Kenning
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
Kenning
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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