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.

Download the Example notebooks and related code

• Clone the AIMET repo as follows to any location: * WORKSPACE=”<absolute_path_to_workspace>” * mkdir $WORKSPACE && cd $WORKSPACE * Go to https://github.com/quic/aimet/releases and identify the release tag (<release_tag>) of the AIMET package that you’re working with. * git clone https://github.com/quic/aimet.git –branch <release_tag> * Update the environment variable as follows: export PYTHONPATH=$PYTHONPATH:${WORKSPACE}/aimet

• The dataloader, evaluator, and trainer utilized in the examples is for the ImageNet dataset. To run the example, please download the ImageNet dataset from here: https://www.image-net.org/download.php

• Install AIMET and its dependencies using the instructions in the Installation section’

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.