Deploy models using Azure CLI and Bicep

• The model name, provider, version, and SKU you want to deploy. Use the Foundry portal or the Azure CLI to find this information. In this example, you deploy the following model:

  • Model name: Phi-4-mini-instruct
  • Provider: Microsoft
  • Version: 1
  • Deployment type: Global standard

Add the model

To add a model, first identify the model that you want to deploy. Query the available models as follows:

1. Sign in to your Azure subscription.

   az login
   

2. If you have more than one subscription, select the subscription where your resource is located.

   subscriptionId="<subscription-id>"
   az account set --subscription $subscriptionId
   

3. Set the following environment variables with the name of the Foundry resource you plan to use and resource group.

   accountName="<ai-services-resource-name>"
   resourceGroupName="<resource-group>"
   location="eastus2"
   

4. If you haven't created a Foundry resource yet, create one.

   az cognitiveservices account create -n $accountName -g $resourceGroupName --custom-domain $accountName --location $location --kind AIServices --sku S0
   

   Reference: az cognitiveservices account

5. Check which models are available to you and under which SKU. Each deployment type is represented by a SKU code in the CLI and ARM, and it defines how Azure infrastructure processes requests. Models might offer different deployment types. The following command lists all the model definitions available:

   az cognitiveservices account list-models \
       -n $accountName \
       -g $resourceGroupName \
   | jq '.[] | { name: .name, format: .format, version: .version, sku: .skus[0].name, capacity: .skus[0].capacity.default }'
   

   The output includes available models with their properties:

   {
     "name": "Phi-4-mini-instruct",
     "format": "Microsoft",
     "version": "1",
     "sku": "GlobalStandard",
     "capacity": 1000
   }
   

   Reference: az cognitiveservices account list-models

6. Identify the model you want to deploy. You need the properties name, format, version, and sku. The property format indicates the provider offering the model. Depending on the type of deployment, you might also need capacity.

7. Add the model deployment to the resource. The following example adds Phi-4-mini-instruct:

   az cognitiveservices account deployment create \
       -n $accountName \
       -g $resourceGroupName \
       --deployment-name Phi-4-mini-instruct \
       --model-name Phi-4-mini-instruct \
       --model-version 1 \
       --model-format Microsoft \
       --sku-capacity 1 \
       --sku-name GlobalStandard
   

   Reference: az cognitiveservices account deployment

8. Verify the deployment completed successfully:

   az cognitiveservices account deployment show \
       --deployment-name Phi-4-mini-instruct \
       -n $accountName \
       -g $resourceGroupName \
   | jq '.properties.provisioningState'
   

   The output should display "Succeeded". The model is ready to use after provisioning completes.

   Reference: az cognitiveservices account deployment show

You can deploy the same model multiple times if needed as long as you give it a different deployment name. This capability is useful if you want to test different configurations for a given model, including content filters.

Add the model

1. Use the template ai-services-deployment-template.bicep to describe model deployments:

   ai-services-deployment-template.bicep

   @description('Name of the Azure AI services account')
   param accountName string
   
   @description('Name of the model to deploy')
   param modelName string
   
   @description('Version of the model to deploy')
   param modelVersion string
   
   @allowed([
     'AI21 Labs'
     'Cohere'
     'Core42'
     'DeepSeek'
     'xAI'
     'Meta'
     'Microsoft'
     'Mistral AI'
     'OpenAI'
   ])
   @description('Model provider')
   param modelPublisherFormat string
   
   @allowed([
       'GlobalStandard'
       'DataZoneStandard'
       'Standard'
       'GlobalProvisioned'
       'Provisioned'
   ])
   @description('Model deployment SKU name')
   param skuName string = 'GlobalStandard'
   
   @description('Content filter policy name')
   param contentFilterPolicyName string = 'Microsoft.DefaultV2'
   
   @description('Model deployment capacity')
   param capacity int = 1
   
   resource modelDeployment 'Microsoft.CognitiveServices/accounts/deployments@2024-04-01-preview' = {
     name: '${accountName}/${modelName}'
     sku: {
       name: skuName
       capacity: capacity
     }
     properties: {
       model: {
         format: modelPublisherFormat
         name: modelName
         version: modelVersion
       }
       raiPolicyName: contentFilterPolicyName == null ? 'Microsoft.Nill' : contentFilterPolicyName
     }
   }
   

2. Run the deployment:

   resourceGroupName="<resource-group-name>"
   accountName="<ai-services-resource-name>"
   modelName="Phi-4-mini-instruct"
   provider="Microsoft"
   version=1
   
   az deployment group create \
       --resource-group $resourceGroupName \
       --template-file ai-services-deployment-template.bicep \
       --parameters accountName=$accountName modelName=$modelName modelVersion=$version modelPublisherFormat=$provider
   

3. Verify the deployment completed successfully:

   az cognitiveservices account deployment show \
       --deployment-name $modelName \
       -n $accountName \
       -g $resourceGroupName \
   | jq '.properties.provisioningState'
   

   The output should display "Succeeded".