Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into rolyon-rbac-roles-categories

Author: rolyon

.openpublishing.publish.config.json

@@ -75,6 +75,12 @@
       "branch": "master",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "azure_storage-snippets",
+      "url": "https://github.com/Azure-Samples/AzureStorageSnippets",
+      "branch": "master",
+      "branch_mapping": {}
+    },
     {
       "path_to_root": "azure_cli_scripts",
       "url": "https://github.com/Azure-Samples/azure-cli-samples",

articles/bastion/configuration-settings.md

@@ -90,7 +90,7 @@ You can configure this setting using the following methods:
 
 An instance is an optimized Azure VM that is created when you configure Azure Bastion. It's fully managed by Azure and runs all of the processes needed for Azure Bastion. An instance is also referred to as a scale unit. You connect to client VMs via an Azure Bastion instance. When you configure Azure Bastion using the Basic SKU, two instances are created. If you use the Standard SKU, you can specify the number of instances. This is called **host scaling**.
 
-Each instance can support 10 concurrent RDP connections and 50 concurrent SSH connections. The number of connections per instances depends on what actions you are taking when connected to the client VM. For example, if you are doing something data intensive, it creates a larger load for the instance to process. Once the concurrent sessions are exceeded, an additional scale unit (instance) is required.
+Each instance can support 25 concurrent RDP connections and 50 concurrent SSH connections for medium workloads (see [Azure subscription limits and quotas](../azure-resource-manager/management/azure-subscription-service-limits.md) for more information). The number of connections per instances depends on what actions you are taking when connected to the client VM. For example, if you are doing something data intensive, it creates a larger load for the instance to process. Once the concurrent sessions are exceeded, an additional scale unit (instance) is required.
 
 Instances are created in the AzureBastionSubnet. To allow for host scaling, the AzureBastionSubnet should be /26 or larger. Using a smaller subnet limits the number of instances you can create. For more information about the AzureBastionSubnet, see the [subnets](#subnet) section in this article.
 

articles/machine-learning/azure-machine-learning-release-notes.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.topic: reference
 ms.author: larryfr
 author: BlackMist
-ms.date: 12/07/2021
+ms.date: 02/21/2022
 ---
 
 # Azure Machine Learning Python SDK release notes
@@ -18,6 +18,32 @@ In this article, learn about Azure Machine Learning Python SDK releases.  For th
 __RSS feed__: Get notified when this page is updated by copying and pasting the following URL into your feed reader:
 `https://docs.microsoft.com/api/search/rss?search=%22Azure+machine+learning+release+notes%22&locale=en-us`
 
+## 2022-02-28
+
+### Azure Machine Learning SDK for Python v1.39.0
+  + **azureml-automl-core**
+    +  Fix incorrect form displayed in PBI for integration with AutoML regression models
+    +  Adding min-label-classes check for both classification tasks (multi-class and multi-label). It will throw an error for the customer's run if the unique number of classes in the input training dataset is fewer than 2. It is meaningless to run classification on fewer than two classes.
+  + **azureml-automl-runtime**
+    +  Converting decimal type y-test into float to allow for metrics computation to proceed without errors.   
+    +  Automl training now supports numpy version 1.8.    
+  + **azureml-contrib-automl-dnn-forecasting**
+    +  Fixed a bug in the TCNForecaster model where not all training data would be used when cross-validation settings were provided.
+    +  TCNForecaster wrapper's forecast method that was corrupting inference-time predictions. Also fixed an issue where the forecast method would not use the most recent context data in train-valid scenarios.
+  + **azureml-interpret**
+    +  For azureml-interpret package, remove shap pin with packaging update. Remove numba and numpy pin after CE env update.
+  + **azureml-responsibleai**
+    +  azureml-responsibleai package to raiwidgets and responsibleai 0.17.0 release
+  + **azureml-synapse**
+    +  Fix the issue that magic widget is disappeared.
+  + **azureml-train-automl-runtime**
+    +  Updating AutoML dependencies to support python 3.8. This change will break compatibility with models trained with SDK 1.37 or below due to newer Pandas interfaces being saved in the model.
+    +  Automl training now supports numpy version 1.19
+    +  Fix automl reset index logic for ensemble models in automl_setup_model_explanations API
+    +  In automl, use lightgbm surrogate model instead of linear surrogate model for sparse case after latest lightgbm version upgrade
+    +  All internal intermediate artifacts that are produced by AutoML are now stored transparently on the parent run (instead of being sent to the default workspace blob store). Users should be able to see the artifacts that AutoML generates under the 'outputs/` directory on the parent run.
+
+ 
 ## 2022-01-24 
 
