Merge pull request #179089 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to master to sync with https://github.com/MicrosoftDocs/azure-docs (branch master)

Author: huypub

articles/azure-resource-manager/management/overview.md

@@ -75,9 +75,15 @@ There are some important factors to consider when defining your resource group:
 
 * The resources in a resource group can be located in different regions than the resource group.
 
-* When creating a resource group, you need to provide a location for that resource group. You may be wondering, "Why does a resource group need a location? And, if the resources can have different locations than the resource group, why does the resource group location matter at all?" The resource group stores metadata about the resources. When you specify a location for the resource group, you're specifying where that metadata is stored. For compliance reasons, you may need to ensure that your data is stored in a particular region.
+* When you create a resource group, you need to provide a location for that resource group. 
 
-   If the resource group's region is temporarily unavailable, you can't update resources in the resource group because the metadata is unavailable. The resources in other regions will still function as expected, but you can't update them. For more information about building reliable applications, see [Designing reliable Azure applications](/azure/architecture/checklist/resiliency-per-service).
+  You may be wondering, "Why does a resource group need a location? And, if the resources can have different locations than the resource group, why does the resource group location matter at all?" 
+
+  The resource group stores metadata about the resources. When you specify a location for the resource group, you're specifying where that metadata is stored. For compliance reasons, you may need to ensure that your data is stored in a particular region. 
+  
+  Except in global resources like Azure Content Delivery Network, Azure Traffic Manager, and Azure Front Door, if a resource group's region is temporarily unavailable, you can't update resources in the resource group because the metadata is unavailable. The resources in other regions will still function as expected, but you can't update them. 
+   
+  For more information about building reliable applications, see [Designing reliable Azure applications](/azure/architecture/checklist/resiliency-per-service).
 
 * A resource group can be used to scope access control for administrative actions. To manage a resource group, you can assign [Azure Policies](../../governance/policy/overview.md), [Azure roles](../../role-based-access-control/role-assignments-portal.md), or [resource locks](lock-resources.md).
 

articles/cognitive-services/Speech-Service/how-to-custom-speech-test-and-train.md

@@ -185,7 +185,7 @@ Additionally, you'll want to account for the following restrictions:
 
 ## Structured text data for training (Public Preview)
 
-Often the expected utterances follow a certain pattern. One common pattern is that utterances only differ by words or phrases from a list. Examples of this could be “I have a question about `product`,” where `product` is a list of possible products. Or, “Make that `object` `color`,” where `object` is a list of geometric shapes and `color` is a list of colors. To simplify the creation of training data and to enable better modeling inside the Custom Language Model, you can use a structured text in markdown format to define lists of items and then reference these inside your training utterances. Additionally, the markdown format also supports specifying the phonetic pronunciation of words. The markdown format shares its format with the `.lu` markdown used to train Language Understanding models, in particular list entities and example utterances. For more information about the complete `.lu` markdown, see the <a href="/azure/bot-service/file-format/bot-builder-lu-file-format" target="_blank"> `.lu` file format</a>.
+Often the expected utterances follow a certain pattern. One common pattern is that utterances only differ by words or phrases from a list. Examples of this could be “I have a question about `product`,” where `product` is a list of possible products. Or, “Make that `object` `color`,” where `object` is a list of geometric shapes and `color` is a list of colors. To simplify the creation of training data and to enable better modeling inside the Custom Language Model, you can use a structured text in markdown format to define lists of items and then reference these inside your training utterances. Additionally, the markdown format also supports specifying the phonetic pronunciation of words. The markdown file should have a `.md` extension. The syntax of the markdown is the same as that from the Language Understanding models, in particular list entities and example utterances. For more information about the complete markdown syntax, see the <a href="/azure/bot-service/file-format/bot-builder-lu-file-format" target="_blank"> Language Understanding markdown</a>.
 
 Here is an example of the markdown format:
 

articles/expressroute/how-to-configure-custom-bgp-communities.md

@@ -86,7 +86,7 @@ BGP communities are groupings of IP prefixes tagged with a community value. This
 1. Update the `VirtualNetworkCommunity` value for your virtual network.
 
