pytorch blender

Intel Core i7-1165G7 testing with a Dell 0GG9PT (3.15.0 BIOS) and Intel Xe TGL GT2 15GB 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 2311188-SYST-PYTORCH48
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November 17
  2 Hours
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November 18
  2 Hours
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November 18
  1 Hour, 58 Minutes
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pytorch blenderOpenBenchmarking.orgPhoronix Test SuiteIntel Core i7-1165G7 @ 4.70GHz (4 Cores / 8 Threads)Dell 0GG9PT (3.15.0 BIOS)Intel Tiger Lake-LP16GBKioxia KBG40ZNS256G NVMe 256GBIntel Xe TGL GT2 15GB (1300MHz)Realtek ALC289Intel Wi-Fi 6 AX201Ubuntu 23.106.5.0-10-generic (x86_64)GNOME Shell 45.0X Server + Wayland4.6 Mesa 23.2.1-1ubuntu3OpenCL 3.0GCC 13.2.0ext41920x1200ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLOpenCLCompilerFile-SystemScreen ResolutionPytorch Blender BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xac - Thermald 2.5.4 - Python 3.11.6- gather_data_sampling: Mitigation of Microcode + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + 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

abcResult OverviewPhoronix Test Suite100%103%105%108%110%PyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchBlenderPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchBlenderPyTorchBlenderCPU - 1 - ResNet-152CPU - 16 - ResNet-50CPU - 32 - ResNet-50CPU - 1 - Efficientnet_v2_lCPU - 32 - ResNet-152CPU - 64 - ResNet-50CPU - 16 - ResNet-152CPU - 64 - Efficientnet_v2_lCPU - 1 - ResNet-50CPU - 512 - ResNet-152CPU - 64 - ResNet-152BMW27 - CPU-OnlyCPU - 256 - ResNet-50CPU - 16 - Efficientnet_v2_lCPU - 32 - Efficientnet_v2_lCPU - 256 - ResNet-152CPU - 256 - Efficientnet_v2_lCPU - 512 - ResNet-50Pabellon Barcelona - CPU-OnlyCPU - 512 - Efficientnet_v2_lFishy Cat - CPU-Only

pytorch blenderpytorch: 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_lblender: BMW27 - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlyabc31.8811.7514.1213.8013.725.5713.465.3613.385.245.355.277.154.023.993.853.984.01438.09556.851472.7331.3410.7013.4313.4113.345.4113.425.4113.395.375.405.437.463.984.034.004.014.02432.24556.981469.9832.3211.8114.4514.2413.975.6513.615.6413.425.395.405.417.564.024.024.003.994.01431.69556.131473.91OpenBenchmarking.org

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50bac81624324031.3431.8832.32MIN: 22.74 / MAX: 33.12MIN: 18.82 / MAX: 32.98MIN: 23.75 / MAX: 33.31

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152bac369121510.7011.7511.81MIN: 8.62 / MAX: 12.74MIN: 8.55 / MAX: 12.87MIN: 10.34 / MAX: 13.05

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50bac4812162013.4314.1214.45MIN: 8.88 / MAX: 13.62MIN: 11.87 / MAX: 17.34MIN: 9.83 / MAX: 17.22

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50bac4812162013.4113.8014.24MIN: 11.76 / MAX: 13.58MIN: 11.74 / MAX: 17.31MIN: 11.11 / MAX: 17.38

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50bac4812162013.3413.7213.97MIN: 8.69 / MAX: 13.57MIN: 9.17 / MAX: 17.4MIN: 11.64 / MAX: 17.55

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152bac1.27132.54263.81395.08526.35655.415.575.65MIN: 4.95 / MAX: 5.48MIN: 4.94 / MAX: 6.57MIN: 5.07 / MAX: 6.69

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50bac369121513.4213.4613.61MIN: 11.82 / MAX: 13.58MIN: 11.82 / MAX: 16.64MIN: 8.8 / MAX: 17.24

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abc1.2692.5383.8075.0766.3455.365.415.64MIN: 4.88 / MAX: 5.41MIN: 3.94 / MAX: 5.46MIN: 4.01 / MAX: 6.72

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50abc369121513.3813.3913.42MIN: 11.78 / MAX: 13.58MIN: 11.46 / MAX: 13.65MIN: 11.85 / MAX: 13.57

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152abc1.21282.42563.63844.85126.0645.245.375.39MIN: 4.85 / MAX: 5.35MIN: 5.08 / MAX: 5.42MIN: 5.09 / MAX: 5.44

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152abc1.2152.433.6454.866.0755.355.405.40MIN: 4.54 / MAX: 5.4MIN: 4.5 / MAX: 5.45MIN: 4.63 / MAX: 5.46

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152acb1.22182.44363.66544.88726.1095.275.415.43MIN: 4.7 / MAX: 5.31MIN: 4.7 / MAX: 5.47MIN: 4.97 / MAX: 5.49

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_labc2468107.157.467.56MIN: 6.42 / MAX: 7.22MIN: 6.36 / MAX: 9.61MIN: 6.3 / MAX: 9.63

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lbac0.90451.8092.71353.6184.52253.984.024.02MIN: 3.1 / MAX: 4.04MIN: 3.5 / MAX: 4.06MIN: 3.41 / MAX: 4.07

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lacb0.90681.81362.72043.62724.5343.994.024.03MIN: 3.42 / MAX: 4.04MIN: 2.79 / MAX: 4.08MIN: 3.16 / MAX: 4.08

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_labc0.91.82.73.64.53.854.004.00MIN: 3.13 / MAX: 3.9MIN: 3.37 / MAX: 4.05MIN: 3.39 / MAX: 4.06

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lacb0.90231.80462.70693.60924.51153.983.994.01MIN: 3.14 / MAX: 4.04MIN: 2.15 / MAX: 4.05MIN: 3.06 / MAX: 4.07

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lacb0.90451.8092.71353.6184.52254.014.014.02MIN: 3.47 / MAX: 4.06MIN: 3.39 / MAX: 4.06MIN: 2.93 / MAX: 4.07

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: BMW27 - Compute: CPU-Onlyabc90180270360450438.09432.24431.69

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Fishy Cat - Compute: CPU-Onlybac120240360480600556.98556.85556.13

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Pabellon Barcelona - Compute: CPU-Onlycab300600900120015001473.911472.731469.98