Using PyTorch in the Cloud: PyTorch Playbook

By Janani Ravi
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.
Play course overview
Course info
Level
Intermediate
Updated
Apr 25, 2019
Duration
2h 20m
Table of contents
Course Overview
Course Overview 2m
Creating PyTorch Solutions on AWS
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
Creating PyTorch Solutions on Azure
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
Creating PyTorch Solutions on the Google Cloud Platform
Module Overview 1m Cloud Datalab and Deep Learning VMs 3m Setting up a Cloud Datalab VM 8m Prototyping PyTorch Models Using Cloud Datalab 2m Create a Deep Learning VM with PyTorch and CUDA Support 5m Using JupyterLab on a GCP Deep Learning VM 2m Summary and Further Study 2m
Description
Course info
Level
Intermediate
Updated
Apr 25, 2019
Duration
2h 20m
Description

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.

About the author
Janani Ravi
About the author

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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Using PyTorch in the Cloud: PyTorch Playbook. 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. In this course, you will gain the ability to use PyTorch on each of the big three cloud providers, Amazon Web Services, Microsoft Azure, and the Google Cloud Platform. First, you will learn how PyTorch can be put to use on AWS, including on AWS SageMaker notebook instances, Amazon machine images, 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 indeed does support GPUs. When you're finished with this course, you will have the skills and knowledge to leverage PyTorch on each of the big three cloud providers.

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