magnitude

Step-by-Step

This document is used to list steps of reproducing Intel® Neural Compressor magnitude pruning feature.

Prerequisite

1. Installation

# Install Intel® Neural Compressor
pip install neural-compressor

2. Install Intel Tensorflow 2.4.0 or above.

pip install intel-tensorflow==2.4.0

3. Train and save a ResNet-V2 model

According to the following link Trains a ResNet on the CIFAR10 dataset., set 'version = 2' and train a ResNet-V2 model as the baseline. Please add a line 'model.save("./ResNetV2_Model")' at the end of the code to save the model to the directory './ResNetV2_Model'.

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# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
model.save("./ResNetV2_Model") # Add a line at the end

Run command to prune the model

Run the command to get pruned model which overwritten and saved into './ResNetV2_Model'.

python main.py

If you want to accelerate pruning with multi-node distributed training and evaluation, you only need to add a small amount of code and use horovod to run main.py. As shown in main.py, uncomment two lines 'prune.train_distributed = True' and 'prune.evaluation_distributed = True' is all you need. Use horovod to run main.py to get pruned model with multi-node distributed training and evaluation.

horovodrun -np <num_of_processes> -H <hosts> python main.py 

Run the command to get pruned model performance.

python benchmark.py