     ```azurepowershell-interactive
-    $vnet.BgpCommunities.VirtualNetworkCommunity = '12076:20002'
+    $vnet.BgpCommunities = @{VirtualNetworkCommunity = '12076:20002'}
     $vnet | Set-AzVirtualNetwork
     ```
 

articles/machine-learning/how-to-troubleshoot-online-endpoints.md

@@ -92,117 +92,145 @@ Add `--help` and/or `--debug` to commands to see more information. Include the `
 
 Below is a list of common deployment errors that are reported as part of the deployment operation status.
 
-### ERR_1100: Not enough quota
+* [OutOfQuota](#error-outofquota)
+* [OutOfCapacity](#error-outofcapacity)
+* [BadArgument](#error-badargument)
+* [ResourceNotReady](#error-resourcenotready)
+* [ResourceNotFound](#error-resourcenotfound)
+* [OperationCancelled](#error-operationcancelled)
+* [InternalServerError](#error-internalservererror)
 
-Before deploying a model, you need to have enough compute quota. This quota defines how much virtual cores are available per subscription, per workspace, per SKU, and per region. Each deployment subtracts from available quota and adds it back after deletion, based on type of the SKU.
+### ERROR: OutOfQuota
 
-A possible mitigation is to check if there are unused deployments that can be deleted. Or you can submit a [request for a quota increase](./how-to-manage-quotas.md).
+Below is a list of common resources that might run out of quota when using Azure services:
 
-### ERR_1101: Out of capacity
+* [CPU](#cpu-quota)
+* [Role assignments](#role-assignment-quota)
+* [Endpoints](#endpoint-quota)
+* [Kubernetes](#kubernetes-quota)
+* [Other](#other-quota)
 
-The specified VM Size failed to provision due to a lack of Azure Machine Learning capacity. Retry later or try deploying to a different region.
+#### CPU Quota
 
-### ERR_1102: No more role assignments
+Before deploying a model, you need to have enough compute quota. This quota defines how much virtual cores are available per subscription, per workspace, per SKU, and per region. Each deployment subtracts from available quota and adds it back after deletion, based on type of the SKU.
 
-Delete some unused role assignments in this subscription. You can check all role assignments in the Azure portal in the Access Control menu.
+A possible mitigation is to check if there are unused deployments that can be deleted. Or you can submit a [request for a quota increase](how-to-manage-quotas.md#request-quota-increases).
 
-### ERR_1103: Endpoint quota reached
+#### Role assignment quota
 
-Delete some unused endpoints in this subscription.
+Try to delete some unused role assignments in this subscription. You can check all role assignments in the Azure portal in the Access Control menu.
 
-### ERR_1200: Unable to download user container image
+#### Endpoint quota
 
-During deployment creation after the compute provisioning, Azure tries to pull the user container image from the workspace private Azure Container Registry (ACR). There could be two possible issues.
+Try to delete some unused endpoints in this subscription.
 
-- The user container image isn't found.
+#### Kubernetes quota
 
-  Make sure container image is available in workspace ACR.
-For example, if image is `testacr.azurecr.io/azureml/azureml_92a029f831ce58d2ed011c3c42d35acb:latest` check the repository with
-`az acr repository show-tags -n testacr --repository azureml/azureml_92a029f831ce58d2ed011c3c42d35acb --orderby time_desc --output table`.
+The requested CPU or memory couldn't be satisfied. Please adjust your request or the cluster.
 
-- There's a permission issue accessing ACR.
+#### Other quota
 
-  To pull the image, Azure uses [managed identities](../active-directory/managed-identities-azure-resources/overview.md) to access ACR. 
+To run the `score.py` provided as part of the deployment, Azure creates a container that includes all the resources that the `score.py` needs, and runs the scoring script on that container.
 
-  - If you created the associated endpoint with SystemAssigned, then Azure role-based access control (RBAC) permission is automatically granted, and no further permissions are needed.
-  - If you created the associated endpoint with UserAssigned, then the user's managed identity must have AcrPull permission for the workspace ACR.
+If your container could not start, this means scoring could not happen. It might be that the container is requesting more resources than what `instance_type` can support. If so, consider updating the `instance_type` of the online deployment.
 
