Deploying PyTorch Models in Production: PyTorch Playbook

Learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs

Using PyTorch, you can build complex deep learning models, while still using Python-native support for debugging and visualization

In this course, Deploying PyTorch Models in Production PyTorch Playbook you will gain the ability to leverage advanced functionality for serializing and deserializing PyTorch models, training, and then deploying them for prediction

First, you will learn how the load_state_dict and the torch

Save() and torch

Load() methods complement and differ from each other, and the relative pros and cons of each

Next, you will discover how to leverage the state_dict which is a handy dictionary with information about parameters as well as hyperparameters

Then, you will see how the multiprocessing, data-parallel, and distributed data-parallel approaches to distributed training can be used in PyTorch

You will train a PyTorch model on a distributed cluster using high-level estimator APIs

Finally, you will explore how to deploy PyTorch models using a Flask application, a Clipper cluster, and a serverless environment

When you’re finished with this course, you will have the skills and knowledge to perform distributed training and deployment of PyTorch models and utilize advanced mechanisms for model serialization and deserialization