5900hx scikit learn

AMD Ryzen 9 5900HX testing with a ASUS G513QY v1.0 (G513QY.318 BIOS) and ASUS AMD Cezanne 512MB on Ubuntu 22.10 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 2305113-NE-5900HXSCI86
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May 10 2023
  7 Hours, 56 Minutes
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May 10 2023
  7 Hours, 34 Minutes
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May 11 2023
  7 Hours, 33 Minutes
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5900hx scikit learnOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 5900HX @ 3.30GHz (8 Cores / 16 Threads)ASUS G513QY v1.0 (G513QY.318 BIOS)AMD Renoir/Cezanne16GB512GB SAMSUNG MZVLQ512HBLU-00B00ASUS AMD Cezanne 512MB (2500/1000MHz)AMD Navi 21/23LQ156M1JW25Realtek RTL8111/8168/8411 + MEDIATEK MT7921 802.11ax PCIUbuntu 22.105.19.0-41-generic (x86_64)GNOME Shell 43.0X Server 1.21.1.4 + Wayland4.6 Mesa 22.2.5 (LLVM 15.0.2 DRM 3.47)1.3.224GCC 12.2.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLVulkanCompilerFile-SystemScreen Resolution5900hx Scikit Learn BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - Platform Profile: balanced - CPU Microcode: 0xa50000c - ACPI Profile: balanced - Python 3.10.7- 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 usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcResult OverviewPhoronix Test Suite100%101%101%102%Scikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnS.W.RTreeK.P.S.T.v.N.CPlot NeighborsH.G.B.C.OH.G.BMNIST DatasetText VectorizersK.P.S.T.v.N.SS.R.P.1.ITSNE MNIST DatasetC.D.BPlot Lasso PathPlot HierarchicalP.I.PH.G.B.H.BFeature ExpansionsP.P.K.AP.S.V.D2.N.L.RH.G.B.APlot WardSAGASGD RegressionLassoPlot OMP vs. LARSLocalOutlierFactorH.G.B.TGLMSparsify

5900hx scikit learnscikit-learn: Sparse Rand Projections / 100 Iterationsscikit-learn: SAGAscikit-learn: Kernel PCA Solvers / Time vs. N Componentsscikit-learn: Lassoscikit-learn: GLMscikit-learn: Covertype Dataset Benchmarkscikit-learn: TSNE MNIST Datasetscikit-learn: Plot Lasso Pathscikit-learn: Kernel PCA Solvers / Time vs. N Samplesscikit-learn: Hist Gradient Boosting Higgs Bosonscikit-learn: Hist Gradient Boosting Threadingscikit-learn: Plot Hierarchicalscikit-learn: Plot Polynomial Kernel Approximationscikit-learn: Plot Neighborsscikit-learn: Plot Singular Value Decompositionscikit-learn: Feature Expansionsscikit-learn: Sample Without Replacementscikit-learn: Sparsifyscikit-learn: Plot OMP vs. LARSscikit-learn: SGD Regressionscikit-learn: Treescikit-learn: Hist Gradient Boostingscikit-learn: Hist Gradient Boosting Adultscikit-learn: LocalOutlierFactorscikit-learn: MNIST Datasetscikit-learn: Text Vectorizersscikit-learn: Plot Wardscikit-learn: Plot Incremental PCAscikit-learn: 20 Newsgroups / Logistic Regressionscikit-learn: Hist Gradient Boosting Categorical Onlyscikit-learn: Glmnetabc760.377732.860169.270430.672406.904378.753236.977196.189193.44867.847183.486180.443149.925138.946135.597134.561119.672114.745104.786101.87540.72492.23671.04159.86859.28258.80954.35534.62742.01715.724767.671733.048170.439430.417407.075376.287235.418194.416193.39967.709183.553179.059150.684138.218136.273133.854122.696114.767104.643101.66341.49193.01071.04159.98559.96958.18554.55734.86442.22515.716762.752735.468172.701431.810406.724375.258237.618194.465195.46067.468183.756180.307150.152140.708136.193134.335120.901114.830104.965102.00241.61793.36670.75859.96959.80058.55754.36834.77242.09515.935OpenBenchmarking.org

