Machine Learning Machine Learning

The machine learning test suite helps to benchmark a system for the popular pattern recognition and computational learning algorithms. Mainly different machine learning / deep learning benchmarks.

See how your system performs with this suite using the Phoronix Test Suite. It's as easy as running the phoronix-test-suite benchmark machine-learning command..

Tests In This Suite

  • Mobile Neural Network
  • NCNN
        Target: CPU
  • NCNN
        Target: Vulkan GPU
  • TNN
        Target: CPU - Model: MobileNet v2
  • TNN
        Target: CPU - Model: SqueezeNet v1.1
  • OpenCV
        Test: DNN - Deep Neural Network
  • Caffe
        Model: AlexNet - Acceleration: CPU - Iterations: 100
  • Caffe
        Model: AlexNet - Acceleration: CPU - Iterations: 200
  • Caffe
        Model: AlexNet - Acceleration: CPU - Iterations: 1000
  • Caffe
        Model: GoogleNet - Acceleration: CPU - Iterations: 100
  • Caffe
        Model: GoogleNet - Acceleration: CPU - Iterations: 200
  • Caffe
        Model: GoogleNet - Acceleration: CPU - Iterations: 1000
  • SHOC Scalable HeterOgeneous Computing
  • R Benchmark
  • Numpy Benchmark
  • AI Benchmark Alpha
  • DeepSpeech
        Acceleration: CPU
  • ECP-CANDLE
        Benchmark: P1B2
  • ECP-CANDLE
        Benchmark: P3B1
  • ECP-CANDLE
        Benchmark: P3B2
  • RNNoise
  • Scikit-Learn
  • Mlpack Benchmark
        Benchmark: scikit_svm
  • Mlpack Benchmark
        Benchmark: scikit_linearridgeregression
  • Mlpack Benchmark
        Benchmark: scikit_qda
  • Mlpack Benchmark
        Benchmark: scikit_ica
  • Numenta Anomaly Benchmark
        Detector: Bayesian Changepoint
  • Numenta Anomaly Benchmark
        Detector: Windowed Gaussian
  • Numenta Anomaly Benchmark
        Detector: Relative Entropy
  • Numenta Anomaly Benchmark
        Detector: Earthgecko Skyline
  • Numenta Anomaly Benchmark
        Detector: EXPoSE
  • Tensorflow
        Build: Cifar10
  • TensorFlow Lite
        Model: Mobilenet Float
  • TensorFlow Lite
        Model: Mobilenet Quant
  • TensorFlow Lite
        Model: NASNet Mobile
  • TensorFlow Lite
        Model: SqueezeNet
  • TensorFlow Lite
        Model: Inception ResNet V2
  • TensorFlow Lite
        Model: Inception V4
  • Numenta Anomaly Benchmark
        Detector: Bayesian Changepoint
  • Numenta Anomaly Benchmark
        Detector: Windowed Gaussian
  • Numenta Anomaly Benchmark
        Detector: Relative Entropy
  • Numenta Anomaly Benchmark
        Detector: Earthgecko Skyline
  • Numenta Anomaly Benchmark
        Detector: EXPoSE
  • oneDNN
        Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
  • oneDNN
        Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
  • oneDNN
        Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
  • oneDNN
        Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
  • oneDNN
        Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
  • oneDNN
        Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
  • oneDNN
        Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
  • oneDNN
        Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
  • oneDNN
        Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
  • oneDNN
        Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
  • OpenVINO
        Model: Face Detection 0106 FP16 - Device: CPU
  • OpenVINO
        Model: Face Detection 0106 FP16 - Device: Intel GPU
  • OpenVINO
        Model: Face Detection 0106 FP32 - Device: CPU
  • OpenVINO
        Model: Face Detection 0106 FP32 - Device: Intel GPU
  • OpenVINO
        Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
  • OpenVINO
        Model: Age Gender Recognition Retail 0013 FP16 - Device: Intel GPU
  • OpenVINO
        Model: Age Gender Recognition Retail 0013 FP32 - Device: CPU
  • OpenVINO
        Model: Age Gender Recognition Retail 0013 FP32 - Device: Intel GPU
  • OpenVINO
        Model: Person Detection 0106 FP16 - Device: CPU
  • OpenVINO
        Model: Person Detection 0106 FP16 - Device: Intel GPU
  • OpenVINO
        Model: Person Detection 0106 FP32 - Device: CPU
  • OpenVINO
        Model: Person Detection 0106 FP32 - Device: Intel GPU
  • ONNX Runtime
        Model: yolov4 - Device: OpenMP CPU
  • ONNX Runtime
        Model: fcn-resnet101-11 - Device: OpenMP CPU
  • ONNX Runtime
        Model: shufflenet-v2-10 - Device: OpenMP CPU
  • ONNX Runtime
        Model: super-resolution-10 - Device: OpenMP CPU
  • ONNX Runtime
        Model: bertsquad-10 - Device: OpenMP CPU
  • PlaidML
        FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU
  • PlaidML
        FP16: No - Mode: Inference - Network: VGG16 - Device: CPU
  • LeelaChessZero
        Backend: BLAS

Revision History Revision History

pts/machine-learning-1.3.3     Sun, 17 Jan 2021 07:57:25 GMT
Add ONNX to machine learning syite.

pts/machine-learning-1.3.2     Thu, 14 Jan 2021 14:01:03 GMT
Add ecp-candle to suite.

pts/machine-learning-1.3.1     Thu, 08 Oct 2020 09:23:48 GMT
Add OpenVINO to suite.

pts/machine-learning-1.3.0     Sun, 04 Oct 2020 13:02:02 GMT
Add OpenCV, TNN, Caffe, and other updates.

pts/machine-learning-1.2.9     Sat, 19 Sep 2020 12:54:40 GMT
Add NCNN to test suite.

pts/machine-learning-1.2.8     Thu, 17 Sep 2020 20:58:30 GMT
Add Alibaba Mobile Neural Network (mnn) test profile.

pts/machine-learning-1.2.7     Sun, 23 Aug 2020 14:17:27 GMT
Add tensorflow-lite test profile.

pts/machine-learning-1.2.6     Wed, 08 Jul 2020 14:28:35 GMT
Add ai-benchmark test profile to machine learning test suite.

pts/machine-learning-1.2.5     Wed, 17 Jun 2020 16:35:07 GMT
Use pts/onednn rather than pts/mkl-dnn due to rename.

pts/machine-learning-1.2.4     Thu, 28 May 2020 15:51:26 GMT
Add additional tests.

pts/machine-learning-1.2.3     Wed, 08 Apr 2020 16:21:57 GMT
Add tensorflow.

pts/machine-learning-1.2.2     Wed, 08 Apr 2020 14:00:00 GMT
Add numenta-nab and deepspeech to test suite.

pts/machine-learning-1.2.10     Tue, 22 Sep 2020 18:11:26 GMT
Add OpenCV DNN test to machine-learning suite.

pts/machine-learning-1.2.1     Mon, 24 Feb 2020 09:14:07 GMT
Update machine-learning test suite with batch mode for mlpack given its new options just added.

pts/machine-learning-1.2.0     Sun, 16 Feb 2020 19:07:13 GMT
Add more tests.

pts/machine-learning-1.1.0     Fri, 10 May 2019 15:47:23 GMT
Update tests.

pts/machine-learning-1.0.0     Mon, 01 Aug 2016 16:30:43 GMT
Initial commit


OpenBenchmarking.org Community User Comments

Post A Comment