-To get more details about this error, run:
+To get the exact reason for an error, run: 
 
 ```azurecli
 az ml online-deployment get-logs -e <endpoint-name> -n <deployment-name> -l 100
 ```
 
-### ERR_1300: Unable to download user model\code artifacts
+### ERROR: OutOfCapacity
+
+The specified VM Size failed to provision due to a lack of Azure Machine Learning capacity. Retry later or try deploying to a different region.
 
-After provisioning the compute resource, during deployment creation, Azure tries to mount the user model and code artifacts into the user container from the workspace storage account.
+### ERROR: BadArgument
 
-- User model\code artifacts not found.
+Below is a list of reasons you might run into this error:
 
-  - Make sure model and code artifacts are registered to the same workspace as the deployment. Use the `show` command to show details for a model or code artifact in a workspace. For example: 
-  
-    ```azurecli
-    az ml model show --name <model-name>
-    az ml code show --name <code-name> --version <version>
-    ```
+* [Resource request was greater than limits](#resource-requests-greater-than-limits)
+* [Unable to download resources](#unable-to-download-resources)
+
+#### Resource requests greater than limits
+
+Requests for resources must be less than or equal to limits. If you don't set limits, we set default values when you attach your compute to an Azure Machine Learning workspace. You can check limits in the Azure portal or by using the `az ml compute show` command.
 
-  - You can also check if the blobs are present in the workspace storage account.
+#### Unable to download resources
 
-    For example, if the blob is `https://foobar.blob.core.windows.net/210212154504-1517266419/WebUpload/210212154504-1517266419/GaussianNB.pkl` you can use this command to check if it exists: `az storage blob exists --account-name foobar --container-name 210212154504-1517266419 --name WebUpload/210212154504-1517266419/GaussianNB.pkl --subscription <sub-name>`
+After provisioning the compute resource, during deployment creation, Azure tries to pull the user container image from the workspace private Azure Container Registry (ACR) and mount the user model and code artifacts into the user container from the workspace storage account.
 
-- Permission issue accessing ACR.
+First, check if there is a permissions issue accessing ACR.
 
-  To pull blobs, Azure uses [managed identities](../active-directory/managed-identities-azure-resources/overview.md) to access the storage account.
+To pull blobs, Azure uses [managed identities](../active-directory/managed-identities-azure-resources/overview.md) to access the storage account.
 
   - If you created the associated endpoint with SystemAssigned, Azure role-based access control (RBAC) permission is automatically granted, and no further permissions are needed.
 
   - If you created the associated endpoint with UserAssigned, the user's managed identity must have Storage blob data reader permission on the workspace storage account.
 
-To get more details about this error, run:
+During this process, you can run into a few different issues depending on which stage the operation failed at:
 
-```azurecli
-az ml online-deployment get-logs -e <endpoint-name> -n <deployment-name> -l 100
-```
+* [Unable to download user container image](#unable-to-download-user-container-image)
+* [Unable to download user model or code artifacts](#unable-to-download-user-model-or-code-artifacts)
 
-### ERR_1350: Unable to download user model, not enough space on the disk
+To get more details about these errors, run:
 
-This issue happens when the size of the model is bigger than the available disk space. Try an SKU with more disk space.
+```azurecli
+az ml online-deployment get-logs -n <endpoint-name> --deployment <deployment-name> --l 100
+``` 
 
-### ERR_2100: Unable to start user container
+#### Unable to download user container image
 
-To run the `score.py` provided as part of the deployment, Azure creates a container that includes all the resources that the `score.py` needs, and runs the scoring script on that container.
+It is possible that the user container could not be found.
 
-This error means that this container couldn't start, which means scoring could not happen. It could be that the container is requesting more resources than what `instance_type` could support. If so, consider updating the `instance_type` of the online deployment.
+Make sure container image is available in workspace ACR.
 