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationsabc170340510680850SE +/- 2.60, N = 3SE +/- 1.20, N = 3SE +/- 3.45, N = 3760.38767.67762.751. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SAGAabc160320480640800SE +/- 6.30, N = 3SE +/- 1.92, N = 3SE +/- 0.31, N = 3732.86733.05735.471. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentsabc4080120160200SE +/- 2.86, N = 12SE +/- 2.38, N = 12SE +/- 2.77, N = 12169.27170.44172.701. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassoabc90180270360450SE +/- 0.52, N = 3SE +/- 0.98, N = 3SE +/- 0.43, N = 3430.67430.42431.811. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMabc90180270360450SE +/- 2.13, N = 3SE +/- 0.79, N = 3SE +/- 0.54, N = 3406.90407.08406.721. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkabc80160240320400SE +/- 1.06, N = 3SE +/- 0.57, N = 3SE +/- 0.82, N = 3378.75376.29375.261. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Isotonic / Pathological

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetabc50100150200250SE +/- 0.39, N = 3SE +/- 0.31, N = 3SE +/- 0.31, N = 3236.98235.42237.621. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Lasso Pathabc4080120160200SE +/- 0.31, N = 3SE +/- 0.18, N = 3SE +/- 0.54, N = 3196.19194.42194.471. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplesabc4080120160200SE +/- 1.38, N = 3SE +/- 0.46, N = 3SE +/- 0.78, N = 3193.45193.40195.461. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Higgs Bosonabc1530456075SE +/- 0.07, N = 3SE +/- 0.18, N = 3SE +/- 0.02, N = 367.8567.7167.471. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Isotonic / Logistic

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingabc4080120160200SE +/- 0.15, N = 3SE +/- 0.23, N = 3SE +/- 0.21, N = 3183.49183.55183.761. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalabc4080120160200SE +/- 0.21, N = 3SE +/- 0.37, N = 3SE +/- 0.84, N = 3180.44179.06180.311. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Plot Fast KMeans

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationabc306090120150SE +/- 0.18, N = 3SE +/- 0.38, N = 3SE +/- 0.07, N = 3149.93150.68150.151. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsabc306090120150SE +/- 1.26, N = 3SE +/- 1.49, N = 3SE +/- 0.77, N = 3138.95138.22140.711. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Isotonic / Perturbed Logarithm

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Singular Value Decompositionabc306090120150SE +/- 0.16, N = 3SE +/- 0.11, N = 3SE +/- 0.42, N = 3135.60136.27136.191. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionsabc306090120150SE +/- 0.35, N = 3SE +/- 0.21, N = 3SE +/- 0.04, N = 3134.56133.85134.341. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementabc306090120150SE +/- 0.18, N = 3SE +/- 0.79, N = 3SE +/- 1.46, N = 3119.67122.70120.901. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifyabc306090120150SE +/- 0.50, N = 3SE +/- 0.20, N = 3SE +/- 0.14, N = 3114.75114.77114.831. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSabc20406080100SE +/- 0.21, N = 3SE +/- 0.08, N = 3SE +/- 0.24, N = 3104.79104.64104.971. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionabc20406080100SE +/- 0.26, N = 3SE +/- 0.16, N = 3SE +/- 0.25, N = 3101.88101.66102.001. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treeabc918273645SE +/- 0.44, N = 5SE +/- 0.36, N = 15SE +/- 0.41, N = 540.7241.4941.621. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingabc20406080100SE +/- 0.17, N = 3SE +/- 0.95, N = 3SE +/- 0.74, N = 392.2493.0193.371. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Adultabc1632486480SE +/- 0.07, N = 3SE +/- 0.30, N = 3SE +/- 0.08, N = 371.0471.0470.761. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactorabc1326395265SE +/- 0.64, N = 3SE +/- 0.58, N = 3SE +/- 0.24, N = 359.8759.9959.971. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST Datasetabc1326395265SE +/- 0.07, N = 3SE +/- 0.13, N = 3SE +/- 0.08, N = 359.2859.9759.801. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizersabc1326395265SE +/- 0.02, N = 3SE +/- 0.07, N = 3SE +/- 0.14, N = 358.8158.1958.561. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardabc1224364860SE +/- 0.29, N = 3SE +/- 0.18, N = 3SE +/- 0.04, N = 354.3654.5654.371. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Incremental PCAabc816243240SE +/- 0.14, N = 3SE +/- 0.50, N = 3SE +/- 0.34, N = 634.6334.8634.771. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic Regressionabc1020304050SE +/- 0.14, N = 3SE +/- 0.17, N = 3SE +/- 0.06, N = 342.0242.2342.101. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Plot Non-Negative Matrix Factorization

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlyabc48121620SE +/- 0.08, N = 3SE +/- 0.08, N = 3SE +/- 0.18, N = 315.7215.7215.941. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: RCV1 Logreg Convergencet

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Benchmark: Plot Parallel Pairwise

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Benchmark: Isolation Forest

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Benchmark: SGDOneClassSVM

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Benchmark: Glmnet

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