c7g.4xlarge

c7g.4xlarge

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c7g.4xlarge
June 25
  10 Hours, 49 Minutes
ARMv8 Neoverse-V1 - - Amazon EC2 c7g.4xlarge (1.0
June 26
  1 Minute
tst11
June 28
  2 Minutes
n
June 28
  2 Minutes
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  2 Hours, 43 Minutes

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c7g.4xlargeProcessorMotherboardChipsetMemoryDiskNetworkOSKernelCompilerFile-SystemSystem LayerVulkanc7g.4xlargeARMv8 Neoverse-V1 - - Amazon EC2 c7g.4xlarge (1.0tst11nARMv8 Neoverse-V1 (16 Cores)Amazon EC2 c7g.4xlarge (1.0 BIOS)Amazon Device 020032GB215GB Amazon Elastic Block StoreAmazon ElasticUbuntu 22.046.5.0-1020-aws (aarch64)GCC 11.4.0ext4amazon1.3.255OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madvisePython Details- c7g.4xlarge: Python 3.7.16- ARMv8 Neoverse-V1 - - Amazon EC2 c7g.4xlarge (1.0: Python 3.11.9Security Details- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Mitigation of CSV2 BHB + srbds: Not affected + tsx_async_abort: Not affected Compiler Details- ARMv8 Neoverse-V1 - - Amazon EC2 c7g.4xlarge (1.0, tst11, n: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --enable-bootstrap --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-fix-cortex-a53-843419 --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-link-serialization=2 --enable-multiarch --enable-nls --enable-objc-gc=auto --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --program-prefix=aarch64-linux-gnu- --target=aarch64-linux-gnu --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-target-system-zlib=auto -v

c7g.4xlargescikit-learn: Isotonic / Perturbed Logarithmscikit-learn: Isotonic / Logisticscikit-learn: Sparse Rand Projections / 100 Iterationsscikit-learn: Hist Gradient Boostingscikit-learn: Covertype Dataset Benchmarkscikit-learn: SGDOneClassSVMscikit-learn: GLMscikit-learn: Lassoscikit-learn: Kernel PCA Solvers / Time vs. N Componentsscikit-learn: Plot Hierarchicalscikit-learn: Sample Without Replacementscikit-learn: Plot Neighborsscikit-learn: Kernel PCA Solvers / Time vs. N Samplesscikit-learn: Plot Polynomial Kernel Approximationscikit-learn: Hist Gradient Boosting Categorical Onlyscikit-learn: Feature Expansionsscikit-learn: Plot Fast KMeansscikit-learn: Hist Gradient Boosting Threadingscikit-learn: Sparsifymlpack: scikit_qdascikit-learn: SGD Regressionscikit-learn: Treescikit-learn: Plot Wardmlpack: scikit_linearridgeregressionscikit-learn: Plot Singular Value Decompositionscikit-learn: Plot Incremental PCAmlpack: scikit_icallama-cpp: Meta-Llama-3-8B-Instruct-Q8_0.ggufmlpack: scikit_svmscikit-learn: 20 Newsgroups / Logistic Regressionai-benchmark: c7g.4xlargeARMv8 Neoverse-V1 - - Amazon EC2 c7g.4xlarge (1.0tst11n1786.1261363.836670.296247.538438.522386.466301.739272.99463.819234.705214.181214.075202.001168.03843.080138.603107.137104.784103.31830.6883.12474.62171.1452.4462.67528.19436.5517.2117.048.67721.5921.87OpenBenchmarking.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: Isotonic / Perturbed Logarithmc7g.4xlarge400800120016002000SE +/- 0.39, N = 31786.131. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Logisticc7g.4xlarge30060090012001500SE +/- 0.34, N = 31363.841. (F9X) gfortran options: -O0

Benchmark: SAGA

c7g.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationsc7g.4xlarge140280420560700SE +/- 0.72, N = 3670.301. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingc7g.4xlarge50100150200250SE +/- 8.01, N = 9247.541. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkc7g.4xlarge100200300400500SE +/- 2.42, N = 3438.521. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGDOneClassSVMc7g.4xlarge80160240320400SE +/- 2.12, N = 3386.471. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMc7g.4xlarge70140210280350SE +/- 0.46, N = 3301.741. (F9X) gfortran options: -O0

Benchmark: Isotonic / Pathological

c7g.4xlarge: The test quit with a non-zero exit status.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassoc7g.4xlarge60120180240300SE +/- 1.58, N = 3272.991. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentsc7g.4xlarge1428425670SE +/- 0.47, N = 1563.821. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalc7g.4xlarge50100150200250SE +/- 0.15, N = 3234.711. (F9X) gfortran options: -O0

