AMD Ryzen 5 7600X 6-Core testing with a ASUS TUF GAMING B650-PLUS WIFI (0823 BIOS) and Gigabyte NVIDIA GeForce RTX 4060 Ti 16GB on Ubuntu 22.04 via the Phoronix Test Suite.
Processor: AMD Ryzen 5 7600X 6-Core @ 4.70GHz (6 Cores / 12 Threads), Motherboard: ASUS TUF GAMING B650-PLUS WIFI (0823 BIOS), Chipset: AMD Device 14d8, Memory: 32GB, Disk: Western Digital WD_BLACK SN850X 2000GB, Graphics: Gigabyte NVIDIA GeForce RTX 4060 Ti 16GB, Audio: NVIDIA Device 22bd, Monitor: DELL P2720DC, Network: Realtek RTL8125 2.5GbE + Realtek Device b852
OS: Ubuntu 22.04, Kernel: 5.19.0-50-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 535.86.05, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 12.2.128, Vulkan: 1.3.224, Compiler: GCC 11.3.0, File-System: ext4, Screen Resolution: 2560x1440
Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-aYxV0E/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-aYxV0E/gcc-11-11.3.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa601203
Graphics Notes: BAR1 / Visible vRAM Size: 16384 MiB - vBIOS Version: 95.06.25.00.ac
OpenCL Notes: GPU Compute Cores: 4352
Python Notes: Python 3.10.6
Security Notes: 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
This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.
Device: CPU - Batch Size: 512 - Model: VGG-16
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Device: CPU - Batch Size: 512 - Model: ResNet-50
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This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.
FP16: No - Mode: Inference - Network: VGG16 - Device: CPU
firstmachinetest: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'Iterable' from 'collections' (/usr/lib/python3.10/collections/__init__.py)
FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU
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LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.
Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.
Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.
Benchmark: GLM
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Benchmark: SAGA
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Benchmark: Tree
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Benchmark: Lasso
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Benchmark: Glmnet
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Benchmark: Sparsify
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Benchmark: Plot Ward
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Benchmark: MNIST Dataset
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Benchmark: Plot Neighbors
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Benchmark: SGD Regression
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Benchmark: SGDOneClassSVM
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Benchmark: Plot Lasso Path
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Benchmark: Isolation Forest
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Benchmark: Plot Fast KMeans
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Benchmark: Text Vectorizers
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Benchmark: Plot Hierarchical
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Benchmark: Plot OMP vs. LARS
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Benchmark: Feature Expansions
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Benchmark: LocalOutlierFactor
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Benchmark: TSNE MNIST Dataset
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Benchmark: Isotonic / Logistic
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Benchmark: Plot Incremental PCA
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Benchmark: Hist Gradient Boosting
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Benchmark: Plot Parallel Pairwise
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Benchmark: Isotonic / Pathological
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Benchmark: RCV1 Logreg Convergencet
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Benchmark: Sample Without Replacement
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Benchmark: Covertype Dataset Benchmark
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Benchmark: Hist Gradient Boosting Adult
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Benchmark: Isotonic / Perturbed Logarithm
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Benchmark: Hist Gradient Boosting Threading
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Benchmark: Plot Singular Value Decomposition
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Benchmark: Hist Gradient Boosting Higgs Boson
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Benchmark: 20 Newsgroups / Logistic Regression
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Benchmark: Plot Polynomial Kernel Approximation
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Benchmark: Plot Non-Negative Matrix Factorization
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Benchmark: Hist Gradient Boosting Categorical Only
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Benchmark: Kernel PCA Solvers / Time vs. N Samples
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Benchmark: Kernel PCA Solvers / Time vs. N Components
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Benchmark: Sparse Random Projections / 100 Iterations
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This test is a quick-running survey of general R performance Learn more via the OpenBenchmarking.org test page.
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This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.
Mozilla DeepSpeech is a speech-to-text engine powered by TensorFlow for machine learning and derived from Baidu's Deep Speech research paper. This test profile times the speech-to-text process for a roughly three minute audio recording. Learn more via the OpenBenchmarking.org test page.
