Merge branch 'intel:master' into parallel-fix

.ci/pipeline/build-and-test-lnx.yml

@@ -24,7 +24,7 @@ steps:
     displayName: 'System info'
   - script: |
       conda update -y -q conda
-      if [ $(echo $(PYTHON_VERSION) | grep '3.7') ] || [ $(echo $(PYTHON_VERSION) | grep '3.11') ]; then export DPCPP_PACKAGE="dpcpp-cpp-rt>=2023.1.0"; else export DPCPP_PACKAGE="dpctl>=0.14 dpcpp-cpp-rt>=2023.1.0"; fi
+      if [ $(echo $(PYTHON_VERSION) | grep '3.7') ] || [ $(echo $(PYTHON_VERSION) | grep '3.11') ]; then export DPCPP_PACKAGE="dpcpp-cpp-rt>=2023.2.0"; else export DPCPP_PACKAGE="dpctl>=0.14 dpcpp-cpp-rt>=2023.2.0"; fi
       conda create -q -y -n CB -c conda-forge -c intel python=$(PYTHON_VERSION) dal-devel mpich pyyaml $DPCPP_PACKAGE
     displayName: 'Conda create'
   - script: |

.ci/scripts/install_dpcpp.sh

@@ -21,6 +21,6 @@ rm GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
 echo "deb https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
 sudo add-apt-repository -y "deb https://apt.repos.intel.com/oneapi all main"
 sudo apt-get update
-sudo apt-get install -y intel-dpcpp-cpp-compiler-2023.1.0
+sudo apt-get install -y intel-dpcpp-cpp-compiler-2023.2.0
 sudo bash -c 'echo libintelocl.so > /etc/OpenCL/vendors/intel-cpu.icd'
 sudo mv -f /opt/intel/oneapi/compiler/latest/linux/lib/oclfpga /opt/intel/oneapi/compiler/latest/linux/lib/oclfpga_

examples/daal4py/adaboost_batch.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,26 +12,28 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py Adaboost example for shared memory systems
 
-import daal4py as d4p
 import numpy as np
 
+import daal4py as d4p
+
 # let's try to use pandas' fast csv reader
 try:
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/adaboost_train.csv"
     testfile = "./data/batch/adaboost_test.csv"
     nClasses = 2
@@ -55,7 +57,7 @@ def main(readcsv=read_csv, method='defaultDense'):
 
     # The prediction result provides prediction
     assert predict_result.prediction.shape == (pdata.shape[0], dep_data.shape[1])
-    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=',', ndmin=2)
+    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=",", ndmin=2)
     assert np.allclose(predict_result.prediction, ptdata)
 
     return (train_result, predict_result, ptdata)
@@ -66,6 +68,6 @@ def main(readcsv=read_csv, method='defaultDense'):
     print("\nGround truth (first 20 observations):\n", ptdata[:20])
     print(
         "Adaboost classification results: (first 20 observations):\n",
-        predict_result.prediction[:20]
+        predict_result.prediction[:20],
     )
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/adagrad_mse_batch.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,27 +12,29 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py AdaGrad (Adaptive Subgradient Method) example for shared memory systems
 # using Mean Squared Error objective function
 
-import daal4py as d4p
 import numpy as np
 
+import daal4py as d4p
+
 # let's try to use pandas' fast csv reader
 try:
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/mse.csv"
     # Read the data, let's have 3 independent variables
     data = readcsv(infile, range(3))
@@ -46,11 +48,13 @@ def main(readcsv=read_csv, method='defaultDense'):
     # configure an AdaGrad object
     lr = np.array([[1.0]], dtype=np.double)
     niters = 1000
-    sgd_algo = d4p.optimization_solver_adagrad(mse_algo,
-                                               learningRate=lr,
-                                               accuracyThreshold=0.0000001,
-                                               nIterations=niters,
-                                               batchSize=1)
+    sgd_algo = d4p.optimization_solver_adagrad(
+        mse_algo,
+        learningRate=lr,
+        accuracyThreshold=0.0000001,
+        nIterations=niters,
+        batchSize=1,
+    )
 
