use mkl instead of mkl-service as dependency to get mkl

Author: ndgrigorian

.github/workflows/build-with-clang.yml

@@ -55,7 +55,7 @@ jobs:
 
       - name: Install mkl_umath dependencies
         run: |
-          pip install meson-python ninja cython cmake mkl-service
+          pip install meson-python ninja cython cmake mkl
           pip install ${{ matrix.numpy_version }}
 
       - name: List oneAPI folder content

.github/workflows/build-with-standard-clang.yml

@@ -47,7 +47,7 @@ jobs:
 
       - name: Install mkl_umath dependencies
         run: |
-          pip install meson-python ninja cmake cython mkl-service
+          pip install meson-python ninja cmake cython mkl
           pip install mkl-devel
           pip install ${{ matrix.numpy_version }}
 

conda-recipe-cf/meta.yaml

@@ -38,7 +38,7 @@ requirements:
     run:
       - python
       - python-gil     # [py>=314]
-      - mkl-service
+      - {{ pin_compatible('mkl') }}
       - {{ pin_compatible('numpy') }}
       - {{ pin_compatible('intel-cmplr-lib-rt') }}
 

conda-recipe/meta.yaml

@@ -42,7 +42,7 @@ requirements:
     run:
       - python
       - python-gil     # [py>=314]
-      - mkl-service
+      - {{ pin_compatible('mkl') }}
       - {{ pin_compatible('intel-cmplr-lib-rt') }}
       {% if use_numpy_base %}
       - numpy-base

pyproject.toml

@@ -56,7 +56,7 @@ classifiers = [
   "Operating System :: POSIX",
   "Operating System :: Unix"
 ]
-dependencies = ["numpy >=1.26.4", "mkl-service"]
+dependencies = ["numpy >=1.26.4", "mkl"]
 description = "Intel (R) MKL-based universal functions for NumPy arrays"
 dynamic = ["version"]
 keywords = ["mkl_umath"]