Summary

In this chapter, we focused on both abstracting out the noisy details involved in model training code and the core components to facilitate the rapid prototyping of models. As PyTorch code can often be cluttered with a lot of such noisy detailed code components, we looked at some of the high-level libraries that are built on top of PyTorch. We also learned how to profile PyTorch code during model inference to better benchmark model performance on the CPU and GPU.

In the next chapter, we will move on to another important and promising aspect of applied machine learning that we already touched upon in both Chapter 2, Deep CNN Architectures, and Chapter 5, Advanced Hybrid Models: we will learn how to effectively use PyTorch for Automated Machine Learning (AutoML). By doing this, we will be able to use AutoML to train machine learning models automatically—that is, without having to decide on and define the model architecture.