mltest

2 x Intel Xeon Gold 6126 testing with a Inspur YZMB-01130-101 and ASPEED ASPEED Family on Ubuntu 16.04 via the Phoronix Test Suite.

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mltest
July 06 2020
 
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mltestOpenBenchmarking.orgPhoronix Test Suite2 x Intel Xeon Gold 6126 @ 3.70GHz (24 Cores)Inspur YZMB-01130-101Intel Device 2020258048MB537GB MR9361-8i + 644GB MR9361-8i + 6018GB MR9361-8iASPEED ASPEED FamilyNVIDIA Device 10f7Intel 82571EB GigabitUbuntu 16.044.4.0-116-generic (x86_64)modesetting 1.18.4GCC 5.4.0 20160609 + CUDA 10.1ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDisplay DriverCompilerFile-SystemScreen ResolutionMltest BenchmarksSystem Logs- --build=x86_64-linux-gnu --disable-browser-plugin --disable-vtable-verify --disable-werror --enable-checking=release --enable-clocale=gnu --enable-gnu-unique-object --enable-gtk-cairo --enable-java-awt=gtk --enable-java-home --enable-languages=c,ada,c++,java,go,d,fortran,objc,obj-c++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-arch-directory=amd64 --with-default-libstdcxx-abi=new --with-multilib-list=m32,m64,mx32 --with-tune=generic -v - Scaling Governor: intel_pstate powersave- Python 3.7.3.

mltestnumpy: Phoronix Test Suite v5.2.1deepspeech: Phoronix Test Suite v5.2.1numenta-nab: EXPoSEnumenta-nab: Relative Entropynumenta-nab: Windowed Gaussiannumenta-nab: Earthgecko Skylinenumenta-nab: Bayesian Changepointscikit-learn: Phoronix Test Suite v5.2.1mltest256.44145.65292.4320.149.80117.9766.4815.47OpenBenchmarking.org

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy BenchmarkPhoronix Test Suite v5.2.1mltest60120180240300SE +/- 2.26, N = 3256.44

DeepSpeech

Mozilla DeepSpeech is a speech-to-text engine powered by TensorFlow for machine learning and derived from Baidu's Deep Speech research paper. This test profile times the speech-to-text process for a roughly three minute audio recording. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterDeepSpeech 0.6Phoronix Test Suite v5.2.1mltest306090120150SE +/- 4.99, N = 6145.65

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: EXPoSEmltest60120180240300SE +/- 3.07, N = 3292.43

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative Entropymltest510152025SE +/- 0.38, N = 620.14

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed Gaussianmltest3691215SE +/- 0.03, N = 39.80

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko Skylinemltest306090120150SE +/- 1.07, N = 3117.97

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian Changepointmltest1530456075SE +/- 0.32, N = 366.48

Scikit-Learn

Scikit-learn is a Python module for machine learning Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 0.22.1Phoronix Test Suite v5.2.1mltest48121620SE +/- 0.10, N = 315.47