MBP M1 Max Machine Learning, sys76-kudu-ML

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

sys76-kudu-ML: AMD Ryzen 9 5900HX testing with a System76 Kudu (1.07.09RSA1 BIOS) and AMD Cezanne on Pop 21.10 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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BLAS (Basic Linear Algebra Sub-Routine) Tests 2 Tests
CPU Massive 7 Tests
Creator Workloads 4 Tests
HPC - High Performance Computing 20 Tests
Machine Learning 20 Tests
Multi-Core 2 Tests
NVIDIA GPU Compute 4 Tests
Intel oneAPI 2 Tests
Python 3 Tests
Server CPU Tests 3 Tests
Single-Threaded 3 Tests
Speech 2 Tests
Telephony 2 Tests

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  Test
  Duration
MBP M1 Max Machine Learning
February 16 2022
  6 Hours, 21 Minutes
ML Tests
February 15 2022
  7 Hours, 15 Minutes
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  6 Hours, 48 Minutes
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MBP M1 Max Machine Learning, sys76-kudu-ML Apple M1 Max testing with a Apple MacBook Pro and Apple M1 Max on macOS 12.1 via the Phoronix Test Suite. sys76-kudu-ML: AMD Ryzen 9 5900HX testing with a System76 Kudu (1.07.09RSA1 BIOS) and AMD Cezanne on Pop 21.10 via the Phoronix Test Suite. ,,"ML Tests","MBP M1 Max Machine Learning" Processor,,AMD Ryzen 9 5900HX @ 3.30GHz (8 Cores / 16 Threads),Apple M1 Max (10 Cores) Motherboard,,System76 Kudu (1.07.09RSA1 BIOS),Apple MacBook Pro Chipset,,AMD Renoir/Cezanne, Memory,,16GB,64GB Disk,,Samsung SSD 970 EVO Plus 500GB,1859GB Graphics,,AMD Cezanne (2100/400MHz),Apple M1 Max Audio,,AMD Renoir Radeon HD Audio, Network,,Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200, Monitor,,,Color LCD OS,,Pop 21.10,macOS 12.1 Kernel,,5.15.15-76051515-generic (x86_64),21.2.0 (arm64) Desktop,,GNOME Shell 40.5, Display Server,,X Server 1.20.13, OpenGL,,4.6 Mesa 21.2.2 (LLVM 12.0.1), Vulkan,,1.2.182, Compiler,,GCC 11.2.0,GCC 13.0.0 + Clang 13.0.0 File-System,,ext4,APFS Screen Resolution,,1920x1080,3456x2234 OpenCL,,,OpenCL 1.2 (Nov 13 2021 00:45:09) ,,"ML Tests","MBP M1 Max Machine Learning" "AI Benchmark Alpha - (Score)",HIB,, "Caffe - Model: AlexNet - Acceleration: CPU - Iterations: 100 (ms)",LIB,33496, "Caffe - Model: AlexNet - Acceleration: CPU - Iterations: 200 (ms)",LIB,65986, "Caffe - Model: AlexNet - Acceleration: CPU - Iterations: 1000 (ms)",LIB,325884, "Caffe - Model: GoogleNet - Acceleration: CPU - Iterations: 100 (ms)",LIB,86567, "Caffe - Model: GoogleNet - Acceleration: CPU - Iterations: 200 (ms)",LIB,173671, "Caffe - Model: GoogleNet - Acceleration: CPU - Iterations: 1000 (ms)",LIB,868758, "DeepSpeech - Acceleration: CPU (sec)",LIB,74.44043, "ECP-CANDLE - Benchmark: P1B2 (sec)",LIB,37.51, "ECP-CANDLE - Benchmark: P3B1 (sec)",LIB,1463.722, "ECP-CANDLE - Benchmark: P3B2 (sec)",LIB,730.736, "LeelaChessZero - Backend: BLAS (Nodes/s)",HIB,563, "Mlpack Benchmark - Benchmark: scikit_ica (sec)",LIB,48.40, "Mlpack Benchmark - Benchmark: scikit_qda (sec)",LIB,65.69, "Mlpack Benchmark - Benchmark: scikit_svm (sec)",LIB,17.60, "Mlpack Benchmark - Benchmark: scikit_linearridgeregression (sec)",LIB,2.10, "Mobile Neural Network - Model: mobilenetV3 (ms)",LIB,1.202,9.152 "Mobile Neural Network - Model: squeezenetv1.1 (ms)",LIB,2.803,7.274 "Mobile Neural Network - Model: resnet-v2-50 (ms)",LIB,22.441,42.428 "Mobile Neural Network - Model: SqueezeNetV1.0 (ms)",LIB,4.546,9.967 "Mobile Neural Network - Model: MobileNetV2_224 (ms)",LIB,2.387,10.677 "Mobile Neural Network - Model: mobilenet-v1-1.0 (ms)",LIB,2.440,8.205 "Mobile Neural Network - Model: inception-v3 (ms)",LIB,31.576,58.253 "NCNN - Target: CPU - Model: mobilenet (ms)",LIB,15.95,20.32 "NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,3.99,5.33 "NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,3.41,4.36 "NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,2.75,3.47 "NCNN - Target: CPU - Model: mnasnet (ms)",LIB,3.25,5.40 "NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,5.22,8.69 "NCNN - Target: CPU - Model: blazeface (ms)",LIB,1.20,1.65 "NCNN - Target: CPU - Model: googlenet (ms)",LIB,13.74,24.96 "NCNN - Target: CPU - Model: vgg16 (ms)",LIB,71.97,71.01 "NCNN - Target: CPU - Model: resnet18 (ms)",LIB,15.78,16.82 "NCNN - Target: CPU - Model: alexnet (ms)",LIB,14.55,29.93 "NCNN - Target: CPU - Model: resnet50 (ms)",LIB,25.17,43.16 "NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,24.97,30.24 "NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,18.56,20.53 "NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,6.90,7.18 "NCNN - Target: Vulkan GPU - Model: mobilenet (ms)",LIB,10.27,20.30 "NCNN - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,3.88,5.30 "NCNN - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,4.69,4.35 "NCNN - Target: Vulkan GPU - Model: shufflenet-v2 (ms)",LIB,3.02,3.46 "NCNN - Target: Vulkan GPU - Model: mnasnet (ms)",LIB,3.89,5.37 "NCNN - Target: Vulkan GPU - Model: efficientnet-b0 (ms)",LIB,10.07,8.71 "NCNN - Target: Vulkan GPU - Model: blazeface (ms)",LIB,1.35,1.64 "NCNN - Target: Vulkan GPU - Model: googlenet (ms)",LIB,8.73,24.9 "NCNN - Target: Vulkan GPU - Model: vgg16 (ms)",LIB,43.99,70.89 "NCNN - Target: Vulkan GPU - Model: resnet18 (ms)",LIB,6.09,16.80 "NCNN - Target: Vulkan GPU - Model: alexnet (ms)",LIB,6.32,29.89 "NCNN - Target: Vulkan GPU - Model: resnet50 (ms)",LIB,13.12,43.08 "NCNN - Target: Vulkan GPU - Model: yolov4-tiny (ms)",LIB,18.82,30.33 "NCNN - Target: Vulkan GPU - Model: squeezenet_ssd (ms)",LIB,15.36,20.55 "NCNN - Target: Vulkan GPU - Model: regnety_400m (ms)",LIB,5.28,7.19 "Numenta Anomaly Benchmark - Detector: EXPoSE (sec)",LIB,, "Numenta Anomaly Benchmark - Detector: Relative Entropy (sec)",LIB,, "Numenta Anomaly Benchmark - Detector: Windowed Gaussian (sec)",LIB,, "Numenta Anomaly Benchmark - Detector: Earthgecko Skyline (sec)",LIB,, "Numenta Anomaly Benchmark - Detector: Bayesian Changepoint (sec)",LIB,, "Numpy Benchmark - (Score)",HIB,422.45, "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,4.25855, "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,12.0926, "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.62985, "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.69210, "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,22.7926, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,8.34789, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,6.74559, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,23.7674, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.11771, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.24784, "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,3579.00, "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,2219.13, "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3587.17, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2228.17, "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,4.59343, "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3577.00, "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2237.65, "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.98659, "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "ONNX Runtime - Model: yolov4 - Device: CPU (Inferences/min)",HIB,, "ONNX Runtime - Model: fcn-resnet101-11 - Device: CPU (Inferences/min)",HIB,, "ONNX Runtime - Model: shufflenet-v2-10 - Device: CPU (Inferences/min)",HIB,, "ONNX Runtime - Model: super-resolution-10 - Device: CPU (Inferences/min)",HIB,, "OpenCV - Test: DNN - Deep Neural Network (ms)",LIB,13787, "OpenVINO - Model: Face Detection 0106 FP16 - Device: CPU (FPS)",HIB,, "OpenVINO - Model: Face Detection 0106 FP32 - Device: CPU (FPS)",HIB,, "OpenVINO - Model: Person Detection 0106 FP16 - Device: CPU (FPS)",HIB,, "OpenVINO - Model: Person Detection 0106 FP32 - Device: CPU (FPS)",HIB,, "OpenVINO - Model: Face Detection 0106 FP16 - Device: Intel GPU (FPS)",HIB,, "OpenVINO - Model: Face Detection 0106 FP32 - Device: Intel GPU (FPS)",HIB,, "OpenVINO - Model: Person Detection 0106 FP16 - Device: Intel GPU (FPS)",HIB,, "OpenVINO - Model: Person Detection 0106 FP32 - Device: Intel GPU (FPS)",HIB,, "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,, "OpenVINO - Model: Age Gender Recognition Retail 0013 FP32 - Device: CPU (FPS)",HIB,, "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: Intel GPU (FPS)",HIB,, "OpenVINO - Model: Age Gender Recognition Retail 0013 FP32 - Device: Intel GPU (FPS)",HIB,, "PlaidML - FP16: No - Mode: Inference - Network: VGG16 - Device: CPU (FPS)",HIB,12.47, "PlaidML - FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU (FPS)",HIB,6.88, "R Benchmark - (sec)",LIB,0.1293, "RNNoise - (sec)",LIB,16.137, "Tensorflow - Build: Cifar10 (sec)",LIB,, "TensorFlow Lite - Model: SqueezeNet (us)",LIB,189764, "TensorFlow Lite - Model: Inception V4 (us)",LIB,2749623, "TensorFlow Lite - Model: NASNet Mobile (us)",LIB,152186, "TensorFlow Lite - Model: Mobilenet Float (us)",LIB,127818, "TensorFlow Lite - Model: Mobilenet Quant (us)",LIB,141174, "TensorFlow Lite - Model: Inception ResNet V2 (us)",LIB,2479080, "TNN - Target: CPU - Model: DenseNet (ms)",LIB,2736.173, "TNN - Target: CPU - Model: MobileNet v2 (ms)",LIB,249.477, "TNN - Target: CPU - Model: SqueezeNet v2 (ms)",LIB,55.434, "TNN - Target: CPU - Model: SqueezeNet v1.1 (ms)",LIB,222.326,