AMD Ryzen Threadripper 3990X 64-Core testing with a Gigabyte TRX40 AORUS PRO WIFI (F6 BIOS) and AMD Radeon RX 5700 8GB 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 2311179-PTS-PYTORCHB94 pytorch bench - Phoronix Test Suite pytorch bench AMD Ryzen Threadripper 3990X 64-Core testing with a Gigabyte TRX40 AORUS PRO WIFI (F6 BIOS) and AMD Radeon RX 5700 8GB on Ubuntu 23.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2311179-PTS-PYTORCHB94&export=pdf&gru&sro .
pytorch bench Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server OpenGL Compiler File-System Screen Resolution a b c AMD Ryzen Threadripper 3990X 64-Core @ 2.90GHz (64 Cores / 128 Threads) Gigabyte TRX40 AORUS PRO WIFI (F6 BIOS) AMD Starship/Matisse 128GB Samsung SSD 970 EVO Plus 500GB AMD Radeon RX 5700 8GB (1750/875MHz) AMD Navi 10 HDMI Audio DELL P2415Q Intel I211 + Intel Wi-Fi 6 AX200 Ubuntu 23.10 6.5.0-10-generic (x86_64) GNOME Shell 45.0 X Server + Wayland 4.6 Mesa 23.2.1-1ubuntu3 (LLVM 15.0.7 DRM 3.54) GCC 13.2.0 ext4 3840x2160 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Processor Details - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x830107a Python Details - Python 3.11.6 Security 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: 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 + 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 + srbds: Not affected + tsx_async_abort: Not affected
pytorch bench pytorch: CPU - 1 - ResNet-50 pytorch: CPU - 1 - ResNet-152 pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 16 - ResNet-152 pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 32 - ResNet-152 pytorch: CPU - 512 - ResNet-50 pytorch: CPU - 64 - ResNet-152 pytorch: CPU - 256 - ResNet-152 pytorch: CPU - 512 - ResNet-152 pytorch: CPU - 1 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l pytorch: CPU - 256 - Efficientnet_v2_l pytorch: CPU - 512 - Efficientnet_v2_l a b c 20.88 8.22 16.88 17.11 15.96 6.69 16.66 6.78 16.59 6.84 7.01 6.89 4.36 3.07 3.01 2.96 2.93 2.92 18.63 7.80 16.03 16.09 15.99 6.81 16.83 6.76 16.06 6.82 6.52 6.66 4.17 2.90 2.99 2.92 2.86 2.91 19.18 8.03 15.57 16.52 16.35 6.76 16.27 6.69 15.64 6.67 6.65 6.51 4.16 2.93 2.96 2.96 3.02 2.99 OpenBenchmarking.org
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 a b c 5 10 15 20 25 SE +/- 0.27, N = 3 20.88 18.63 19.18 MIN: 19.27 / MAX: 22.47 MIN: 17.58 / MAX: 19.74 MIN: 18.22 / MAX: 20.52
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 a b c 2 4 6 8 10 SE +/- 0.10, N = 3 8.22 7.80 8.03 MIN: 7.88 / MAX: 8.56 MIN: 7.62 / MAX: 8.01 MIN: 7.82 / MAX: 8.21
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 a b c 4 8 12 16 20 SE +/- 0.19, N = 3 16.88 16.03 15.57 MIN: 15.77 / MAX: 18.22 MIN: 14.93 / MAX: 16.68 MIN: 14.78 / MAX: 16.29
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 a b c 4 8 12 16 20 SE +/- 0.20, N = 3 17.11 16.09 16.52 MIN: 15.92 / MAX: 18.06 MIN: 15.44 / MAX: 16.98 MIN: 15.52 / MAX: 17.2
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 a b c 4 8 12 16 20 SE +/- 0.13, N = 3 15.96 15.99 16.35 MIN: 14.8 / MAX: 17.08 MIN: 15.31 / MAX: 16.83 MIN: 15.49 / MAX: 17.11
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 a b c 2 4 6 8 10 SE +/- 0.06, N = 3 6.69 6.81 6.76 MIN: 6.41 / MAX: 6.91 MIN: 6.67 / MAX: 7 MIN: 6.34 / MAX: 6.95
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 a b c 4 8 12 16 20 SE +/- 0.21, N = 3 16.66 16.83 16.27 MIN: 15.65 / MAX: 17.93 MIN: 15.98 / MAX: 17.49 MIN: 15.45 / MAX: 16.87
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 a b c 2 4 6 8 10 SE +/- 0.06, N = 3 6.78 6.76 6.69 MIN: 6.21 / MAX: 7.03 MIN: 6.63 / MAX: 6.89 MIN: 6.25 / MAX: 6.86
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 a b c 4 8 12 16 20 SE +/- 0.14, N = 8 16.59 16.06 15.64 MIN: 15.41 / MAX: 17.89 MIN: 14.68 / MAX: 16.72 MIN: 15.06 / MAX: 16.39
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 a b c 2 4 6 8 10 SE +/- 0.08, N = 3 6.84 6.82 6.67 MIN: 6.51 / MAX: 7.13 MIN: 6.66 / MAX: 6.95 MIN: 6.48 / MAX: 6.83
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 a b c 2 4 6 8 10 SE +/- 0.05, N = 3 7.01 6.52 6.65 MIN: 6.51 / MAX: 7.22 MIN: 6.32 / MAX: 6.71 MIN: 6.45 / MAX: 6.82
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: ResNet-152 a b c 2 4 6 8 10 SE +/- 0.01, N = 3 6.89 6.66 6.51 MIN: 6.48 / MAX: 7.08 MIN: 6.29 / MAX: 6.85 MIN: 6.34 / MAX: 6.69
PyTorch Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l a b c 0.981 1.962 2.943 3.924 4.905 SE +/- 0.04, N = 3 4.36 4.17 4.16 MIN: 4.07 / MAX: 4.56 MIN: 3.98 / MAX: 4.35 MIN: 3.9 / MAX: 4.35
PyTorch Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l a b c 0.6908 1.3816 2.0724 2.7632 3.454 SE +/- 0.03, N = 3 3.07 2.90 2.93 MIN: 2.85 / MAX: 3.2 MIN: 2.73 / MAX: 3.05 MIN: 2.82 / MAX: 3.07
PyTorch Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l a b c 0.6773 1.3546 2.0319 2.7092 3.3865 SE +/- 0.04, N = 4 3.01 2.99 2.96 MIN: 2.81 / MAX: 3.25 MIN: 2.79 / MAX: 3.27 MIN: 2.83 / MAX: 3.07
PyTorch Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l a b c 0.666 1.332 1.998 2.664 3.33 SE +/- 0.00, N = 3 2.96 2.92 2.96 MIN: 2.81 / MAX: 3.09 MIN: 2.76 / MAX: 3.04 MIN: 2.82 / MAX: 3.09
PyTorch Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l a b c 0.6795 1.359 2.0385 2.718 3.3975 SE +/- 0.02, N = 3 2.93 2.86 3.02 MIN: 2.74 / MAX: 3.09 MIN: 2.76 / MAX: 2.99 MIN: 2.9 / MAX: 3.14
PyTorch Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l a b c 0.6728 1.3456 2.0184 2.6912 3.364 SE +/- 0.02, N = 3 2.92 2.91 2.99 MIN: 2.78 / MAX: 3.12 MIN: 2.77 / MAX: 3.03 MIN: 2.84 / MAX: 3.16
Phoronix Test Suite v10.8.4