pytorch 2.2.1 ryzen

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/2403270-PTS-PYTORCH233&sro.

pytorch 2.2.1 ryzenProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLOpenCLCompilerFile-SystemScreen ResolutionabcdAMD 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.orgKernel Details- Transparent Huge Pages: madviseProcessor Details- Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xa601206 Python Details- Python 3.11.6Security Details- 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 2.2.1 ryzenpytorch: 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 - 512 - ResNet-50pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 512 - 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_lpytorch: CPU - 512 - Efficientnet_v2_labcd72.7530.3349.1848.6448.1920.1048.8919.8148.4420.3020.5419.4016.5511.7911.4211.8011.7011.7672.6629.8148.6749.0247.9720.0147.4920.2648.8319.6419.9119.6916.0911.7711.7311.8711.7811.6772.2529.3249.7348.8548.5220.1648.6120.0649.3220.0619.7919.9815.7711.6211.7811.5911.7511.5671.2328.9647.6148.3448.5120.1848.3820.2448.1920.1419.7519.9316.2211.8411.7311.8811.7411.76OpenBenchmarking.org

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abcd1632486480SE +/- 0.34, N = 372.7572.6672.2571.23MIN: 69.82 / MAX: 74.29MIN: 67.96 / MAX: 74.36MIN: 57.14 / MAX: 74.16MIN: 64.96 / MAX: 73.24

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abcd714212835SE +/- 0.11, N = 330.3329.8129.3228.96MIN: 23.35 / MAX: 30.76MIN: 28.63 / MAX: 30.21MIN: 28.93 / MAX: 29.59MIN: 22.78 / MAX: 29.7

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abcd1122334455SE +/- 0.11, N = 349.1848.6749.7347.61MIN: 48.1 / MAX: 49.79MIN: 46.88 / MAX: 49.75MIN: 48.72 / MAX: 50.57MIN: 45.13 / MAX: 48.9

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abcd1122334455SE +/- 0.13, N = 348.6449.0248.8548.34MIN: 47.2 / MAX: 49.77MIN: 47.89 / MAX: 49.85MIN: 46.2 / MAX: 49.69MIN: 45.85 / MAX: 49.33

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50abcd1122334455SE +/- 0.35, N = 348.1947.9748.5248.51MIN: 45.76 / MAX: 49.14MIN: 45.74 / MAX: 49.08MIN: 47.06 / MAX: 49.51MIN: 46.37 / MAX: 49.94

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abcd510152025SE +/- 0.04, N = 320.1020.0120.1620.18MIN: 19.61 / MAX: 20.33MIN: 17.16 / MAX: 20.31MIN: 19.7 / MAX: 20.4MIN: 19.67 / MAX: 20.58

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50abcd1122334455SE +/- 0.66, N = 348.8947.4948.6148.38MIN: 46.38 / MAX: 49.85MIN: 46.32 / MAX: 48.77MIN: 46.63 / MAX: 49.33MIN: 45.71 / MAX: 50.11

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abcd510152025SE +/- 0.03, N = 319.8120.2620.0620.24MIN: 19.34 / MAX: 20.05MIN: 19.92 / MAX: 20.55MIN: 19.66 / MAX: 20.42MIN: 19.62 / MAX: 20.59

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50abcd1122334455SE +/- 0.19, N = 348.4448.8349.3248.19MIN: 46.94 / MAX: 49.13MIN: 47.52 / MAX: 50.16MIN: 47.73 / MAX: 50.31MIN: 37.3 / MAX: 49.6

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152abcd510152025SE +/- 0.04, N = 320.3019.6420.0620.14MIN: 20 / MAX: 20.47MIN: 19.34 / MAX: 19.92MIN: 19.3 / MAX: 20.27MIN: 19.6 / MAX: 20.53

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152abcd510152025SE +/- 0.13, N = 320.5419.9119.7919.75MIN: 20.16 / MAX: 20.7MIN: 19.61 / MAX: 20.22MIN: 19.52 / MAX: 19.99MIN: 19.22 / MAX: 20.44

PyTorch

Device: CPU - Batch Size: 512 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152abcd510152025SE +/- 0.08, N = 319.4019.6919.9819.93MIN: 18.86 / MAX: 19.68MIN: 19.19 / MAX: 20.13MIN: 19.59 / MAX: 20.45MIN: 17.21 / MAX: 20.37

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_labcd48121620SE +/- 0.07, N = 316.5516.0915.7716.22MIN: 14.5 / MAX: 16.83MIN: 15.88 / MAX: 16.28MIN: 13.93 / MAX: 16.03MIN: 15.75 / MAX: 16.51

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_labcd3691215SE +/- 0.03, N = 311.7911.7711.6211.84MIN: 9.57 / MAX: 12.78MIN: 9.55 / MAX: 12.38MIN: 9.52 / MAX: 12.22MIN: 9.62 / MAX: 12.79

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_labcd3691215SE +/- 0.03, N = 311.4211.7311.7811.73MIN: 9.66 / MAX: 12.53MIN: 9.61 / MAX: 12.62MIN: 9.57 / MAX: 12.66MIN: 9.64 / MAX: 12.74

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_labcd3691215SE +/- 0.14, N = 311.8011.8711.5911.88MIN: 9.74 / MAX: 12.67MIN: 9.59 / MAX: 12.72MIN: 9.5 / MAX: 12.72MIN: 9.72 / MAX: 12.8

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_labcd3691215SE +/- 0.10, N = 311.7011.7811.7511.74MIN: 9.54 / MAX: 12.17MIN: 9.74 / MAX: 12.5MIN: 9.7 / MAX: 12.73MIN: 9.67 / MAX: 12.77

PyTorch

Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_labcd3691215SE +/- 0.01, N = 311.7611.6711.5611.76MIN: 9.55 / MAX: 12.64MIN: 9.73 / MAX: 12.76MIN: 9.64 / MAX: 12.51MIN: 9.68 / MAX: 12.8


Phoronix Test Suite v10.8.5