1te

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HTML result view exported from: https://openbenchmarking.org/result/2405272-NE-1TE21756861&grr.

1teProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDisplay ServerDisplay DriverVulkanCompilerFile-SystemScreen Resolution3090 back from deadAMD Ryzen 9 3900X 12-Core @ 4.50GHz (12 Cores / 24 Threads)Gigabyte B550M AORUS PRO-P (F14e BIOS)AMD Starship/Matisse128GB2000GB Corsair Force MP600 + PC SN730 NVMe WDC 256GBNVIDIA GeForce RTX 3090 24GBNVIDIA GA102 HD AudioQ32V3WG5Realtek RTL8125 2.5GbEUbuntu 22.045.15.0-107-generic (x86_64)X Server 1.21.1.4NVIDIA1.3.242GCC 11.4.0 + CUDA 12.5ext41024x768OpenBenchmarking.org- Transparent Huge Pages: madvise- Scaling Governor: acpi-cpufreq schedutil (Boost: Disabled) - CPU Microcode: 0x8701021 - Python 3.10.12- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

1tepytorch: NVIDIA CUDA GPU - 16 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 64 - ResNet-152pytorch: NVIDIA CUDA GPU - 32 - ResNet-152pytorch: NVIDIA CUDA GPU - 256 - ResNet-152pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 1 - ResNet-152pytorch: NVIDIA CUDA GPU - 256 - ResNet-50pytorch: NVIDIA CUDA GPU - 32 - ResNet-50pytorch: NVIDIA CUDA GPU - 16 - ResNet-50pytorch: NVIDIA CUDA GPU - 64 - ResNet-50pytorch: NVIDIA CUDA GPU - 512 - ResNet-50pytorch: NVIDIA CUDA GPU - 1 - ResNet-503090 back from dead72.6772.7637.5037.6637.7537.8437.8973.3873.3873.6738.1871.47206.28204.72205.60206.25207.15208.89OpenBenchmarking.org

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-1523090 back from dead1632486480SE +/- 0.44, N = 1572.67MIN: 66.15 / MAX: 74.19

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-1523090 back from dead1632486480SE +/- 0.65, N = 772.76MIN: 67.7 / MAX: 74.31

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l3090 back from dead918273645SE +/- 0.16, N = 337.50MIN: 35.25 / MAX: 37.95

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l3090 back from dead918273645SE +/- 0.02, N = 337.66MIN: 35.7 / MAX: 37.99

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l3090 back from dead918273645SE +/- 0.08, N = 337.75MIN: 35.68 / MAX: 38.06

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l3090 back from dead918273645SE +/- 0.24, N = 337.84MIN: 35.45 / MAX: 38.29

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l3090 back from dead918273645SE +/- 0.11, N = 337.89MIN: 35.81 / MAX: 38.2

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-1523090 back from dead1632486480SE +/- 0.06, N = 373.38MIN: 68.42 / MAX: 73.88

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-1523090 back from dead1632486480SE +/- 0.31, N = 373.38MIN: 68.15 / MAX: 74.16

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-1523090 back from dead1632486480SE +/- 0.09, N = 373.67MIN: 68.62 / MAX: 74.23

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l3090 back from dead918273645SE +/- 0.24, N = 338.18MIN: 35.52 / MAX: 38.74

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-1523090 back from dead1632486480SE +/- 0.87, N = 471.47MIN: 65.98 / MAX: 74.07

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-503090 back from dead50100150200250SE +/- 0.41, N = 3206.28MIN: 184.63 / MAX: 209.46

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-503090 back from dead4080120160200SE +/- 1.75, N = 3204.72MIN: 183.33 / MAX: 210.03

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-503090 back from dead50100150200250SE +/- 0.54, N = 3205.60MIN: 181.67 / MAX: 207.62

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-503090 back from dead50100150200250SE +/- 0.65, N = 3206.25MIN: 182.91 / MAX: 209.25

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-503090 back from dead50100150200250SE +/- 0.29, N = 3207.15MIN: 183.26 / MAX: 209.01

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-503090 back from dead50100150200250SE +/- 2.02, N = 3208.89MIN: 184.84 / MAX: 212.57

Phoronix Test Suite v10.8.5