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Benchmark suite for scikit-learn performances
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Benchmark suite for scikit-learn performances using airspeed velocity.

To get started,

pip install asv
  • Clone this repo:
git clone
cd scikit-learn_benchmarks
  • To run the full benchmark suite (warning: this can take several hours) use the following (see the docs to change the environments config or run only selected benchmarks):
asv run -b _bench
  • To run the benchmarks for a specific model, use the -b <modelname> flag, e.g.:
asv run -b KMeans
  • You can configure the benchmarks by editing the benchmark/config.json file.

  • To publish the results in html format, run asv publish and asv preview

Special instructions to run the benchmarks with the daal4py patches of scikit-learn:

  • Create a conda environment with scikit-learn, joblib, pillow and daal4py installed.
  • Install asv with pip install git+git:// (see this PR)
  • To benchmark scikit-learn vanilla, run:
asv run --python=same --commit-label=vanilla_sklearn -b _bench
  • To benchmark scikit-learn with the patches, first edit the benchmarks/ file with:
import daal4py.sklearn
  • Then run:
asv run --python=same --commit-label=daal4py_sklearn -b _bench

Compare benchmarks

  • To get a quick overview of the comparison of 2 benchmarks, run:
asv compare commit_hash_1 commit_hash_2

Commit hashes can be replaced by labels if you run the benchmarks against an existing environment (see above), for instance:

asv run --python=same --commit-label=daal4py_sklearn -b _bench
  • To get a csv file of the results run:
python commit_hash_1 commit_hash_2

Commit hashes can be replaced by labels also.

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