machine-learning-macbook-pro-big-sur
Apple M1 testing with a Apple MacBook Pro and Apple M1 on macOS 11.7 via the Phoronix Test Suite.
machine-learning-macbook-pro-big-sur
Processor: Apple M1 (8 Cores), Motherboard: Apple MacBook Pro, Memory: 16GB, Disk: 927GB, Graphics: Apple M1, Monitor: Color LCD
OS: macOS 11.7, Kernel: 20.6.0 (arm64), Compiler: GCC 13.0.0 + Clang 13.0.0, File-System: APFS, Screen Resolution: 2880x1800
Environment Notes: XPC_FLAGS=0x0
Python Notes: Python 2.7.16 + Python 3.10.8
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
machine-learning-macbook-pro-big-sur: 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: onednn: line 6: ./benchdnn: No such file or directory
Numpy Benchmark
This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.
DeepSpeech
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.
Acceleration: CPU
machine-learning-macbook-pro-big-sur: 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: Reason: image not found
Mobile Neural Network
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.
NCNN
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.
PlaidML
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
machine-learning-macbook-pro-big-sur: 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: plaidml: line 24: /.local/bin/plaidbench: No such file or directory
FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU
machine-learning-macbook-pro-big-sur: 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: plaidml: line 24: /.local/bin/plaidbench: No such file or directory
Numenta Anomaly Benchmark
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 timeseries 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.
Detector: EXPoSE
machine-learning-macbook-pro-big-sur: 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: ModuleNotFoundError: No module named 'sklearn'
AI Benchmark Alpha
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.
machine-learning-macbook-pro-big-sur
Processor: Apple M1 (8 Cores), Motherboard: Apple MacBook Pro, Memory: 16GB, Disk: 927GB, Graphics: Apple M1, Monitor: Color LCD
OS: macOS 11.7, Kernel: 20.6.0 (arm64), Compiler: GCC 13.0.0 + Clang 13.0.0, File-System: APFS, Screen Resolution: 2880x1800
Environment Notes: XPC_FLAGS=0x0
Python Notes: Python 2.7.16 + Python 3.10.8
Testing initiated at 23 October 2022 00:03 by user progdan.