ripper-pytorch

AMD Ryzen Threadripper 7960X 24-Cores testing with a ASRock TRX50 WS (7.09 BIOS) and NVIDIA GeForce RTX 4060 Ti 16GB on Ubuntu 22.04 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 2403309-NE-RIPPERPYT22
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ripper-pytorch
March 30
  1 Hour, 50 Minutes
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ripper-pytorchOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen Threadripper 7960X 24-Cores @ 8.23GHz (24 Cores / 48 Threads)ASRock TRX50 WS (7.09 BIOS)AMD Device 14a4128GB2000GB Samsung SSD 980 PRO with Heatsink 2TBNVIDIA GeForce RTX 4060 Ti 16GBAMD Device 14ccSyncMasterAquantia Device 04c0 + Realtek RTL8125 2.5GbE + MEDIATEK Device 0616Ubuntu 22.046.5.0-26-generic (x86_64)GNOME Shell 42.9X Server 1.21.1.4NVIDIA 545.29.064.6.0OpenCL 3.0 CUDA 12.3.991.3.260GCC 11.4.0ext41680x1050ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLOpenCLVulkanCompilerFile-SystemScreen ResolutionRipper-pytorch BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: amd-pstate-epp powersave (EPP: performance) - CPU Microcode: 0xa108105- 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: 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

ripper-pytorchpytorch: 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_lpytorch: NVIDIA CUDA GPU - 1 - ResNet-50pytorch: NVIDIA CUDA GPU - 1 - ResNet-152pytorch: NVIDIA CUDA GPU - 16 - ResNet-50pytorch: NVIDIA CUDA GPU - 32 - ResNet-50pytorch: NVIDIA CUDA GPU - 64 - ResNet-50pytorch: NVIDIA CUDA GPU - 16 - ResNet-152pytorch: NVIDIA CUDA GPU - 256 - ResNet-50pytorch: NVIDIA CUDA GPU - 32 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-50pytorch: NVIDIA CUDA GPU - 64 - ResNet-152pytorch: NVIDIA CUDA GPU - 256 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-152pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_lripper-pytorch64.4725.2149.4849.0049.0219.1649.3019.3048.9519.2719.2619.2015.2010.8210.8210.7710.9210.82334.53118.70308.84313.80310.95118.70316.14119.37316.76118.01120.96120.4562.7961.2261.1061.0760.0060.24OpenBenchmarking.org

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50ripper-pytorch1428425670SE +/- 0.27, N = 364.47MIN: 49.55 / MAX: 66.67

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152ripper-pytorch612182430SE +/- 0.15, N = 325.21MIN: 20.13 / MAX: 25.88

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50ripper-pytorch1122334455SE +/- 0.11, N = 349.48MIN: 46.91 / MAX: 50.12

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50ripper-pytorch1122334455SE +/- 0.31, N = 349.00MIN: 45.84 / MAX: 50.07

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50ripper-pytorch1122334455SE +/- 0.26, N = 349.02MIN: 46.16 / MAX: 49.85

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152ripper-pytorch510152025SE +/- 0.12, N = 319.16MIN: 17.87 / MAX: 19.55

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50ripper-pytorch1122334455SE +/- 0.22, N = 349.30MIN: 46.71 / MAX: 50.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152ripper-pytorch510152025SE +/- 0.02, N = 319.30MIN: 18.88 / MAX: 19.46

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50ripper-pytorch1122334455SE +/- 0.17, N = 348.95MIN: 45.69 / MAX: 49.93

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152ripper-pytorch510152025SE +/- 0.02, N = 319.27MIN: 18.8 / MAX: 19.46

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152ripper-pytorch510152025SE +/- 0.02, N = 319.26MIN: 18.76 / MAX: 19.4

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152ripper-pytorch510152025SE +/- 0.07, N = 319.20MIN: 18.72 / MAX: 19.42

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lripper-pytorch48121620SE +/- 0.07, N = 315.20MIN: 12.84 / MAX: 15.51

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lripper-pytorch3691215SE +/- 0.01, N = 310.82MIN: 9.7 / MAX: 11.03

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lripper-pytorch3691215SE +/- 0.01, N = 310.82MIN: 9.47 / MAX: 11.07

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lripper-pytorch3691215SE +/- 0.07, N = 310.77MIN: 9.05 / MAX: 11.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lripper-pytorch3691215SE +/- 0.04, N = 310.92MIN: 9.71 / MAX: 11.17

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lripper-pytorch3691215SE +/- 0.02, N = 310.82MIN: 9.59 / MAX: 11.05

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50ripper-pytorch70140210280350SE +/- 2.80, N = 3334.53MIN: 278.54 / MAX: 343.94

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152ripper-pytorch306090120150SE +/- 0.39, N = 3118.70MIN: 104.59 / MAX: 121.02

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50ripper-pytorch70140210280350SE +/- 1.97, N = 3308.84MIN: 286.79 / MAX: 316.03

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50ripper-pytorch70140210280350SE +/- 2.54, N = 3313.80MIN: 289.68 / MAX: 320.89

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50ripper-pytorch70140210280350SE +/- 3.16, N = 3310.95MIN: 284.77 / MAX: 319.52

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152ripper-pytorch306090120150SE +/- 1.27, N = 3118.70MIN: 105.17 / MAX: 122.87

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50ripper-pytorch70140210280350SE +/- 1.84, N = 3316.14MIN: 296.19 / MAX: 323.31

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152ripper-pytorch306090120150SE +/- 0.31, N = 3119.37MIN: 106.4 / MAX: 120.94

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50ripper-pytorch70140210280350SE +/- 1.73, N = 3316.76MIN: 288.88 / MAX: 324.66

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152ripper-pytorch306090120150SE +/- 0.89, N = 3118.01MIN: 105.65 / MAX: 120.3

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152ripper-pytorch306090120150SE +/- 1.01, N = 3120.96MIN: 107.66 / MAX: 123.82

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152ripper-pytorch306090120150SE +/- 0.12, N = 3120.45MIN: 107.48 / MAX: 122.04

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_lripper-pytorch1428425670SE +/- 0.30, N = 362.79MIN: 54.78 / MAX: 63.93

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_lripper-pytorch1428425670SE +/- 0.18, N = 361.22MIN: 53.84 / MAX: 62.11

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_lripper-pytorch1428425670SE +/- 0.60, N = 361.10MIN: 54.19 / MAX: 62.82

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_lripper-pytorch1428425670SE +/- 0.60, N = 561.07MIN: 52.92 / MAX: 62.55

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_lripper-pytorch1326395265SE +/- 0.52, N = 360.00MIN: 52.58 / MAX: 61.37

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_lripper-pytorch1326395265SE +/- 0.62, N = 360.24MIN: 53.1 / MAX: 61.93