 ### Azure Machine Learning SDK for Python v1.38.0 

articles/machine-learning/how-to-auto-train-image-models.md

@@ -154,6 +154,8 @@ The following is a sample JSONL file for image classification:
 Once your data is in JSONL format, you can create a TabularDataset with the following code:
 
 ```python
+ws = Workspace.from_config()
+ds = ws.get_default_datastore()
 from azureml.core import Dataset
 
 training_dataset = Dataset.Tabular.from_json_lines_files(

articles/search/search-file-storage-integration.md

@@ -15,9 +15,9 @@ ms.date: 01/19/2022
 > [!IMPORTANT] 
 > Azure Files indexer is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). Use a [preview REST API (2020-06-30-preview or later)](search-api-preview.md) to create the indexer data source.
 
-Configure a [search indexer](search-indexer-overview.md) to extract content from Azure File Storage and make it searchable in Azure Cognitive Search. 
+In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure File Storage and makes it searchable in Azure Cognitive Search. Inputs to the indexer are your files in a single share. Output is a search index with searchable content and metadata stored in individual fields.
 
-This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information specific to indexing files in Azure Storage. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
+This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing files in Azure Storage. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
 ## Prerequisites
 
@@ -29,6 +29,8 @@ This article supplements [**Create an indexer**](search-howto-create-indexers.md
 
 + Read permissions on Azure Storage. A "full access" connection string includes a key that grants access to the content, but if you're using Azure roles instead, make sure the [search service managed identity](search-howto-managed-identities-data-sources.md) has **Data and Reader** permissions.
 
++ A REST client, such as [Postman](search-get-started-rest.md) or [Visual Studio Code with the extension for Azure Cognitive Search](search-get-started-vs-code.md) to send REST calls that create the data source, index, and indexer.
+
 ## Supported document formats
 
 The Azure Files indexer can extract text from the following document formats:

articles/search/search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md

@@ -13,9 +13,9 @@ ms.date: 02/28/2022
 
 # Index data from Azure SQL
 
-In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure SQL and makes it searchable in Azure Cognitive Search. The workflow creates a search index and loads it with text extracted from Azure SQL Database and Azure SQL managed instances.
+In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure SQL and makes it searchable in Azure Cognitive Search. 
 
-This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information about settings that are specific to Azure SQL. You can create indexers using the [Azure portal](https://portal.azure.com), [Search REST APIs](/rest/api/searchservice/Indexer-operations) or an Azure SDK. This article uses REST to explain each step.
+This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to Azure SQL. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
 ## Prerequisites
 

articles/search/search-howto-connecting-azure-sql-iaas-to-azure-search-using-indexers.md

@@ -14,7 +14,7 @@ ms.date: 03/19/2021
 
 # Indexer connections to SQL Server on an Azure virtual machine
 
-When configuring an [Azure SQL indexer](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md#faq) to extract content from a database on an Azure virtual machine, additional steps are required for secure connections. 
+When configuring an [Azure SQL indexer](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md) to extract content from a database on an Azure virtual machine, additional steps are required for secure connections. 
 
 A connection from Azure Cognitive Search to SQL Server on a virtual machine is a public internet connection. In order for secure connections to succeed, complete the following steps:
 

articles/search/search-howto-connecting-azure-sql-mi-to-azure-search-using-indexers.md

@@ -13,7 +13,7 @@ ms.date: 06/26/2021
 
 # Indexer connections to Azure SQL Managed Instance through a public endpoint
 
-If you are setting up an Azure Cognitive Search indexer that connects to an Azure SQL managed instance, you will need to enable a public endpoint on the managed instance as a prerequisite. An indexer connects to a managed instance over a public endpoint.
+If you are setting up an [Azure SQL indexer](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md) that connects to an Azure SQL managed instance, you'll need to enable a public endpoint on the managed instance as a prerequisite. By default, an indexer connects to a managed instance over a public endpoint. You can also use a [private endpoint](search-indexer-howto-access-private.md).
 
 This article provides basic steps that include collecting information necessary for data source configuration. For more information and methodologies, see [Configure public endpoint in Azure SQL Managed Instance](../azure-sql/managed-instance/public-endpoint-configure.md).
 

articles/search/search-howto-index-azure-data-lake-storage.md

@@ -14,24 +14,24 @@ ms.date: 02/11/2022
 
 # Index data from Azure Data Lake Storage Gen2
 
-Configure a [search indexer](search-indexer-overview.md) to extract content and metadata from Azure Data Lake Storage (ADLS) Gen2 and make it searchable in Azure Cognitive Search. 
+In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Data Lake Storage (ADLS) Gen2 and makes it searchable in Azure Cognitive Search. Inputs to the indexer are your blobs, in a single container. Output is a search index with searchable content and metadata stored in individual fields.
 
-ADLS Gen2 is available through Azure Storage. When setting up a storage account, you have the option of enabling [hierarchical namespace](../storage/blobs/data-lake-storage-namespace.md), organizing files into a hierarchy of directories and nested subdirectories. By enabling a hierarchical namespace, you enable ADLS Gen2.
