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Azure Batch provides cloud-scale rendering capabilities on a pay-per-use basis. Azure Batch supports rendering apps including Autodesk Maya, 3ds Max, Arnold, and V-Ray. This tutorial shows you the steps to render a small scene with Batch using the Azure Command-Line Interface. You learn how to:

• Upload a scene to Azure storage
• Create a Batch pool for rendering
• Render a single-frame scene
• Scale the pool, and render a multi-frame scene
• Download rendered output

In this tutorial, you render a 3ds Max scene with Batch using the Arnold ray-tracing renderer. The Batch pool uses an Azure Marketplace image with pre-installed graphics and rendering applications that provide pay-per-use licensing.

Prerequisites

• You need a pay-as-you-go subscription or other Azure purchase option to use rendering applications in Batch on a pay-per-use basis. Pay-per-use licensing isn't supported if you use a free Azure offer that provides a monetary credit.

• The sample 3ds Max scene for this tutorial is on GitHub, along with a sample Bash script and JSON configuration files. The 3ds Max scene is from the Autodesk 3ds Max sample files. (Autodesk 3ds Max sample files are available under a Creative Commons Attribution-NonCommercial-Share Alike license. Copyright © Autodesk, Inc.)

• Use Azure Cloud Shell using the bash environment.

• If you prefer, install the Azure CLI to run CLI reference commands.

  • If you're using a local install, sign in with Azure CLI by using the az login command. To finish the authentication process, follow the steps displayed in your terminal. See Sign in with Azure CLI for additional sign-in options.
  • When you're prompted, install Azure CLI extensions on first use. For more information about extensions, see Use extensions with Azure CLI.
  • Run az version to find the version and dependent libraries that are installed. To upgrade to the latest version, run az upgrade.

• This tutorial requires version 2.0.20 or later of the Azure CLI. If using Azure Cloud Shell, the latest version is already installed.

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Tip

You can view Arnold job templates in the Azure Batch Extension Templates GitHub repository.

Create a Batch account

If you haven't already, create a resource group, a Batch account, and a linked storage account in your subscription.

Create a resource group with the az group create command. The following example creates a resource group named myResourceGroup in the eastus2 location.

Create an Azure Storage account in your resource group with the az storage account create command. For this tutorial, you use the storage account to store an input 3ds Max scene and the rendered output.

Create a Batch account with the az batch account create command. The following example creates a Batch account named mybatchaccount in myResourceGroup, and links the storage account you created.

To create and manage compute pools and jobs, you need to authenticate with Batch. Log in to the account with the az batch account login command. After you log in, your az batch commands use this account context. The following example uses shared key authentication, based on the Batch account name and key. Batch also supports authentication through Azure Active Directory, to authenticate individual users or an unattended application.

Upload a scene to storage

To upload the input scene to storage, you first need to access the storage account and create a destination container for the blobs. To access the Azure storage account, export the AZURE_STORAGE_KEY and AZURE_STORAGE_ACCOUNT environment variables. The first Bash shell command uses the az storage account keys list command to get the first account key. After you set these environment variables, your storage commands use this account context.

Now, create a blob container in the storage account for the scene files. The following example uses the az storage container create command to create a blob container named scenefiles that allows public read access.

Download the scene MotionBlur-Dragon-Flying.max from GitHub to a local working directory. For example:

Upload the scene file from your local working directory to the blob container. The following example uses the az storage blob upload-batch command, which can upload multiple files:

Create a rendering pool

Create a Batch pool for rendering using the az batch pool create command. In this example, you specify the pool settings in a JSON file. Within your current shell, create a file name mypool.json, then copy and paste the following contents. Be sure all the text copies correctly. (You can download the file from GitHub.)

Batch supports dedicated nodes and low-priority nodes, and you can use either or both in your pools. Dedicated nodes are reserved for your pool. Low-priority nodes are offered at a reduced price from surplus VM capacity in Azure. Low-priority nodes become unavailable if Azure does not have enough capacity.