-To get the exact reason for an error, run: 
+For example, if image is `testacr.azurecr.io/azureml/azureml_92a029f831ce58d2ed011c3c42d35acb:latest` check the repository with
+`az acr repository show-tags -n testacr --repository azureml/azureml_92a029f831ce58d2ed011c3c42d35acb --orderby time_desc --output table`.
 
-```azurecli
-az ml online-deployment get-logs -e <endpoint-name> -n <deployment-name> -l 100
-```
+#### Unable to download user model or code artifacts
 
-### ERR_2101: Kubernetes unschedulable
+It is possible that the user model or code artifacts can't be found.
 
-The requested CPU or memory can't be satisfied. Please adjust your request or the cluster.
+Make sure model and code artifacts are registered to the same workspace as the deployment. Use the `show` command to show details for a model or code artifact in a workspace. 
 
-### ERR_2102: Resources requests invalid
+- For example: 
+  
+  ```azurecli
+  az ml model show --name <model-name>
+  az ml code show --name <code-name> --version <version>
+  ```
+ 
+  You can also check if the blobs are present in the workspace storage account.
 
-Requests for resources must be less than or equal to limits. If you don't set limits, we set default values when you attach your compute to an Azure Machine Learning workspace. You can check limits in the Azure portal or by using the `az ml compute show` command.
+- For example, if the blob is `https://foobar.blob.core.windows.net/210212154504-1517266419/WebUpload/210212154504-1517266419/GaussianNB.pkl`, you can use this command to check if it exists:
 
-### ERR_2200: User container has crashed\terminated
+  `az storage blob exists --account-name foobar --container-name 210212154504-1517266419 --name WebUpload/210212154504-1517266419/GaussianNB.pkl --subscription <sub-name>`
 
-To run the `score.py` provided as part of the deployment, Azure creates a container that includes all the resources that the `score.py` needs, and runs the scoring script on that container.  The error in this scenario is that this container is crashing when running, which means scoring couldn't happen. This error happens when:
+### ERROR: ResourceNotReady
+
+To run the `score.py` provided as part of the deployment, Azure creates a container that includes all the resources that the `score.py` needs, and runs the scoring script on that container. The error in this scenario is that this container is crashing when running, which means scoring can't happen. This error happens when:
 
 - There's an error in `score.py`. Use `get-logs` to help diagnose common problems:
-    - A package that was  imported but is not in the conda environment
-    - A syntax error
-    - A failure in the `init()` method
+    - A package that was  imported but is not in the conda environment.
+    - A syntax error.
+    - A failure in the `init()` method.
 - If `get-logs` isn't producing any logs, it usually means that the container has failed to start. To debug this issue, try [deploying locally](https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/how-to-troubleshoot-online-endpoints.md#deploy-locally) instead.
 - Readiness or liveness probes are not set up correctly.
 - There's an error in the environment setup of the container, such as a missing dependency.
 
-### ERR_5000: Internal error
+### ERROR: ResourceNotFound
+
+This error occurs when Azure Resource Manager can't find a required resource. For example, you will receive this error if a storage account was referred to but cannot be found at the path on which it was specified. Be sure to double check resources which might have been supplied by exact path or the spelling of their names.
+
+For more information, see [Resolve resource not found errors](../azure-resource-manager/troubleshooting/error-not-found.md). 
+
+### ERROR: OperationCancelled
+
+Azure operations have a certain priority level and are executed from highest to lowest. This error happens when your operation happened to be overridden by another operation that has a higher priority. Retrying the operation might allow it to be performed without cancellation.
+
+### ERROR: InternalServerError
 
-While we do our best to provide a stable and reliable service, sometimes things don't go according to plan. If you get this error, it means something isn't right on our side and we need to fix it. Submit a [customer support ticket](https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade/newsupportrequest) with all related information and we'll address the issue.  
+Although we do our best to provide a stable and reliable service, sometimes things don't go according to plan. If you get this error, it means that something isn't right on our side, and we need to fix it. Submit a [customer support ticket](https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade/newsupportrequest) with all related information and we'll address the issue. 
 
 ## Autoscaling issues