machine_learning_test

Nano_machine_learning_test_result

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machine_learning_test_result
July 24 2023
  1 Day, 13 Hours, 21 Minutes
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machine_learning_test_result

Processor: ARMv8 Cortex-A78E @ 1.51GHz (6 Cores), Motherboard: EDK II 3.1-32827747, Memory: 8GB, Disk: 256GB TEAM Ind N745-M80W, Graphics: NVIDIA Tegra Orin, Monitor: BenQ GW2381, Network: Realtek RTL8111/8168/8411 + Realtek RTL8822CE 802.11ac PCIe

OS: Ubuntu 20.04, Kernel: 5.10.104-tegra (aarch64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Display Driver: NVIDIA 35.3.1, OpenGL: 4.6.0, Vulkan: 1.3.212, Compiler: GCC 9.4.0 + CUDA 11.4, File-System: ext4, Screen Resolution: 1920x1200

Kernel Notes: Transparent Huge Pages: always
Compiler Notes: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --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++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --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-default-libstdcxx-abi=new --with-gcc-major-version-only --with-target-system-zlib=auto -v
Processor Notes: Scaling Governor: tegra194 schedutil
Python Notes: Python 2.7.18 + Python 3.8.10
Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected

machine_learning_testOpenBenchmarking.orgPhoronix Test SuiteARMv8 Cortex-A78E @ 1.51GHz (6 Cores)EDK II 3.1-328277478GB256GB TEAM Ind N745-M80WNVIDIA Tegra OrinBenQ GW2381Realtek RTL8111/8168/8411 + Realtek RTL8822CE 802.11ac PCIeUbuntu 20.045.10.104-tegra (aarch64)GNOME Shell 3.36.9X Server 1.20.13NVIDIA 35.3.14.6.01.3.212GCC 9.4.0 + CUDA 11.4ext41920x1200ProcessorMotherboardMemoryDiskGraphicsMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen ResolutionMachine_learning_test BenchmarksSystem Logs- Transparent Huge Pages: always- --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --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++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --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-default-libstdcxx-abi=new --with-gcc-major-version-only --with-target-system-zlib=auto -v - Scaling Governor: tegra194 schedutil- Python 2.7.18 + Python 3.8.10- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected

machine_learning_testlczero: BLASnumpy: rbenchmark: rnnoise: tensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2mnn: nasnetmnn: mobilenetV3mnn: squeezenetv1.1mnn: resnet-v2-50mnn: SqueezeNetV1.0mnn: MobileNetV2_224mnn: mobilenet-v1-1.0mnn: inception-v3ncnn: CPU - mobilenetncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU - shufflenet-v2ncnn: CPU - mnasnetncnn: CPU - efficientnet-b0ncnn: CPU - blazefacencnn: CPU - googlenetncnn: CPU - vgg16ncnn: CPU - resnet18ncnn: CPU - alexnetncnn: CPU - resnet50ncnn: CPU - yolov4-tinyncnn: CPU - squeezenet_ssdncnn: CPU - regnety_400mncnn: CPU - vision_transformerncnn: CPU - FastestDetncnn: Vulkan GPU - mobilenetncnn: Vulkan GPU-v2-v2 - mobilenet-v2ncnn: Vulkan GPU-v3-v3 - mobilenet-v3ncnn: Vulkan GPU - shufflenet-v2ncnn: Vulkan GPU - mnasnetncnn: Vulkan GPU - efficientnet-b0ncnn: Vulkan GPU - blazefacencnn: Vulkan GPU - googlenetncnn: Vulkan GPU - vgg16ncnn: Vulkan GPU - resnet18ncnn: Vulkan GPU - alexnetncnn: Vulkan GPU - resnet50ncnn: Vulkan GPU - yolov4-tinyncnn: Vulkan GPU - squeezenet_ssdncnn: Vulkan GPU - regnety_400mncnn: Vulkan GPU - vision_transformerncnn: Vulkan GPU - FastestDettnn: CPU - DenseNettnn: CPU - MobileNet v2tnn: CPU - SqueezeNet v2tnn: CPU - SqueezeNet v1.1numenta-nab: KNN CADnumenta-nab: Relative Entropynumenta-nab: Windowed Gaussiannumenta-nab: Earthgecko Skylinenumenta-nab: Bayesian Changepointnumenta-nab: Contextual Anomaly Detector OSEscikit-learn: GLMscikit-learn: SAGAscikit-learn: Treescikit-learn: Lassoscikit-learn: Sparsifyscikit-learn: Plot Wardscikit-learn: MNIST Datasetscikit-learn: Plot Neighborsscikit-learn: SGD Regressionscikit-learn: Plot Lasso Pathscikit-learn: Text Vectorizersscikit-learn: Plot Hierarchicalscikit-learn: Plot OMP vs. LARSscikit-learn: Feature Expansionsscikit-learn: LocalOutlierFactorscikit-learn: TSNE MNIST Datasetopencv: DNN - Deep Neural Networkmachine_learning_test_result253165.050.371136.74914679.220503030199.910162.55186.4318743026.4423.1017.31166.26512.8587.83711.66684.63420.106.105.634.965.509.712.3617.8661.6513.4715.4031.6029.4917.4412.83805.535.5713.394.085.275.104.448.612.938.2119.345.589.039.6723.2113.416.29935.564.867490.176544.128132.243483.095739.30192.50238.467550.591210.464260.8181051.9722449.384181.662793.006183.637156.972205.606687.955181.386589.354206.412473.073159.485347.101272.7501313.492284715OpenBenchmarking.org

LeelaChessZero

LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.28Backend: BLASmachine_learning_test_result60120180240300SE +/- 2.31, N = 92531. (CXX) g++ options: -flto -pthread

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.

Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU

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

Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU

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

Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU

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

Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU

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

Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU

machine_learning_test_result: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU

machine_learning_test_result: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU

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

Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU

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

Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU

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

Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU

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

Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU

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

Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU

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

Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU

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

Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU

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

Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU

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

Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU

machine_learning_test_result: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU

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

Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU

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

Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU

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

Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU

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

Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU

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

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 Benchmarkmachine_learning_test_result4080120160200SE +/- 0.31, N = 3165.05

R Benchmark

This test is a quick-running survey of general R performance Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterR Benchmarkmachine_learning_test_result0.08350.1670.25050.3340.4175SE +/- 0.0008, N = 30.37111. R scripting front-end version 3.6.3 (2020-02-29)

RNNoise

RNNoise is a recurrent neural network for audio noise reduction developed by Mozilla and Xiph.Org. This test profile is a single-threaded test measuring the time to denoise a sample 26 minute long 16-bit RAW audio file using this recurrent neural network noise suppression library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRNNoise 2020-06-28machine_learning_test_result816243240SE +/- 0.00, N = 336.751. (CC) gcc options: -O2 -pedantic -fvisibility=hidden

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: SqueezeNetmachine_learning_test_result3K6K9K12K15KSE +/- 22.88, N = 314679.2

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Inception V4machine_learning_test_result40K80K120K160K200KSE +/- 286.58, N = 3205030

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: NASNet Mobilemachine_learning_test_result6K12K18K24K30KSE +/- 2.71, N = 330199.9

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Mobilenet Floatmachine_learning_test_result2K4K6K8K10KSE +/- 6.27, N = 310162.5

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Mobilenet Quantmachine_learning_test_result11002200330044005500SE +/- 7.51, N = 35186.43

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Inception ResNet V2machine_learning_test_result40K80K120K160K200KSE +/- 147.73, N = 3187430

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

Device: CPU - Batch Size: 16 - Model: VGG-16

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 32 - Model: VGG-16

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 64 - Model: VGG-16

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 16 - Model: AlexNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 256 - Model: VGG-16

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 32 - Model: AlexNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 512 - Model: VGG-16

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 64 - Model: AlexNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 256 - Model: AlexNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 512 - Model: AlexNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 16 - Model: GoogLeNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 16 - Model: ResNet-50

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 32 - Model: GoogLeNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 32 - Model: ResNet-50

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 64 - Model: GoogLeNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 64 - Model: ResNet-50

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 256 - Model: GoogLeNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 256 - Model: ResNet-50

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 512 - Model: GoogLeNet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Device: CPU - Batch Size: 512 - Model: ResNet-50

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Neural Magic DeepSparse

This is a benchmark of Neural Magic's DeepSparse using its built-in deepsparse.benchmark utility and various models from their SparseZoo (https://sparsezoo.neuralmagic.com/). Learn more via the OpenBenchmarking.org test page.

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/usr/local/lib/python3.8/dist-packages/typing_extensions.py)

spaCy

The spaCy library is an open-source solution for advanced neural language processing (NLP). The spaCy library leverages Python and is a leading neural language processing solution. This test profile times the spaCy CPU performance with various models. Learn more via the OpenBenchmarking.org test page.

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Caffe

This is a benchmark of the Caffe deep learning framework and currently supports the AlexNet and Googlenet model and execution on both CPUs and NVIDIA GPUs. Learn more via the OpenBenchmarking.org test page.

Model: AlexNet - Acceleration: CPU - Iterations: 100

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: AlexNet - Acceleration: CPU - Iterations: 200

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: AlexNet - Acceleration: CPU - Iterations: 1000

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: GoogleNet - Acceleration: CPU - Iterations: 100

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: GoogleNet - Acceleration: CPU - Iterations: 200

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: GoogleNet - Acceleration: CPU - Iterations: 1000

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. This MNN test profile is building the OpenMP / CPU threaded version for processor benchmarking and not any GPU-accelerated test. MNN does allow making use of AVX-512 extensions. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: nasnetmachine_learning_test_result612182430SE +/- 0.07, N = 326.44MIN: 25.77 / MAX: 56.321. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: mobilenetV3machine_learning_test_result0.69771.39542.09312.79083.4885SE +/- 0.011, N = 33.101MIN: 2.97 / MAX: 8.171. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: squeezenetv1.1machine_learning_test_result246810SE +/- 0.070, N = 37.311MIN: 7.03 / MAX: 27.941. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: resnet-v2-50machine_learning_test_result1530456075SE +/- 0.13, N = 366.27MIN: 65.16 / MAX: 96.131. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: SqueezeNetV1.0machine_learning_test_result3691215SE +/- 0.13, N = 312.86MIN: 12.3 / MAX: 33.191. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: MobileNetV2_224machine_learning_test_result246810SE +/- 0.034, N = 37.837MIN: 7.56 / MAX: 28.871. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: mobilenet-v1-1.0machine_learning_test_result3691215SE +/- 0.02, N = 311.67MIN: 11.41 / MAX: 23.931. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: inception-v3machine_learning_test_result20406080100SE +/- 0.14, N = 384.63MIN: 83.31 / MAX: 127.651. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: mobilenetmachine_learning_test_result510152025SE +/- 0.03, N = 320.10MIN: 19.54 / MAX: 25.391. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU-v2-v2 - Model: mobilenet-v2machine_learning_test_result246810SE +/- 0.01, N = 36.10MIN: 5.72 / MAX: 16.211. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU-v3-v3 - Model: mobilenet-v3machine_learning_test_result1.26682.53363.80045.06726.334SE +/- 0.01, N = 35.63MIN: 5.32 / MAX: 14.811. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: shufflenet-v2machine_learning_test_result1.1162.2323.3484.4645.58SE +/- 0.02, N = 34.96MIN: 4.75 / MAX: 9.31. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: mnasnetmachine_learning_test_result1.23752.4753.71254.956.1875SE +/- 0.01, N = 35.50MIN: 5.28 / MAX: 6.681. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: efficientnet-b0machine_learning_test_result3691215SE +/- 0.02, N = 39.71MIN: 9.31 / MAX: 15.621. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: blazefacemachine_learning_test_result0.5311.0621.5932.1242.655SE +/- 0.02, N = 32.36MIN: 2.26 / MAX: 5.541. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: googlenetmachine_learning_test_result48121620SE +/- 0.06, N = 317.86MIN: 17.35 / MAX: 43.131. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: vgg16machine_learning_test_result1428425670SE +/- 0.03, N = 361.65MIN: 59.81 / MAX: 67.571. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: resnet18machine_learning_test_result3691215SE +/- 0.07, N = 313.47MIN: 12.82 / MAX: 31.581. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: alexnetmachine_learning_test_result48121620SE +/- 0.04, N = 315.40MIN: 15.05 / MAX: 40.441. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: resnet50machine_learning_test_result714212835SE +/- 0.07, N = 331.60MIN: 30.84 / MAX: 34.641. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: yolov4-tinymachine_learning_test_result714212835SE +/- 0.10, N = 329.49MIN: 28.54 / MAX: 55.91. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: squeezenet_ssdmachine_learning_test_result48121620SE +/- 0.07, N = 317.44MIN: 16.82 / MAX: 21.081. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: regnety_400mmachine_learning_test_result3691215SE +/- 0.16, N = 312.83MIN: 12.15 / MAX: 32.191. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: vision_transformermachine_learning_test_result2004006008001000SE +/- 0.80, N = 3805.53MIN: 792.04 / MAX: 835.041. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: CPU - Model: FastestDetmachine_learning_test_result1.25332.50663.75995.01326.2665SE +/- 0.03, N = 35.57MIN: 5.29 / MAX: 8.651. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: mobilenetmachine_learning_test_result3691215SE +/- 0.68, N = 613.39MIN: 11.26 / MAX: 18.781. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2machine_learning_test_result0.9181.8362.7543.6724.59SE +/- 0.06, N = 64.08MIN: 3.89 / MAX: 6.571. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3machine_learning_test_result1.18582.37163.55744.74325.929SE +/- 0.15, N = 65.27MIN: 4.87 / MAX: 8.291. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: shufflenet-v2machine_learning_test_result1.14752.2953.44254.595.7375SE +/- 0.06, N = 65.10MIN: 4.75 / MAX: 5.961. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: mnasnetmachine_learning_test_result0.9991.9982.9973.9964.995SE +/- 0.13, N = 64.44MIN: 3.86 / MAX: 7.021. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: efficientnet-b0machine_learning_test_result246810SE +/- 0.14, N = 68.61MIN: 8.01 / MAX: 13.991. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: blazefacemachine_learning_test_result0.65931.31861.97792.63723.2965SE +/- 0.02, N = 62.93MIN: 2.46 / MAX: 3.921. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: googlenetmachine_learning_test_result246810SE +/- 0.13, N = 68.21MIN: 7.6 / MAX: 14.121. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: vgg16machine_learning_test_result510152025SE +/- 0.09, N = 619.34MIN: 19.03 / MAX: 20.051. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: resnet18machine_learning_test_result1.25552.5113.76655.0226.2775SE +/- 0.14, N = 65.58MIN: 4.96 / MAX: 9.281. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: alexnetmachine_learning_test_result3691215SE +/- 0.05, N = 69.03MIN: 8.81 / MAX: 9.571. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: resnet50machine_learning_test_result3691215SE +/- 0.05, N = 69.67MIN: 9.34 / MAX: 11.271. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: yolov4-tinymachine_learning_test_result612182430SE +/- 0.50, N = 623.21MIN: 18.67 / MAX: 25.11. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: squeezenet_ssdmachine_learning_test_result3691215SE +/- 0.42, N = 613.41MIN: 10.48 / MAX: 19.861. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: regnety_400mmachine_learning_test_result246810SE +/- 0.15, N = 66.29MIN: 5.59 / MAX: 10.121. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: vision_transformermachine_learning_test_result2004006008001000SE +/- 0.66, N = 6935.56MIN: 907.84 / MAX: 971.111. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20220729Target: Vulkan GPU - Model: FastestDetmachine_learning_test_result1.09352.1873.28054.3745.4675SE +/- 0.03, N = 64.86MIN: 4.51 / MAX: 5.471. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: DenseNetmachine_learning_test_result16003200480064008000SE +/- 46.30, N = 37490.18MIN: 7150.37 / MAX: 7917.481. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: MobileNet v2machine_learning_test_result120240360480600SE +/- 1.98, N = 3544.13MIN: 537.28 / MAX: 552.431. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v2machine_learning_test_result306090120150SE +/- 0.18, N = 3132.24MIN: 131.39 / MAX: 134.131. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v1.1machine_learning_test_result100200300400500SE +/- 0.10, N = 3483.10MIN: 481.83 / MAX: 485.581. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.

FP16: No - Mode: Inference - Network: VGG16 - Device: CPU

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./plaidml: line 24: /.local/bin/plaidbench: No such file or directory

FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./plaidml: line 24: /.local/bin/plaidbench: No such file or directory

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

Benchmark: P1B2

machine_learning_test_result: The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Benchmark: P3B1

machine_learning_test_result: The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Benchmark: P3B2

machine_learning_test_result: The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

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 time-series 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: KNN CADmachine_learning_test_result160320480640800SE +/- 5.05, N = 3739.30

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative Entropymachine_learning_test_result20406080100SE +/- 0.40, N = 392.50

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed Gaussianmachine_learning_test_result918273645SE +/- 0.20, N = 338.47

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko Skylinemachine_learning_test_result120240360480600SE +/- 4.48, N = 3550.59

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian Changepointmachine_learning_test_result50100150200250SE +/- 1.96, N = 3210.46

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Contextual Anomaly Detector OSEmachine_learning_test_result60120180240300SE +/- 1.75, N = 3260.82

ONNX Runtime

ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Model Zoo. Learn more via the OpenBenchmarking.org test page.

Model: GPT-2 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: GPT-2 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: yolov4 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: yolov4 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: bertsquad-12 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: bertsquad-12 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: fcn-resnet101-11 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: super-resolution-10 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: super-resolution-10 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

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.

machine_learning_test_result: The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Mlpack Benchmark

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

Benchmark: scikit_ica

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'float'.

Benchmark: scikit_qda

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'float'.

Benchmark: scikit_svm

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'float'.

Benchmark: scikit_linearridgeregression

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'float'.

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: GLMmachine_learning_test_result2004006008001000SE +/- 3.81, N = 31051.971. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SAGAmachine_learning_test_result5001000150020002500SE +/- 0.80, N = 32449.381. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treemachine_learning_test_result4080120160200SE +/- 0.81, N = 3181.661. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassomachine_learning_test_result2004006008001000SE +/- 2.89, N = 3793.011. (F9X) gfortran options: -O0

Benchmark: Glmnet

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifymachine_learning_test_result4080120160200SE +/- 0.31, N = 3183.641. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardmachine_learning_test_result306090120150SE +/- 0.37, N = 3156.971. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST Datasetmachine_learning_test_result50100150200250SE +/- 0.39, N = 3205.611. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsmachine_learning_test_result150300450600750SE +/- 6.64, N = 3687.961. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionmachine_learning_test_result4080120160200SE +/- 0.91, N = 3181.391. (F9X) gfortran options: -O0

Benchmark: SGDOneClassSVM

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Lasso Pathmachine_learning_test_result130260390520650SE +/- 7.20, N = 4589.351. (F9X) gfortran options: -O0

Benchmark: Isolation Forest

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

Benchmark: Plot Fast KMeans

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizersmachine_learning_test_result50100150200250SE +/- 0.43, N = 3206.411. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalmachine_learning_test_result100200300400500SE +/- 0.71, N = 3473.071. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSmachine_learning_test_result4080120160200SE +/- 0.44, N = 3159.491. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionsmachine_learning_test_result80160240320400SE +/- 0.16, N = 3347.101. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactormachine_learning_test_result60120180240300SE +/- 0.81, N = 3272.751. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetmachine_learning_test_result30060090012001500SE +/- 146.33, N = 71313.491. (F9X) gfortran options: -O0

Benchmark: Isotonic / Logistic

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Plot Incremental PCA

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Hist Gradient Boosting

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Plot Parallel Pairwise

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Isotonic / Pathological

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: RCV1 Logreg Convergencet

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Sample Without Replacement

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Covertype Dataset Benchmark

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Hist Gradient Boosting Adult

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy.random' has no attribute 'BitGenerator'

Benchmark: Isotonic / Perturbed Logarithm

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Hist Gradient Boosting Threading

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Plot Singular Value Decomposition

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Hist Gradient Boosting Higgs Boson

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: AttributeError: module 'numpy.random' has no attribute 'BitGenerator'

Benchmark: 20 Newsgroups / Logistic Regression

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Plot Polynomial Kernel Approximation

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Plot Non-Negative Matrix Factorization

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Hist Gradient Boosting Categorical Only

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

Benchmark: Kernel PCA Solvers / Time vs. N Samples

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Kernel PCA Solvers / Time vs. N Components

