Update k8s compute TSG and log info

Author: jiaochenlu

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

@@ -62,7 +62,7 @@ You can use AzureML CLI command `k8s-extension create` to deploy AzureML extensi
    |`sslSecret`| The name of the Kubernetes secret in the `azureml` namespace. This config is used to store `cert.pem` (PEM-encoded TLS/SSL cert) and `key.pem` (PEM-encoded TLS/SSL key), which are required for inference HTTPS endpoint support when ``allowInsecureConnections`` is set to `False`. For a sample YAML definition of `sslSecret`, see [Configure sslSecret](./how-to-secure-kubernetes-online-endpoint.md#configure-sslsecret). Use this config or a combination of `sslCertPemFile` and `sslKeyPemFile` protected config settings. |N/A| Optional |  Optional |
    |`sslCname` |An TLS/SSL CNAME is used by inference HTTPS endpoint. **Required** if `allowInsecureConnections=False`  |  N/A | Optional | Optional|
    | `inferenceRouterHA` |`True` or `False`, default `True`. By default, AzureML extension will deploy three inference router replicas for high availability, which requires at least three worker nodes in a cluster. Set to `False` if your cluster has fewer than three worker nodes, in this case only one inference router service is deployed. | N/A| Optional |  Optional |
-   |`nodeSelector` | By default, the deployed kubernetes resources are randomly deployed to one 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 with label `key1=value1` and `key2=value2`, use `nodeSelector.key1=value1`, `nodeSelector.key2=value2` correspondingly. | Optional| Optional |  Optional |
+   |`nodeSelector` | By default, the deployed kubernetes resources and your machine learning workloads are randomly deployed to one or more nodes of the cluster, and DaemonSet resources are deployed to ALL nodes. If you want to restrict the extension deployment and your training/inference workloads to specific nodes with label `key1=value1` and `key2=value2`, use `nodeSelector.key1=value1`, `nodeSelector.key2=value2` correspondingly. | Optional| Optional |  Optional |
    |`installNvidiaDevicePlugin`  | `True` or `False`, default `False`. [NVIDIA Device Plugin](https://github.com/NVIDIA/k8s-device-plugin#nvidia-device-plugin-for-kubernetes) is required for ML workloads on NVIDIA GPU hardware. By default, AzureML extension deployment won't install NVIDIA Device Plugin regardless Kubernetes cluster has GPU hardware or not. User can specify this setting to `True`, to install it, but make sure to fulfill [Prerequisites](https://github.com/NVIDIA/k8s-device-plugin#prerequisites). | Optional |Optional |Optional |
    |`installPromOp`|`True` or `False`, default `True`. AzureML extension needs prometheus operator to manage prometheus. Set to `False` to reuse the existing prometheus operator. For more information about reusing the existing  prometheus operator, refer to [reusing the prometheus operator](./how-to-troubleshoot-kubernetes-extension.md#prometheus-operator)| Optional| Optional |  Optional |
    |`installVolcano`| `True` or `False`, default `True`. AzureML extension needs volcano scheduler to schedule the job. Set to `False` to reuse existing volcano scheduler. For more information about reusing the existing volcano scheduler, refer to [reusing volcano scheduler](./how-to-troubleshoot-kubernetes-extension.md#volcano-scheduler)   | Optional| N/A |  Optional |

articles/machine-learning/how-to-manage-kubernetes-instance-types.md

@@ -24,6 +24,14 @@ and [resources](https://kubernetes.io/docs/concepts/configuration/manage-resourc
 
 In short, a `nodeSelector` lets you specify which node a pod should run on.  The node must have a corresponding label.  In the `resources` section, you can set the compute resources (CPU, memory and NVIDIA GPU) for the pod.
 
+>[!IMPORTANT]
+> 
+> If you have [specified a nodeSelector when deploying the AzureML extension](./how-to-deploy-kubernetes-extension.md#review-azureml-extension-configuration-settings), the nodeSelector will be applied to all instance types.  This means that:
+> - For each instance type creating, the specified nodeSelector should be a subset of the extension-specified nodeSelector. 
+> - If you use an instance type **with nodeSelector**, the workload will run on any node matching both the extension-specified nodeSelector and the instance type-specified nodeSelector.
+> - If you use an instance type **without a nodeSelector**, the workload will run on any node mathcing the extension-specified nodeSelector.
+
+
 ## Default instance type
 
 By default, a `defaultinstancetype` with the following definition is created when you attach a Kubernetes cluster to an AzureML workspace: