MBP M1 Max Machine Learning

Apple M1 Max testing with a Apple MacBook Pro and Apple M1 Max on macOS 12.1 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2202161-NE-MBPM1MAXM40
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Result
Identifier
Performance Per
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Date
Run
  Test
  Duration
MBP M1 Max Machine Learning
February 16 2022
  6 Hours, 21 Minutes
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MBP M1 Max Machine Learning

Processor: Apple M1 Max (10 Cores), Motherboard: Apple MacBook Pro, Memory: 64GB, Disk: 1859GB, Graphics: Apple M1 Max, Monitor: Color LCD

OS: macOS 12.1, Kernel: 21.2.0 (arm64), OpenCL: OpenCL 1.2 (Nov 13 2021 00:45:09), Compiler: GCC 13.0.0 + Clang 13.0.0, File-System: APFS, Screen Resolution: 3456x2234

Environment Notes: XPC_FLAGS=0x0
Python Notes: Python 2.7.18 + Python 3.8.9

MBP M1 Max Machine LearningOpenBenchmarking.orgPhoronix Test SuiteApple M1 Max (10 Cores)Apple MacBook Pro64GB1859GBApple M1 MaxColor LCDmacOS 12.121.2.0 (arm64)OpenCL 1.2 (Nov 13 2021 00:45:09)GCC 13.0.0 + Clang 13.0.0APFS3456x2234ProcessorMotherboardMemoryDiskGraphicsMonitorOSKernelOpenCLCompilerFile-SystemScreen ResolutionMBP M1 Max Machine Learning BenchmarksSystem Logs- XPC_FLAGS=0x0- Python 2.7.18 + Python 3.8.9

MBP M1 Max Machine Learningmnn: 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: 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_400mai-benchmark: MBP M1 Max Machine Learning9.1527.27442.4289.96710.6778.20558.25320.325.334.363.475.408.691.6524.9671.0116.8229.9343.1630.2420.537.1820.305.304.353.465.378.711.6424.970.8916.8029.8943.0830.3320.557.19OpenBenchmarking.org

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 initiative. Learn more via the OpenBenchmarking.org test page.

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

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

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU

MBP M1 Max Machine Learning: 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: onednn: line 6: ./benchdnn: No such file or directory

Numpy Benchmark

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

MBP M1 Max Machine Learning: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

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.

Acceleration: CPU

MBP M1 Max Machine Learning: 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.

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: mobilenetV3MBP M1 Max Machine Learning3691215SE +/- 0.487, N = 99.152MIN: 3.37 / MAX: 58.791. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: squeezenetv1.1MBP M1 Max Machine Learning246810SE +/- 0.345, N = 97.274MIN: 2.75 / MAX: 117.921. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: resnet-v2-50MBP M1 Max Machine Learning1020304050SE +/- 4.17, N = 942.43MIN: 24 / MAX: 197.771. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: SqueezeNetV1.0MBP M1 Max Machine Learning3691215SE +/- 0.664, N = 99.967MIN: 4.34 / MAX: 49.521. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: MobileNetV2_224MBP M1 Max Machine Learning3691215SE +/- 0.19, N = 910.68MIN: 5.12 / MAX: 61.591. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: mobilenet-v1-1.0MBP M1 Max Machine Learning246810SE +/- 0.384, N = 98.205MIN: 4.27 / MAX: 48.51. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: inception-v3MBP M1 Max Machine Learning1326395265SE +/- 6.12, N = 958.25MIN: 30.46 / MAX: 200.211. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -arch -isysroot

