Merge remote-tracking branch 'origin/master' into xgb-nvidia-datasets

Author: Igor Rukhovich

README.md

@@ -40,7 +40,7 @@ Create a suitable conda environment for each framework to test. Each item in the
 * [**scikit-learn**](sklearn_bench#how-to-create-conda-environment-for-benchmarking)
 
 ```bash
-conda create -n bench -c intel python=3.7 scikit-learn daal4py pandas
+conda create -n bench -c intel python=3.7 scikit-learn scikit-learn-intelex pandas
 ```
 
 * [**daal4py**](daal4py_bench#how-to-create-conda-environment-for-benchmarking)
@@ -67,7 +67,7 @@ Run `python runner.py --configs configs/config_example.json [--output-file resul
 
 runner options:
 * ``configs`` : configuration files paths
-* ``no-intel-optimized`` : use no intel optimized version. Now avalible for scikit-learn benchmarks. Default is intel-optimized version.
+* ``no-intel-optimized`` : using Scikit-learn without Intel(R) Extension for Scikit-learn*. Now avalible for scikit-learn benchmarks. Default starts with using Intel(R) Extension for Scikit-learn*.
 * ``output-file``: output file name for result benchmarks. Default is `result.json`
 * ``report``: create an Excel report based on benchmarks results. Need library `openpyxl`.
 * ``dummy-run`` : run configuration parser and datasets generation without benchmarks running.

azure-pipelines.yml

@@ -6,7 +6,7 @@ jobs:
   - script: |
       conda update -y -q conda
       export FORCE_DAAL4PY_SKLEARN=yes
-      conda create -q -y -n bench -c conda-forge python=3.7 pandas scikit-learn daal4py tqdm
+      conda create -q -y -n bench -c conda-forge python=3.7 pandas scikit-learn scikit-learn-intelex tqdm
     displayName: Create Anaconda environment
   - script: |
       . /usr/share/miniconda/etc/profile.d/conda.sh
@@ -37,7 +37,7 @@ jobs:
   - script: |
       . /usr/share/miniconda/etc/profile.d/conda.sh
       conda activate bench
-      python runner.py --configs configs/testing/daal4py.json
+      python runner.py --configs configs/testing/daal4py.json --no-intel-optimized
     displayName: Run bench
 - job: Linux_XGBoost_and_daal4py
   pool:
@@ -50,7 +50,7 @@ jobs:
   - script: |
       . /usr/share/miniconda/etc/profile.d/conda.sh
       conda activate bench
-      python runner.py --configs configs/testing/daal4py_xgboost.json
+      python runner.py --configs configs/testing/daal4py_xgboost.json --no-intel-optimized
     displayName: Run bench
 - job: Pep8
   pool:

bench.py

@@ -198,10 +198,10 @@ def parse_args(parser, size=None, loop_types=(),
 
     if not params.no_intel_optimized:
         try:
-            from daal4py.sklearn import patch_sklearn
+            from sklearnex import patch_sklearn
             patch_sklearn()
         except ImportError:
-            logging.info('Failed to import daal4py.sklearn.patch_sklearn.'
+            logging.info('Failed to import sklearnex.patch_sklearn.'
                          'Use stock version scikit-learn', file=sys.stderr)
             params.device = 'None'
     else: