Azure (AzureML)

An AzureML stack runs each Kitaru execution as a managed AzureML job and stores checkpoint outputs in Azure storage.

Use this page when your team wants Azure-managed job execution. If you want the broader stack model first, start with Stacks.

Prerequisites

Before creating the stack, make sure these resources already exist:

• a Kitaru server you are connected to with kitaru login ...
• an Azure storage URI for artifacts, for example az://my-container/kitaru, abfs://my-container/kitaru, or abfss://my-container/kitaru
• an Azure Container Registry repository, for example demo.azurecr.io/kitaru
• an Azure subscription ID, resource group, and AzureML workspace
• Azure credentials available to the Kitaru server / stack setup path
• optionally, the Azure region you want reported on the stack

Kitaru creates the stack definition and component records. It does not create your storage account, container registry, AzureML workspace, resource group, or IAM setup for you.

Create the stack

kitaru stack create prod-azureml \
  --type azureml \
  --artifact-store az://my-container/kitaru \
  --container-registry demo.azurecr.io/kitaru \
  --subscription-id 00000000-0000-0000-0000-000000000123 \
  --resource-group ml-platform \
  --workspace team-ml \
  --region westeurope

The required AzureML fields are:

--region is optional for AzureML stack creation, but it is useful for humans reading kitaru stack show output.

You can add an optional credentials reference with --credentials when your server setup uses named cloud credentials.

Set advanced AzureML defaults

Named flags cover the common setup. Use --extra for lower-level component fields that Kitaru does not expose as first-class flags.

For example, write the asynchronous runner default explicitly with --extra:

kitaru stack create prod-azureml \
  --type azureml \
  --artifact-store az://my-container/kitaru \
  --container-registry demo.azurecr.io/kitaru \
  --subscription-id 00000000-0000-0000-0000-000000000123 \
  --resource-group ml-platform \
  --workspace team-ml \
  --region westeurope \
  --extra orchestrator.synchronous=false

--async is shorthand for that same orchestrator.synchronous=false setting. If you provide both, the explicit --extra value wins.

If you need provider-specific settings not shown here, keep them in a reviewed stack YAML template and pass them through extra: / --extra.

Use YAML for repeatable setup

name: prod-azureml
type: azureml
artifact_store: az://my-container/kitaru
container_registry: demo.azurecr.io/kitaru
subscription_id: 00000000-0000-0000-0000-000000000123
resource_group: ml-platform
workspace: team-ml
region: westeurope
extra:
  orchestrator:
    synchronous: false

Create it with:

kitaru stack create -f stack.yaml

CLI flags override YAML values, and --extra values merge on top of the YAML extra: block.

Inspect and use it

kitaru stack show prod-azureml
kitaru stack use prod-azureml
kitaru stack current

kitaru stack show reports the translated Kitaru view: runner, storage, image registry, subscription, resource group, workspace, active status, and whether the stack was created by Kitaru.

Once active, normal flow runs use the AzureML stack unless a flow-level or run-level stack override is present.

Delete it

kitaru stack delete prod-azureml

Use --recursive if you want Kitaru to remove Kitaru-managed component records too. Kitaru does not delete your cloud storage, registry, workspace, resource group, or IAM resources.

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