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

Author: rolyon

.openpublishing.redirection.json

@@ -9,7 +9,7 @@ ms.service: active-directory
 ms.subservice: domain-services
 ms.workload: identity
 ms.topic: conceptual
-ms.date: 06/20/2022
+ms.date: 09/21/2022
 ms.author: justinha
 
 ---
@@ -108,14 +108,21 @@ The following sections cover network security groups and Inbound and Outbound po
 
 ### Inbound connectivity
 
-The following network security group Inbound rules are required for the managed domain to provide authentication and management services. Don't edit or delete these network security group rules for the virtual network subnet your managed domain is deployed into.
+The following network security group Inbound rules are required for the managed domain to provide authentication and management services. Don't edit or delete these network security group rules for the virtual network subnet for your managed domain.
 
 | Inbound port number | Protocol | Source                             | Destination | Action | Required | Purpose |
 |:-----------:|:--------:|:----------------------------------:|:-----------:|:------:|:--------:|:--------|
 | 5986        | TCP      | AzureActiveDirectoryDomainServices | Any         | Allow  | Yes      | Management of your domain. |
 | 3389        | TCP      | CorpNetSaw                         | Any         | Allow  | Optional      | Debugging for support. |
 
-An Azure standard load balancer is created that requires these rules to be place. This network security group secures Azure AD DS and is required for the managed domain to work correctly. Don't delete this network security group. The load balancer won't work correctly without it.
+Azure AD DS also relies on the Default Security rules AllowVnetInBound and AllowAzureLoadBalancerInBound.
+
+:::image type="content" border="true" source="./media/network-considerations/nsg.png" alt-text="Screenshot of network security group rules.":::
+
+The AllowVnetInBound rule allows all traffic within the VNet which allows the DCs to properly communicate and replicate as well as allow domain join and other domain services to domain members. For more information about required ports for Windows, see [Service overview and network port requirements for Windows](/troubleshoot/windows-server/networking/service-overview-and-network-port-requirements).
+
+
+The AllowAzureLoadBalancerInBound rule is also required so that the service can properly communicate over the loadbalancer to manage the DCs. This network security group secures Azure AD DS and is required for the managed domain to work correctly. Don't delete this network security group. The load balancer won't work correctly without it. 
 
 If needed, you can [create the required network security group and rules using Azure PowerShell](powershell-create-instance.md#create-a-network-security-group).
 

articles/active-directory-domain-services/media/network-considerations/nsg.png

@@ -242,6 +242,9 @@ image_object_detection_job = automl.image_object_detection(
 
 Provide a [compute target](concept-azure-machine-learning-architecture.md#compute-targets) for automated ML to conduct model training. Automated ML models for computer vision tasks require GPU SKUs and support NC and ND families. We recommend the NCsv3-series (with v100 GPUs) for faster training. A compute target with a multi-GPU VM SKU leverages multiple GPUs to also speed up training. Additionally, when you set up a compute target with multiple nodes you can conduct faster model training through parallelism when tuning hyperparameters for your model.
 
+> [!NOTE]
+> If you are using a [compute instance](concept-compute-instance.md) as your compute target, please make sure that multiple AutoML jobs are not run at the same time. Also, please make sure that `max_concurrent_trials` is set to 1 in your [job limits](#job-limits).
+
 The compute target is passed in using the `compute` parameter. For example:
 
 # [Azure CLI](#tab/cli)
@@ -423,7 +426,7 @@ When sweeping hyperparameters, you need to specify the sampling method to use fo
 |[Bayesian Sampling](how-to-tune-hyperparameters.md#bayesian-sampling)| `bayesian` |
 
 > [!NOTE]
-> Currently only random sampling supports conditional hyperparameter spaces.
+> Currently only random and grid sampling support conditional hyperparameter spaces.
 
 ### Early termination policies
 

articles/active-directory-domain-services/network-considerations.md

@@ -187,6 +187,9 @@ automl_image_config = AutoMLImageConfig(training_data=training_dataset)
 
 Provide a [compute target](../v1/concept-azure-machine-learning-architecture.md#compute-targets) for automated ML to conduct model training. Automated ML models for computer vision tasks require GPU SKUs and support NC and ND families. We recommend the NCsv3-series (with v100 GPUs) for faster training. A compute target with a multi-GPU VM SKU leverages multiple GPUs to also speed up training. Additionally, when you set up a compute target with multiple nodes you can conduct faster model training through parallelism when tuning hyperparameters for your model.
 
