Deploying PyTorch Models in Production: PyTorch Playbook

Course Overview
Hi! My name is Janani Ravi, and welcome to this course on Deploying PyTorch Models in Production. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease of use, and built-in support for optimized hardware such as GPUs. In this course, you will gain the ability to leverage advanced functionality for serializing and deserializing PyTorch models, training and then deploying them for production. First, you will learn how the load_state_dict under 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 dictionary, which is a handy dictionary with information about parameters, as well as hyper-parameters. You will then see how the multi-processing 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'll 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 skills and knowledge to perform distributed training and deployment of PyTorch models and utilize advanced mechanisms for model serialization and deserialization.