PyTorch is quickly emerging as a popular choice for building deep learning models due to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. But, as a relatively recent entrant in the fast-moving world of deep learning frameworks, PyTorch is only now being fully supported by the major cloud providers. In this course, Using PyTorch in the Cloud: PyTorch Playbook, you will gain the ability to use PyTorch on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP). First, you will learn how PyTorch can be put to use on AWS, including on AWS Sagemaker notebook instances, Amazon Machine Images (AMIs), and using the Sagemaker PyTorch estimator for distributed training. Next, you will discover how Microsoft Azure supports PyTorch, including Azure notebooks, Azure deep learning VMs, and PyTorch Estimators, which run using the Azure machine learning service. Finally, you will round out the course by understanding GCP support for PyTorch, including both Cloud Datalab (which does not have GPU support), and JupyterLab on GCP Deep Learning VMs (which does). When you are finished with this course, you will have the skills and knowledge to leverage PyTorch on each of the big three cloud providers.
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
• Creating PyTorch Solutions on AWS
  67mins
• Creating PyTorch Solutions on Azure
  49mins
• Creating PyTorch Solutions on the Google Cloud Platform
  22mins