UpdatingCLIAzureMLNIMDeployment_for_demo

cloud-service-providers/azure/azureml/cli/1_set_credentials.sh

@@ -2,18 +2,46 @@
 set -x
 source config.sh
 
+CREATE_RESOURCE_GROUP=false
+CREATE_CONTAINER_REGISTRY=false
 CREATE_WORKSPACE=false
 
 for i in "$@"; do
   case $i in
-    --create_new_workspace) CREATE_WORKSPACE=true ;;
-    -*|--*) echo "Unknown option $i"; exit 1 ;;
+    --create_new_resource_group)
+      CREATE_RESOURCE_GROUP=true
+      shift # past argument with no value
+      ;;
+    --create_new_container_registry)
+      CREATE_CONTAINER_REGISTRY=true
+      shift # past argument with no value
+      ;;
+    --create_new_workspace)
+      CREATE_WORKSPACE=true
+      shift # past argument with no value
+      ;;
+    -*|--*)
+      echo "Unknown option $i"
+      exit 1
+      ;;
+    *)
+      ;;
   esac
 done
 
+# Create new resource group
+if $CREATE_RESOURCE_GROUP; then
+    az group create --name $resource_group --location $location
+fi
+
+# Create new container registry
+if $CREATE_CONTAINER_REGISTRY; then
+    az acr create --resource-group $resource_group --name $acr_registry_name --sku Basic
+fi
+
 # Create new workspace
 if $CREATE_WORKSPACE; then
-    az ml workspace create --name $workspace --resource-group $resource_group --location $location
+    az ml workspace create --name $workspace --resource-group $resource_group --location $location --container-registry /subscriptions/${subscription_id}/resourceGroups/${resource_group}/providers/Microsoft.ContainerRegistry/registries/${acr_registry_name}
 fi
 
 # Assign role permission to read secrets from workspace connections

cloud-service-providers/azure/azureml/cli/4_create_endpoint.sh

@@ -5,7 +5,7 @@ source config.sh
 # Create new endpoint in this workspace
 cp azureml_files/endpoint.yml actual_endpoint_aml.yml
 # sed -i "s/endpoint_name_placeholder/${endpoint_name}/g" actual_endpoint_aml.yml
-sed -i '' "s|endpoint_name_placeholder|$endpoint_name|g" actual_endpoint_aml.yml
+sed -i "s|endpoint_name_placeholder|$endpoint_name|g" actual_endpoint_aml.yml
 echo "Creating Online Endpoint ${endpoint_name}"
 az ml online-endpoint create -f actual_endpoint_aml.yml --resource-group $resource_group --workspace-name $workspace
 rm actual_endpoint_aml.yml

cloud-service-providers/azure/azureml/cli/5_create_deployment.sh

@@ -10,12 +10,12 @@ cp azureml_files/deployment.yml actual_deployment_aml.yml
 connection_path="\${{azureml://connections/ngc/credentials/NGC_API_KEY}}"
 
 # Replace placeholders in the actual_deployment_aml.yml file
-sed -i '' "s|ngc_api_key_placeholder|${connection_path}|g" actual_deployment_aml.yml
-sed -i '' "s|endpoint_name_placeholder|$endpoint_name|g" actual_deployment_aml.yml
-sed -i '' "s|deployment_name_placeholder|$deployment_name|g" actual_deployment_aml.yml
-sed -i '' "s|acr_registry_placeholder|$acr_registry_name|g" actual_deployment_aml.yml
-sed -i '' "s|image_name_placeholder|$image_name|g" actual_deployment_aml.yml
-sed -i '' "s|instance_type_placeholder|$instance_type|g" actual_deployment_aml.yml
+sed -i "s|ngc_api_key_placeholder|${connection_path}|g" actual_deployment_aml.yml
+sed -i "s|endpoint_name_placeholder|$endpoint_name|g" actual_deployment_aml.yml
+sed -i "s|deployment_name_placeholder|$deployment_name|g" actual_deployment_aml.yml
+sed -i "s|acr_registry_placeholder|$acr_registry_name|g" actual_deployment_aml.yml
+sed -i "s|image_name_placeholder|$image_name|g" actual_deployment_aml.yml
+sed -i "s|instance_type_placeholder|$instance_type|g" actual_deployment_aml.yml
 
