Cloud Computing platforms, such as Microsoft Azure, bring the availability of massive compute resources on a consumption billing model. The Azure Batch service simplifies the task of running distributed parallel compute jobs in the cloud.
This course will focus on the use of the Azure Batch service for job processing, using the rendering of a 3D ray-traced animation as an example.
The demos and real-world scenarios covered in this course will provide you with the knowledge and skills to utilize the power and flexibility of the Azure Batch service in distributed processing scenarios.
Alan Smith is a Windows Azure developer, trainer, mentor and evangelist at Active Solution in Stockholm. He has a strong hands-on philosophy and focusses on embracing the power and flexibility of cloud computing to deliver engaging and exciting demos.
Course Overview Hello and welcome to my Pluralsight course, Microsoft Azure Batch: Getting Started. My name's Alan Smith, and I work as a consultant for Active Solution, based in Stockholm. In this course, we'll be gaining an understanding of using Azure Batch to manage distributed processing workloads in Azure. Microsoft Azure Batch provides a functionality for managing pools of processing resources, scheduling and managing jobs, and visualizing the execution and completion status of jobs. Batch processing involves the use of dynamically scalable compute resources to perform the parallel processing of tasks in a job in order to complete the job processing in a time- and cost-efficient manner. We'll start the course by looking at cloud computing in Microsoft Azure and how cloud- based environments can be optimal for parallel job processing. We'll then take a look at parallel job processing scenarios and the kinds of jobs that are suitable for running in Azure Batch. We'll then introduce the core Azure Batch functionality and introduce a simple scenario which we'll use throughout the course, rendering a 3D animation. In following modules, we'll take a more in-depth look at the Azure Batch features and architecture. We'll look at the ways that we can create workloads in Azure Batch programmatically. We'll also focus on the management and optimization of jobs in Azure Batch and how we can handle and fix errors during job processing. We'll round off the course by looking at how we provision Batch services and automatically run Batch workloads using PowerShell and Azure CLI. The requirement for this course is that you have some knowledge of C# programming. And to use Azure Batch, you'll need a Microsoft Azure subscription and Visual Studio, either 2015, 2017, or any future versions.
Cloud Computing, Azure Batch, and Parallel Job Processing Scenarios Welcome to the Pluralsight course on Microsoft Azure Batch: Getting Started. My name's Alan Smith, and I'm a senior consultant at Active Solution in Sweden. In this module, we'll introduce the concepts of cloud computing. We'll then take an overview of parallel job processing and look at some real-world scenarios where companies leverage parallel processing. We'll then take an overview of the Microsoft Azure platform and focus on the main functionality of the Azure Batch service. We'll round off the module with a demo looking at how we can render a 3D animation using Azure Batch.
Azure Batch Features and Architecture [Autogenerated] in this model will take a look at the architecture and features of as Old Batch. We'll start out by taking an overview of the whole batch. Architecture. Well, then take a look at the core features. Well, look at how we can integrate without our batch, both with the user interface and programmatically. We'll take an overview of the limits and quotas we have been working with that old batch. We'll round off the module with a couple of demos. Well, look at how we can provision and manage and as a batch processing architecture, using the as or portal and also how we can use Batch Explorer to manage as our batch resources.
Creating Workloads with Azure Batch In this module, we're going to look at creating workloads with Azure Batch. We'll start out by looking at how we can analyze the processing requirements of the batch jobs that we're going to run. We'll then run through a demo looking at how we can analyze the processing requirements for rendering 3D animations. We'll then take an overview of the Azure Batch client API that allows us to programmatically manage Azure Batch resources and workflows. We'll look at how we can use this API to create Batch workloads, and round off the module with a demo looking at how we can create Batch workloads using the Batch API.
Managing Batch Processing In this module, we're going to look at some of the more advanced aspects of managing batch processing in Azure Batch. The module will consist of three main topics and feature three demos. We'll start out by looking at how Azure Batch can handle failed tasks, how we can examine the statistics for successful task processing, and also how we can determine what was causing the task to fail. We'll move on to look at how we can optimize job processing by configuring a pool to run multiple concurrent tasks on the node instances. And we'll round off the module by looking at managing scalability and configuring autoscaling by using autoscaling formulas in Azure Batch.
Azure Batch Automation and Monitoring In this module, we're going to look at Azure Batch automation, how we can use command-line tools to deploy resources and execute batches on Azure Batch. We'll start out by looking at how we can manage Azure Batch resources using PowerShell, and then run through a demo showing how we can script Batch service provisioning using PowerShell. We'll then take a look at how we can run Azure Batch jobs using Azure CLI, and round off the module with a demo showing how we can script batch job processing with Azure CLI 2. 0.