scikit-learn_bench

scikit-learn_bench benchmarks various 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.

See benchmark results here.

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
• Legacy automatic building and running

Prerequisites

• python and scikit-learn to run python versions
• pandas when using its DataFrame as input data format
• icc, ifort, mkl, daal to compile and run native benchmarks
• machine learning frameworks, that you want to test. Check this item to get additional information how to set environment.

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
• daal4py
• cuml
• xgboost

How to enable daal4py patching for scikit-learn benchmarks

Set specific environment variable export FORCE_DAAL4PY_SKLEARN=YES

Running Python benchmarks with runner script

Run python runner.py --configs configs/config_example.json [--output-format json --verbose] to launch benchmarks.

runner options:

• configs : configuration files paths
• dummy-run : run configuration parser and datasets generation without benchmarks running
• verbose : print additional information during benchmarks running
• output-format: json or csv. Output type of benchmarks to use with their runner

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

algorithm	benchmark name	sklearn	daal4py	cuml	xgboost
DBSCAN	dbscan	✅	✅	✅	❌
RandomForestClassifier	df_clfs	✅	✅	✅	❌
RandomForestRegressor	df_regr	✅	✅	✅	❌
pairwise_distances	distances	✅	✅	❌	❌
KMeans	kmeans	✅	✅	✅	❌
KNeighborsClassifier	knn_clsf	✅	❌	✅	❌
LinearRegression	linear	✅	✅	✅	❌
LogisticRegression	log_reg	✅	✅	✅	❌
PCA	pca	✅	✅	✅	❌
Ridge	ridge	✅	✅	✅	❌
SVM	svm	✅	✅	✅	❌
train_test_split	train_test_split	✅	❌	✅	❌
GradientBoostingClassifier	gbt	❌	❌	❌	✅
GradientBoostingRegressor	gbt	❌	❌	❌	✅

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

Legacy automatic building and running

• Run make. This will generate data, compile benchmarks, and run them.
  • To run only scikit-learn benchmarks, use make sklearn.
  • To run only native benchmarks, use make native.
  • To run only daal4py benchmarks, use make daal4py.
  • To run a specific implementation of a specific benchmark, directly request the corresponding file: make output/<impl>/<bench>.out.
  • If you have activated a conda environment, the build will use daal from the conda environment, if available.