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

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

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