This course covers the important aspects of using PyTorch on Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP), including the use of cloud-hosted notebooks, deep learning VM instances with GPU support, and PyTorch estimators.
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.
A problem solver at heart, 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.