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.

HTML result view exported from: https://openbenchmarking.org/result/2311188-SYST-PYTORCH48&grr.

pytorch blenderProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLOpenCLCompilerFile-SystemScreen ResolutionabcIntel 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 13.2.0ext41920x1200OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseProcessor Details- Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xac - Thermald 2.5.4 Python Details- Python 3.11.6Security Details- 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

pytorch blenderblender: Pabellon Barcelona - CPU-Onlyblender: Fishy Cat - CPU-Onlypytorch: CPU - 64 - Efficientnet_v2_lblender: BMW27 - CPU-Onlypytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 256 - ResNet-152pytorch: CPU - 512 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 32 - ResNet-152pytorch: CPU - 16 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 512 - ResNet-50pytorch: CPU - 256 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 1 - ResNet-50abc1472.73556.853.85438.093.984.014.023.995.355.275.245.365.577.1513.3813.4613.7214.1213.8011.7531.881469.98556.984.00432.244.014.023.984.035.405.435.375.415.417.4613.3913.4213.3413.4313.4110.7031.341473.91556.134.00431.693.994.014.024.025.405.415.395.645.657.5613.4213.6113.9714.4514.2411.8132.32OpenBenchmarking.org

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Pabellon Barcelona - Compute: CPU-Onlyabc300600900120015001472.731469.981473.91

Blender

Blend File: Fishy Cat - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Fishy Cat - Compute: CPU-Onlyabc120240360480600556.85556.98556.13

PyTorch

Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l

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

Blender

Blend File: BMW27 - Compute: CPU-Only

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

PyTorch

Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_labc0.90231.80462.70693.60924.51153.984.013.99MIN: 3.14 / MAX: 4.04MIN: 3.06 / MAX: 4.07MIN: 2.15 / MAX: 4.05

PyTorch

Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_labc0.90451.8092.71353.6184.52254.014.024.01MIN: 3.47 / MAX: 4.06MIN: 2.93 / MAX: 4.07MIN: 3.39 / MAX: 4.06

PyTorch

Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_labc0.90451.8092.71353.6184.52254.023.984.02MIN: 3.5 / MAX: 4.06MIN: 3.1 / MAX: 4.04MIN: 3.41 / MAX: 4.07

PyTorch

Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_labc0.90681.81362.72043.62724.5343.994.034.02MIN: 3.42 / MAX: 4.04MIN: 3.16 / MAX: 4.08MIN: 2.79 / MAX: 4.08

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-152

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

PyTorch

Device: CPU - Batch Size: 512 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152abc1.22182.44363.66544.88726.1095.275.435.41MIN: 4.7 / MAX: 5.31MIN: 4.97 / MAX: 5.49MIN: 4.7 / MAX: 5.47

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-152

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

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-152

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

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abc1.27132.54263.81395.08526.35655.575.415.65MIN: 4.94 / MAX: 6.57MIN: 4.95 / MAX: 5.48MIN: 5.07 / MAX: 6.69

PyTorch

Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l

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

PyTorch

Device: CPU - Batch Size: 512 - Model: ResNet-50

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

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50abc369121513.4613.4213.61MIN: 11.82 / MAX: 16.64MIN: 11.82 / MAX: 13.58MIN: 8.8 / MAX: 17.24

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50abc4812162013.7213.3413.97MIN: 9.17 / MAX: 17.4MIN: 8.69 / MAX: 13.57MIN: 11.64 / MAX: 17.55

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abc4812162014.1213.4314.45MIN: 11.87 / MAX: 17.34MIN: 8.88 / MAX: 13.62MIN: 9.83 / MAX: 17.22

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abc4812162013.8013.4114.24MIN: 11.74 / MAX: 17.31MIN: 11.76 / MAX: 13.58MIN: 11.11 / MAX: 17.38

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abc369121511.7510.7011.81MIN: 8.55 / MAX: 12.87MIN: 8.62 / MAX: 12.74MIN: 10.34 / MAX: 13.05

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abc81624324031.8831.3432.32MIN: 18.82 / MAX: 32.98MIN: 22.74 / MAX: 33.12MIN: 23.75 / MAX: 33.31

Phoronix Test Suite v10.8.5