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. 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.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Persisting and Loading PyTorch Models
  42mins
• Implementing Training Using Single and Multiple Processors
  25mins
• Implementing Distributed Training on Multiple Machines
  30mins
• Deploying PyTorch Models to Production
  33mins