Using PyTorch in the Cloud: PyTorch Playbook
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.
What you'll learn
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.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 2m
- Machine Learning on the Cloud 3m
- PyTorch: Taxonomy of Solutions 4m
- Introducing SageMaker 3m
- Creating a SageMaker Notebook Instance 7m
- Prototyping a PyTorch Model on SageMaker Notebooks 10m
- PyTorch Estimators on SageMaker 2m
- Distributed Data Loading in PyTorch 5m
- Distributed Training in PyTorch 6m
- Using PyTorch Estimators for Distributed Training 6m
- Model Deployment and Prediction Using Estimators 4m
- AWS Deep Learning AMIs 2m
- Instantiating a Deep Learning VM 6m
- Building Models with GPU Support on the AWS Deep Learning VM 6m
- Module Overview 1m
- Introducing Azure Machine Learning Service 2m
- Prototyping PyTorch Models on Azure Notebooks 7m
- Azure Machine Learning Service Workflow 2m
- Understanding Terms in Azure Machine Learning 3m
- Horovod for Distributed Training 2m
- Distributed Training in PyTorch Using the Horovod Framework 9m
- Instantiating the PyTorch Estimator for Distributed Training 7m
- Distributed Run Using the PyTorch Estimator 4m
- The Azure Deep Learning VM 2m
- Instantiating an Azure Deep Learning VM 6m
- Building PyTorch Models with GPU Support on Azure Deep Learning VMs 6m
About the author
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