Summary

In this chapter, you learned that simplifying a model by reducing the number of parameters can accelerate the network training process, besides making the model feasible to run on resource-constrained platforms.

Then, we saw that the simplification process consists of two phases: pruning and compression. The former is responsible for determining which parameters must be dropped off from the network, whereas the latter effectively removes the parameters from the model.

Although PyTorch provides an API to prune the model, it is not fully useful to simplify a model. Thus, you were introduced to Microsoft NNI, a powerful toolkit to automate tasks related to deep learning modes. Among the features provided by NNI, this tool offers a complete workflow to simplify a model. All of this is achieved with a couple of new lines added to the original code.

In the next chapter, you will learn how to reduce the numeric precision adopted by the neural network to accelerate the training...