pytorch tensorflow

AMD Ryzen 9 7950X 16-Core testing with a ASUS ROG STRIX X670E-E GAMING WIFI (1905 BIOS) and NVIDIA GeForce RTX 3080 10GB on Ubuntu 23.10 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2403274-PTS-PYTORCHT32&grw.

pytorch tensorflowProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLOpenCLCompilerFile-SystemScreen ResolutionAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080AMD Ryzen 9 7950X 16-Core @ 5.88GHz (16 Cores / 32 Threads)ASUS ROG STRIX X670E-E GAMING WIFI (1905 BIOS)AMD Device 14d82 x 16GB DRAM-6000MT/s G Skill F5-6000J3038F16G2000GB Samsung SSD 980 PRO 2TB + 123GB SanDisk 3.2Gen1NVIDIA GeForce RTX 3080 10GBNVIDIA GA102 HD AudioDELL U2723QEIntel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411Ubuntu 23.106.7.0-060700-generic (x86_64)GNOME Shell 45.2X Server 1.21.1.7NVIDIA 550.54.144.6.0OpenCL 3.0 CUDA 12.4.89GCC 13.2.0ext43840x2160OpenBenchmarking.org- Transparent Huge Pages: madvise- Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xa601206 - Python 3.11.6- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

pytorch tensorflowtensorflow: CPU - 1 - VGG-16tensorflow: CPU - 1 - AlexNettensorflow: CPU - 16 - VGG-16tensorflow: CPU - 32 - VGG-16tensorflow: CPU - 64 - VGG-16tensorflow: CPU - 16 - AlexNettensorflow: CPU - 32 - AlexNettensorflow: CPU - 64 - AlexNettensorflow: CPU - 1 - GoogLeNettensorflow: CPU - 1 - ResNet-50tensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 32 - GoogLeNettensorflow: CPU - 32 - ResNet-50tensorflow: CPU - 64 - GoogLeNettensorflow: CPU - 64 - ResNet-50pytorch: CPU - 1 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: CPU - 32 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_lAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30805.6516.1118.0518.9919.68170.76254.51334.7461.1016.15142.2441.34141.4142.48138.7642.6871.7729.3948.8548.5448.5720.0848.5119.5219.6919.9016.5311.5611.8611.7711.84OpenBenchmarking.org

TensorFlow

Device: CPU - Batch Size: 1 - Model: VGG-16

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: VGG-16AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801.27132.54263.81395.08526.3565SE +/- 0.00, N = 35.65

TensorFlow

Device: CPU - Batch Size: 1 - Model: AlexNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: AlexNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 308048121620SE +/- 0.01, N = 316.11

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: VGG-16AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 308048121620SE +/- 0.12, N = 318.05

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: VGG-16AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080510152025SE +/- 0.02, N = 318.99

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: VGG-16AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080510152025SE +/- 0.01, N = 319.68

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30804080120160200SE +/- 0.23, N = 3170.76

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: AlexNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 308060120180240300SE +/- 0.04, N = 3254.51

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: AlexNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 308070140210280350SE +/- 0.08, N = 3334.74

TensorFlow

Device: CPU - Batch Size: 1 - Model: GoogLeNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: GoogLeNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801428425670SE +/- 0.30, N = 361.10

TensorFlow

Device: CPU - Batch Size: 1 - Model: ResNet-50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 308048121620SE +/- 0.03, N = 316.15

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080306090120150SE +/- 0.23, N = 3142.24

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080918273645SE +/- 0.04, N = 341.34

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: GoogLeNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080306090120150SE +/- 0.10, N = 3141.41

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801020304050SE +/- 0.01, N = 342.48

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: GoogLeNetAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080306090120150SE +/- 0.01, N = 3138.76

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801020304050SE +/- 0.01, N = 342.68

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801632486480SE +/- 0.19, N = 371.77MIN: 57.05 / MAX: 74.1

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080714212835SE +/- 0.31, N = 329.39MIN: 22.95 / MAX: 30.32

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801122334455SE +/- 0.29, N = 348.85MIN: 47.27 / MAX: 50.05

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801122334455SE +/- 0.47, N = 348.54MIN: 38.25 / MAX: 50.46

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801122334455SE +/- 0.40, N = 348.57MIN: 45.94 / MAX: 49.92

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080510152025SE +/- 0.15, N = 320.08MIN: 15.43 / MAX: 20.67

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30801122334455SE +/- 0.15, N = 348.51MIN: 46.12 / MAX: 49.98

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080510152025SE +/- 0.14, N = 319.52MIN: 18.86 / MAX: 20.1

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080510152025SE +/- 0.21, N = 319.69MIN: 19.03 / MAX: 20.31

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080510152025SE +/- 0.21, N = 319.90MIN: 19.05 / MAX: 20.71

PyTorch

Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 308048121620SE +/- 0.05, N = 316.53MIN: 14.7 / MAX: 16.81

PyTorch

Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30803691215SE +/- 0.15, N = 311.56MIN: 9.38 / MAX: 12.7

PyTorch

Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30803691215SE +/- 0.05, N = 311.86MIN: 9.68 / MAX: 12.71

PyTorch

Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30803691215SE +/- 0.08, N = 311.77MIN: 9.64 / MAX: 12.77

PyTorch

Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lAMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 30803691215SE +/- 0.10, N = 311.84MIN: 9.61 / MAX: 12.83

Phoronix Test Suite v10.8.5