Merge pull request #221802 from Bozhong68/k8s-doc-update

update doc according to Studio Compute UI changes

Author: Maggiemouse1

articles/machine-learning/how-to-attach-kubernetes-to-workspace.md

@@ -32,7 +32,7 @@ Otherwise, if a user-assigned managed identity is specified in Azure Machine Lea
 |Azure resource name |Role to be assigned|Description|
 |--|--|--|
 |Azure Relay|Azure Relay Owner|Only applicable for Arc-enabled Kubernetes cluster. Azure Relay isn't created for AKS cluster without Arc connected.|
-|Azure Arc-enabled Kubernetes or AKS|Reader|Applicable for both Arc-enabled Kubernetes cluster and AKS cluster.|
+|Kubernetes - Azure Arc or Azure Kubernetes Service|Reader|Applicable for both Arc-enabled Kubernetes cluster and AKS cluster.|
 
 Azure Relay resource is created during the extension deployment under the same Resource Group as the Arc-enabled Kubernetes cluster.
 
@@ -68,10 +68,10 @@ Attaching a Kubernetes cluster makes it available to your workspace for training
 
 1. Navigate to [Azure Machine Learning studio](https://ml.azure.com).
 1. Under **Manage**, select **Compute**.
-1. Select the **Attached computes** tab.
+1. Select the **Kubernetes clusters** tab.
 1. Select **+New > Kubernetes**
 
-   :::image type="content" source="media/how-to-attach-kubernetes-to-workspace/attach-kubernetes-cluster.png" alt-text="Screenshot of settings for Kubernetes cluster to make available in your workspace.":::
+   :::image type="content" source="media/how-to-attach-arc-kubernetes/kubernetes-attach.png" alt-text="Screenshot of settings for Kubernetes cluster to make available in your workspace.":::
 
 1. Enter a compute name and select your Kubernetes cluster from the dropdown.
 
@@ -83,9 +83,9 @@ Attaching a Kubernetes cluster makes it available to your workspace for training
 
 1. Select **Attach**
 
-    In the Attached compute tab, the initial state of your cluster is *Creating*. When the cluster is successfully attached, the state changes to *Succeeded*. Otherwise, the state changes to *Failed*.
+    In the Kubernetes clusters tab, the initial state of your cluster is *Creating*. When the cluster is successfully attached, the state changes to *Succeeded*. Otherwise, the state changes to *Failed*.
 
-    :::image type="content" source="media/how-to-attach-kubernetes-to-workspace/provision-resources.png" alt-text="Screenshot of attached settings for configuration of Kubernetes cluster.":::
+    :::image type="content" source="media/how-to-attach-arc-kubernetes/kubernetes-creating.png" alt-text="Screenshot of attached settings for configuration of Kubernetes cluster.":::
 
 ---
 

articles/machine-learning/how-to-create-attach-compute-studio.md

@@ -35,7 +35,7 @@ To see all compute targets for your workspace, use the following steps:
 
 1. Select tabs at the top to show each type of compute target.
 
-    :::image type="content" source="media/how-to-create-attach-studio/view-compute-targets.png" alt-text="View list of compute targets":::
+    :::image type="content" source="media/how-to-create-attach-studio/compute-targets.png" alt-text="View list of compute targets":::
 
 ## Compute instance and clusters
 
@@ -46,9 +46,9 @@ You can create compute instances and compute clusters in your workspace, using t
 
 In addition, you can use the [VS Code extension](how-to-manage-resources-vscode.md#compute-clusters) to create compute instances and compute clusters in your workspace.
 
-## Kubernetes cluster
+## Kubernetes clusters
 
-For information on configuring and attaching a Kubrnetes cluster to your workspace, see [Configure Kubernetes cluster for Azure Machine Learning](how-to-attach-kubernetes-anywhere.md).
+For information on configuring and attaching a Kubernetes cluster to your workspace, see [Configure Kubernetes cluster for Azure Machine Learning](how-to-attach-kubernetes-anywhere.md).
 
 ## Other compute targets
 
@@ -58,7 +58,8 @@ To use VMs created outside the Azure Machine Learning workspace, you must first
 
 1. Under __Manage__, select __Compute__.
 
-1. In the tabs at the top, select **Attached compute** to attach a compute target for **training**.  Or select **Inference clusters** to attach an AKS cluster for **inferencing**.
+1. In the tabs at the top, select **Attached compute** to attach a compute target for **training**.  
+
 1. Select +New, then select the type of compute to attach. Not all compute types can be attached from Azure Machine Learning studio.
 