     # finally do the computation
     inp = np.array([[8], [2], [1], [4]], dtype=np.double)
@@ -66,4 +70,4 @@ def main(readcsv=read_csv, method='defaultDense'):
     res = main()
     print("\nMinimum:\n", res.minimum)
     print("\nNumber of iterations performed:\n", res.nIterations[0][0])
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/association_rules_batch.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,26 +12,28 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py assiciation rules example for shared memory systems
 
-import daal4py as d4p
 import numpy as np
 
+import daal4py as d4p
+
 # let's try to use pandas' fast csv reader
 try:
     import pandas
 
     def read_csv(f, c=None, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c=None, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/apriori.csv"
 
     # configure a association_rules object
@@ -57,6 +59,6 @@ def main(readcsv=read_csv, method='defaultDense'):
 
 if __name__ == "__main__":
     result1 = main()
-    print('Confidence: (20 first)')
+    print("Confidence: (20 first)")
     print(result1.confidence[0:20])
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/bacon_outlier_batch.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,26 +12,28 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py outlier detection bacon example for shared memory systems
 
-import daal4py as d4p
 import numpy as np
 
+import daal4py as d4p
+
 # let's try to use pandas' fast csv reader
 try:
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     # Input file
     infile = "./data/batch/outlierdetection.csv"
 
@@ -55,4 +57,4 @@ def main(readcsv=read_csv, method='defaultDense'):
 
     print("\nInput data\n", data)
     print("\nOutlier detection result (Bacon method) weights:\n", res.weights)
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/bf_knn_classification_batch.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2020 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,30 +12,33 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py Brute Force KNN example for shared memory systems
 
-import daal4py as d4p
-import numpy as np
 import os
 
+import numpy as np
+
+import daal4py as d4p
+
 # let's try to use pandas' fast csv reader
 try:
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     # Input data set parameters
-    train_file = os.path.join('data', 'batch', 'k_nearest_neighbors_train.csv')
-    predict_file = os.path.join('data', 'batch', 'k_nearest_neighbors_test.csv')
+    train_file = os.path.join("data", "batch", "k_nearest_neighbors_train.csv")
+    predict_file = os.path.join("data", "batch", "k_nearest_neighbors_test.csv")
 
     # Read data. Let's use 5 features per observation
     nFeatures = 5
@@ -69,6 +72,5 @@ def main(readcsv=read_csv, method='defaultDense'):
     print("Brute Force kNN classification results:")
     print("Ground truth(observations #30-34):\n", predict_labels[30:35])
     print(
-        "Classification results(observations #30-34):\n",
-        predict_result.prediction[30:35]
+        "Classification results(observations #30-34):\n", predict_result.prediction[30:35]
     )

examples/daal4py/brownboost_batch.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,26 +12,28 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py Brownboost example for shared memory systems
 
-import daal4py as d4p
 import numpy as np
 
+import daal4py as d4p
+
 # let's try to use pandas' fast csv reader
 try:
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/brownboost_train.csv"
     testfile = "./data/batch/brownboost_test.csv"
 
@@ -54,7 +56,7 @@ def main(readcsv=read_csv, method='defaultDense'):
 
     # The prediction result provides prediction
     assert predict_result.prediction.shape == (pdata.shape[0], dep_data.shape[1])
-    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=',', ndmin=2)
+    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=",", ndmin=2)
     assert np.allclose(predict_result.prediction, ptdata)
 
     return (train_result, predict_result, ptdata)
@@ -65,6 +67,6 @@ def main(readcsv=read_csv, method='defaultDense'):
     print("\nGround truth (first 20 observations):\n", ptdata[:20])
     print(
         "Brownboost classification results: (first 20 observations):\n",
-        predict_result.prediction[:20]
+        predict_result.prediction[:20],
     )
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/cholesky_batch.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,26 +12,28 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py cholesky example for shared memory systems
 
-import daal4py as d4p
 import numpy as np
 
+import daal4py as d4p
+
 # let's try to use pandas' fast csv reader
 try:
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/cholesky.csv"
 
     # configure a cholesky object
@@ -45,4 +47,4 @@ def main(readcsv=read_csv, method='defaultDense'):
 if __name__ == "__main__":
     result = main()
     print("\nFactor:\n", result.choleskyFactor)
-    print('All looks good!')
+    print("All looks good!")