scikit learn 5950X

AMD Ryzen 9 7950X 16-Core testing with a ASUS ROG CROSSHAIR X670E HERO (1101 BIOS) and AMD Radeon RX 7900 XTX 24GB on Ubuntu 22.04 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 2305115-NE-SCIKITLEA14
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May 10 2023
  6 Hours, 55 Minutes
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May 10 2023
  6 Hours, 16 Minutes
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May 11 2023
  6 Hours, 24 Minutes
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  6 Hours, 32 Minutes

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scikit learn 5950XOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 7950X 16-Core @ 4.50GHz (16 Cores / 32 Threads)ASUS ROG CROSSHAIR X670E HERO (1101 BIOS)AMD Device 14d832GB2048GB SOLIDIGM SSDPFKKW020X7 + 2000GBAMD Radeon RX 7900 XTX 24GB (2304/1249MHz)AMD Device ab30ASUS MG28UIntel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411Ubuntu 22.046.3.0-060300rc7daily20230417-generic (x86_64)GNOME Shell 42.5X Server 1.21.1.3 + Wayland4.6 Mesa 23.2.0-devel (git-f6fb189 2023-04-18 jammy-oibaf-ppa) (LLVM 15.0.7 DRM 3.52)1.3.246GCC 11.3.0ext43840x2160ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLVulkanCompilerFile-SystemScreen ResolutionScikit Learn 5950X BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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-build-config=bootstrap-lto-lean --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) - CPU Microcode: 0xa601203- Python 3.10.6- 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 Enhanced / Automatic IBRS IBPB: conditional RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcResult OverviewPhoronix Test Suite100%114%129%143%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-LearnScikit-LearnScikit-LearnScikit-LearnScikit-LearnPlot OMP vs. LARSLassoSGD RegressionGLMSGDOneClassSVMPlot Lasso PathP.I.PK.P.S.T.v.N.CK.P.S.T.v.N.SH.G.B.H.BIsolation ForestTree2.N.L.RPlot NeighborsFeature ExpansionsH.G.B.TH.G.B.ATSNE MNIST DatasetPlot Fast KMeansPlot WardText VectorizersMNIST DatasetS.R.P.1.IH.G.B.C.OH.G.BI.LS.W.RPlot HierarchicalC.D.BSparsifyLocalOutlierFactorSAGAP.P.K.AP.S.V.D

