pytorch 2.2 raptor lake

Intel Core i9-14900K testing with a ASUS PRIME Z790-P WIFI (1402 BIOS) and ASUS Intel RPL-S 31GB on Ubuntu 23.10 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2403267-PTS-PYTORCH238&rdt&grr.

pytorch 2.2 raptor lakeProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionabcIntel Core i9-14900K @ 5.70GHz (24 Cores / 32 Threads)ASUS PRIME Z790-P WIFI (1402 BIOS)Intel Device 7a272 x 16GB DRAM-6000MT/s Corsair CMK32GX5M2B6000C36Western Digital WD_BLACK SN850X 1000GBASUS Intel RPL-S 31GB (1650MHz)Realtek ALC897ASUS VP28UUbuntu 23.106.8.0-phx (x86_64)GNOME Shell 45.1X Server 1.21.1.74.6 Mesa 24.0~git2312240600.c05261~oibaf~m (git-c05261a 2023-12-24 mantic-oibaf-ppa)GCC 13.2.0ext43840x2160OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseProcessor Details- Scaling Governor: intel_pstate powersave (EPP: performance) - CPU Microcode: 0x122 - Thermald 2.5.4Python 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 + reg_file_data_sampling: Mitigation of Clear Register File + retbleed: Not affected + spec_rstack_overflow: Not affected + 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 RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected

pytorch 2.2 raptor lakepytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 512 - ResNet-152pytorch: CPU - 32 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 16 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 512 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 256 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 16 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 1 - ResNet-50abc8.6511.078.5411.3811.1516.8115.7616.8716.8616.8212.9339.6343.5743.6243.5742.9728.5571.8111.628.6411.4610.3611.5314.6716.6616.7916.7116.8313.0038.4338.8342.9643.6944.2328.3557.4610.4011.5411.439.6911.4714.8216.8516.8016.8616.8213.0243.5142.4443.7039.1043.5528.4757.44OpenBenchmarking.org

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_labc36912158.6511.6210.40MIN: 4.77 / MAX: 8.82MIN: 4.95 / MAX: 12.01MIN: 4.47 / MAX: 10.8

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_labc369121511.078.6411.54MIN: 4.44 / MAX: 11.86MIN: 4.69 / MAX: 8.91MIN: 4.96 / MAX: 11.96

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_labc36912158.5411.4611.43MIN: 4.87 / MAX: 8.84MIN: 5.04 / MAX: 11.96MIN: 4.47 / MAX: 11.86

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_labc369121511.3810.369.69MIN: 5.03 / MAX: 11.89MIN: 4.64 / MAX: 10.76MIN: 5.03 / MAX: 11.97

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_labc369121511.1511.5311.47MIN: 4.59 / MAX: 11.97MIN: 4.97 / MAX: 12MIN: 4.87 / MAX: 11.96

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152abc4812162016.8114.6714.82MIN: 6.53 / MAX: 17.63MIN: 5.69 / MAX: 15.6MIN: 5.9 / MAX: 17.52

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abc4812162015.7616.6616.85MIN: 5.85 / MAX: 17.62MIN: 6.55 / MAX: 17.53MIN: 5.47 / MAX: 17.6

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152abc4812162016.8716.7916.80MIN: 6.32 / MAX: 17.66MIN: 6.54 / MAX: 17.58MIN: 6.4 / MAX: 17.6

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152abc4812162016.8616.7116.86MIN: 7.09 / MAX: 17.62MIN: 6.13 / MAX: 17.52MIN: 6.25 / MAX: 17.63

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abc4812162016.8216.8316.82MIN: 5.94 / MAX: 17.56MIN: 6.26 / MAX: 17.61MIN: 6.29 / MAX: 17.63

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_labc369121512.9313.0013.02MIN: 4.5 / MAX: 16.83MIN: 3.43 / MAX: 17.41MIN: 4.51 / MAX: 17.18

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50abc102030405039.6338.4343.51MIN: 11.63 / MAX: 44.96MIN: 9.91 / MAX: 43.83MIN: 10.48 / MAX: 45.09

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50abc102030405043.5738.8342.44MIN: 9.83 / MAX: 45.65MIN: 9.43 / MAX: 42.63MIN: 10.09 / MAX: 44.94

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50abc102030405043.6242.9643.70MIN: 10.53 / MAX: 45.39MIN: 11.33 / MAX: 44.99MIN: 10.24 / MAX: 45.38

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abc102030405043.5743.6939.10MIN: 10.28 / MAX: 45.23MIN: 10 / MAX: 44.93MIN: 38.31 / MAX: 39.18

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abc102030405042.9744.2343.55MIN: 9.78 / MAX: 44.95MIN: 9.75 / MAX: 45.13MIN: 9.79 / MAX: 45.11

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abc71421283528.5528.3528.47MIN: 27.88 / MAX: 28.63MIN: 28 / MAX: 28.43MIN: 28 / MAX: 28.54

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abc163248648071.8157.4657.44MIN: 70.12 / MAX: 72.04MIN: 56.35 / MAX: 68.07MIN: 56.19 / MAX: 58.2

Phoronix Test Suite v10.8.5