Use cuda-python cuda.bindings import names.

python/cudf/cudf/core/udf/utils.py

@@ -9,7 +9,7 @@
 import cupy as cp
 import llvmlite.binding as ll
 import numpy as np
-from cuda import cudart
+from cuda.bindings import runtime
 from numba import cuda, typeof
 from numba.core.datamodel import default_manager, models
 from numba.core.errors import TypingError
@@ -356,8 +356,8 @@ def set_malloc_heap_size(size=None):
     if size is None:
         size = _STRINGS_UDF_DEFAULT_HEAP_SIZE
     if size != _heap_size:
-        (ret,) = cudart.cudaDeviceSetLimit(
-            cudart.cudaLimit.cudaLimitMallocHeapSize, size
+        (ret,) = runtime.cudaDeviceSetLimit(
+            runtime.cudaLimit.cudaLimitMallocHeapSize, size
         )
         if ret.value != 0:
             raise RuntimeError("Unable to set cudaMalloc heap size")

python/cudf/cudf/utils/gpu_utils.py

@@ -15,7 +15,7 @@ def validate_setup():
 
     import warnings
 
-    from cuda.cudart import cudaDeviceAttr, cudaError_t
+    from cuda.bindings.runtime import cudaDeviceAttr, cudaError_t
 
     from rmm._cuda.gpu import (
         CUDARuntimeError,

python/cudf/pyproject.toml

@@ -98,8 +98,6 @@ filterwarnings = [
     "error",
     "ignore:::.*xdist.*",
     "ignore:::.*pytest.*",
-    # https://github.com/rapidsai/build-planning/issues/116
-    "ignore:.*cuda..* module is deprecated.*:DeprecationWarning",
     # some third-party dependencies (e.g. 'boto3') still using datetime.datetime.utcnow()
     "ignore:.*datetime.*utcnow.*scheduled for removal.*:DeprecationWarning:botocore",
     # Deprecation warning from Pyarrow Table.to_pandas() with pandas-2.2+

python/cudf_kafka/pyproject.toml

@@ -60,8 +60,6 @@ addopts = "--tb=native --strict-config --strict-markers"
 empty_parameter_set_mark = "fail_at_collect"
 filterwarnings = [
   "error",
-  # https://github.com/rapidsai/build-planning/issues/116
-  "ignore:.*cuda..* module is deprecated.*:DeprecationWarning",
 ]
 xfail_strict = true
 

python/cudf_polars/pyproject.toml

@@ -66,8 +66,6 @@ addopts = "--tb=native --strict-config --strict-markers"
 empty_parameter_set_mark = "fail_at_collect"
 filterwarnings = [
   "error",
-  # https://github.com/rapidsai/build-planning/issues/116
-  "ignore:.*cuda..* module is deprecated.*:DeprecationWarning",
 ]
 xfail_strict = true
 

python/custreamz/pyproject.toml

@@ -93,8 +93,6 @@ addopts = "--tb=native --strict-config --strict-markers"
 empty_parameter_set_mark = "fail_at_collect"
 filterwarnings = [
     "error",
-    # https://github.com/rapidsai/build-planning/issues/116
-    "ignore:.*cuda..* module is deprecated.*:DeprecationWarning",
     "ignore:unclosed <socket.socket:ResourceWarning",
     "ignore:Port .* is already in use.:UserWarning:distributed",
     # Should be fixed in the next streamz release

python/dask_cudf/pyproject.toml

@@ -96,8 +96,6 @@ empty_parameter_set_mark = "fail_at_collect"
 filterwarnings = [
     "error::FutureWarning",
     "error::DeprecationWarning",
-    # https://github.com/rapidsai/build-planning/issues/116
-    "ignore:.*cuda..* module is deprecated.*:DeprecationWarning",
     # some third-party dependencies (e.g. 'boto3') still using datetime.datetime.utcnow()
     "ignore:.*datetime.*utcnow.*scheduled for removal:DeprecationWarning:botocore",
     "ignore:create_block_manager_from_blocks is deprecated and will be removed in a future version. Use public APIs instead.:DeprecationWarning",

python/pylibcudf/pylibcudf/utils.pyx

@@ -5,7 +5,7 @@ from cython.operator import dereference
 from libc.stdint cimport uintptr_t
 from libcpp.functional cimport reference_wrapper
 from libcpp.vector cimport vector
-from cuda import cudart
+from cuda.bindings import runtime
 
 from pylibcudf.libcudf.scalar.scalar cimport scalar
 from pylibcudf.libcudf.types cimport bitmask_type
@@ -44,13 +44,13 @@ def _is_concurrent_managed_access_supported():
     """
 
     # Ensure CUDA is initialized before checking cudaDevAttrConcurrentManagedAccess
-    cudart.cudaFree(0)
+    runtime.cudaFree(0)
 
     device_id = 0
-    err, supports_managed_access = cudart.cudaDeviceGetAttribute(
-        cudart.cudaDeviceAttr.cudaDevAttrConcurrentManagedAccess, device_id
+    err, supports_managed_access = runtime.cudaDeviceGetAttribute(
+        runtime.cudaDeviceAttr.cudaDevAttrConcurrentManagedAccess, device_id
     )
-    if err != cudart.cudaError_t.cudaSuccess:
+    if err != runtime.cudaError_t.cudaSuccess:
         raise RuntimeError(
             f"Failed to check cudaDevAttrConcurrentManagedAccess with error {err}"
         )

python/pylibcudf/pyproject.toml

@@ -99,8 +99,6 @@ addopts = "--tb=native --strict-config --strict-markers --import-mode=importlib"
 empty_parameter_set_mark = "fail_at_collect"
 filterwarnings = [
   "error",
-  # https://github.com/rapidsai/build-planning/issues/116
-  "ignore:.*cuda..* module is deprecated.*:DeprecationWarning",
   "ignore:::.*xdist.*",
   "ignore:::.*pytest.*"
 ]