update

Author: mrbullwinkle

articles/ai-services/openai/how-to/role-based-access-control.md

@@ -99,7 +99,7 @@ This role is typically granted access at the resource group level for a user in
 ✅ Create new model deployments or edit existing model deployments (via API) <br>
 ✅ Create custom fine-tuned models **[Added Fall 2023]**<br>
 ✅ Upload datasets for fine-tuning **[Added Fall 2023]**<br>
-✅ Create new model deployments or edit existing model deployments (via Azure AI Foundry) **[Added Fall 2023]**
+✅ Create new model deployments or edit existing model deployments (via Azure AI Foundry) **[Added Fall 2023]**<br>
 ✅ View, query, filter Stored completions data <br>
 
 

articles/ai-services/openai/includes/fine-tuning-python.md

@@ -503,6 +503,55 @@ print(r.json())
 
 To deploy between the same subscription, but different regions you would just have subscription and resource groups be identical for both source and destination variables and only the source and destination resource names would need to be unique.
 
+### Cross tenant deployment
+
+The accounts used to generate access tokens with `az account get-access-token --tenant` should have Cognitive Services OpenAI Contributor permissions to the associated Azure OpenAI resources.
+
+```python
+import requests
+
+subscription = "DESTINATION-SUBSCRIPTION-ID"
+resource_group = "DESTINATION-RESOURCE-GROUP"
+resource_name = "DESTINATION-AZURE-OPENAI-RESOURCE-NAME"
+model_deployment_name = "DESTINATION-MODEL-DEPLOYMENT-NAME"
+fine_tuned_model = "gpt-4o-mini-2024-07-18.ft-f8838e7c6d4a4cbe882a002815758510" #source fine-tuned model id example id provided
+source_subscription_id = "SOURCE-SUBSCRIPTION-ID"
+source_resource_group = "SOURCE-RESOURCE-GROUP" 
+source_account = "SOURCE-AZURE-OPENAI-RESOURCE-NAME"
+
+dest_token = "DESTINATION-ACCESS-TOKEN" # az account get-access-token --tenant DESTINATION-TENANT-ID
+source_token = "SOURCE-ACCESS-TOKEN"  # az account get-access-token --tenant SOURCE-TENANT-ID
+
+headers = {
+    "Authorization": f"Bearer {dest_token}", 
+    "x-ms-authorization-auxiliary": f"Bearer {source_token}", 
+    "Content-Type": "application/json"
+}
+
+url = f"https://management.azure.com/subscriptions/{subscription}/resourceGroups/{resource_group}/providers/Microsoft.CognitiveServices/accounts/{resource_name}/deployments/{model_deployment_name}?api-version=2024-10-01"
+
+payload = {
+    "sku": {
+        "name": "standard",
+        "capacity": 1
+    },
+    "properties": {
+        "model": {
+            "format": "OpenAI",
+            "name": fine_tuned_model,
+            "version": "1",
+            "sourceAccount": f"/subscriptions/{source_subscription_id}/resourceGroups/{source_resource_group}/providers/Microsoft.CognitiveServices/accounts/{source_account}"
+        }
+    }
+}
+
+response = requests.put(url, headers=headers, json=payload)
+
+# Check response
+print(f"Status Code: {response.status_code}")
+print(f"Response: {response.json()}")
+```
+
 ### Deploy a model with Azure CLI
 
 The following example shows how to use the Azure CLI to deploy your customized model. With the Azure CLI, you must specify a name for the deployment of your customized model. For more information about how to use the Azure CLI to deploy customized models, see [`az cognitiveservices account deployment`](/cli/azure/cognitiveservices/account/deployment).