+> [!NOTE]
+> If you are using a [compute instance](../concept-compute-instance.md) as your compute target, please make sure that multiple AutoML jobs are not run at the same time. Also, please make sure that `max_concurrent_iterations` is set to 1 in your [experiment resources](#resources-for-the-sweep).
+
 The compute target is a required parameter and is passed in using the `compute_target` parameter of the `AutoMLImageConfig`. For example:
 
 ```python
@@ -280,7 +283,7 @@ When sweeping hyperparameters, you need to specify the sampling method to use fo
 * [Bayesian sampling](../how-to-tune-hyperparameters.md#bayesian-sampling) 
 
 > [!NOTE]
-> Currently only random sampling supports conditional hyperparameter spaces.
+> Currently only random and grid sampling support conditional hyperparameter spaces.
 
 ### Early termination policies
 

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

@@ -235,9 +235,17 @@ step1_output_ds = step1_output_data.register_on_complete(name='processed_data',
 
 Azure does not automatically delete intermediate data written with `OutputFileDatasetConfig`. To avoid storage charges for large amounts of unneeded data, you should either:
 
-* Programmatically delete intermediate data at the end of a pipeline job, when it is no longer needed
-* Use blob storage with a short-term storage policy for intermediate data (see [Optimize costs by automating Azure Blob Storage access tiers](/azure/storage/blobs/lifecycle-management-overview)) 
-* Regularly review and delete no-longer-needed data
+> [!CAUTION]
+> Only delete intermediate data after 30 days from the last change date of the data. Deleting the data earlier could cause the pipeline run to fail because the pipeline will assume the intermediate data exists within 30 day period for reuse.
+
+* Programmatically delete intermediate data at the end of a pipeline job, when it is no longer needed. 
+* Use blob storage with a short-term storage policy for intermediate data (see [Optimize costs by automating Azure Blob Storage access tiers](/azure/storage/blobs/lifecycle-management-overview)). This policy can only be set to a workspace's non-default datastore. Use `OutputFileDatasetConfig` to export intermediate data to another datastore that isn't the default.
+  ```Python
+  # Get adls gen 2 datastore already registered with the workspace
+  datastore = workspace.datastores['my_adlsgen2']
+  step1_output_data = OutputFileDatasetConfig(name="processed_data", destination=(datastore, "mypath/{run-id}/{output-name}")).as_upload()
+  ```
+* Regularly review and delete no-longer-needed data.
 
 For more information, see [Plan and manage costs for Azure Machine Learning](../concept-plan-manage-cost.md).
 

articles/machine-learning/v1/how-to-auto-train-image-models-v1.md

@@ -6,7 +6,7 @@ ms.subservice: partnercenter-marketplace-publisher
 ms.topic: conceptual
 ms.author: mingshen
 author: mingshen-ms
-ms.date: 09/22/2021
+ms.date: 09/23/2022
 ---
 
 # Commercial marketplace submission API overview
@@ -17,22 +17,23 @@ Use API to programmatically query, create submissions for, and publish offers. A
 
 There are two sets of submission API available:
 
-- **Partner Center submission API** – The common set of APIs that work across consumer and commercial products to publish through Partner Center. New capabilities are continuously added to this set of APIs. For more information on how to integrate with this API, see [Partner Center submission API onboarding](submission-api-onboard.md). 
+- **Partner Center submission API** – The common set of APIs that work across consumer and commercial products to publish through Partner Center. New capabilities are continuously added to this set of APIs. For more information on how to integrate with this API, see [Partner Center submission API onboarding](submission-api-onboard.md).
+- **Product Ingestion API** – The new set of modern APIs to create and manage commercial offers through Partner Center. New capabilities are continuously added to this set of APIs. The APIs are in preview state and will soon be launched for all offer types and will eventually replace the Partner Center submission and Legacy Cloud Partner Portal APIs. For more information on how to integrate with the modern Product Ingestion API, see [Product Ingestion API for the commercial marketplace](product-ingestion-api.md).  
 - **Legacy Cloud Partner Portal API** – The APIs carried over from the deprecated Cloud Partner Portal; it is integrated with and continues to work in Partner Center. This set of APIs is in maintenance mode only; new capabilities introduced in Partner Center may not be supported, and it should only be used for existing products that were already integrated before transition to Partner Center. For more information on how to continue to use the Cloud Partner Portal APIs, see [Cloud Partner Portal API Reference](cloud-partner-portal-api-overview.md).
 
 Refer to the following table for supported submission APIs for each offer type.
 
-| Offer type | Legacy Cloud Partner Portal API Support | Partner Center submission API support |
-| --- | :---: | :---: |
-| Azure Application |  | ✔ |
-| Azure Container | ✔ |  |
-| Azure Virtual Machine | ✔ |  |
-| Consulting Service | ✔ |  |
-| Dynamics 365 |  | ✔ |
-| IoT Edge Module | ✔ |  |
-| Managed Service | ✔ |  |
-| Power BI App | ✔ |  |
-| Software as a Service |  | ✔ |
+| Offer type | Legacy Cloud Partner Portal API Support | Partner Center submission API support | Product Ingestion API support |
+| --- | :---: | :---: |--------------------|
+| Azure Application |  | ✔ | |
+| Azure Container | ✔ |  | |
+| Azure Virtual Machine | ✔ |  | ✔|
+| Consulting Service | ✔ |  | |
+| Dynamics 365 |  | ✔ | |
+| IoT Edge Module | ✔ |  | |
+| Managed Service | ✔ |  | |
+| Power BI App | ✔ |  | |
+| Software as a Service |  | ✔ | |
 
 Microsoft 365 Office add-ins, Microsoft 365 SharePoint solutions, Microsoft 365 Teams apps, and Power BI Visuals don’t have submission API support.
 

articles/machine-learning/v1/how-to-move-data-in-out-of-pipelines.md

@@ -104,6 +104,7 @@ A dedicated SQL pool consumes billable resources as long as it's active. You can
     SELECT PassengerCount,
           SUM(TripDistanceMiles) as SumTripDistance,
           AVG(TripDistanceMiles) as AvgTripDistance
+    INTO dbo.PassengerCountStats
     FROM  dbo.NYCTaxiTripSmall
     WHERE TripDistanceMiles > 0 AND PassengerCount > 0
     GROUP BY PassengerCount