Use cupy instead of cuda-python.

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -9,7 +9,6 @@ dependencies:
 - c-compiler
 - click
 - cmake>=3.26.4,!=3.30.0
-- cuda-python>=11.7.1,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0

conda/environments/all_cuda-125_arch-x86_64.yaml

@@ -11,7 +11,6 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-cudart-dev
 - cuda-nvcc
-- cuda-python>=12.0,<13.0a0
 - cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler

dependencies.yaml

@@ -322,13 +322,3 @@ dependencies:
         packages:
           # Already added to requirements via docs. This is for tests.
           - numpydoc>=1.5
-    specific:
-      - output_types: [conda, requirements, pyproject]
-        matrices:
-          - matrix: {cuda: "12.*"}
-            packages:
-              - cuda-python>=12.0,<13.0a0
-          - matrix: {cuda: "11.*"}
-            packages: &test_cuda_python_cu11
-              - cuda-python>=11.7.1,<12.0a0
-          - {matrix: null, packages: *test_cuda_python_cu11}

python/cucim/pyproject.toml

@@ -57,7 +57,6 @@ Tracker = "https://github.com/rapidsai/cucim/issues"
 
 [project.optional-dependencies]
 test = [
-    "cuda-python>=11.7.1,<12.0a0",
     "imagecodecs>=2021.6.8; platform_machine=='x86_64'",
     "matplotlib",
     "numpydoc>=1.5",

python/cucim/tests/performance/clara/test_read_region_memory_usage.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 #
 
-import cuda.cudart
+import cupy as cp
 import pytest
 
 from ...util.io import open_image_cucim
@@ -25,14 +25,13 @@
 def test_read_region_cuda_memleak(testimg_tiff_stripe_4096x4096_256_jpeg):
     def get_used_gpu_memory_mib():
         """Get the used GPU memory in MiB."""
-        status, free, total = cuda.cudart.cudaMemGetInfo()
-        if status != cuda.cudart.cudaError_t.cudaSuccess:
-            raise RuntimeError("Failed to get GPU memory info.")
+        dev = cp.cuda.Device()
+        free, total = dev.mem_info
         memory_used = (total - free) / (2**20)
         return memory_used
 
-    status, num_gpus = cuda.cudart.cudaGetDeviceCount()
-    if status != cuda.cudart.cudaError_t.cudaSuccess or num_gpus == 0:
+    num_gpus = cp.cuda.runtime.getDeviceCount()
+    if num_gpus == 0:
         pytest.skip("No gpu available")
 
     img = open_image_cucim(testimg_tiff_stripe_4096x4096_256_jpeg)