 1. Fill out the form and provide values for the required properties.
@@ -71,10 +72,6 @@ To use VMs created outside the Azure Machine Learning workspace, you must first
 
 1. Select __Attach__.
 
-[!INCLUDE [arc-enabled-machine-learning-create-training-compute](../../includes/machine-learning-create-arc-enabled-training-computer-target.md)]
-
-> [!IMPORTANT]
-> To attach an Azure Kubernetes Services (AKS) or Azure Arc-enabled Kubernetes cluster, you must be subscription owner or have permission to access AKS cluster resources under the subscription. Otherwise, the cluster list on "attach new compute" page will be blank.
 
 To detach your compute use the following steps:
 

articles/machine-learning/how-to-deploy-kubernetes-extension.md

@@ -28,8 +28,7 @@ In this article, you can learn:
 
 ## Prerequisites
 
-* An AKS cluster is up and running in Azure.
-  * If you have not previously used cluster extensions, you need to [register the KubernetesConfiguration service provider](../aks/dapr.md#register-the-kubernetesconfiguration-service-provider).
+* An AKS cluster running in Azure. If you have not previously used cluster extensions, you need to [register the KubernetesConfiguration service provider](../aks/dapr.md#register-the-kubernetesconfiguration-service-provider).
 * Or an Arc Kubernetes cluster is up and running. Follow instructions in [connect existing Kubernetes cluster to Azure Arc](../azure-arc/kubernetes/quickstart-connect-cluster.md).
   * If the cluster is an Azure RedHat OpenShift Service (ARO) cluster or OpenShift Container Platform (OCP) cluster, you must satisfy other prerequisite steps as documented in the [Reference for configuring Kubernetes cluster](./reference-kubernetes.md#prerequisites-for-aro-or-ocp-clusters) article.
 * For production purposes, the Kubernetes cluster must have a minimum of **4 vCPU cores and 14-GB memory**. For more information on resource detail and cluster size recommendations, see [Recommended resource planning](./reference-kubernetes.md).
@@ -46,6 +45,7 @@ In this article, you can learn:
 - Azure Machine Learning does not guarantee support for all preview stage features in AKS. For example, [Azure AD pod identity](../aks/use-azure-ad-pod-identity.md) is not supported.
 - If you've previously followed the steps from [AzureML AKS v1 document](./v1/how-to-create-attach-kubernetes.md) to create or attach your AKS as inference cluster, use the following link to [clean up the legacy azureml-fe related resources](./v1/how-to-create-attach-kubernetes.md#delete-azureml-fe-related-resources) before you continue the next step. 
 
+
 ## Review AzureML extension configuration settings
 
 You can use AzureML CLI command `k8s-extension create` to deploy AzureML extension. CLI `k8s-extension create` allows you to specify a set of configuration settings in `key=value` format using `--config` or `--config-protected` parameter. Following is the list of available configuration settings to be specified during AzureML extension deployment.
@@ -188,7 +188,7 @@ Upon AzureML extension deployment completes, you can use `kubectl get deployment
 
 > [!IMPORTANT]
    > * Azure Relay resource  is under the same resource group as the Arc cluster resource. It is used to communicate with the Kubernetes cluster and modifying them will break attached compute targets.
-   > * By default, the kubernetes deployment resources are randomly deployed to 1 or more nodes of the cluster, and daemonset resources are deployed to ALL nodes. If you want to restrict the extension deployment to specific nodes, use `nodeSelector` configuration setting described as below.
+   > * By default, the kubernetes deployment resources are randomly deployed to 1 or more nodes of the cluster, and daemonset resources are deployed to ALL nodes. If you want to restrict the extension deployment to specific nodes, use `nodeSelector` configuration setting described in [configuration settings table](#review-azureml-extension-configuration-settings).
 
 > [!NOTE]
    > * **{EXTENSION-NAME}:** is the extension name specified with `az k8s-extension create --name` CLI command. 

articles/machine-learning/media/how-to-attach-arc-kubernetes/kubernetes-attach.png

@@ -103,38 +103,6 @@ For AzureML extension deployment on ARO or OCP cluster, grant privileged access
 > * `{EXTENSION-NAME}`: is the extension name specified with the `az k8s-extension create --name` CLI command. 
 >* `{KUBERNETES-COMPUTE-NAMESPACE}`: is the namespace of the Kubernetes compute specified when attaching the compute to the Azure Machine Learning workspace. Skip configuring `system:serviceaccount:{KUBERNETES-COMPUTE-NAMESPACE}:default` if `KUBERNETES-COMPUTE-NAMESPACE` is `default`.
 
