Merge pull request #443 from MicrosoftDocs/main

9/23/2024 AM Publish

articles/machine-learning/how-to-customize-compute-instance.md

@@ -9,17 +9,19 @@ ms.topic: how-to
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: vijetaj
-ms.date: 03/13/2024
+ms.date: 09/23/2024
 ms.custom: update-code
 ---
 
 # Customize the compute instance with a script
 
-Use a setup script for an automated way to customize and configure a compute instance at provisioning time. 
+Use a setup script for an automated way to customize and configure a compute instance at provisioning time.
 
 Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#training-compute-targets) or for an [inference target](concept-compute-target.md#compute-targets-for-inference).  A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
-As an administrator, you can write a customization script to be used to provision all compute instances in the workspace according to your requirements. You can configure your setup script as a Creation script, which will run once when the compute instance is created. Or you can configure it as a Startup script, which will run every time the compute instance is started (including initial creation).
+As an administrator, you can write a customization script to be used to provision all compute instances in the workspace according to your requirements. You can configure your setup script as a:
+* Creation script - runs once when the compute instance is created. 
+* Startup script- runs every time the compute instance is started (including initial creation).
 
 Some examples of what you can do in a setup script:
 
@@ -97,10 +99,12 @@ In a Resource Manager [template](https://github.com/Azure/azure-quickstart-templ
 }
 ```
 
-`scriptData` above specifies the location of the creation script in the notebooks file share such as `Users/admin/testscript.sh`.
-`scriptArguments` is optional above and specifies the arguments for the creation script.
+`scriptData` in this code specifies the location of the creation script in the notebooks file share such as `Users/admin/testscript.sh`.
+`scriptArguments` is optional and specifies the arguments for the creation script.
 
-You could instead provide the script inline for a Resource Manager template. The shell command can refer to any dependencies uploaded into the notebooks file share. When you use an inline string, the working directory for the script is `/mnt/batch/tasks/shared/LS_root/mounts/clusters/**\<ciname\>**/code/Users`.
+You could instead provide the script inline for a Resource Manager template. The shell command can refer to any dependencies uploaded into the notebooks file share. When you use an inline string, the working directory for the script is `/mnt/batch/tasks/shared/LS_root/mounts/clusters/<ciname>/code/Users`, where .`<ciname>` is the name of the compute instance.
+
+```json
 
 For example, specify a base64 encoded command string for `scriptData`:
 

articles/machine-learning/quickstart-create-resources.md

@@ -79,7 +79,7 @@ You'll use the *compute instance* to run Jupyter notebooks and Python scripts in
     :::image type="content" source="media/quickstart-create-resources/create-compute.png" alt-text="Screenshot shows create compute in the New list.":::
 
 1. Supply a name.
-1. Keep the default values for the rest of the page.
+1. Keep the default values for the rest of the page, unless your organization policy requires you to change other settings.
 1. Select **Review + Create**.
 1. Select **Create**.
 

articles/search/search-sku-tier.md

@@ -33,7 +33,7 @@ Billing rates are shown in the portal's **Select Pricing Tier** page. You can ch
 
 Tiers include **Free**, **Basic**, **Standard**, and **Storage Optimized**. Standard and Storage Optimized are available with several configurations and capacities. The following screenshot from Azure portal shows the available tiers, minus pricing (which you can find in the portal and on the [pricing page](https://azure.microsoft.com/pricing/details/search/)). 
 
-:::image type="content" source="media/search-sku-tier/tiers.png" alt-text="Pricing tier chart" border="true":::
+:::image type="content" source="media/search-sku-tier/tiers.png" lightbox="media/search-sku-tier/tiers.png" alt-text="Pricing tier chart" border="true":::
 
 **Free** creates a [limited search service](search-limits-quotas-capacity.md#subscription-limits) for smaller projects, like running tutorials and code samples. Internally, system resources are shared among multiple subscribers. You can't scale a free service, run significant workloads, and some premium features aren't available. You can only have one free search service per Azure subscription.
 

articles/search/tutorial-rag-build-solution-pipeline.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: tutorial
-ms.date: 09/12/2024
+ms.date: 09/23/2024
 
 ---
 
@@ -99,7 +99,7 @@ print(f"{result.name} created")
 
 In this step, set up the sample data and a connection to Azure Blob Storage. The indexer retrieves PDFs from a container. You create the container and upload files in this step.
 
-The original ebook is large, over 100 pages and 35 MB in size. We broke it up into smaller PDFs, one per page of text, to stay under the REST API payload limit of 16 MB per API call. For simplicity, we omit image vectorization for this exercise.
+The original ebook is large, over 100 pages and 35 MB in size. We broke it up into smaller PDFs, one per page of text, to stay under the [API payload limit](search-limits-quotas-capacity.md#api-request-limits) of 16 MB per API call and also the [AI enrichment data limits](search-limits-quotas-capacity.md#data-limits-ai-enrichment). For simplicity, we omit image vectorization for this exercise.
 
 1. Sign in to the Azure portal and find your Azure Storage account.
 

articles/search/vector-search-index-size.md

@@ -52,11 +52,11 @@ Newer services created after April 3, 2024 offer five to ten times more vector s
 
 1. Select the deployment. If you have more than one, click through to see if it resolves to your search service.
 
-    :::image type="content" source="media/vector-search-index-size/resource-group-deployments.png" alt-text="Screenshot of a filtered deployments list.":::
+    :::image type="content" source="media/vector-search-index-size/resource-group-deployments.png" lightbox="media/vector-search-index-size/resource-group-deployments.png" alt-text="Screenshot of a filtered deployments list.":::
 
 1. Expand deployment details. You should see *Created* and the creation date.
 
-   :::image type="content" source="media/vector-search-index-size/deployment-details.png" alt-text="Screenshot of the deployment details showing creation date.":::
+   :::image type="content" source="media/vector-search-index-size/deployment-details.png" lightbox="media/vector-search-index-size/deployment-details.png" alt-text="Screenshot of the deployment details showing creation date.":::
 
 1. Now that you know the age of your search service, review the vector quota limits based on service creation: [Vector index size limits](search-limits-quotas-capacity.md#vector-index-size-limits).