-
-This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information specific to indexing from ADLS Gen2. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
+This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing from ADLS Gen2. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
 For a code sample in C#, see [Index Data Lake Gen2 using Azure AD](https://github.com/Azure-Samples/azure-search-dotnet-samples/blob/master/data-lake-gen2-acl-indexing/README.md) on GitHub.
 
 ## Prerequisites
 
-+ [ADLS Gen2](../storage/blobs/data-lake-storage-introduction.md) with [hierarchical namespace](../storage/blobs/data-lake-storage-namespace.md) enabled.
++ [ADLS Gen2](../storage/blobs/data-lake-storage-introduction.md) with [hierarchical namespace](../storage/blobs/data-lake-storage-namespace.md) enabled. ADLS Gen2 is available through Azure Storage. When setting up a storage account, you have the option of enabling [hierarchical namespace](../storage/blobs/data-lake-storage-namespace.md), organizing files into a hierarchy of directories and nested subdirectories. By enabling a hierarchical namespace, you enable ADLS Gen2.
 
 + [Access tiers](../storage/blobs/access-tiers-overview.md) for ADLS Gen2 include hot, cool, and archive. Only hot and cool can be accessed by search indexers.
 
 + Blobs containing text. If you have binary data, you can include [AI enrichment](cognitive-search-concept-intro.md) for image analysis. Blob content cannot exceed the [indexer limits](search-limits-quotas-capacity.md#indexer-limits) for your search service tier.
 
 + Read permissions on Azure Storage. A "full access" connection string includes a key that grants access to the content, but if you're using Azure roles instead, make sure the [search service managed identity](search-howto-managed-identities-data-sources.md) has **Storage Blob Data Reader** permissions.
 
++ A REST client, such as [Postman](search-get-started-rest.md) or [Visual Studio Code with the extension for Azure Cognitive Search](search-get-started-vs-code.md) to send REST calls that create the data source, index, and indexer.
+
 ## Access control
 
 ADLS Gen2 implements an [access control model](../storage/blobs/data-lake-storage-access-control.md) that supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs). 

articles/search/search-howto-index-cosmosdb-gremlin.md

@@ -17,13 +17,11 @@ ms.date: 02/15/2022
 > [!IMPORTANT]
 > The Gremlin API indexer is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). Currently, there is no SDK support.
 
-This article shows you how to configure an Azure Cosmos DB indexer to extract content and make it searchable in Azure Cognitive Search. This workflow creates a search index on Azure Cognitive Search and loads it with existing content extracted from Azure Cosmos DB using the [Gremlin API](../cosmos-db/choose-api.md#gremlin-api).
+In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Cosmos DB and makes it searchable in Azure Cognitive Search. 
 
-Because terminology can be confusing, it's worth noting that [Azure Cosmos DB indexing](../cosmos-db/index-overview.md) and [Azure Cognitive Search indexing](search-what-is-an-index.md) are different operations. Indexing in Cognitive Search creates and loads a search index on your search service.
+This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to Cosmos DB [Gremlin API](../cosmos-db/choose-api.md#gremlin-api). It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
-By default the Azure Cognitive Search Cosmos DB Gremlin API indexer will make every vertex in your graph a document in the index. Edges will be ignored. Alternatively, you could set the query to only index the edges.
-
-Although Cosmos DB indexing is easiest with the [Import data wizard](search-import-data-portal.md), this article uses the REST APIs to explain concepts and steps. 
+Because terminology can be confusing, it's worth noting that [Cosmos DB indexing](../cosmos-db/index-overview.md) and [Cognitive Search indexing](search-what-is-an-index.md) are different operations. Indexing in Cognitive Search creates and loads a search index on your search service.
 
 ## Prerequisites
 
@@ -35,7 +33,7 @@ Although Cosmos DB indexing is easiest with the [Import data wizard](search-impo
 
 + Read permissions. A "full access" connection string includes a key that grants access to the content, but if you're using Azure roles, make sure the [search service managed identity](search-howto-managed-identities-data-sources.md) has **Cosmos DB Account Reader Role** permissions.
 
-Unfamiliar with indexers? See [**Create an indexer**](search-howto-create-indexers.md) before you get started.
++ A REST client, such as [Postman](search-get-started-rest.md) or [Visual Studio Code with the extension for Azure Cognitive Search](search-get-started-vs-code.md) to send REST calls that create the data source, index, and indexer. 
 
 ## Define the data source
 
@@ -74,7 +72,9 @@ For this call, specify a [preview REST API version](search-api-preview.md) (2020
 
 1. Set "credentials" to a connection string.  The next section describes the supported formats.
 
-1. Set "container" to the collection. The "name" property is required and it specifies the ID of the graph. The "query" property is optional. The query default is `g.V()`. To index the edges, set the query to `g.E()`.
+1. Set "container" to the collection. The "name" property is required and it specifies the ID of the graph. 
+
+   The "query" property is optional. By default the Azure Cognitive Search Cosmos DB Gremlin API indexer will make every vertex in your graph a document in the index. Edges will be ignored. The query default is `g.V()`. Alternatively, you could set the query to only index the edges. To index the edges, set the query to `g.E()`.
 
 1. [Set "dataChangeDetectionPolicy"](#DataChangeDetectionPolicy) if data is volatile and you want the indexer to pick up just the new and updated items on subsequent runs. Incremental progress will be enabled by default using `_ts` as the high water mark column.