scikit-learn_bench

Benchmark for optimizations to scikit-learn in the Intel(R) Distribution for Python*. See benchmark results here.

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

How to create conda environment for benchmarking

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

Running Python benchmarks with runner script

python runner.py --config config_example.json [--output-format json --verbose]

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.