title	description	services	author	manager	ms.service	ms.topic	ms.date	ms.author	ms.custom
Render a scene in the cloud - Azure Batch	Tutorial - How to render an Autodesk 3ds Max scene with Arnold using the Batch Rendering Service and Azure Command-Line Interface	batch	dlepow	jeconnoc	batch	tutorial	04/19/2018	danlep	mvc

Tutorial: Render a scene with Azure Batch

Azure Batch provides cloud-scale rendering capabilities on a pay-per-use basis. The Batch Rendering service 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:

[!div class="checklist"]

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.

[!INCLUDE quickstarts-free-trial-note.md]

Prerequisites

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.)

[!INCLUDE cloud-shell-try-it.md]

If you choose to install and use the CLI locally, this tutorial requires that you are running the Azure CLI version 2.0.20 or later. Run az --version to find the version. If you need to install or upgrade, see Install Azure CLI 2.0.

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.

az group create \
    --name myResourceGroup \
    --location eastus2

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.

az storage account create \
    --resource-group myResourceGroup \
    --name mystorageaccount \
    --location eastus2 \
    --sku Standard_LRS

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.

az batch account create \
    --name mybatchaccount \
    --storage-account mystorageaccount \
    --resource-group myResourceGroup \
    --location eastus2

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.

az batch account login \
    --name mybatchaccount \
    --resource-group myResourceGroup \
    --shared-key-auth

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.

export AZURE_STORAGE_KEY=$(az storage account keys list --account-name mystorageaccount --resource-group myResourceGroup -o tsv --query [0].value)

export AZURE_STORAGE_ACCOUNT=mystorageaccount

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.

az storage container create \
    --public-access blob \
    --name scenefiles

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

wget -O MotionBlur-DragonFlying.max https://github.com/Azure/azure-docs-cli-python-samples/raw/master/batch/render-scene/MotionBlur-DragonFlying.max

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:

az storage blob upload-batch \
    --destination scenefiles \
    --source ./

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.)

{
  "id": "myrenderpool",
  "vmSize": "standard_d2_v2",
  "virtualMachineConfiguration": {
    "imageReference": {
      "publisher": "batch",
      "offer": "rendering-windows2016",
      "sku": "rendering",
      "version": "1.2.1"
    },
    "nodeAgentSKUId": "batch.node.windows amd64"
  },
  "targetDedicatedNodes": 0,
  "targetLowPriorityNodes": 1,
  "enableAutoScale": false,
  "applicationLicenses":[
         "3dsmax",
         "arnold"
      ],
  "enableInterNodeCommunication": false 
}

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:

az batch pool create \
    --json-file mypool.json

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:

az batch pool show \
    --pool-id myrenderpool \
    --query "allocationState"

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.

az storage container create \
    --public-access blob \
    --name job-myrenderjob

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, 2018:

az storage account generate-sas \
    --permissions w \
    --resource-types co \
    --services b \
    --expiry 2018-11-15

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

se=2018-11-15&sp=rw&sv=2017-04-17&ss=b&srt=co&sig=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

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.

az batch job create \
    --id myrenderjob \
    --pool-id myrenderpool

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:

https://mystorageaccount.blob.core.windows.net/job-myrenderjob/$TaskOutput?se=2018-11-15&sp=rw&sv=2017-04-17&ss=b&srt=co&sig=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

{
  "id": "myrendertask",
  "commandLine": "cmd /c \"%3DSMAX_2018%3dsmaxcmdio.exe -secure off -v:5 -rfw:0 -start:1 -end:1 -outputName:\"dragon.jpg\" -w 400 -h 300 MotionBlur-DragonFlying.max\"",
  "resourceFiles": [
    {
        "blobSource": "https://mystorageaccount.blob.core.windows.net/scenefiles/MotionBlur-DragonFlying.max",
        "filePath": "MotionBlur-DragonFlying.max"
    }
  ],
    "outputFiles": [
        {
            "filePattern": "dragon*.jpg",
            "destination": {
                "container": {
                    "containerUrl": "https://mystorageaccount.blob.core.windows.net/job-myrenderjob/myrendertask/$TaskOutput?Add_Your_SAS_Token_Here"
                }
            },
            "uploadOptions": {
                "uploadCondition": "TaskSuccess"
            }
        }
    ],
  "userIdentity": {
    "autoUser": {
      "scope": "task",
      "elevationLevel": "nonAdmin"
    }
  }
}

Add the task to the job with the following command:

az batch task create \
    --job-id myrenderjob \
    --json-file myrendertask.json

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

View task output

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

az batch task show \
    --job-id myrenderjob \
    --task-id myrendertask

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.

az storage blob download \
    --container-name job-myrenderjob \
    --file dragon.jpg \
    --name dragon0001.jpg

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:

az batch pool resize --pool-id myrenderpool --target-dedicated-nodes 0 --target-low-priority-nodes 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:

az batch task create --job-id myrenderjob --json-file myrendertask_multi.json

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:

az batch task list \
    --job-id myrenderjob \
    --output table

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

az batch task show \
    --job-id myrenderjob \
    --task-id mymultitask1

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:

az storage blob download-batch \
    --source job-myrenderjob \
    --destination .

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:

az group delete --name myResourceGroup

Next steps

In this tutorial, you learned about how to:

[!div class="checklist"]

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.

[!div class="nextstepaction"] Batch Rendering service

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tutorial-rendering-cli.md

tutorial-rendering-cli.md

Tutorial: Render a scene with Azure Batch

Prerequisites

Create a Batch account

Upload a scene to storage

Create a rendering pool

Create a blob container for output

Render a single-frame scene

Create a job

Create a task

View task output

Scale the pool

Render a multiframe scene

View task output

Clean up resources

Next steps

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tutorial-rendering-cli.md

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tutorial-rendering-cli.md

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Tutorial: Render a scene with Azure Batch

Prerequisites

Create a Batch account

Upload a scene to storage

Create a rendering pool

Create a blob container for output

Render a single-frame scene

Create a job

Create a task

View task output

Scale the pool

Render a multiframe scene

View task output

Clean up resources

Next steps