Using PyTorch in the Cloud: PyTorch Playbook

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Janani Ravi

2:21:12

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1. Course Overview

1. Course Overview.mp4

01:40

2. Creating PyTorch Solutions on AWS

01. Version Check.mp4

00:16

02. Module Overview.mp4

00:52

03. Prerequisites and Course Outline.mp4

01:41

04. Machine Learning on the Cloud.mp4

02:47

05. PyTorch - Taxonomy of Solutions.mp4

03:51

06. Introducing SageMaker.mp4

02:51

07. Creating a SageMaker Notebook Instance.mp4

07:09

08. Prototyping a PyTorch Model on SageMaker Notebooks.mp4

09:42

09. PyTorch Estimators on SageMaker.mp4

01:48

10. Distributed Data Loading in PyTorch.mp4

05:26

11. Distributed Training in PyTorch.mp4

06:08

12. Using PyTorch Estimators for Distributed Training.mp4

06:17

13. Model Deployment and Prediction Using Estimators.mp4

04:20

14. AWS Deep Learning AMIs.mp4

02:06

15. Instantiating a Deep Learning VM.mp4

06:09

16. Building Models with GPU Support on the AWS Deep Learning VM.mp4

06:25

3. Creating PyTorch Solutions on Azure

01. Module Overview.mp4

01:07

02. Introducing Azure Machine Learning Service.mp4

01:55

03. Prototyping PyTorch Models on Azure Notebooks.mp4

06:35

04. Azure Machine Learning Service Workflow.mp4

02:26

05. Understanding Terms in Azure Machine Learning.mp4

02:47

06. Horovod for Distributed Training.mp4

01:31

07. Distributed Training in PyTorch Using the Horovod Framework.mp4

08:37

08. Instantiating the PyTorch Estimator for Distributed Training.mp4

07:20

09. Distributed Run Using the PyTorch Estimator.mp4

04:15

10. The Azure Deep Learning VM.mp4

01:44

11. Instantiating an Azure Deep Learning VM.mp4

05:31

12. Building PyTorch Models with GPU Support on Azure Deep Learning VMs.mp4

05:34

4. Creating PyTorch Solutions on the Google Cloud Platform

1. Module Overview.mp4

00:47

2. Cloud Datalab and Deep Learning VMs.mp4

03:02

3. Setting up a Cloud Datalab VM.mp4

07:49

4. Prototyping PyTorch Models Using Cloud Datalab.mp4

02:03

5. Create a Deep Learning VM with PyTorch and CUDA Support.mp4

04:57

6. Using JupyterLab on a GCP Deep Learning VM.mp4

02:14

7. Summary and Further Study.mp4

01:30

Description

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.

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Python

Microsoft Azure

Google Cloud

Janani Ravi

Instructor's Courses

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 high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.