You can run this solution using GitHub Codespaces. The button will open a web-based VS Code instance in your browser:
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Open the solution accelerator (this may take several minutes):
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Accept the default values on the create Codespaces page
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Open a terminal window if it is not already open
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Continue with the deploying steps
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Navigate to the repo
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Click the code button
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Click the Codespaces tab
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Click "Create Codespaces on main"
This step will create the codespaces environment for you and launch a web based VS Code session.
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In the terminal window (usually below by default) you can select the layout of the window in the upper right corner.
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Log into your Azure subscription by leveraging the “azd auth login” command. Type the command “azd auth login”. It will display a code to copy and paste into the authorization window that will appear when you hit the enter button.
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Return to the codespaces window and type “az login”. The Azure CLI is used to validate available AI model quota.
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Return to the codespaces window now. In the terminal window, begin by initializing the environment by typing the command “azd init”
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Enter the name for your environment
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Now start the deployment of the infrastructure by typing the command “azd up”
⚠️ Note: The latest version of the Azure Developer CLI (AZD) is currently limited on prompting for missing parameters. The feature flag parameters in this solution have been temporarily defaulted to'disabled'until this limitation is lifted and prompting will resume.
This step will allow you to choose from the subscriptions you have available, based on the account you logged in with in the login step. Next it will prompt you for the region to deploy the resources into as well as any additional Azure resources to be provisioned and configured.
**Be sure to remember the vm password. This will be used in a later step. You are still required to log into Azure once you connect through the virtual machine.
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The automated model quota check will run, and will check if the location selected will have the necessary quota for the AI Models that are listed in the parameters file prior to deploying any resources.
If the location selected has sufficient quota for the models you plan to deploy, the provisioning will begin without notification.
If the location selected does not have the available quota for the models selected in your parameters, there will be a message back to the user, prior to any provisioning of resources. This will allow the developer to change the location of the provisiong and try again. Note that in our example, Italy North had capacity for gpt-4o but not for text-embedding-ada-002. This terminated the entire provisioning, because both models could not be deployed due to a quota issue.
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After completeing the required paramters that you were prompted for, and a successful model quota validation, the provisioning of resources will run and deploy the Network Isolated AI Foundry development portal and dependent resources in about 20 minutes.
These steps will help to check that the isolated environment was set up correctly. Follow these steps to check the creation of the required private endpoints in the environment (when set to networkIsolation = true).
One way to check if the access is private to the hub is to launch the AI Foundry hub from the portal.
When a user that is not connected through the virtual network via an RDP approved connection will see the following screen in their browser. This is the intended behavior!
A more thourough check is to look for the networking settings and checking for private end points.
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Go to the Azure Portal and select your Azure AI hub that was just created.
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Click on Settings and then Networking.
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Open the Workspace managed outbound access tab.
Here, you will find the private endpoints that are connected to the resources within the hub managed virtual network. Ensure that these private endpoints are active. The hub should show that Public access is ‘disabled’.
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Navigate to the resource group where the isolated AI Foundry was deployed to and select the virtual machine.
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Be sure that the Virtual Machine is running. If not, start the VM.
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Select “Bastion” under the ‘Connect’ heading in the VM resource.
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Supply the username and the password you created as environment variables and press the connect button.
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Your virtual machine will launch and you will see a different screen.
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Launch Edge browser and navigate to your AI Foundry Hub. https://ai.azure.com Sign in using your credentials.
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You are challenged by MFA to connect.
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You will now be able to view the Foundry Hub which is contained in an isolated network.
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