How can I deploy an Azure ML Compute Instance with Custom Startup Scripts using Terraform?

Below is a summary of the options and their trade‐offs:

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What You’re Facing

The Azure ML Compute Instance resource (azurerm_machine_learning_compute_instance) doesn’t currently expose a way to inject a custom startup script. This means that if you need to install additional agents at startup, you must work around that limitation.

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Option 1: Use a VM Extension

Using a VM extension (typically a Custom Script Extension) is the most common workaround. With a VM extension, you can run your startup script on the underlying VM which is created as part of the managed compute instance. Note these points:

• Timing Is Critical: The extension must be applied after the compute instance is fully provisioned. Use Terraform’s depends_on attribute to ensure ordering.
• Limited Control: Because the instance is managed by Azure ML, adding your own VM extension means you’re taking responsibility for that extra layer of configuration. If Azure ML ever re-images the VM (for maintenance, etc.) your extension’s state might be lost, and you’ll have to reapply it.
• Documentation and Support: This use case isn’t “first class” in the compute instance resource, so you’ll need to test carefully in your environment.

A sample Terraform snippet might look like this:

HCLCopied
resource "azurerm_virtual_machine_extension" "custom_script" {
  name                       = "CustomScriptExtension"
  location                   = azurerm_machine_learning_compute_instance.compute_instance.location
  resource_group_name        = var.resource_group_name
  virtual_machine_name       = azurerm_machine_learning_compute_instance.compute_instance.name
  publisher                = "Microsoft.Azure.Extensions"
  type                     = "CustomScript"
  type_handler_version     = "2.0"

  settings = <<SETTINGS
    {
      "fileUris": ["https://<your-storage-endpoint>/startup-script.sh"]
    }
SETTINGS

  protected_settings = <<PROTECTED
    {
      "commandToExecute": "bash startup-script.sh"
    }
PROTECTED

  depends_on = [
    azurerm_machine_learning_compute_instance.compute_instance
  ]
}

Caveat: You’ll need to verify that the compute instance’s underlying VM name (or ID) is available for use with the VM extension. In some cases, the managed compute instance may not expose all the typical VM properties. You might have to extract the resource ID from outputs (if available) or coordinate with Azure ML support if this pattern is officially supported in your subscription.

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Option 2: Build a Custom Image

Another approach is to bake your customizations into your compute instance:

• Create a VM image (or container image if you’re using containerized workloads) that already includes your extra agents.
• Use that image to create the compute instance. This way, the agents are already installed at creation.

This method offloads the customization from runtime to image creation, which can be easier to manage long term. It does, however, require you to manage image updates and versioning.

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Option 3: Use an Automation Script (Outside of Terraform)

If you want to avoid using a VM extension resource in Terraform, you can consider running a post-deployment automation (for example, using an Azure DevOps job or an Azure Automation Runbook) that executes your custom script once your compute instance is up. This isn’t as integrated with Terraform, but it does provide a clear separation of provisioning versus configuration.

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Conclusion

While there isn’t a direct property on azurerm_machine_learning_compute_instance to inject startup scripts, the most common workaround is to attach a VM extension (e.g., Custom Script Extension) after the instance is created. However, be aware of timing and potential re-imaging issues inherent with managed compute instances. Alternatively, consider baking your changes into a custom image if that fits your lifecycle process better.

Remember to test any chosen approach in a non‐production environment to ensure that it meets your enterprise agent requirements reliably.