Intel Corp has built a bundle of Python libraries with accelerations for High-Performance Computing (HPC) on CPUs. The vast majority of the accelerations come with no code changes, because they are snuck in under the hood. All the concepts and libraries introduced in the rest of the book will run faster in the HPC Intel Python environment. Luckily, Intel has a Conda version of their distribution, so you can add it as a new Conda environment via the following few command lines in the Anaconda prompt:
(base) $ Conda create -n idp -c channel intelpython3_full Python=3
(base) $ Conda activate idp
Full disclosure: I work for Intel, so I won't focus too much on this HPC distribution. I will merely let the performance numbers speak for themselves. See the following graph for raw speedup numbers (optimized versus stock) when using unchanged Scikit-learn code on CPU:
