1. Isolating AI Training Environments Using NSG Rules


    When setting up AI training environments, especially in the cloud, it's important to ensure that these environments are isolated and secure to prevent unauthorized access and to maintain the integrity of your data and models. In Azure, one of the primary mechanisms for achieving network isolation and security is through the use of Azure Network Security Groups (NSGs).

    NSGs are used to filter network traffic to and from Azure resources in an Azure virtual network. An NSG contains a list of security rules that allow or deny network traffic based on several properties, including source and destination IP address, port, and protocol.

    To set up an isolated AI training environment using Pulumi with NSG rules, you would typically:

    1. Define a virtual network where your AI training environment will reside.
    2. Create a subnet within the virtual network.
    3. Associate an NSG with the subnet or specific network interfaces of the VMs within the subnet.
    4. Define NSG rules to control the inbound and outbound traffic to and from your AI environment.

    Here's how you could structure this setup using Pulumi and the Azure Python SDK:

    import pulumi import pulumi_azure_native as azure_native # Define a resource group, which is a logical container for your resources. resource_group = azure_native.resources.ResourceGroup('ai_resource_group') # Define a virtual network for your AI training environment. vnet = azure_native.network.VirtualNetwork( 'ai_vnet', resource_group_name=resource_group.name, location=resource_group.location, address_space=azure_native.network.AddressSpaceArgs( address_prefixes=[''] ) ) # Define a subnet within the virtual network. subnet = azure_native.network.Subnet( 'ai_subnet', resource_group_name=resource_group.name, virtual_network_name=vnet.name, address_prefix='' ) # Create a network security group for securing the AI training environment. nsg = azure_native.network.NetworkSecurityGroup( 'ai_nsg', resource_group_name=resource_group.name, location=resource_group.location ) # Associate the NSG with the subnet. subnet_nsg_association = azure_native.network.SubnetNetworkSecurityGroupAssociation( 'ai_subnet_nsg_association', subnet_name=subnet.name, network_security_group_id=nsg.id, resource_group_name=resource_group.name, virtual_network_name=vnet.name ) # Define a security rule that allows SSH access to your AI training environment. # Only allow SSH from your IP address for security. ssh_rule = azure_native.network.SecurityRule( 'ssh_rule', resource_group_name=resource_group.name, network_security_group_name=nsg.name, protocol='Tcp', direction='Inbound', source_address_prefix='YOUR_PUBLIC_IP_ADDRESS', # Replace with your public IP address source_port_range='*', destination_address_prefix='*', destination_port_range='22', access='Allow', priority=1000 # The priority for this rule - lower numbers are processed first. ) # Define additional rules as necessary, for example, to allow outbound internet access # for downloading updates or accessing APIs. # Export the public IP address of your training environment (if applicable). pulumi.export('ssh_rule_id', ssh_rule.id)

    In the program above, we set up a resource group for our AI environment to keep it logically separated from other resources. Next, we define a virtual network (vnet) with its own address space. Inside the vnet, a subnet is defined where our AI training VMs would reside.

    We then create a network security group (NSG) and associate it with the subnet through the SubnetNetworkSecurityGroupAssociation resource. This step effectively applies the rules defined in the NSG to all resources within the subnet.

    Finally, we set up a security rule ssh_rule to allow SSH access from a specific public IP address to the subnet. This allows us to access our VMs securely while keeping the environment isolated from other network traffic.

    Replace 'YOUR_PUBLIC_IP_ADDRESS' with your actual public IP address to allow SSH access to your environment.

    Keep in mind that security is a complex topic and requires careful consideration. You should tailor your NSG rules to the specific needs of your environment, considering both the security implications and the requirements of your AI workloads.