Azure Batch

Official Documentation

Service Description

The automated execution of large computational processes (e.g., for mathematical calculations, video rendering, data analyses) normally poses several challenges: the temporary provision of sufficient compute capacity and distribution of the computation logic and associated data are difficult or impossible to implement in conventional IT. Azure Batch can handle these tasks, making the capabilities of batch processing available to every Azure user. The service provides the cluster environments required, distributes the calculation logic provided by EXE file or script, manages the execution, and releases the computational resources following completion so that only the required resources are billed.

Getting Started

  1. 12/12/2014, Video, 0:11:46
    Mark tells Scott all about how to run compute intensive jobs at scale in the cloud.Areas covered in this video: Azure batch example scenarios View Azure batch service in the...
  2. 5/9/2018, Video, 0:20:55
    Manage your workloads, not the infrastructure. This session covers how to use Azure Batch to build cloud-native Big Compute and HPC solutions. Batch provides APIs for creating...
  3. 2/10/2017, Video, 1:12:08
    High Performance Computing (HPC) applications are some of the most challenging to run in the cloud due to requirements that include fast processors, low-latency networking,...
  4. 11/6/2017, Video, 0:14:10
    Dave Fellows joins Donovan Brown to chat about a new service called Azure Batch Rendering, which is built on the Azure Batch service to provide capabilities for rendering 3D...
  5. 9/30/2016, Video, 0:58:50
    High performance computing (HPC) applications are some of the most challenging to run in the cloud due to requirements that can include fast processors, low-latency...

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Web Content

Azure Batch Documentation

1. Overview
     1.1. What is Azure Batch?
2. Quickstarts
     2.1. Run a Batch job - CLI
     2.2. Run a Batch job - Portal
     2.3. Run a Batch job - .NET
     2.4. Run a Batch job - Python
3. Tutorials
     3.1. Parallel file processing - .NET
     3.2. Parallel file processing - Python
     3.3. Scene rendering with Arnold
     3.4. Rendering using Batch Explorer
     3.5. Parallel R simulation
4. Samples
     4.1. Code samples
          4.1.1. Azure code samples
          4.1.2. Batch samples repo
     4.2. Azure CLI
5. Concepts
     5.1. Developer features
     5.2. APIs and tools
     5.3. Quotas and limits
     5.4. Supported VM sizes
6. How-to guides
     6.1. Manage Batch accounts
          6.1.1. Manage Batch accounts with the portal
          6.1.2. Manage Batch accounts with Batch Management .NET
     6.2. Authenticate with Azure AD
          6.2.1. Azure AD with Batch service
          6.2.2. Azure AD with Batch Management
     6.3. Create application packages
     6.4. Create and manage pools
          6.4.1. Autoscale compute nodes
          6.4.2. Choose a VM size for compute nodes
          6.4.3. Configure access to compute nodes
          6.4.4. Create a pool with a custom image
          6.4.5. Create a pool in a virtual network
          6.4.6. Mount an Azure file share
          6.4.7. Use RDMA or GPU instances
          6.4.8. Use Linux compute nodes
          6.4.9. Use low-priority VMs
          6.4.10. Checking for pool and node errors
     6.5. Manage jobs and tasks
          6.5.1. Job preparation and completion tasks
          6.5.2. Concurrent node tasks
          6.5.3. Task dependencies
          6.5.4. User accounts for running tasks
          6.5.5. Submit a large number of tasks
     6.6. Persist job and task output
          6.6.1. Persist output with Batch API
          6.6.2. Persist output with File Conventions library
     6.7. Monitor Batch solutions
          6.7.1. Use metrics and diagnostic logs
          6.7.2. Monitor with Application Insights
          6.7.3. Count resources by state
          6.7.4. Query resources efficiently
     6.8. Use scripting tools
          6.8.1. Use Azure PowerShell
          6.8.2. Use Azure CLI
      Use Batch CLI templates
     6.9. Use Batch Node.js SDK
7. Run workloads
     7.1. MPI jobs
     7.2. Container workloads
     7.3. Rendering
          7.3.1. Rendering using Azure
          7.3.2. Rendering VM images
          7.3.3. Batch rendering capabilities
          7.3.4. Using Batch rendering
          7.3.5. Using render managers
          7.3.6. Rendering application reference
          7.3.7. Storage and data movement
          7.3.8. Rendering architectures
     7.4. Data processing with Batch and Data Factory
8. Reference
     8.1. Azure CLI
     8.2. Azure PowerShell
     8.3. .NET
     8.4. Java
     8.5. Node.js
     8.6. Python
     8.7. REST
          8.7.1. Batch Service
          8.7.2. Batch Management
     8.8. Resource Manager template
     8.9. Batch Analytics
          8.9.1. Pool create event
          8.9.2. Pool delete start event
          8.9.3. Pool delete complete event
          8.9.4. Pool resize start event
          8.9.5. Pool resize complete event
          8.9.6. Task start event
          8.9.7. Task complete event
          8.9.8. Task fail event
     8.10. Batch compute node environment variables
9. Resources
     9.1. Azure Roadmap
     9.2. Batch Community
     9.3. Batch Shipyard
     9.4. Blog
     9.5. HPC solutions in Azure
     9.6. Pricing
     9.7. Pricing calculator
     9.8. Resource Manager template
     9.9. Service updates
     9.10. Stack Overflow
     9.11. Videos

Web Pages

Content Type
Engineering Simulation on Azure Website

Online Training Content

Date Title


Tool Description
Azure Batch Explorer The Azure Batch Explorer is a Windows Presentation Foundation (WPF) application used for viewing, managing, monitoring, and debugging entities within an Azure Batch account. While this application is not officially supported, it is updated periodically, and is an invaluable tool not only for those new to Batch, but anyone developing or managing Batch applications.


Date Title Length
Faster, more accessible edge HPC with Avere vFXT for Azure | Azure Friday
Faster, more accessible edge HPC with Avere vFXT for Azure
Running Cray Supercomputers in Microsoft Azure - BRK3303
Leveraging the Azure GPU and AI infrastructure for accelerated computing scenarios - BRK2306
Managing Azure resources for high performance computing with Azure CycleCloud - BRK2309
Running Cray Supercomputers in Microsoft Azure - BRK3303
Managing Azure resources for high performance computing with Azure CycleCloud - BRK2309
Getting Started with Visual Studio Tools for AI : Build 2018
Getting Started with Visual Studio Tools for AI
Developing large-scale parallel compute and HPC solutions with Azure Batch

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