m2 scikit learn apple

Apple M2 testing with a Apple MacBook Air (13 h M2 2022) and llvmpipe on Arch rolling via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2211216-NE-M2SCIKITL85
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a
November 20 2022
  1 Hour, 3 Minutes
b
November 20 2022
  3 Hours, 9 Minutes
c
November 21 2022
  1 Hour, 3 Minutes
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  1 Hour, 45 Minutes

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m2 scikit learn appleOpenBenchmarking.orgPhoronix Test SuiteApple M2 @ 2.42GHz (4 Cores / 8 Threads)Apple MacBook Air (13 h M2 2022)8GB251GB APPLE SSD AP0256Z + 2 x 0GB APPLE SSD AP0256ZllvmpipeBroadcom Device 4433 + Broadcom Device 5f71Arch rolling5.19.0-rc7-asahi-2-1-ARCH (aarch64)KDE Plasma 5.25.4X Server 1.21.1.44.5 Mesa 22.1.6 (LLVM 14.0.6 128 bits)GCC 12.1.0 + Clang 14.0.6ext42560x1600ProcessorMotherboardMemoryDiskGraphicsNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionM2 Scikit Learn Apple BenchmarksSystem Logs- Scaling Governor: apple-cpufreq schedutil- Python 3.10.5- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcResult OverviewPhoronix Test Suite100%101%102%103%103%Scikit-LearnScikit-LearnScikit-LearnTSNE MNIST DatasetMNIST DatasetS.R.P.1.I

Scikit-Learn

Scikit-learn is a Python module for machine learning Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.1.3Benchmark: Sparse Random Projections, 100 Iterationsabc7001400210028003500SE +/- 0.14, N = 33422.753422.933423.45

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.1.3Benchmark: MNIST Datasetabc50100150200250SE +/- 0.70, N = 3246.49246.92248.18

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.1.3Benchmark: TSNE MNIST Datasetbac20406080100SE +/- 0.09, N = 3103.23103.93106.76