0,<2.0.0",
+    "cuda-core[cu13]>=1.0.0,<2.0.0",
     "cupy-cuda13x[ctk]>=14.0.1,!=14.1.0",
     "libnvforest==26.12.*,>=0.0.0a0",
     "numpy>=2.0,<3.0a0",