The pool specified contains a single low-priority node running a Windows Server image with software for the Batch Rendering service. This pool is licensed to render with 3ds Max and Arnold. In a later step, you scale the pool to a larger number of nodes.

Create the pool by passing the JSON file to the az batch pool create command:

It takes a few minutes to provision the pool. To see the status of the pool, run the az batch pool show command. The following command gets the allocation state of the pool:

Continue the following steps to create a job and tasks while the pool state is changing. The pool is completely provisioned when the allocation state is steady and the nodes are running.

Create a blob container for output

In the examples in this tutorial, every task in the rendering job creates an output file. Before scheduling the job, create a blob container in your storage account as the destination for the output files. The following example uses the az storage container create command to create the job-myrenderjob container with public read access.

To write output files to the container, Batch needs to use a Shared Access Signature (SAS) token. Create the token with the az storage account generate-sas command. This example creates a token to write to any blob container in the account, and the token expires on November 15, 2020:

Take note of the token returned by the command, which looks similar to the following. You use this token in a later step.

Render a single-frame scene

Create a job

Create a rendering job to run on the pool by using the az batch job create command. Initially the job has no tasks.

Create a task

Use the az batch task create command to create a rendering task in the job. In this example, you specify the task settings in a JSON file. Within your current shell, create a file named myrendertask.json, then copy and paste the following contents. Be sure all the text copies correctly. (You can download the file from GitHub.)

The task specifies a 3ds Max command to render a single frame of the scene MotionBlur-DragonFlying.max.

Modify the blobSource and containerURL elements in the JSON file so that they include the name of your storage account and your SAS token.

Tip

Your containerURL ends with your SAS token and is similar to:

Add the task to the job with the following command:

Batch schedules the task, and the task runs as soon as a node in the pool is available.

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View task output

The task takes a few minutes to run. Use the az batch task show command to view details about the task.

The task generates dragon0001.jpg on the compute node and uploads it to the job-myrenderjob container in your storage account. To view the output, download the file from storage to your local computer using the az storage blob download command.

Open dragon.jpg on your computer. The rendered image looks similar to the following:

Scale the pool

Now modify the pool to prepare for a larger rendering job, with multiple frames. Batch provides a number of ways to scale the compute resources, including autoscaling which adds or removes nodes as task demands change. For this basic example, use the az batch pool resize command to increase the number of low-priority nodes in the pool to 6:

The pool takes a few minutes to resize. While that process takes place, set up the next tasks to run in the existing rendering job.

Render a multiframe scene

As in the single-frame example, use the az batch task create command to create rendering tasks in the job named myrenderjob. Here, specify the task settings in a JSON file called myrendertask_multi.json. (You can download the file from GitHub.) Each of the six tasks specifies an Arnold command line to render one frame of the 3ds Max scene MotionBlur-DragonFlying.max.

Create a file in your current shell named myrendertask_multi.json, and copy and paste the contents from the downloaded file. Modify the blobSource and containerURL elements in the JSON file to include the name of your storage account and your SAS token. Be sure to change the settings for each of the six tasks. Save the file, and run the following command to queue the tasks:

View task output

The task takes a few minutes to run. Use the az batch task list command to view the state of the tasks. For example:

Use the az batch task show command to view details about individual tasks. For example:

The tasks generate output files named dragon0002.jpg - dragon0007.jpg on the compute nodes and upload them to the job-myrenderjob container in your storage account. To view the output, download the files to a folder on your local computer using the az storage blob download-batch command. For example:

Open one of the files on your computer. Rendered frame 6 looks similar to the following:

Clean up resources

When no longer needed, you can use the az group delete command to remove the resource group, Batch account, pools, and all related resources. Delete the resources as follows:

Next steps

In this tutorial, you learned about how to:

• Upload scenes to Azure storage
• Create a Batch pool for rendering
• Render a single-frame scene with Arnold
• Scale the pool, and render a multi-frame scene
• Download rendered output

To learn more about cloud-scale rendering, see the options for the Batch Rendering service.

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