SCIKIT-leaRn tests

AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 on Ubuntu 24.04 via the Phoronix Test Suite. Noble python 3.12 performance vs. python compiled without frame pointers.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2405056-VPA1-MERGE7223
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Identifier
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Date
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  Test
  Duration
noble
May 02
  12 Hours, 54 Minutes
scikit-learn-python-disabled-fp
May 03
  10 Hours, 58 Minutes
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  11 Hours, 56 Minutes
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SCIKIT-leaRn tests Suite 1.0.0 System Test suite extracted from SCIKIT-leaRn tests. pts/scikit-learn-2.0.0 isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset perturbed_logarithm Benchmark: Isotonic / Perturbed Logarithm pts/scikit-learn-2.0.0 isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset logistic Benchmark: Isotonic / Logistic pts/scikit-learn-2.0.0 saga.py Benchmark: SAGA pts/scikit-learn-2.0.0 random_projections.py --n-times 100 Benchmark: Sparse Random Projections / 100 Iterations pts/scikit-learn-2.0.0 online_ocsvm.py Benchmark: SGDOneClassSVM pts/scikit-learn-2.0.0 glm.py Benchmark: GLM pts/scikit-learn-2.0.0 isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset pathological Benchmark: Isotonic / Pathological pts/scikit-learn-2.0.0 covertype.py Benchmark: Covertype Dataset Benchmark pts/scikit-learn-2.0.0 lasso.py Benchmark: Lasso pts/scikit-learn-2.0.0 isolation_forest.py Benchmark: Isolation Forest pts/scikit-learn-2.0.0 kernel_pca_solvers_time_vs_n_samples.py Benchmark: Kernel PCA Solvers / Time vs. N Samples pts/scikit-learn-2.0.0 tsne_mnist.py Benchmark: TSNE MNIST Dataset pts/scikit-learn-2.0.0 plot_lasso_path.py Benchmark: Plot Lasso Path pts/scikit-learn-2.0.0 sgd_regression.py Benchmark: SGD Regression pts/scikit-learn-2.0.0 plot_hierarchical.py Benchmark: Plot Hierarchical pts/scikit-learn-2.0.0 sparsify.py Benchmark: Sparsify pts/scikit-learn-2.0.0 sample_without_replacement.py Benchmark: Sample Without Replacement pts/scikit-learn-2.0.0 plot_fastkmeans.py Benchmark: Plot Fast KMeans pts/scikit-learn-2.0.0 hist_gradient_boosting_higgsboson.py Benchmark: Hist Gradient Boosting Higgs Boson pts/scikit-learn-2.0.0 plot_polynomial_kernel_approximation.py Benchmark: Plot Polynomial Kernel Approximation pts/scikit-learn-2.0.0 plot_neighbors.py Benchmark: Plot Neighbors pts/scikit-learn-2.0.0 feature_expansions.py Benchmark: Feature Expansions pts/scikit-learn-2.0.0 tree.py Benchmark: Tree pts/scikit-learn-2.0.0 hist_gradient_boosting.py Benchmark: Hist Gradient Boosting pts/scikit-learn-2.0.0 kernel_pca_solvers_time_vs_n_components.py Benchmark: Kernel PCA Solvers / Time vs. N Components pts/scikit-learn-2.0.0 hist_gradient_boosting_adult.py Benchmark: Hist Gradient Boosting Adult pts/scikit-learn-2.0.0 hist_gradient_boosting_threading.py Benchmark: Hist Gradient Boosting Threading pts/scikit-learn-2.0.0 plot_incremental_pca.py Benchmark: Plot Incremental PCA pts/scikit-learn-2.0.0 plot_svd.py Benchmark: Plot Singular Value Decomposition pts/scikit-learn-2.0.0 plot_omp_lars.py Benchmark: Plot OMP vs. LARS pts/scikit-learn-2.0.0 plot_ward.py Benchmark: Plot Ward pts/scikit-learn-2.0.0 mnist.py Benchmark: MNIST Dataset pts/scikit-learn-2.0.0 text_vectorizers.py Benchmark: Text Vectorizers pts/scikit-learn-2.0.0 lof.py Benchmark: LocalOutlierFactor pts/scikit-learn-2.0.0 20newsgroups.py -e logistic_regression Benchmark: 20 Newsgroups / Logistic Regression pts/scikit-learn-2.0.0 plot_nmf.py Benchmark: Plot Non-Negative Matrix Factorization pts/scikit-learn-2.0.0 hist_gradient_boosting_categorical_only.py Benchmark: Hist Gradient Boosting Categorical Only pts/scikit-learn-2.0.0 rcv1_logreg_convergence.py Benchmark: RCV1 Logreg Convergencet pts/scikit-learn-2.0.0 plot_parallel_pairwise.py Benchmark: Plot Parallel Pairwise pts/scikit-learn-2.0.0 glmnet.py Benchmark: Glmnet