Benchmark: Isolation Forest

c7g.4xlarge: The test quit with a non-zero exit status. E: ImportError: Using `parser='pandas'` wit dense data requires pandas to be installed. Alternatively, explicitly set `parser='liac-arff'`.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementc7g.4xlarge50100150200250SE +/- 0.28, N = 3214.181. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsc7g.4xlarge50100150200250SE +/- 0.94, N = 3214.081. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplesc7g.4xlarge4080120160200SE +/- 2.57, N = 3202.001. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationc7g.4xlarge4080120160200SE +/- 0.70, N = 3168.041. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlyc7g.4xlarge1020304050SE +/- 0.82, N = 1343.081. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionsc7g.4xlarge306090120150SE +/- 0.14, N = 3138.601. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Fast KMeansc7g.4xlarge20406080100SE +/- 0.17, N = 3107.141. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingc7g.4xlarge20406080100SE +/- 0.59, N = 3104.781. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifyc7g.4xlarge20406080100SE +/- 0.01, N = 3103.321. (F9X) gfortran options: -O0

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_qdac7g.4xlarge714212835SE +/- 0.06, N = 330.68

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: SGD Regressionc7g.4xlarge20406080100SE +/- 0.18, N = 383.121. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treec7g.4xlarge20406080100SE +/- 0.62, N = 374.621. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardc7g.4xlarge1632486480SE +/- 0.09, N = 371.151. (F9X) gfortran options: -O0

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_linearridgeregressionc7g.4xlarge0.5491.0981.6472.1962.745SE +/- 0.00, N = 32.44

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 Singular Value Decompositionc7g.4xlarge1428425670SE +/- 0.39, N = 362.681. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Incremental PCAc7g.4xlarge714212835SE +/- 0.23, N = 328.191. (F9X) gfortran options: -O0

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_icac7g.4xlarge816243240SE +/- 0.03, N = 336.55

Llama.cpp

OpenBenchmarking.orgTokens Per Second, More Is BetterLlama.cpp b3067Model: Meta-Llama-3-8B-Instruct-Q8_0.ggufc7g.4xlargetst11n510152025SE +/- 0.10, N = 3SE +/- 0.14, N = 3SE +/- 0.03, N = 317.2121.5921.871. (CXX) g++ options: -std=c++11 -fPIC -O3 -pthread -mcpu=native -lopenblas

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmc7g.4xlarge48121620SE +/- 0.02, N = 317.04

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.

Benchmark: LocalOutlierFactor

c7g.4xlarge: The test quit with a non-zero exit status. E: ImportError: Using `parser='pandas'` wit dense data requires pandas to be installed. Alternatively, explicitly set `parser='liac-arff'`.

Benchmark: RCV1 Logreg Convergencet

c7g.4xlarge: The test quit with a non-zero exit status. E: IndexError: list index out of range

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic Regressionc7g.4xlarge246810SE +/- 0.023, N = 38.6771. (F9X) gfortran options: -O0

Benchmark: 20 Newsgroups / Logistic Regression

ARMv8 Neoverse-V1 - - Amazon EC2 c7g.4xlarge (1.0: The test quit with a non-zero exit status. E: ImportError: /lib/aarch64-linux-gnu/liblapack.so.3: undefined symbol: gotoblas

Benchmark: MNIST Dataset

c7g.4xlarge: The test quit with a non-zero exit status. E: ImportError: Returning pandas objects requires pandas to be installed. Alternatively, explicitly set `as_frame=False` and `parser='liac-arff'`.

Benchmark: Plot Parallel Pairwise

c7g.4xlarge: 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

Benchmark: Plot Lasso Path

c7g.4xlarge: The test quit with a non-zero exit status. E: sklearn.utils._param_validation.InvalidParameterError: The 'effective_rank' parameter of make_regression must be an int in the range [1, inf) or None. Got 1.5 instead.

Benchmark: TSNE MNIST Dataset

c7g.4xlarge: The test quit with a non-zero exit status. E: ImportError: Returning pandas objects requires pandas to be installed. Alternatively, explicitly set `as_frame=False` and `parser='liac-arff'`.

Benchmark: Plot OMP vs. LARS

c7g.4xlarge: The test quit with a non-zero exit status. E: TypeError: got an unexpected keyword argument 'data_transposed'

Benchmark: Glmnet

c7g.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'

Benchmark: Plot Non-Negative Matrix Factorization

c7g.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'

Benchmark: Hist Gradient Boosting Higgs Boson

c7g.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'

Benchmark: Hist Gradient Boosting Adult

c7g.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'

Benchmark: Text Vectorizers

c7g.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'

AI Benchmark Alpha

AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.

c7g.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tensorflow'