# AzureML Stacks

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](/kitaru/agent-runtime-stacks/stacks.md).

## 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

```bash
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:

| Field                  | Meaning                                                                      |
| ---------------------- | ---------------------------------------------------------------------------- |
| `--artifact-store`     | Azure storage URI where Kitaru writes checkpoint outputs and saved artifacts |
| `--container-registry` | Azure Container Registry repository where Kitaru pushes the run image        |
| `--subscription-id`    | Azure subscription containing the AzureML workspace                          |
| `--resource-group`     | Resource group containing the AzureML workspace                              |
| `--workspace`          | AzureML workspace name                                                       |

`--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`:

```bash
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

```yaml
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:

```bash
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

```bash
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

```bash
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.

## Related

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Stacks</strong></td><td>The shared stack model, precedence rules, YAML, --extra, and --async</td><td><a href="/pages/Md0YgNiF5z5NwLEvQ5aR">/pages/Md0YgNiF5z5NwLEvQ5aR</a></td></tr><tr><td><strong>Containerization</strong></td><td>How Kitaru builds and configures remote execution images</td><td><a href="/pages/9JL1jz8yokIPcq1wr5RE">/pages/9JL1jz8yokIPcq1wr5RE</a></td></tr></tbody></table>


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