15 Deploying to production

This chapter covers

  • Options for deploying PyTorch models
  • Working with the PyTorch JIT
  • Deploying a model server and exporting models
  • Running exported and natively implemented models from C++
  • Running models on mobile

In part 1 of this book, we learned a lot about models; and part 2 left us with a detailed path for creating good models for a particular problem. Now that we have these great models, we need to take them where they can be useful. Maintaining infrastructure for executing inference of deep learning models at scale can be impactful from an architectural as well as cost standpoint. While PyTorch started off as a framework focused on research, beginning with the 1.0 release, a set of production-oriented features ...

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