-## AzureML extension components
-
-For Arc-connected cluster, AzureML extension deployment will create [Azure Relay](../azure-relay/relay-what-is-it.md) in Azure cloud, used to route traffic between Azure services and the Kubernetes cluster. For AKS cluster without Arc connected, Azure Relay resource won't be created.
-
-Upon AzureML extension deployment completes, it will create following resources in Kubernetes cluster, depending on each AzureML extension deployment scenario:
-
-   |Resource name  |Resource type |Training |Inference |Training and Inference| Description | Communication with cloud|
-   |--|--|--|--|--|--|--|
-   |relayserver|Kubernetes deployment|**✓**|**✓**|**✓**|relay server is only needed in arc-connected cluster, and won't be installed in AKS cluster. Relay server works with Azure Relay to communicate with the cloud services.|Receive the request of job creation, model deployment from cloud service; sync the job status with cloud service.|
-   |gateway|Kubernetes deployment|**✓**|**✓**|**✓**|The gateway is used to communicate and send data back and forth.|Send nodes and cluster resource information to cloud services.|
-   |aml-operator|Kubernetes deployment|**✓**|N/A|**✓**|Manage the lifecycle of training jobs.| Token exchange with the cloud token service for authentication and authorization of Azure Container Registry.|
-   |metrics-controller-manager|Kubernetes deployment|**✓**|**✓**|**✓**|Manage the configuration for Prometheus|N/A|
-   |{EXTENSION-NAME}-kube-state-metrics|Kubernetes deployment|**✓**|**✓**|**✓**|Export the cluster-related metrics to Prometheus.|N/A|
-   |{EXTENSION-NAME}-prometheus-operator|Kubernetes deployment|Optional|Optional|Optional| Provide Kubernetes native deployment and management of Prometheus and related monitoring components.|N/A|
-   |amlarc-identity-controller|Kubernetes deployment|N/A|**✓**|**✓**|Request and renew Azure Blob/Azure Container Registry token through managed identity.|Token exchange with the cloud token service for authentication and authorization of Azure Container  Registry and Azure Blob used by inference/model deployment.|
-   |amlarc-identity-proxy|Kubernetes deployment|N/A|**✓**|**✓**|Request and renew Azure Blob/Azure Container Registry token  through managed identity.|Token exchange with the cloud token service for authentication and authorization of Azure Container  Registry and Azure Blob used by inference/model deployment.|
-   |azureml-fe-v2|Kubernetes deployment|N/A|**✓**|**✓**|The front-end component that routes incoming inference requests to deployed services.|Send service logs to Azure Blob.|
-   |inference-operator-controller-manager|Kubernetes deployment|N/A|**✓**|**✓**|Manage the lifecycle of inference endpoints. |N/A|
-   |volcano-admission|Kubernetes deployment|Optional|N/A|Optional|Volcano admission webhook.|N/A|
-   |volcano-controllers|Kubernetes deployment|Optional|N/A|Optional|Manage the lifecycle of Azure Machine Learning training job pods.|N/A|
-   |volcano-scheduler |Kubernetes deployment|Optional|N/A|Optional|Used to perform in-cluster job scheduling.|N/A|
-   |fluent-bit|Kubernetes daemonset|**✓**|**✓**|**✓**|Gather the components' system log.| Upload the components' system log to cloud.|
-   |{EXTENSION-NAME}-dcgm-exporter|Kubernetes daemonset|Optional|Optional|Optional|dcgm-exporter exposes GPU metrics for Prometheus.|N/A|
-   |nvidia-device-plugin-daemonset|Kubernetes daemonset|Optional|Optional|Optional|nvidia-device-plugin-daemonset exposes GPUs on each node of your cluster| N/A|
-   |prometheus-prom-prometheus|Kubernetes statefulset|**✓**|**✓**|**✓**|Gather and send job metrics to cloud.|Send job metrics like cpu/gpu/memory utilization to cloud.|
-
-> [!IMPORTANT]
-   > * Azure Relay resource  is under the same resource group as the Arc cluster resource. It is used to communicate with the Kubernetes cluster and modifying them will break attached compute targets.
-   > * By default, the kubernetes deployment resources are randomly deployed to 1 or more nodes of the cluster, and daemonset resources are deployed to ALL nodes. If you want to restrict the extension deployment to specific nodes, use `nodeSelector` configuration setting described as below.
-
-> [!NOTE]
-   > * **{EXTENSION-NAME}:** is the extension name specified with ```az k8s-extension create --name``` CLI command. 
 
 ## AzureML jobs connect with custom data storage