pytorch 2.2.1

AMD Ryzen Threadripper 7980X 64-Cores testing with a System76 Thelio Major (FA Z5 BIOS) and AMD Radeon Pro W7900 45GB on Ubuntu 23.10 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 2403260-PTS-PYTORCH257
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pytorch 2.2.1OpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen Threadripper 7980X 64-Cores @ 7.79GHz (64 Cores / 128 Threads)System76 Thelio Major (FA Z5 BIOS)AMD Device 14a44 x 32GB DRAM-4800MT/s Micron MTC20F1045S1RC48BA21000GB CT1000T700SSD5AMD Radeon Pro W7900 45GB (1760/1124MHz)AMD Device 14ccDELL P2415QAquantia AQC113C NBase-T/IEEE + Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX210/AX211/AX411Ubuntu 23.106.5.0-26-generic (x86_64)GNOME Shell 45.2X Server + Wayland4.6 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 DRM 3.54)GCC 13.2.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionPytorch 2.2.1 BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xa108105- 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

abcdResult OverviewPhoronix Test Suite100%101%103%104%105%PyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchCPU - 64 - ResNet-152CPU - 16 - ResNet-152CPU - 32 - ResNet-50CPU - 256 - ResNet-152CPU - 32 - ResNet-152CPU - 256 - ResNet-50CPU - 512 - ResNet-152CPU - 512 - ResNet-50CPU - 64 - ResNet-50CPU - 1 - ResNet-152CPU - 1 - ResNet-50CPU - 16 - ResNet-50CPU - 16 - Efficientnet_v2_lCPU - 1 - Efficientnet_v2_lCPU - 256 - Efficientnet_v2_lCPU - 512 - Efficientnet_v2_lCPU - 64 - Efficientnet_v2_lCPU - 32 - Efficientnet_v2_l

pytorch 2.2.1pytorch: CPU - 64 - ResNet-152pytorch: CPU - 16 - ResNet-152pytorch: CPU - 32 - ResNet-50pytorch: CPU - 256 - ResNet-152pytorch: CPU - 32 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: CPU - 512 - ResNet-152pytorch: CPU - 512 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 1 - ResNet-50pytorch: CPU - 16 - ResNet-50pytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_labcd18.9718.7148.4319.0219.0048.5018.8147.8448.2521.8259.8748.107.8011.997.787.787.787.8119.3419.4346.9119.4718.9347.9819.0548.1247.8421.8760.4448.047.8111.947.777.797.807.7918.5119.1847.3518.8819.0947.6318.7448.1347.7921.7559.7647.597.8212.007.747.797.767.7719.4318.6247.1118.9219.3748.7119.1647.2747.5222.0459.7847.907.7412.057.817.757.807.79OpenBenchmarking.org

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152dcba510152025SE +/- 0.15, N = 319.4318.5119.3418.97MIN: 19.02 / MAX: 19.6MIN: 17.93 / MAX: 18.68MIN: 18.65 / MAX: 19.48MIN: 18.22 / MAX: 19.28

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152dcba510152025SE +/- 0.07, N = 318.6219.1819.4318.71MIN: 18.1 / MAX: 18.77MIN: 18.6 / MAX: 19.36MIN: 18.98 / MAX: 19.61MIN: 18.06 / MAX: 18.98

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50dcba1122334455SE +/- 0.09, N = 347.1147.3546.9148.43MIN: 44.02 / MAX: 47.74MIN: 43.64 / MAX: 48.24MIN: 43.81 / MAX: 47.55MIN: 43.88 / MAX: 49.3

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152dcba510152025SE +/- 0.05, N = 318.9218.8819.4719.02MIN: 18.19 / MAX: 19.1MIN: 18.14 / MAX: 19.04MIN: 18.9 / MAX: 19.63MIN: 18.43 / MAX: 19.26

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152dcba510152025SE +/- 0.02, N = 319.3719.0918.9319.00MIN: 18.65 / MAX: 19.53MIN: 18.53 / MAX: 19.25MIN: 18.35 / MAX: 19.07MIN: 18.39 / MAX: 19.17

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50dcba1122334455SE +/- 0.17, N = 348.7147.6347.9848.50MIN: 44.82 / MAX: 49.49MIN: 43.91 / MAX: 48.42MIN: 43.06 / MAX: 48.63MIN: 44.96 / MAX: 49.46

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152dcba510152025SE +/- 0.12, N = 319.1618.7419.0518.81MIN: 18.7 / MAX: 19.35MIN: 18.24 / MAX: 18.88MIN: 18.53 / MAX: 19.21MIN: 18.12 / MAX: 19.1

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50dcba1122334455SE +/- 0.10, N = 347.2748.1348.1247.84MIN: 43.83 / MAX: 48.07MIN: 43.45 / MAX: 48.9MIN: 44.65 / MAX: 48.96MIN: 43.86 / MAX: 48.56

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50dcba1122334455SE +/- 0.16, N = 347.5247.7947.8448.25MIN: 43.99 / MAX: 48.23MIN: 44.49 / MAX: 48.52MIN: 44.44 / MAX: 48.63MIN: 43.55 / MAX: 49.28

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152dcba510152025SE +/- 0.08, N = 322.0421.7521.8721.82MIN: 21.04 / MAX: 22.24MIN: 20.95 / MAX: 21.96MIN: 20.98 / MAX: 22.14MIN: 21.05 / MAX: 22.15

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50dcba1428425670SE +/- 0.10, N = 359.7859.7660.4459.87MIN: 53.76 / MAX: 60.69MIN: 54.51 / MAX: 60.81MIN: 55.61 / MAX: 61.31MIN: 53.95 / MAX: 61.32

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50dcba1122334455SE +/- 0.25, N = 347.9047.5948.0448.10MIN: 44.12 / MAX: 48.66MIN: 44.44 / MAX: 48.39MIN: 44.19 / MAX: 48.74MIN: 42 / MAX: 49.24

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_ldcba246810SE +/- 0.02, N = 37.747.827.817.80MIN: 7 / MAX: 8.01MIN: 7.22 / MAX: 8.18MIN: 7.14 / MAX: 8.1MIN: 7.02 / MAX: 8.19

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_ldcba3691215SE +/- 0.09, N = 312.0512.0011.9411.99MIN: 11.9 / MAX: 12.2MIN: 10.62 / MAX: 12.16MIN: 11.71 / MAX: 12.13MIN: 11.68 / MAX: 12.24

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_ldcba246810SE +/- 0.01, N = 37.817.747.777.78MIN: 7.08 / MAX: 8.17MIN: 7 / MAX: 8.06MIN: 7.09 / MAX: 8.11MIN: 7.1 / MAX: 8.12

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_ldcba246810SE +/- 0.02, N = 37.757.797.797.78MIN: 7.2 / MAX: 8.05MIN: 7.14 / MAX: 8.11MIN: 7.21 / MAX: 8.14MIN: 7.11 / MAX: 8.12

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_ldcba246810SE +/- 0.02, N = 37.807.767.807.78MIN: 7.12 / MAX: 8.13MIN: 7.17 / MAX: 8.13MIN: 7.04 / MAX: 8.09MIN: 7.07 / MAX: 8.15

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_ldcba246810SE +/- 0.01, N = 37.797.777.797.81MIN: 7.2 / MAX: 8.08MIN: 7.04 / MAX: 8.06MIN: 7.08 / MAX: 8.09MIN: 7.09 / MAX: 8.15