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  • Introduction
  • Kenning
  • Deep Learning deployment stack
  • Defining optimization pipelines in Kenning
  • Using Kenning via command line arguments
  • Kenning measurements
  • ONNX support in deep learning frameworks
  • Sample autogenerated report
  • Developing Kenning blocks
  • Kenning API
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Kenning
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  • Kenning

KenningΒΆ

  • Introduction
  • Kenning
    • Kenning installation
    • Kenning structure
    • Kenning usage
    • Example use case of Kenning - optimizing a classifier
    • Using Kenning as a library in Python scripts
    • Adding new implementations
  • Deep Learning deployment stack
    • From training to deployment
    • Dataset preparation
    • Model preparation and training
    • Model optimization
    • Model compilation and deployment
  • Defining optimization pipelines in Kenning
    • JSON specification
    • Model evaluation using its native framework
    • Optimizing and running a model on a single device
    • Compiling a model and running it remotely
  • Using Kenning via command line arguments
    • Command-line arguments for classes
    • Model training
    • In-framework inference performance measurements
    • ONNX conversion
    • Testing inference on target hardware
    • Running inference
    • Generating performance reports
  • Kenning measurements
    • Performance metrics
  • ONNX support in deep learning frameworks
    • ONNX support grid in deep learning frameworks
    • ONNX conversion support grid
  • Sample autogenerated report
    • Pet Dataset classification using TVM-compiled TensorFlow model
    • Inference performance metrics for build.local-cpu-tvm-tensorflow-classification.json
    • Inference quality metrics for build.local-cpu-tvm-tensorflow-classification.json
  • Developing Kenning blocks
    • Model metadata
    • Implementing a new Kenning component
  • Kenning API
    • Deployment API overview
    • Dataset
    • ModelWrapper
    • Optimizer
    • Runtime
    • RuntimeProtocol
    • Measurements
    • ONNXConversion
    • DataProvider
    • OutputCollector
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