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 supports the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms.

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Table of content

• How to create conda environment for benchmarking
• Running Python benchmarks with runner script
• Benchmark supported algorithms
  • Scikit-learn benchmakrs
• Algorithm 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

pip install -r sklearn_bench/requirements.txt
# or
conda install -c intel scikit-learn scikit-learn-intelex pandas tqdm

• daal4py

conda install -c conda-forge scikit-learn daal4py pandas tqdm

• cuml

conda install -c rapidsai -c conda-forge cuml pandas cudf tqdm

• xgboost

pip install -r xgboost_bench/requirements.txt
# or
conda install -c conda-forge xgboost scikit-learn pandas tqdm

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.

Options:

• --configs: specify the path to a configuration file or a folder that contains configuration files.
• --no-intel-optimized: use Scikit-learn without Intel(R) Extension for Scikit-learn*. Now available for scikit-learn benchmarks. By default, the runner uses Intel(R) Extension for Scikit-learn.
• --output-file: specify the name of the output file 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 out additional information when the benchmarks are running. The 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

Scikit-learn benchmakrs

When you run scikit-learn benchmarks on CPU, Intel(R) Extension for Scikit-learn is used by default. Use the --no-intel-optimized option to run the benchmarks without the extension.

For the algorithms with both CPU and GPU support, you may use the same configuration file to run the scikit-learn benchmarks on CPU and GPU.

Algorithm 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