 # Display the modified file
 cat actual_deployment_aml.yml

cloud-service-providers/azure/azureml/cli/README.md

@@ -27,14 +27,16 @@ az account set -s ${subscription_id}
 
 ## Setup AzureML Workspace
 
-Create a new AzureML workspace with the "Azure ML Secrets Reader" role assignment. All necessary commands are provided in the `1_setup_credentials.sh` script file.
+The deployment procedure requires an Azure AI Workspace with an associated Azure AI private container registry. The workspace will also require to assign the "Azure ML Secrets Reader" role assignment to the user. The user could use any pre-existent workspace with those characteristics, populating accordingly the names of the resource group, container registry name and workspace name in the config.sh file. 
+
+The `1_setup_credentials.sh` script file, provides the role assigment, optionally it can create the necessary resource group, container registry and workspace (with the required association to the container registry, all using the names provided in the config.sh file) by the use of flags `--create_new_resource_group`, `--create_new_container_registry` and `--create_new_workspace` respectively. 
+
+Create a new AzureML resource group, container registry and workspace (if needed) with the "Azure ML Secrets Reader" role assignment. All necessary commands are provided in the `1_setup_credentials.sh` script file.
 
 ```bash
-./1_setup_credentials.sh --create_new_workspace
+./1_setup_credentials.sh --create_new_resource --create_new_container_registry --create_new_workspace
 ```
 
-The above command creates a new workspace with the workspace name provided in the `config.sh` file. If the workspace name already exists, omit the `--create_new_workspace` flag to skip the creation process and update the workspace with the necessary role assignments.
-
 ## Store NGC API Key for Use in the AzureML Deployment
 
 The NGC API Key needs to be stored within Azure so the AzureML workspace can access it during deployment. The API key is required to pull the correct model from the NGC model catalog. The key can be stored in Azure Key Vault or provided as a workspace connection to the AzureML workspace.
@@ -87,7 +89,7 @@ Required commands are provided in the `5_create_deployment.sh` script.
 ./5_create_deployment.sh
 ```
 
-## Verify Your Connection
+## Querying the model
 
 Verify your deployment using the `test_chat_completions.sh` script. Modify the URL to your endpoint URL and add the following headers:
 `-H 'Authorization: Bearer <your-azureml-endpoint-token>' -H 'azureml-model-deployment: <your-azureml-model-deployment-name>'`
@@ -96,9 +98,10 @@ For example:
 
 ```bash
 curl -X 'POST' \
-  'https://llama3-8b-nim-endpoint-aml-1.westeurope.inference.ml.azure.com/v1/chat/completions' \
+  'https://llama3-8b-nim-endpoint-aml-1.southcentralus.inference.ml.azure.com/v1/chat/completions' \
   -H 'accept: application/json' \
-  -H 'Authorization: Bearer xxxxxxxxxxxxxxxxxxxxxx' \
+  -H 'Authorization: Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX' \
+  -H 'azureml-model-deployment: llama3-8b-nim-deployment-aml-1' \
   -H 'Content-Type: application/json' \
   -d '{
   "messages": [
@@ -112,11 +115,11 @@ curl -X 'POST' \
     }
   ],
   "model": "meta/llama3-8b-instruct",
-  "max_tokens": 16,
+  "max_tokens": 400,
   "top_p": 1,
   "n": 1,
   "stream": false,
-  "stop": "\n",
   "frequency_penalty": 0.0
 }'
-```
+```
+

cloud-service-providers/azure/azureml/cli/azureml_files/deployment.yml

@@ -3,7 +3,7 @@ name: deployment_name_placeholder
 endpoint_name: endpoint_name_placeholder
 environment: 
   name: image_name_placeholder-env
-  image: acr_registry_placeholder.azurecr.io/image_name_placeholder:v1
+  image: acr_registry_placeholder.azurecr.io/image_name_placeholder:latest
   inference_config:
       liveness_route:
           path: /v1/health/ready