Summary

In this chapter, we covered an important practical aspect of machine learning, that is, how to optimize the model training process. We explored the extent and power of distributed training using PyTorch, both on CPUs as well as GPUs. We then learned how to use mixed precision training to further optimize the model training process.

In the next chapter, we will focus on some practical aspects of working with PyTorch in production. We will discuss how to deploy trained models into production systems, converting PyTorch models into universal formats such as ONNX, as well as translating PyTorch code written in Python into C++ and creating executable binaries.