Machine Learning Benchmarks

Machine Learning Benchmarks contains implementations of machine learning algorithms across data analytics frameworks. Scikit-learn_bench can be extended to add new frameworks and algorithms. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms.

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Table of content

• Prerequisites
• How to create conda environment for benchmarking
• How to enable daal4py patching for scikit-learn benchmarks
• Running Python benchmarks with runner script
• Supported algorithms
• Algorithms parameters

How to create conda environment for benchmarking

Create a suitable conda environment for each framework to test. Each item in the list below links to instructions to create an appropriate conda environment for the framework.

• scikit-learn

conda create -n bench -c intel python=3.7 scikit-learn daal4py pandas

• daal4py

conda create -n bench -c intel python=3.7 scikit-learn daal4py pandas

• cuml

conda create -n bench -c rapidsai -c conda-forge python=3.7 cuml pandas cudf

• xgboost

conda create -n bench -c conda-forge python=3.7 xgboost pandas

Running Python benchmarks with runner script

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 : use no intel optimized version. Now avalible for scikit-learn benchmarks. Default is intel-optimized version.
• 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

Benchmarks currently support the following frameworks:

• scikit-learn
• daal4py
• cuml
• xgboost

The configuration of benchmarks allows you to select the frameworks to run, select datasets for measurements and configure the parameters of the algorithms.

You can configure benchmarks by editing a config file. Check config.json schema for more details.

Benchmark supported algorithms

Algorithms parameters

You can launch benchmarks for each algorithm separately. To do this, go to the directory with the benchmark:

cd <framework>

Run the following command:

python <benchmark_file> --dataset-name <path to the dataset> <other algorithm parameters>

The list of supported parameters for each algorithm you can find here:

• scikit-learn
• daal4py
• cuml
• xgboost