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[P] AI Benchmark: A New Standard for ML Performance Assessment of CPUs, GPUs and TPUs

AI Benchmark is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. The benchmark is relying on TensorFlow machine learning library, and is providing a lightweight solution for assessing inference and training speed for key Deep Learning models, including:

  1. MobileNet-V2
  2. Inception-V3
  3. Inception-V4
  4. Inception-ResNet-V2
  5. ResNet-V2-50
  6. ResNet-V2-152
  7. VGG-16
  8. SRCNN 9-5-5
  9. VGG-19
  10. ResNet-SRGAN
  11. ResNet-DPED
  12. U-Net
  13. Nvidia-SPADE
  14. ICNet
  15. PSPNet
  16. DeepLab
  17. Pixel-RNN
  18. LSTM
  19. GNMT

It is currently distributed as a Python pip package, installation instructions can be found at http://ai-benchmark.com/alpha.html and https://pypi.org/project/ai-benchmark/

Note: Fast installation [if TensorFlow is already installed]:

  1. pip install ai-benchmark
  2. Run benchmark using the following python code:
    1. from ai_benchmark import AIBenchmark
    2. results = AIBenchmark().run()

A detailed information about test setups: http://ai-benchmark.com/ranking_cpus_and_gpus_detailed.html

A short preliminary ranking is available here: http://ai-benchmark.com/ranking_cpus_and_gpus.html

A global ranking with the results of various hardware and software platforms, drivers / configs and TF builds should be available soon. Original post: http://ai-benchmark.com/alpha.html

submitted by /u/aiff22
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.