pytorch1

AMD Ryzen 7 7700X 8-Core testing with a MSI PRO B650M-A WIFI (MS-7D77) v1.0 (1.F0 BIOS) and MSI NVIDIA GeForce RTX 4070 SUPER 12GB on Pop 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 2403300-NE-PYTORCH1166
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Result
Identifier
Performance Per
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Date
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  Test
  Duration
pytorch1
March 30
  4 Minutes
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pytorch1OpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 7 7700X 8-Core @ 5.57GHz (8 Cores / 16 Threads)MSI PRO B650M-A WIFI (MS-7D77) v1.0 (1.F0 BIOS)AMD Device 14d864GB2000GB Samsung SSD 990 PRO 2TBMSI NVIDIA GeForce RTX 4070 SUPER 12GBNVIDIA Device 22bcLC49G95TRealtek RTL8125 2.5GbE + MEDIATEK Device 0616Pop 22.046.8.0-76060800daily20240311-generic (x86_64)GNOME Shell 42.5X Server 1.21.1.4NVIDIA 550.674.6.0OpenCL 3.0 CUDA 12.4.1251.3.277GCC 11.4.0ext42560x1440ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLOpenCLVulkanCompilerFile-SystemScreen ResolutionPytorch1 BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xa601206 - 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

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50pytorch180160240320400SE +/- 1.08, N = 3377.25MIN: 209.86 / MAX: 387.68

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152pytorch1306090120150SE +/- 1.02, N = 3136.71MIN: 117.53 / MAX: 141.43

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_lpytorch11530456075SE +/- 0.32, N = 368.84MIN: 58.96 / MAX: 69.96