c6i.4xlarge

c6i.4xlarge

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c6i.4xlarge
June 21
  13 Hours, 57 Minutes
Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2
June 24
  4 Minutes
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c6i.4xlarge Suite 1.0.0 System Test suite extracted from c6i.4xlarge. pts/scikit-learn-2.0.0 20newsgroups.py -e logistic_regression Benchmark: 20 Newsgroups / Logistic Regression pts/onednn-3.4.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --engine=cpu Harness: Recurrent Neural Network Inference - Engine: CPU pts/onednn-3.4.0 --rnn --batch=inputs/rnn/perf_rnn_training --engine=cpu Harness: Recurrent Neural Network Training - Engine: CPU pts/onednn-3.4.0 --deconv --batch=inputs/deconv/shapes_3d --engine=cpu Harness: Deconvolution Batch shapes_3d - Engine: CPU pts/onednn-3.4.0 --ip --batch=inputs/ip/shapes_3d --engine=cpu Harness: IP Shapes 3D - Engine: CPU pts/onednn-3.4.0 --ip --batch=inputs/ip/shapes_1d --engine=cpu Harness: IP Shapes 1D - Engine: CPU pts/onednn-3.4.0 --conv --batch=inputs/conv/shapes_auto --engine=cpu Harness: Convolution Batch Shapes Auto - Engine: CPU pts/scikit-learn-2.0.0 random_projections.py --n-times 100 Benchmark: Sparse Random Projections / 100 Iterations 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 kernel_pca_solvers_time_vs_n_samples.py Benchmark: Kernel PCA Solvers / Time vs. N Samples pts/scikit-learn-2.0.0 hist_gradient_boosting_categorical_only.py Benchmark: Hist Gradient Boosting Categorical Only pts/scikit-learn-2.0.0 plot_polynomial_kernel_approximation.py Benchmark: Plot Polynomial Kernel Approximation pts/scikit-learn-2.0.0 hist_gradient_boosting_higgsboson.py Benchmark: Hist Gradient Boosting Higgs Boson pts/scikit-learn-2.0.0 plot_svd.py Benchmark: Plot Singular Value Decomposition pts/scikit-learn-2.0.0 hist_gradient_boosting_threading.py Benchmark: Hist Gradient Boosting Threading 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 hist_gradient_boosting_adult.py Benchmark: Hist Gradient Boosting Adult pts/scikit-learn-2.0.0 covertype.py Benchmark: Covertype Dataset Benchmark pts/scikit-learn-2.0.0 sample_without_replacement.py Benchmark: Sample Without Replacement pts/scikit-learn-2.0.0 hist_gradient_boosting.py Benchmark: Hist Gradient Boosting pts/scikit-learn-2.0.0 plot_incremental_pca.py Benchmark: Plot Incremental PCA 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 tsne_mnist.py Benchmark: TSNE MNIST Dataset pts/scikit-learn-2.0.0 lof.py Benchmark: LocalOutlierFactor pts/scikit-learn-2.0.0 feature_expansions.py Benchmark: Feature Expansions pts/scikit-learn-2.0.0 plot_omp_lars.py Benchmark: Plot OMP vs. LARS pts/scikit-learn-2.0.0 plot_hierarchical.py Benchmark: Plot Hierarchical pts/scikit-learn-2.0.0 text_vectorizers.py Benchmark: Text Vectorizers pts/scikit-learn-2.0.0 plot_fastkmeans.py Benchmark: Plot Fast KMeans pts/scikit-learn-2.0.0 isolation_forest.py Benchmark: Isolation Forest pts/scikit-learn-2.0.0 plot_lasso_path.py Benchmark: Plot Lasso Path pts/scikit-learn-2.0.0 online_ocsvm.py Benchmark: SGDOneClassSVM pts/scikit-learn-2.0.0 sgd_regression.py Benchmark: SGD Regression pts/scikit-learn-2.0.0 plot_neighbors.py Benchmark: Plot Neighbors pts/scikit-learn-2.0.0 mnist.py Benchmark: MNIST Dataset pts/scikit-learn-2.0.0 plot_ward.py Benchmark: Plot Ward pts/scikit-learn-2.0.0 sparsify.py Benchmark: Sparsify pts/scikit-learn-2.0.0 lasso.py Benchmark: Lasso pts/scikit-learn-2.0.0 tree.py Benchmark: Tree pts/scikit-learn-2.0.0 saga.py Benchmark: SAGA pts/scikit-learn-2.0.0 glm.py Benchmark: GLM pts/mlpack-1.0.2 SCIKIT_LINEARRIDGEREGRESSION Benchmark: scikit_linearridgeregression pts/mlpack-1.0.2 SCIKIT_SVM Benchmark: scikit_svm pts/mlpack-1.0.2 SCIKIT_QDA Benchmark: scikit_qda pts/mlpack-1.0.2 SCIKIT_ICA Benchmark: scikit_ica pts/onednn-3.4.0 --deconv --batch=inputs/deconv/shapes_1d --engine=cpu Harness: Deconvolution Batch shapes_1d - Engine: CPU pts/scikit-learn-2.0.0 plot_nmf.py Benchmark: Plot Non-Negative Matrix Factorization pts/scikit-learn-2.0.0 rcv1_logreg_convergence.py Benchmark: RCV1 Logreg Convergencet 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 plot_parallel_pairwise.py Benchmark: Plot Parallel Pairwise pts/scikit-learn-2.0.0 glmnet.py Benchmark: Glmnet