Deep-Learning-with-PyTorch

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 were added that today make PyTorch an ideal end-toend
platform from research to large-scale production.
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