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: Matplotlib requires numpy>=1.20; you have 1.17.4

Benchmark: Sparse Random Projections / 100 Iterations

machine_learning_test_result: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 232 from C header, got 216 from PyObject

OpenCV

This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenCV 4.7Test: DNN - Deep Neural Networkmachine_learning_test_result60K120K180K240K300KSE +/- 53435.38, N = 62847151. (CXX) g++ options: -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -fvisibility=hidden -O3 -ldl -lm -lpthread -lrt

79 Results Shown

LeelaChessZero
Numpy Benchmark
R Benchmark
RNNoise
TensorFlow Lite:
  SqueezeNet
  Inception V4
  NASNet Mobile
  Mobilenet Float
  Mobilenet Quant
  Inception ResNet V2
Mobile Neural Network:
  nasnet
  mobilenetV3
  squeezenetv1.1
  resnet-v2-50
  SqueezeNetV1.0
  MobileNetV2_224
  mobilenet-v1-1.0
  inception-v3
NCNN:
  CPU - mobilenet
  CPU-v2-v2 - mobilenet-v2
  CPU-v3-v3 - mobilenet-v3
  CPU - shufflenet-v2
  CPU - mnasnet
  CPU - efficientnet-b0
  CPU - blazeface
  CPU - googlenet
  CPU - vgg16
  CPU - resnet18
  CPU - alexnet
  CPU - resnet50
  CPU - yolov4-tiny
  CPU - squeezenet_ssd
  CPU - regnety_400m
  CPU - vision_transformer
  CPU - FastestDet
  Vulkan GPU - mobilenet
  Vulkan GPU-v2-v2 - mobilenet-v2
  Vulkan GPU-v3-v3 - mobilenet-v3
  Vulkan GPU - shufflenet-v2
  Vulkan GPU - mnasnet
  Vulkan GPU - efficientnet-b0
  Vulkan GPU - blazeface
  Vulkan GPU - googlenet
  Vulkan GPU - vgg16
  Vulkan GPU - resnet18
  Vulkan GPU - alexnet
  Vulkan GPU - resnet50
  Vulkan GPU - yolov4-tiny
  Vulkan GPU - squeezenet_ssd
  Vulkan GPU - regnety_400m
  Vulkan GPU - vision_transformer
  Vulkan GPU - FastestDet
TNN:
  CPU - DenseNet
  CPU - MobileNet v2
  CPU - SqueezeNet v2
  CPU - SqueezeNet v1.1
Numenta Anomaly Benchmark:
  KNN CAD
  Relative Entropy
  Windowed Gaussian
  Earthgecko Skyline
  Bayesian Changepoint
  Contextual Anomaly Detector OSE
Scikit-Learn:
  GLM
  SAGA
  Tree
  Lasso
  Sparsify
  Plot Ward
  MNIST Dataset
  Plot Neighbors
  SGD Regression
  Plot Lasso Path
  Text Vectorizers
  Plot Hierarchical
  Plot OMP vs. LARS
  Feature Expansions
  LocalOutlierFactor
  TSNE MNIST Dataset
OpenCV

machine_learning_test_result

Processor: ARMv8 Cortex-A78E @ 1.51GHz (6 Cores), Motherboard: EDK II 3.1-32827747, Memory: 8GB, Disk: 256GB TEAM Ind N745-M80W, Graphics: NVIDIA Tegra Orin, Monitor: BenQ GW2381, Network: Realtek RTL8111/8168/8411 + Realtek RTL8822CE 802.11ac PCIe

OS: Ubuntu 20.04, Kernel: 5.10.104-tegra (aarch64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Display Driver: NVIDIA 35.3.1, OpenGL: 4.6.0, Vulkan: 1.3.212, Compiler: GCC 9.4.0 + CUDA 11.4, File-System: ext4, Screen Resolution: 1920x1200

Kernel Notes: Transparent Huge Pages: always
Compiler Notes: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --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++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --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-default-libstdcxx-abi=new --with-gcc-major-version-only --with-target-system-zlib=auto -v
Processor Notes: Scaling Governor: tegra194 schedutil
Python Notes: Python 2.7.18 + Python 3.8.10
Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 24 July 2023 03:56 by user root.