RNNoise is a recurrent neural network for audio noise reduction developed by Mozilla and Xiph.Org. This test profile is a single-threaded test measuring the time to denoise a sample 26 minute long 16-bit RAW audio file using this recurrent neural network noise suppression library. Learn more via the OpenBenchmarking.org test page.
AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.
The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.
Benchmark: P1B2
firstmachinetest: The test quit with a non-zero exit status. E: ValueError: decay is deprecated in the new Keras optimizer, please check the docstring for valid arguments, or use the legacy optimizer, e.g., tf.keras.optimizers.legacy.RMSprop.
Benchmark: P3B1
firstmachinetest: The test quit with a non-zero exit status. E: ValueError: decay is deprecated in the new Keras optimizer, please check the docstring for valid arguments, or use the legacy optimizer, e.g., tf.keras.optimizers.legacy.SGD.
Benchmark: P3B2
firstmachinetest: The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.
MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. This MNN test profile is building the OpenMP / CPU threaded version for processor benchmarking and not any GPU-accelerated test. MNN does allow making use of AVX-512 extensions. Learn more via the OpenBenchmarking.org test page.
ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Model Zoo. Learn more via the OpenBenchmarking.org test page.
Model: GPT-2 - Device: CPU - Executor: Parallel
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Model: GPT-2 - Device: CPU - Executor: Standard
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Model: yolov4 - Device: CPU - Executor: Parallel
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Model: yolov4 - Device: CPU - Executor: Standard
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Model: bertsquad-12 - Device: CPU - Executor: Parallel
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Model: bertsquad-12 - Device: CPU - Executor: Standard
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Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel
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Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard
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Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel
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Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
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Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel
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Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
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Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel
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Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard
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Model: super-resolution-10 - Device: CPU - Executor: Parallel
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Model: super-resolution-10 - Device: CPU - Executor: Standard
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Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel
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Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard
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This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
The spaCy library is an open-source solution for advanced neural language processing (NLP). The spaCy library leverages Python and is a leading neural language processing solution. This test profile times the spaCy CPU performance with various models. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.
TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the Caffe deep learning framework and currently supports the AlexNet and Googlenet model and execution on both CPUs and NVIDIA GPUs. Learn more via the OpenBenchmarking.org test page.
Model: AlexNet - Acceleration: CPU - Iterations: 100
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Model: AlexNet - Acceleration: CPU - Iterations: 200
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Model: AlexNet - Acceleration: CPU - Iterations: 1000
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Model: GoogleNet - Acceleration: CPU - Iterations: 100
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Model: GoogleNet - Acceleration: CPU - Iterations: 200
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Model: GoogleNet - Acceleration: CPU - Iterations: 1000
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The CUDA and OpenCL version of Vetter's Scalable HeterOgeneous Computing benchmark suite. SHOC provides a number of different benchmark programs for evaluating the performance and stability of compute devices. Learn more via the OpenBenchmarking.org test page.
NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.
Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.
This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
Processor: AMD Ryzen 5 7600X 6-Core @ 4.70GHz (6 Cores / 12 Threads), Motherboard: ASUS TUF GAMING B650-PLUS WIFI (0823 BIOS), Chipset: AMD Device 14d8, Memory: 32GB, Disk: Western Digital WD_BLACK SN850X 2000GB, Graphics: Gigabyte NVIDIA GeForce RTX 4060 Ti 16GB, Audio: NVIDIA Device 22bd, Monitor: DELL P2720DC, Network: Realtek RTL8125 2.5GbE + Realtek Device b852
OS: Ubuntu 22.04, Kernel: 5.19.0-50-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 535.86.05, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 12.2.128, Vulkan: 1.3.224, Compiler: GCC 11.3.0, File-System: ext4, Screen Resolution: 2560x1440
Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-aYxV0E/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-aYxV0E/gcc-11-11.3.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa601203
Graphics Notes: BAR1 / Visible vRAM Size: 16384 MiB - vBIOS Version: 95.06.25.00.ac
OpenCL Notes: GPU Compute Cores: 4352
Python Notes: Python 3.10.6
Security Notes: 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
Testing initiated at 28 July 2023 22:18 by user andbo.