Add GPU benchmarks support to readme (#59)

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Author: vlad-nazarov

README.md

@@ -26,11 +26,10 @@ We publish blogs on Medium, so [follow us](https://medium.com/intel-analytics-so
 
 ## Table of content
 
-* [Prerequisites](#prerequisites)
 * [How to create conda environment for benchmarking](#how-to-create-conda-environment-for-benchmarking)
-* [How to enable daal4py patching for scikit-learn benchmarks](#how-to-enable-daal4py-patching-for-scikit-learn-benchmarks)
 * [Running Python benchmarks with runner script](#running-python-benchmarks-with-runner-script)
-* [Supported algorithms](#supported-algorithms)
+* [Benchmark supported algorithms](#benchmark-supported-algorithms)
+* [Intel(R) Extension for Scikit-learn* support](#intelr-extension-for-scikit-learn-support)
 * [Algorithms parameters](#algorithms-parameters)
 
 ## How to create conda environment for benchmarking
@@ -65,13 +64,13 @@ conda create -n bench -c conda-forge python=3.7 xgboost pandas
 
 Run `python runner.py --configs configs/config_example.json [--output-file result.json --verbose INFO --report]` to launch benchmarks.
 
-runner options:
-* ``configs`` : configuration files paths
-* ``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.
-* ``verbose`` : *WARNING*, *INFO*, *DEBUG*. print additional information during benchmarks running. Default is *INFO*
+Options:
+* ``--configs``: specify the path to a configuration file.
+* ``--no-intel-optimized``: use Scikit-learn without [Intel(R) Extension for Scikit-learn*](#intelr-extension-for-scikit-learn-support). Now available for [scikit-learn benchmarks](https://github.com/IntelPython/scikit-learn_bench/tree/master/sklearn_bench). By default, the runner uses Intel(R) Extension for Scikit-learn.
+* ``--output-file``: output file name for the benchmark result. The default name is `result.json`
+* ``--report``: create an Excel report based on benchmark results. The `openpyxl` library is required.
+* ``--dummy-run``: run configuration parser and dataset generation without benchmarks running.
+* ``--verbose``: *WARNING*, *INFO*, *DEBUG*. print additional information during benchmarks running. Default is *INFO*.
 
 |   Level   |  Description  |
 |-----------|---------------|
@@ -108,6 +107,18 @@ The configuration of benchmarks allows you to select the frameworks to run, sele
 |**[GradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html)**|gbt|:x:|:x:|:x:|:white_check_mark:|
 |**[GradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)**|gbt|:x:|:x:|:x:|:white_check_mark:|
 
+## Intel(R) Extension for Scikit-learn support
+
+When you run scikit-learn benchmarks on CPU, [Intel(R) Extension for Scikit-learn](https://github.com/intel/scikit-learn-intelex) is used by default. Use the ``--no-intel-optimized`` option to run the benchmarks without the extension. 
+
+The following benchmarks have a GPU support:
+* dbscan
+* kmeans
+* linear
+* log_reg
+
+You may use the [configuration file for these benchmarks](https://github.com/IntelPython/scikit-learn_bench/blob/master/configs/skl_xpu_config.json) to run them on both CPU and GPU.
+
 ##  Algorithms parameters
 
 You can launch benchmarks for each algorithm separately.

configs/README.md

@@ -27,6 +27,7 @@ Refer to the tables below for descriptions of all fields in the configuration fi
 |data-order| array[string] | **REQUIRED**  input data order. Data order: *C* (row-major, default) or *F* (column-major) |
 |dtype| array[string] | **REQUIRED**  input data type. Data type: *float64* (default) or *float32* |
 |check-finitness| array[] | Check finiteness in sklearn input check(disabled by default) |
+|device| array[string] | For scikit-learn only. The list of devices to run the benchmarks on. It can be *None* (default, run on CPU without sycl context) or one of the types of sycl devices: *cpu*, *gpu*, *host*. Refer to [SYCL specification](https://www.khronos.org/files/sycl/sycl-2020-reference-guide.pdf) for details|
 
 ###  Case Object