scikit learn 5950Xscikit-learn: Plot OMP vs. LARSscikit-learn: Lassoscikit-learn: SGD Regressionscikit-learn: GLMscikit-learn: SGDOneClassSVMscikit-learn: Plot Lasso Pathscikit-learn: Hist Gradient Boosting Higgs Bosonscikit-learn: Isolation Forestscikit-learn: Treescikit-learn: 20 Newsgroups / Logistic Regressionscikit-learn: Plot Neighborsscikit-learn: Feature Expansionsscikit-learn: Hist Gradient Boosting Threadingscikit-learn: Hist Gradient Boosting Adultscikit-learn: TSNE MNIST Datasetscikit-learn: Plot Fast KMeansscikit-learn: Plot Wardscikit-learn: Text Vectorizersscikit-learn: MNIST Datasetscikit-learn: Sparse Rand Projections / 100 Iterationsscikit-learn: Hist Gradient Boosting Categorical Onlyscikit-learn: Hist Gradient Boostingscikit-learn: Isotonic / Logisticscikit-learn: Sample Without Replacementscikit-learn: Plot Hierarchicalscikit-learn: Covertype Dataset Benchmarkscikit-learn: Sparsifyscikit-learn: LocalOutlierFactorscikit-learn: SAGAscikit-learn: Plot Polynomial Kernel Approximationscikit-learn: Plot Singular Value Decompositionscikit-learn: Kernel PCA Solvers / Time vs. N Componentsscikit-learn: Kernel PCA Solvers / Time vs. N Samplesscikit-learn: Plot Incremental PCAscikit-learn: Glmnetabc52.051309.79279.631167.138238.929112.27331.812168.47334.48424.632103.85479.06165.38684.066141.755123.57933.77239.49142.813381.42315.82988.8361020.78365.763115.026270.85367.55723.360568.36492.65744.70134.33183.71047.94233.088208.53958.841142.180204.919123.74832.731171.26835.37324.589102.51079.48166.50584.648142.709125.40133.81539.50642.453378.06715.93789.0081015.23865.591114.506270.39967.55223.386571.01993.01244.87533.96185.35147.11533.414208.91759.164141.992208.572125.34532.450173.11634.74725.179104.83880.57365.36883.208144.040124.30834.17839.92042.834379.12115.80789.4881022.45465.308114.279271.99167.93923.482569.63793.02244.80235.19286.72745.343OpenBenchmarking.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: Plot OMP vs. LARScba1224364860SE +/- 0.14, N = 3SE +/- 0.09, N = 3SE +/- 0.38, N = 333.4133.0952.051. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassocba70140210280350SE +/- 1.28, N = 3SE +/- 0.81, N = 3SE +/- 0.34, N = 3208.92208.54309.791. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressioncba20406080100SE +/- 0.12, N = 3SE +/- 0.13, N = 3SE +/- 0.29, N = 359.1658.8479.631. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMcba4080120160200SE +/- 0.73, N = 3SE +/- 0.63, N = 3SE +/- 0.97, N = 3141.99142.18167.141. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGDOneClassSVMcba50100150200250SE +/- 2.09, N = 5SE +/- 0.37, N = 3SE +/- 0.51, N = 3208.57204.92238.931. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Lasso Pathcba306090120150SE +/- 0.63, N = 3SE +/- 0.89, N = 3SE +/- 0.58, N = 3125.35123.75112.271. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Higgs Bosoncba816243240SE +/- 0.15, N = 3SE +/- 0.23, N = 3SE +/- 0.26, N = 332.4532.7331.811. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isolation Forestcba4080120160200SE +/- 1.54, N = 3SE +/- 0.30, N = 3SE +/- 0.26, N = 3173.12171.27168.471. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treecba816243240SE +/- 0.43, N = 15SE +/- 0.38, N = 15SE +/- 0.43, N = 1534.7535.3734.481. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic Regressioncba612182430SE +/- 0.21, N = 3SE +/- 0.20, N = 3SE +/- 0.32, N = 325.1824.5924.631. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborscba20406080100SE +/- 0.52, N = 3SE +/- 1.07, N = 3SE +/- 0.38, N = 3104.84102.51103.851. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionscba20406080100SE +/- 0.47, N = 3SE +/- 0.12, N = 3SE +/- 0.33, N = 380.5779.4879.061. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingcba1530456075SE +/- 0.28, N = 3SE +/- 0.52, N = 3SE +/- 0.31, N = 365.3766.5165.391. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Adultcba20406080100SE +/- 0.32, N = 3SE +/- 0.69, N = 3SE +/- 0.38, N = 383.2184.6584.071. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetcba306090120150SE +/- 0.84, N = 3SE +/- 0.37, N = 3SE +/- 1.56, N = 5144.04142.71141.761. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Fast KMeanscba306090120150SE +/- 0.61, N = 3SE +/- 0.83, N = 3SE +/- 0.29, N = 3124.31125.40123.581. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardcba816243240SE +/- 0.28, N = 3SE +/- 0.42, N = 3SE +/- 0.05, N = 334.1833.8233.771. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizerscba918273645SE +/- 0.22, N = 3SE +/- 0.08, N = 3SE +/- 0.14, N = 339.9239.5139.491. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST Datasetcba1020304050SE +/- 0.22, N = 3SE +/- 0.01, N = 3SE +/- 0.27, N = 342.8342.4542.811. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationscba80160240320400SE +/- 0.62, N = 3SE +/- 0.26, N = 3SE +/- 2.15, N = 3379.12378.07381.421. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlycba48121620SE +/- 0.01, N = 3SE +/- 0.14, N = 3SE +/- 0.13, N = 315.8115.9415.831. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingcba20406080100SE +/- 0.49, N = 3SE +/- 0.58, N = 3SE +/- 0.73, N = 389.4989.0188.841. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Logisticcba2004006008001000SE +/- 2.35, N = 3SE +/- 0.65, N = 3SE +/- 5.97, N = 31022.451015.241020.781. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementcba1530456075SE +/- 0.43, N = 3SE +/- 0.34, N = 3SE +/- 0.03, N = 365.3165.5965.761. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalcba306090120150SE +/- 0.41, N = 3SE +/- 0.15, N = 3SE +/- 0.26, N = 3114.28114.51115.031. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkcba60120180240300SE +/- 1.29, N = 3SE +/- 0.09, N = 3SE +/- 1.22, N = 3271.99270.40270.851. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifycba1530456075SE +/- 0.72, N = 3SE +/- 0.56, N = 3SE +/- 0.71, N = 367.9467.5567.561. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactorcba612182430SE +/- 0.07, N = 3SE +/- 0.05, N = 3SE +/- 0.11, N = 323.4823.3923.361. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SAGAcba120240360480600SE +/- 1.46, N = 3SE +/- 3.47, N = 3SE +/- 2.25, N = 3569.64571.02568.361. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationcba20406080100SE +/- 0.42, N = 3SE +/- 0.28, N = 3SE +/- 0.11, N = 393.0293.0192.661. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Singular Value Decompositioncba1020304050SE +/- 0.52, N = 3SE +/- 0.38, N = 3SE +/- 0.46, N = 1544.8044.8844.701. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentscba816243240SE +/- 0.52, N = 15SE +/- 0.59, N = 15SE +/- 0.52, N = 1535.1933.9634.331. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplescba20406080100SE +/- 1.74, N = 15SE +/- 1.27, N = 15SE +/- 2.28, N = 1286.7385.3583.711. (F9X) gfortran options: -O0

Benchmark: Plot Non-Negative Matrix Factorization

a: The test quit with a non-zero exit status. E: KeyError:

b: The test quit with a non-zero exit status. E: KeyError:

c: The test quit with a non-zero exit status. E: KeyError:

Benchmark: Isotonic / Perturbed Logarithm

a: The test quit with a non-zero exit status.

b: The test quit with a non-zero exit status.

c: The test quit with a non-zero exit status.

Benchmark: RCV1 Logreg Convergencet

a: The test quit with a non-zero exit status. E: IndexError: list index out of range

b: The test quit with a non-zero exit status. E: IndexError: list index out of range

c: The test quit with a non-zero exit status. E: IndexError: list index out of range

Benchmark: Isotonic / Pathological

a: The test quit with a non-zero exit status.

b: The test quit with a non-zero exit status.

c: The test quit with a non-zero exit status.

Benchmark: Plot Parallel Pairwise

a: The test quit with a non-zero exit status. E: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 74.5 GiB for an array with shape (100000, 100000) and data type float64

b: The test quit with a non-zero exit status. E: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 74.5 GiB for an array with shape (100000, 100000) and data type float64

c: The test quit with a non-zero exit status. E: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 74.5 GiB for an array with shape (100000, 100000) and data type float64

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Incremental PCAcba1122334455SE +/- 1.03, N = 15SE +/- 0.78, N = 15SE +/- 0.72, N = 1545.3447.1247.941. (F9X) gfortran options: -O0

Benchmark: Glmnet

a: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'

b: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'

c: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'