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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- How to create conda environment for benchmarking
- Running Python benchmarks with runner script
- Benchmark supported algorithms
- Intel(R) Extension for Scikit-learn* support
- Algorithms parameters
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.
pip install -r sklearn_bench/requirements.txt
# or
conda install -c intel scikit-learn scikit-learn-intelex pandas tqdm
conda install -c conda-forge scikit-learn daal4py pandas tqdm
conda install -c rapidsai -c conda-forge cuml pandas cudf tqdm
pip install -r xgboost_bench/requirements.txt
# or
conda install -c conda-forge xgboost scikit-learn pandas tqdm
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.--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 isresult.json
--report
: create an Excel report based on benchmark results. Theopenpyxl
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.
Level | Description |
---|---|
DEBUG | etailed information, typically of interest only when diagnosing problems. Usually at this level the logging output is so low level that it’s not useful to users who are not familiar with the software’s internals. |
INFO | Confirmation that things are working as expected. |
WARNING | An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected. |
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.
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 | ❌ | ❌ | ❌ | ✅ |
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.
The following benchmarks have a GPU support:
- dbscan
- kmeans
- linear
- log_reg
You may use the configuration file for these benchmarks to run them on both CPU and GPU.
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: