Secure an Azure Machine Learning training environment with virtual networks (SDKv1)

Članek
08/28/2024

In this article, you learn how to secure training environments with a virtual network in Azure Machine Learning using the Python SDK v1.

Azure Machine Learning compute instance and compute cluster can be used to securely train models in a virtual network. When planning your environment, you can configure the compute instance/cluster with or without a public IP address. The general differences between the two are:

No public IP: Reduces costs as it doesn't have the same networking resource requirements. Improves security by removing the requirement for inbound traffic from the internet. However, there are additional configuration changes required to enable outbound access to required resources (Microsoft Entra ID, Azure Resource Manager, etc.).
Public IP: Works by default, but costs more due to additional Azure networking resources. Requires inbound communication from the Azure Machine Learning service over the public internet.

The following table contains the differences between these configurations:

Configuration	With public IP	Without public IP
Inbound traffic	`AzureMachineLearning` service tag.	None
Outbound traffic	By default, can access the public internet with no restrictions. You can restrict what it accesses using a Network Security Group or firewall.	By default, it can't access the internet. If it can still send outbound traffic to internet, it is because of Azure default outbound access and you have an NSG that allows outbound to the internet. We don't recommend using the default outbound access. If you need outbound access to the internet, we recommend using a Virtual Network NAT gateway or Firewall instead if you need to route outbound traffic to required resources on the internet.
Azure networking resources	Public IP address, load balancer, network interface	None

You can also use Azure Databricks or HDInsight to train models in a virtual network.

Tip

You can use Azure Machine Learning managed virtual networks instead of the steps in this article. With a managed virtual network, Azure Machine Learning handles the job of network isolation for your workspace and managed computes. You can also add private endpoints for resources needed by the workspace, such as Azure Storage Account. For more information, see Workspace managed network isolation.

Note

For information on using the Azure Machine Learning studio and the Python SDK v2, see Secure training environment (v2).

For a tutorial on creating a secure workspace, see Tutorial: Create a secure workspace in Azure portal, Bicep template, or Terraform template.

In this article you learn how to secure the following training compute resources in a virtual network:

Azure Machine Learning compute cluster
Azure Machine Learning compute instance
Azure Databricks
Virtual Machine
HDInsight cluster

Important

Items in this article marked as "preview" are currently in public preview. The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Prerequisites

Read the Network security overview article to understand common virtual network scenarios and overall virtual network architecture.
An existing virtual network and subnet to use with your compute resources. This VNet must be in the same subscription as your Azure Machine Learning workspace.
- We recommend putting the storage accounts used by your workspace and training jobs in the same Azure region that you plan to use for your compute instances and clusters. If they aren't in the same Azure region, you may incur data transfer costs and increased network latency.
- Make sure that WebSocket communication is allowed to *.instances.azureml.net and *.instances.azureml.ms in your VNet. WebSockets are used by Jupyter on compute instances.
An existing subnet in the virtual network. This subnet is used when creating compute instances and clusters.
- Make sure that the subnet isn't delegated to other Azure services.
- Make sure that the subnet contains enough free IP addresses. Each compute instance requires one IP address. Each node within a compute cluster requires one IP address.
If you have your own DNS server, we recommend using DNS forwarding to resolve the fully qualified domain names (FQDN) of compute instances and clusters. For more information, see Use a custom DNS with Azure Machine Learning.

To deploy resources into a virtual network or subnet, your user account must have permissions to the following actions in Azure role-based access control (Azure RBAC):
- "Microsoft.Network/*/read" on the virtual network resource. This permission isn't needed for Azure Resource Manager (ARM) template deployments.
- "Microsoft.Network/virtualNetworks/join/action" on the virtual network resource.
- "Microsoft.Network/virtualNetworks/subnets/join/action" on the subnet resource.
For more information on Azure RBAC with networking, see the Networking built-in roles

Limitations

Azure Machine Learning compute cluster/instance

Compute clusters can be created in a different region and VNet than your workspace. However, this functionality is only available using the SDK v2, CLI v2, or studio. For more information, see the v2 version of secure training environments.
Compute cluster/instance deployment in virtual network isn't supported with Azure Lighthouse.
Port 445 must be open for private network communications between your compute instances and the default storage account during training. For example, if your computes are in one VNet and the storage account is in another, don't block port 445 to the storage account VNet.

Azure Databricks

The virtual network must be in the same subscription and region as the Azure Machine Learning workspace.
If the Azure Storage Account(s) for the workspace are also secured in a virtual network, they must be in the same virtual network as the Azure Databricks cluster.
In addition to the databricks-private and databricks-public subnets used by Azure Databricks, the default subnet created for the virtual network is also required.
Azure Databricks doesn't use a private endpoint to communicate with the virtual network.

For more information on using Azure Databricks in a virtual network, see Deploy Azure Databricks in your Azure Virtual Network.

Azure HDInsight or virtual machine

Azure Machine Learning supports only virtual machines that are running Ubuntu.

Compute instance/cluster with no public IP

Important

If you have been using compute instances or compute clusters configured for no public IP without opting-in to the preview, you will need to delete and recreate them after January 20, 2023 (when the feature is generally available).

If you were previously using the preview of no public IP, you may also need to modify what traffic you allow inbound and outbound, as the requirements have changed for general availability:

Outbound requirements - Two additional outbound, which are only used for the management of compute instances and clusters. The destination of these service tags are owned by Microsoft:
- AzureMachineLearning service tag on UDP port 5831.
- BatchNodeManagement service tag on TCP port 443.

The following configurations are in addition to those listed in the Prerequisites section, and are specific to creating a compute instances/clusters configured for no public IP:

You must use a workspace private endpoint for the compute resource to communicate with Azure Machine Learning services from the VNet. For more information, see Configure a private endpoint for Azure Machine Learning workspace.

In your VNet, allow outbound traffic to the following service tags or fully qualified domain names (FQDN):

Service tag	Protocol	Port	Notes
`AzureMachineLearning`	TCP UDP	443/8787/18881 5831	Communication with the Azure Machine Learning service.
`BatchNodeManagement.<region>`	ANY	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. Communication with Azure Batch. Compute instance and compute cluster are implemented using the Azure Batch service.
`Storage.<region>`	TCP	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. This service tag is used to communicate with the Azure Storage account used by Azure Batch.

Important

The outbound access to Storage.<region> could potentially be used to exfiltrate data from your workspace. By using a Service Endpoint Policy, you can mitigate this vulnerability. For more information, see the Azure Machine Learning data exfiltration prevention article.

FQDN	Protocol	Port	Notes
`<region>.tundra.azureml.ms`	UDP	5831	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace.
`graph.windows.net`	TCP	443	Communication with the Microsoft Graph API.
`*.instances.azureml.ms`	TCP	443/8787/18881	Communication with Azure Machine Learning.
`*.<region>.batch.azure.com`	ANY	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. Communication with Azure Batch.
`*.<region>.service.batch.azure.com`	ANY	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. Communication with Azure Batch.
`*.blob.core.windows.net`	TCP	443	Communication with Azure Blob storage.
`*.queue.core.windows.net`	TCP	443	Communication with Azure Queue storage.
`*.table.core.windows.net`	TCP	443	Communication with Azure Table storage.

Create either a firewall and outbound rules or a NAT gateway and network service groups to allow outbound traffic. Since the compute has no public IP address, it can't communicate with resources on the public internet without this configuration. For example, it wouldn't be able to communicate with Microsoft Entra ID or Azure Resource Manager. Installing Python packages from public sources would also require this configuration.

For more information on the outbound traffic that is used by Azure Machine Learning, see the following articles:
- Configure inbound and outbound network traffic.
- Azure's outbound connectivity methods.

Use the following information to create a compute instance or cluster with no public IP address:

To create a compute instance or compute cluster with no public IP, use the Azure Machine Learning studio UI to create the resource:

Sign in to the Azure Machine Learning studio, and then select your subscription and workspace.
Select the Compute page from the left navigation bar.
Select the + New from the navigation bar of compute instance or compute cluster.
Configure the VM size and configuration you need, then select Next.
From the Advanced Settings, Select Enable virtual network, your virtual network and subnet, and finally select the No Public IP option under the VNet/subnet section.

Tip

You can also use the Azure Machine Learning SDK v2 or Azure CLI extension for ML v2. For information on creating a compute instance or cluster with no public IP, see the v2 version of Secure an Azure Machine Learning training environment article.

Compute instance/cluster with public IP

The following configurations are in addition to those listed in the Prerequisites section, and are specific to creating compute instances/clusters that have a public IP:

If you put multiple compute instances/clusters in one virtual network, you may need to request a quota increase for one or more of your resources. The Machine Learning compute instance or cluster automatically allocates networking resources in the resource group that contains the virtual network. For each compute instance or cluster, the service allocates the following resources:
- A network security group (NSG) is automatically created. This NSG allows inbound TCP traffic on port 44224 from the AzureMachineLearning service tag.
  
  Important
  
  Compute instance and compute cluster automatically create an NSG with the required rules.
  
  If you have another NSG at the subnet level, the rules in the subnet level NSG mustn't conflict with the rules in the automatically created NSG.
  
  To learn how the NSGs filter your network traffic, see How network security groups filter network traffic.
- One load balancer
For compute clusters, these resources are deleted every time the cluster scales down to 0 nodes and created when scaling up.

For a compute instance, these resources are kept until the instance is deleted. Stopping the instance doesn't remove the resources.

Important

These resources are limited by the subscription's resource quotas. If the virtual network resource group is locked then deletion of compute cluster/instance will fail. Load balancer cannot be deleted until the compute cluster/instance is deleted. Also please ensure there is no Azure Policy assignment which prohibits creation of network security groups.
In your VNet, allow inbound TCP traffic on port 44224 from the AzureMachineLearning service tag.

Important

The compute instance/cluster is dynamically assigned an IP address when it is created. Since the address is not known before creation, and inbound access is required as part of the creation process, you cannot statically assign it on your firewall. Instead, if you are using a firewall with the VNet you must create a user-defined route to allow this inbound traffic.

In your VNet, allow outbound traffic to the following service tags:

Service tag	Protocol	Port	Notes
`AzureMachineLearning`	TCP UDP	443/8787/18881 5831	Communication with the Azure Machine Learning service.
`BatchNodeManagement.<region>`	ANY	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. Communication with Azure Batch. Compute instance and compute cluster are implemented using the Azure Batch service.
`Storage.<region>`	TCP	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. This service tag is used to communicate with the Azure Storage account used by Azure Batch.

Important

FQDN	Protocol	Port	Notes
`<region>.tundra.azureml.ms`	UDP	5831	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace.
`graph.windows.net`	TCP	443	Communication with the Microsoft Graph API.
`*.instances.azureml.ms`	TCP	443/8787/18881	Communication with Azure Machine Learning.
`*.<region>.batch.azure.com`	ANY	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. Communication with Azure Batch.
`*.<region>.service.batch.azure.com`	ANY	443	Replace `<region>` with the Azure region that contains your Azure Machine Learning workspace. Communication with Azure Batch.
`*.blob.core.windows.net`	TCP	443	Communication with Azure Blob storage.
`*.queue.core.windows.net`	TCP	443	Communication with Azure Queue storage.
`*.table.core.windows.net`	TCP	443	Communication with Azure Table storage.

Compute instance
Compute cluster

APPLIES TO: Python SDK azureml v1

import datetime
import time

from azureml.core.compute import ComputeTarget, ComputeInstance
from azureml.core.compute_target import ComputeTargetException

# Choose a name for your instance
# Compute instance name should be unique across the azure region
compute_name = "ci{}".format(ws._workspace_id)[:10]

# Verify that instance does not exist already
try:
    instance = ComputeInstance(workspace=ws, name=compute_name)
    print('Found existing instance, use it.')
except ComputeTargetException:
    compute_config = ComputeInstance.provisioning_configuration(
        vm_size='STANDARD_D3_V2',
        ssh_public_access=False,
        vnet_resourcegroup_name='vnet_resourcegroup_name',
        vnet_name='vnet_name',
        subnet_name='subnet_name',
        # admin_user_ssh_public_key='<my-sshkey>'
    )
    instance = ComputeInstance.create(ws, compute_name, compute_config)
    instance.wait_for_completion(show_output=True)

APPLIES TO: Python SDK azureml v1

from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

# The Azure virtual network name, subnet, and resource group
vnet_name = 'mynetwork'
subnet_name = 'default'
vnet_resourcegroup_name = 'mygroup'

# Choose a name for your CPU cluster
cpu_cluster_name = "cpucluster"

# Verify that cluster does not exist already
try:
    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
    print("Found existing cpucluster")
except ComputeTargetException:
    print("Creating new cpucluster")

    # Specify the configuration for the new cluster
    compute_config = AmlCompute.provisioning_configuration(vm_size="STANDARD_D2_V2",
                                                           min_nodes=0,
                                                           max_nodes=4,
                                                           location="westus2",
                                                           vnet_resourcegroup_name=vnet_resourcegroup_name,
                                                           vnet_name=vnet_name,
                                                           subnet_name=subnet_name)

    # Create the cluster with the specified name and configuration
    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)

    # Wait for the cluster to be completed, show the output log
    cpu_cluster.wait_for_completion(show_output=True)

When the creation process finishes, you train your model. For more information, see Select and use a compute target for training.

Azure Databricks

The virtual network must be in the same subscription and region as the Azure Machine Learning workspace.
If the Azure Storage Account(s) for the workspace are also secured in a virtual network, they must be in the same virtual network as the Azure Databricks cluster.
In addition to the databricks-private and databricks-public subnets used by Azure Databricks, the default subnet created for the virtual network is also required.
Azure Databricks doesn't use a private endpoint to communicate with the virtual network.

For specific information on using Azure Databricks with a virtual network, see Deploy Azure Databricks in your Azure Virtual Network.

Required public internet access to train models

Important

While previous sections of this article describe configurations required to create compute resources, the configuration information in this section is required to use these resources to train models.

Azure Machine Learning requires both inbound and outbound access to the public internet. The following tables provide an overview of the required access and what purpose it serves. For service tags that end in .region, replace region with the Azure region that contains your workspace. For example, Storage.westus:

Tip

The required tab lists the required inbound and outbound configuration. The situational tab lists optional inbound and outbound configurations required by specific configurations you may want to enable.

Required
Situational

Direction	Protocol & ports	Service tag	Purpose
Outbound	TCP: 80, 443	`AzureActiveDirectory`	Authentication using Microsoft Entra ID.
Outbound	TCP: 443, 18881 UDP: 5831	`AzureMachineLearning`	Using Azure Machine Learning services. Python intellisense in notebooks uses port 18881. Creating, updating, and deleting an Azure Machine Learning compute instance uses port 5831.
Outbound	ANY: 443	`BatchNodeManagement.region`	Communication with Azure Batch back-end for Azure Machine Learning compute instances/clusters.
Outbound	TCP: 443	`AzureResourceManager`	Creation of Azure resources with Azure Machine Learning, Azure CLI, and Azure Machine Learning SDK.
Outbound	TCP: 443	`Storage.region`	Access data stored in the Azure Storage Account for compute cluster and compute instance. For information on preventing data exfiltration over this outbound, see Data exfiltration protection.
Outbound	TCP: 443	`AzureFrontDoor.FrontEnd` * Not needed in Microsoft Azure operated by 21Vianet.	Global entry point for Azure Machine Learning studio. Store images and environments for AutoML. For information on preventing data exfiltration over this outbound, see Data exfiltration protection.
Outbound	TCP: 443	`MicrosoftContainerRegistry.region` Note that this tag has a dependency on the `AzureFrontDoor.FirstParty` tag	Access docker images provided by Microsoft. Setup of the Azure Machine Learning router for Azure Kubernetes Service.

Direction	Protocol & ports	Service tag	Purpose
Inbound	TCP: 44224	`AzureMachineLearning`	Create, update, and delete of Azure Machine Learning compute instance/cluster. Required if instance/cluster configured with a Public IP option.
Outbound	TCP: 8787	`AzureMachineLearning`	Using Azure Machine Learning services. Port 8787 is required if you use RStudio.
Outbound	TCP: 445	`Storage.region`	Access data stored in the Azure Storage Account for compute cluster and compute instance. For information on preventing data exfiltration over this outbound, see Data exfiltration protection. 445 is only required if you have a firewall between your virtual network for Azure ML and a private endpoint for your storage accounts.
Outbound	TCP: 443	`AzureMonitor`	Used to log monitoring and metrics to App Insights and Azure Monitor. Only needed if you haven't secured Azure Monitor for the workspace. * This outbound is also used to log information for support incidents.
Outbound	TCP: 443	`Keyvault.region`	Access the key vault for the Azure Batch service. Only needed if you enabled the hbi_workspace flag when creating the workspace.

Tip

If you need the IP addresses instead of service tags, use one of the following options:

Download a list from Azure IP Ranges and Service Tags.
Use the Azure CLI az network list-service-tags command.
Use the Azure PowerShell Get-AzNetworkServiceTag command.

The IP addresses may change periodically.

You may also need to allow outbound traffic to Visual Studio Code and non-Microsoft sites for the installation of packages required by your machine learning project. The following table lists commonly used repositories for machine learning:

Host name	Purpose
`anaconda.com` `*.anaconda.com`	Used to install default packages.
`*.anaconda.org`	Used to get repo data.
`pypi.org`	Used to list dependencies from the default index, if any, and the index isn't overwritten by user settings. If the index is overwritten, you must also allow `*.pythonhosted.org`.
`cloud.r-project.org`	Used when installing CRAN packages for R development.
`*.pytorch.org`	Used by some examples based on PyTorch.
`*.tensorflow.org`	Used by some examples based on Tensorflow.
`code.visualstudio.com`	Required to download and install Visual Studio Code desktop. This isn't required for Visual Studio Code Web.
`update.code.visualstudio.com` `*.vo.msecnd.net`	Used to retrieve Visual Studio Code server bits that are installed on the compute instance through a setup script.
`marketplace.visualstudio.com` `vscode.blob.core.windows.net` `*.gallerycdn.vsassets.io`	Required to download and install Visual Studio Code extensions. These hosts enable the remote connection to Compute Instances provided by the Azure ML extension for Visual Studio Code. For more information, see Connect to an Azure Machine Learning compute instance in Visual Studio Code.
`raw.githubusercontent.com/microsoft/vscode-tools-for-ai/master/azureml_remote_websocket_server/*`	Used to retrieve websocket server bits, which are installed on the compute instance. The websocket server is used to transmit requests from Visual Studio Code client (desktop application) to Visual Studio Code server running on the compute instance.

Note

When using the Azure Machine Learning VS Code extension the remote compute instance will require an access to public repositories to install the packages required by the extension. If the compute instance requires a proxy to access these public repositories or the Internet, you will need to set and export the HTTP_PROXY and HTTPS_PROXY environment variables in the ~/.bashrc file of the compute instance. This process can be automated at provisioning time by using a custom script.

When using Azure Kubernetes Service (AKS) with Azure Machine Learning, allow the following traffic to the AKS VNet:

General inbound/outbound requirements for AKS as described in the Restrict egress traffic in Azure Kubernetes Service article.
Outbound to mcr.microsoft.com.
When deploying a model to an AKS cluster, use the guidance in the Deploy ML models to Azure Kubernetes Service article.

For information on using a firewall solution, see Use a firewall with Azure Machine Learning.

Next steps

This article is part of a series on securing an Azure Machine Learning workflow. See the other articles in this series:

Deli z drugimi prek

Secure an Azure Machine Learning training environment with virtual networks (SDKv1)

Prerequisites

Limitations

Azure Machine Learning compute cluster/instance

Azure Databricks

Azure HDInsight or virtual machine

Compute instance/cluster with no public IP

Compute instance/cluster with public IP

Azure Databricks

Required public internet access to train models

Next steps

Povratne informacije

Dodatni viri