Merge pull request #263316 from MicrosoftDocs/repo_sync_working_branch

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

Author: Taojunshen

articles/ai-services/computer-vision/how-to/shelf-analyze.md

@@ -36,7 +36,7 @@ To analyze a shelf image, do the following steps:
 1. Copy the following `curl` command into a text editor.
 
     ```bash
-    curl.exe -H "Ocp-Apim-Subscription-Key: <subscriptionKey>" -H "Content-Type: application/json" "https://<endpoint>/computervision/productrecognition/ms-pretrained-product-detection/runs/<your_run_name>?api-version=2023-04-01-preview" -d "{
+    curl -X PUT -H "Ocp-Apim-Subscription-Key: <subscriptionKey>" -H "Content-Type: application/json" "https://<endpoint>/computervision/productrecognition/ms-pretrained-product-detection/runs/<your_run_name>?api-version=2023-04-01-preview" -d "{
         'url':'<your_url_string>'
     }"
     ```

articles/ai-services/openai/how-to/switching-endpoints.md

@@ -31,10 +31,11 @@ We recommend using environment variables. If you haven't done this before our [P
 <td>
 
 ```python
+import os
 from openai import OpenAI
 
 client = OpenAI(
-  api_key=os.environ["OPENAI_API_KEY"]  
+    api_key=os.getenv("OPENAI_API_KEY")
 )
 
 
@@ -51,7 +52,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
     api_key=os.getenv("AZURE_OPENAI_KEY"),  
     api_version="2023-12-01-preview",
-    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
 )
 ```
 
@@ -71,10 +72,11 @@ client = AzureOpenAI(
 <td>
 
 ```python
+import os
 from openai import OpenAI
 
 client = OpenAI(
-  api_key=os.environ["OPENAI_API_KEY"]  
+    api_key=os.getenv("OPENAI_API_KEY")
 )
 
 
@@ -93,7 +95,9 @@ client = OpenAI(
 from azure.identity import DefaultAzureCredential, get_bearer_token_provider
 from openai import AzureOpenAI
 
-token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
+token_provider = get_bearer_token_provider(
+    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+)
 
 api_version = "2023-12-01-preview"
 endpoint = "https://my-resource.openai.azure.com"
@@ -126,7 +130,7 @@ OpenAI uses the `model` keyword argument to specify what model to use. Azure Ope
 ```python
 completion = client.completions.create(
     model="gpt-3.5-turbo-instruct",
-    prompt="<prompt>")
+    prompt="<prompt>"
 )
 
 chat_completion = client.chat.completions.create(
@@ -135,8 +139,8 @@ chat_completion = client.chat.completions.create(
 )
 
 embedding = client.embeddings.create(
-    input="<input>",
-    model="text-embedding-ada-002"
+    model="text-embedding-ada-002",
+    input="<input>"
 )
 ```
 
@@ -146,17 +150,17 @@ embedding = client.embeddings.create(
 ```python
 completion = client.completions.create(
     model="gpt-35-turbo-instruct", # This must match the custom deployment name you chose for your model.
-    prompt=<"prompt">
+    prompt="<prompt>"
 )
 
 chat_completion = client.chat.completions.create(
     model="gpt-35-turbo", # model = "deployment_name".
-    messages=<"messages">
+    messages="<messages>"
 )
 
 embedding = client.embeddings.create(
-    input = "<input>",
-    model= "text-embedding-ada-002" # model = "deployment_name".
+    model="text-embedding-ada-002", # model = "deployment_name".
+    input="<input>"
 )
 ```
 
@@ -179,8 +183,8 @@ OpenAI and Azure OpenAI currently support input arrays up to 2048 input items fo
 inputs = ["A", "B", "C"] 
 
 embedding = client.embeddings.create(
-  input=inputs,
-  model="text-embedding-ada-002"
+    input=inputs,
+    model="text-embedding-ada-002"
 )
 
 
@@ -193,9 +197,9 @@ embedding = client.embeddings.create(
 inputs = ["A", "B", "C"] #max array size=2048
 
 embedding = client.embeddings.create(
-  input=inputs,
-  model="text-embedding-ada-002" # This must match the custom deployment name you chose for your model.
-  #engine="text-embedding-ada-002"
+    input=inputs,
+    model="text-embedding-ada-002" # This must match the custom deployment name you chose for your model.
+    # engine="text-embedding-ada-002"
 )
 
 ```

articles/ai-services/openai/how-to/use-blocklists.md

@@ -20,7 +20,7 @@ The configurable content filters are sufficient for most content moderation need
 
 - An Azure subscription. <a href="https://azure.microsoft.com/free/ai-services" target="_blank">Create one for free</a>.
 - Once you have your Azure subscription, create an Azure OpenAI resource in the Azure portal to get your token, key and endpoint. Enter a unique name for your resource, select the subscription you entered on the application form, select a resource group, supported region, and supported pricing tier. Then select **Create**.
-    - The resource takes a few minutes to deploy. After it finishes, sSelect **go to resource**. In the left pane, under **Resource Management**,  select **Subscription Key and Endpoint**. The endpoint and either of the keys are used to call APIs.
+    - The resource takes a few minutes to deploy. After it finishes, select **go to resource**. In the left pane, under **Resource Management**, select **Subscription Key and Endpoint**. The endpoint and either of the keys are used to call APIs.
 - [Azure CLI](/cli/azure/install-azure-cli) installed
 - [cURL](https://curl.haxx.se/) installed
 
@@ -279,4 +279,4 @@ You can also create custom blocklists in the Azure OpenAI Studio as part of your
 
 - Read more about [content filtering categories and severity levels](/azure/ai-services/openai/concepts/content-filter?tabs=python) with Azure OpenAI Service. 
 
-- Learn more about red teaming from our: [Introduction to red teaming large language models (LLMs)](/azure/ai-services/openai/concepts/red-teaming) article. 
+- Learn more about red teaming from our: [Introduction to red teaming large language models (LLMs)](/azure/ai-services/openai/concepts/red-teaming) article. 

articles/azure-portal/azure-portal-dashboards-create-programmatically.md

@@ -81,8 +81,10 @@ Declare required template metadata and the parameters at the top of the JSON tem
         }
     },
     "variables": {},
-
-    ... rest of template omitted ...
+    "resources": [
+        ... rest of template omitted ...
+    ]
+}
 ```
 
 Once you've configured your template, deploy it using any of the following methods:

articles/machine-learning/how-to-secure-training-vnet.md

@@ -285,7 +285,7 @@ serverless_compute:
 Update workspace:
 
 ```azurecli
-az ml workspace update -n <workspace-name> -g <resource-group-name> -file serverlesscomputevnetsettings.yml
+az ml workspace update -n <workspace-name> -g <resource-group-name> --file serverlesscomputevnetsettings.yml
 ```
 
 ```yaml
@@ -452,7 +452,7 @@ serverless_compute:
 Update workspace:
 
 ```azurecli
-az ml workspace update -n <workspace-name> -g <resource-group-name> -file serverlesscomputevnetsettings.yml
+az ml workspace update -n <workspace-name> -g <resource-group-name> --file serverlesscomputevnetsettings.yml
 ```
 
 ```yaml

articles/storage/blobs/immutable-storage-overview.md

@@ -146,6 +146,9 @@ When you enable blob inventory, Azure Storage generates an inventory report on a
 
 For more information about blob inventory, see [Azure Storage blob inventory](blob-inventory.md).
 
+> [!NOTE]
+> You can't configure an inventory policy in an account if support for version-level immutability is enabled on that account, or if support for version-level immutability is enabled on the destination container that is defined in the inventory policy. 
+
 ## Pricing
 
 There is no additional capacity charge for using immutable storage. Immutable data is priced in the same way as mutable data. For pricing details on Azure Blob Storage, see the [Azure Storage pricing page](https://azure.microsoft.com/pricing/details/storage/blobs/).

articles/virtual-machines/nd-h100-v5-series.md

@@ -43,7 +43,7 @@ Ubuntu 20.04: 5.4.0-1046-azure
 
 | Size                | vCPU | Memory: GiB | Temp storage (SSD) GiB | GPU                        | GPU Memory GiB | Max data disks | Max uncached disk throughput: IOPS/MBps | Max network bandwidth  | Max NICs |
 |---------------------|------|------------|------------------------|----------------------------|----------------|----------------|-----------------------------------------|------------------------------|----------|
-| Standard_ND96isr_v5 | 96   | 1900       | 1000                   | 8 H100 80 GB GPUs(NVLink)  | 80             | 32             | 40800/612                               | 80,000 Mbps                         | 8        |
+| Standard_ND96isr_H100_v5 | 96   | 1900       | 1000                   | 8 H100 80 GB GPUs(NVLink)  | 80             | 32             | 40800/612                               | 80,000 Mbps                         | 8        |
 
 [!INCLUDE [virtual-machines-common-sizes-table-defs](../../includes/virtual-machines-common-sizes-table-defs.md)]