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 20210720Target: CPU - Model: mobilenetMBP M1 Max Machine Learning510152025SE +/- 0.02, N = 320.32MIN: 20.23 / MAX: 21.331. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU-v2-v2 - Model: mobilenet-v2MBP M1 Max Machine Learning1.19932.39863.59794.79725.9965SE +/- 0.03, N = 35.33MIN: 5.27 / MAX: 5.611. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU-v3-v3 - Model: mobilenet-v3MBP M1 Max Machine Learning0.9811.9622.9433.9244.905SE +/- 0.03, N = 34.36MIN: 4.32 / MAX: 4.611. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: shufflenet-v2MBP M1 Max Machine Learning0.78081.56162.34243.12323.904SE +/- 0.02, N = 33.47MIN: 3.43 / MAX: 3.841. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: mnasnetMBP M1 Max Machine Learning1.2152.433.6454.866.075SE +/- 0.03, N = 35.40MIN: 5.35 / MAX: 5.681. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: efficientnet-b0MBP M1 Max Machine Learning246810SE +/- 0.04, N = 38.69MIN: 8.59 / MAX: 9.151. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: blazefaceMBP M1 Max Machine Learning0.37130.74261.11391.48521.8565SE +/- 0.01, N = 31.65MIN: 1.64 / MAX: 1.721. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: googlenetMBP M1 Max Machine Learning612182430SE +/- 0.07, N = 324.96MIN: 24.82 / MAX: 25.911. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: vgg16MBP M1 Max Machine Learning1632486480SE +/- 0.15, N = 371.01MIN: 70.58 / MAX: 74.441. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: resnet18MBP M1 Max Machine Learning48121620SE +/- 0.04, N = 316.82MIN: 16.69 / MAX: 17.581. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: alexnetMBP M1 Max Machine Learning714212835SE +/- 0.05, N = 329.93MIN: 29.79 / MAX: 31.031. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: resnet50MBP M1 Max Machine Learning1020304050SE +/- 0.07, N = 343.16MIN: 42.92 / MAX: 44.811. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: yolov4-tinyMBP M1 Max Machine Learning714212835SE +/- 0.03, N = 330.24MIN: 29.85 / MAX: 31.871. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: squeezenet_ssdMBP M1 Max Machine Learning510152025SE +/- 0.05, N = 320.53MIN: 20.37 / MAX: 21.531. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: regnety_400mMBP M1 Max Machine Learning246810SE +/- 0.00, N = 37.18MIN: 7.14 / MAX: 8.131. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: mobilenetMBP M1 Max Machine Learning510152025SE +/- 0.02, N = 320.30MIN: 20.23 / MAX: 21.481. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2MBP M1 Max Machine Learning1.19252.3853.57754.775.9625SE +/- 0.01, N = 35.30MIN: 5.28 / MAX: 5.981. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3MBP M1 Max Machine Learning0.97881.95762.93643.91524.894SE +/- 0.00, N = 34.35MIN: 4.32 / MAX: 4.631. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: shufflenet-v2MBP M1 Max Machine Learning0.77851.5572.33553.1143.8925SE +/- 0.01, N = 33.46MIN: 3.44 / MAX: 3.821. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: mnasnetMBP M1 Max Machine Learning1.20832.41663.62494.83326.0415SE +/- 0.00, N = 35.37MIN: 5.35 / MAX: 5.621. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: efficientnet-b0MBP M1 Max Machine Learning246810SE +/- 0.02, N = 38.71MIN: 8.6 / MAX: 9.431. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: blazefaceMBP M1 Max Machine Learning0.3690.7381.1071.4761.845SE +/- 0.00, N = 31.64MIN: 1.63 / MAX: 1.791. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: googlenetMBP M1 Max Machine Learning612182430SE +/- 0.00, N = 324.9MIN: 24.82 / MAX: 25.791. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: vgg16MBP M1 Max Machine Learning1632486480SE +/- 0.02, N = 370.89MIN: 70.59 / MAX: 73.621. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: resnet18MBP M1 Max Machine Learning48121620SE +/- 0.01, N = 316.80MIN: 16.69 / MAX: 18.251. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: alexnetMBP M1 Max Machine Learning714212835SE +/- 0.00, N = 329.89MIN: 29.79 / MAX: 31.071. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: resnet50MBP M1 Max Machine Learning1020304050SE +/- 0.01, N = 343.08MIN: 42.9 / MAX: 45.661. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: yolov4-tinyMBP M1 Max Machine Learning714212835SE +/- 0.07, N = 330.33MIN: 29.85 / MAX: 32.581. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: squeezenet_ssdMBP M1 Max Machine Learning510152025SE +/- 0.05, N = 320.55MIN: 20.39 / MAX: 22.131. (CXX) g++ options: -O3 -arch -isysroot

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: Vulkan GPU - Model: regnety_400mMBP M1 Max Machine Learning246810SE +/- 0.00, N = 37.19MIN: 7.15 / MAX: 7.721. (CXX) g++ options: -O3 -arch -isysroot

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.

Detector: EXPoSE

MBP M1 Max Machine Learning: 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 'pandas'

Detector: Relative Entropy

MBP M1 Max Machine Learning: 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 'pandas'

Detector: Windowed Gaussian

MBP M1 Max Machine Learning: 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 'pandas'

Detector: Earthgecko Skyline

MBP M1 Max Machine Learning: 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 'pandas'

Detector: Bayesian Changepoint

MBP M1 Max Machine Learning: 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 '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.

MBP M1 Max Machine Learning: The test quit with a non-zero exit status. E: SyntaxError: invalid syntax

MBP M1 Max Machine Learning

Processor: Apple M1 Max (10 Cores), Motherboard: Apple MacBook Pro, Memory: 64GB, Disk: 1859GB, Graphics: Apple M1 Max, Monitor: Color LCD

OS: macOS 12.1, Kernel: 21.2.0 (arm64), OpenCL: OpenCL 1.2 (Nov 13 2021 00:45:09), Compiler: GCC 13.0.0 + Clang 13.0.0, File-System: APFS, Screen Resolution: 3456x2234

Environment Notes: XPC_FLAGS=0x0
Python Notes: Python 2.7.18 + Python 3.8.9

Testing initiated at 16 February 2022 14:41 by user chrisf.