Accuracy of autosklearn can be improved with Intel® Extension for Scikit-learn

What is this?

Intel® Extension for Scikit-learn provides drop-in replacement patching functionality for a seamless way to speed up Scikit-learn application.

Our results

I used automlbenchmarks on large datasets to compare accuracy of autosklearn with patching and without.

The table below represent the difference of autosklearn with patching and w/o patching

Accuracy was improved because the number of trained models was increased. The full list of algorithms, that can be accelerated with intel extension for scikit-learn can be founded here.

How to reproduce our results

To add the intel extension for scikit-learn to the benchmark, you just need to add 2 lines at the beginning of the autosklearn exec file:

from sklearnex import patch_sklearn
patch_sklearn()

and add scikit-learn-intelex to the requirements.

I also change constraints for a more honest comparison:

test:
  folds: 2
  max_runtime_seconds: 1800
  cores: 72

And remove environment settings from autosklearn exec file.

os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'

All measurements were done on AWS c5.18xlarge instance (Intel Xeon Platinum with 36 cores)

Some benefits of Intel® Extension for Scikit-learn

• Library uses all capabilities of the hardware, which allows you to get a significant performance boost for the classic machine learning algorithms. Check their patching section and medium articles for more details.

• All optimizations can be easily integrated into scikit-learn application by changing one line of code. Check their get started section for more details.

I also think, that Intel® Extension for Scikit-learn can help to solve these problems: automl#445, automl#923, automl#1153

What do you think?