../_images/logo-quic-on@h68.png

AIMET Examples

AIMET Examples provide reference code (in the form of Jupyter Notebooks) to learn how to apply AIMET quantization and compression features. It is also a quick way to become familiar with AIMET usage and APIs.

For more details on each of the features and APIs please refer: Links to User Guide and API Documentation

Browse the notebooks

The following table has links to browsable versions of the notebooks for different features.

Model Quantization Examples

Features

PyTorch

TensorFlow

Keras

Quantization-Aware Training (QAT)

Link

Link

QAT with Range Learning

Link

Link

Cross-Layer Equalization (CLE)

Link

Link

Link

Adaptive Rounding (AdaRound)

Link

Link

Link

AutoQuant

Link

Link


Model Compression Examples

Features

PyTorch

TensorFlow

Channel Pruning

Link

Link

Spatial SVD

Link

Link

Spatial SVD + Channel Pruning

Link

Link


Running the notebooks

Install Jupyter

  • Install the Jupyter metapackage as follows (pre-pend with “sudo -H” if appropriate):

python3 -m pip install jupyter

  • Start the notebook server as follows (please customize the command line options if appropriate):

jupyter notebook –ip=* –no-browser &

  • The above command will generate and display a URL in the terminal. Copy and paste it into your browser.

Run the notebooks

  • Navigate to one of the following paths under the Examples directory and launch your chosen Jupyter Notebook (.ipynb extension): - Examples/torch/quantization/ - Examples/torch/compression/ - Examples/tensorflow/quantization/ - Examples/tensorflow/compression/

  • Follow the instructions therein to execute the code.