Builds comparison

Read each cell like in the software/implementations dashboard: fitTraining a model on data. More in the scikit-learn glossary or predictMaking predictions with a fitted model. More in the scikit-learn glossary speed-upBaseline time divided by the compared time: 2x is twice as fast as the baseline, 0.5x twice as slow. (log scale) per estimator category, one line per build.

• MKLIntel's Math Kernel Library (oneMKL): a BLAS implementation tuned for Intel CPUs, available as a conda-forge build option. is a good pick for linear models: it speeds up BLASBasic Linear Algebra Subprograms: the library that runs the matrix and vector products of NumPy and SciPy. OpenBLAS and MKL are two implementations of it.-bound linear model fits, commonly by 1.3x to 1.5x.
• On the laptop, every conda-forgeCommunity-maintained channel of conda packages. Its scikit-learn can be installed with different BLAS and OpenMP libraries. build fits LogisticRegression much faster than the PyPIThe Python Package Index, where pip installs packages from. Its scikit-learn wheels bundle their own OpenMP runtime, and NumPy and SciPy bundle OpenBLAS. one, up to ~10x on some datasets. This comes from the OpenBLASOpen-source BLAS implementation. The NumPy and SciPy wheels on PyPI each ship their own copy of it. shipped in scipy and is going to be fixed in scipy soon, see scipy#26193.
• HistGradientBoosting varies more between builds. On laptops, this comes from differences in active waitIdle OpenMP threads spin on the CPU for a while before sleeping, so they start the next parallel loop faster, at the cost of CPU time. (how long idle OpenMP threadsStandard for running the loops of compiled code on several threads. scikit-learn's Cython code (HistGradientBoosting, KMeans, ...) uses it. spin before sleeping), which conda-forge disables by default. Without active wait, HGB fits on small and medium datasets can be much slower. On the high-end server, the LLVM/Intel OpenMPLLVM's OpenMP runtime (libomp) and Intel's (libiomp5), which share most of their code. runtimes seem faster than libgompGCC's OpenMP runtime, used by the PyPI scikit-learn wheels on Linux.. See scikit-learn#34764 for the analysis, and scikit-learn#34935 for a fix in progress, based on the insights from the HistGradientBoosting thread-scalability breakdown plots.
• ExtraTrees fits are ~25% slower on conda-forge builds. scikit-learn#34876 fixes it and will land in the next release.