Microsoft Azure Batch: Fundamentals

Whether you’re doing simulations, machine learning, or something else entirely, this course teaches you how to configure and run your workload in Azure Batch - including using GPUs and Docker - to produce the results you need.
Course info
Rating
(14)
Level
Intermediate
Updated
Jul 25, 2018
Duration
1h 49m
Table of contents
Description
Course info
Rating
(14)
Level
Intermediate
Updated
Jul 25, 2018
Duration
1h 49m
Description

Today you might be doing some simulations, machine learning, or rendering with your own hardware. But what if you could do those tasks in a tenth of the time? In this course, Microsoft Azure Batch: Fundamentals, you’ll learn how to utilize the Batch service to run and scale your HPC workload in the Azure cloud. First, you’ll discover what workloads and architecture work with Batch, allowing you to decide whether it’s a good fit for your situation. Next, you’ll learn how to run a Batch job via the Azure portal and then in C# code via the Batch SDK. Finally, you’ll see how Batch supports more advanced scenarios such as using GPUs for performance and Docker for portability. When you’re finished with this course, you’ll understand how to build a production-ready application leveraging the Azure Batch service. All proceeds of this course are donated to Pluralsight One.

About the author
About the author

Gerald is a technical Agilist and software craftsman who loves to help teams delight the customer by delivering value early and often.

Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone, my name is Gerald Ho, and welcome to my course, Microsoft Azure Batch: Fundamentals. I'm a software developer and architect with over 15 years of experience, and I love harnessing the power of new tools and technologies. And so this course is all about taking what was once the dark art of high-performance computing and making it easy and available to everyone through the Azure Batch service. Some of the major topics that we'll cover include understanding the workloads and architecture that can work with Azure Batch; executing your workload by creating a compute pool, job, and tasks, both with and without code; utilizing the GPU to give a huge performance boost; and using a Docker container to make your solution portable and maintainable. By the end of this course, you'll know how to take an existing workload, be it simulations, machine learning, rendering, or something else entirely, and move it into the Azure cloud with the Batch service. Or if you're starting from scratch you'll learn how to implement it more quickly and with a more cost-effective result by doing it directly in the cloud. Before beginning the course, you should be familiar with C#, but other than that I'll take you through everything else you need to know. I hope you'll join me on this journey to learn high-performance computing in the cloud with the Microsoft Azure Batch: Fundamentals course at Pluralsight.