Accelerating scikit-learn with Intel’s accelerated Python requires absolutely no code changes, thereby giving us a nearly effortless way to enhance performance.
However, scikit-learn is designed for machine learning operations on in-memory homogeneous data.
Think of it as “scikit-learn meets MPI (Message Passing Interface)” without requiring us to actually program in MPI.
We get the benefits of MPI, and our programs get higher performance by utilizing parallelism across multiple nodes of CPUs.
You might want to read my previous piece about accelerating Python in “How Does a 20X Speed-Up in Python Grab You?” Although I wrote it a couple of years ago, it’s still valid today.
(And the efforts to accelerate Python have only gotten better in the meantime.)