pytorch hx

AMD Ryzen 9 5900HX testing with a ASUS G513QY v1.0 (G513QY.318 BIOS) and ASUS AMD Cezanne 512MB on Ubuntu 22.10 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 2311169-PTS-PYTORCHH78
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November 16 2023
  1 Hour, 31 Minutes
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November 16 2023
  24 Minutes
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November 16 2023
  24 Minutes
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pytorch hxOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 5900HX @ 3.30GHz (8 Cores / 16 Threads)ASUS G513QY v1.0 (G513QY.318 BIOS)AMD Renoir/Cezanne16GB512GB SAMSUNG MZVLQ512HBLU-00B00ASUS AMD Cezanne 512MB (2500/1000MHz)AMD Navi 21/23LQ156M1JW25Realtek RTL8111/8168/8411 + MEDIATEK MT7921 802.11ax PCIUbuntu 22.105.19.0-46-generic (x86_64)GNOME Shell 43.0X Server 1.21.1.4 + Wayland4.6 Mesa 22.2.5 (LLVM 15.0.2 DRM 3.47)1.3.224GCC 12.2.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLVulkanCompilerFile-SystemScreen ResolutionPytorch Hx BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - Platform Profile: balanced - CPU Microcode: 0xa50000c - ACPI Profile: balanced - Python 3.10.7- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: 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 Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcResult OverviewPhoronix Test Suite100%103%107%110%113%PyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchCPU - 1 - ResNet-50CPU - 512 - ResNet-50CPU - 16 - ResNet-152CPU - 16 - ResNet-50CPU - 512 - ResNet-152CPU - 1 - ResNet-152CPU - 512 - Efficientnet_v2_lCPU - 1 - Efficientnet_v2_lCPU - 16 - Efficientnet_v2_l

pytorch hxpytorch: CPU - 1 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 512 - ResNet-50pytorch: CPU - 512 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_labc34.8314.9019.548.9918.388.749.376.286.2338.1715.2519.419.0719.208.729.366.276.2933.6915.2218.729.4217.588.509.416.286.17OpenBenchmarking.org

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50cba918273645SE +/- 0.24, N = 333.6938.1734.83MIN: 28.41 / MAX: 34.46MIN: 28.84 / MAX: 39.86MIN: 27.03 / MAX: 36.83

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152cba48121620SE +/- 0.16, N = 415.2215.2514.90MIN: 13.18 / MAX: 16.33MIN: 13.45 / MAX: 16.23MIN: 11.55 / MAX: 16.23

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50cba510152025SE +/- 0.17, N = 1518.7219.4119.54MIN: 16.31 / MAX: 19.95MIN: 18.05 / MAX: 20.45MIN: 14.52 / MAX: 21.45

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152cba3691215SE +/- 0.13, N = 39.429.078.99MIN: 8.78 / MAX: 10.02MIN: 8.38 / MAX: 9.61MIN: 8.32 / MAX: 9.79

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50cba510152025SE +/- 0.12, N = 317.5819.2018.38MIN: 15.72 / MAX: 20.02MIN: 17.86 / MAX: 19.94MIN: 16.56 / MAX: 20.37

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152cba246810SE +/- 0.04, N = 38.508.728.74MIN: 8.24 / MAX: 9.46MIN: 8.29 / MAX: 9.49MIN: 8.1 / MAX: 9.63

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lcba3691215SE +/- 0.05, N = 39.419.369.37MIN: 8.85 / MAX: 9.68MIN: 8.35 / MAX: 9.62MIN: 8.24 / MAX: 9.75

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lcba246810SE +/- 0.03, N = 36.286.276.28MIN: 5.98 / MAX: 6.52MIN: 5.82 / MAX: 6.55MIN: 5.55 / MAX: 6.58

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lcba246810SE +/- 0.05, N = 36.176.296.23MIN: 5.86 / MAX: 6.44MIN: 5.89 / MAX: 6.52MIN: 5.69 / MAX: 6.5