fokioccupy.blogg.se - march 2023

#Gehc learning factory for inferencing how to
#Gehc learning factory for inferencing install
#Gehc learning factory for inferencing code

If this command returns a value of msi, use the following command to identify the principal ID for the managed identity: az aks show -n -resource-group -query identity.principalId Replace with the name of the resource group that contains the AKS cluster: az aks show -n -resource-group -query servicePrincipalProfile.clientId To find the service principal or managed identity ID for AKS, use the following Azure CLI commands.

To add the identity as network contributor, use the following steps: If you create or attach an AKS cluster by providing a virtual network you previously created, you must grant the service principal (SP) or managed identity for your AKS cluster the Network Contributor role to the resource group that contains the virtual network.

#Gehc learning factory for inferencing how to

For more information, see How to deploy to AKS.įor more information on using Role-Based Access Control with Kubernetes, see Use Azure RBAC for Kubernetes authorization. When the creation process is completed, you can run inference, or model scoring, on an AKS cluster behind a virtual network. # Create the compute configuration and set virtual network informationĬonfig = AksCompute.provisioning_configuration(location="eastus2")Ĭonfig.vnet_resourcegroup_name = "mygroup"Ĭonfig.docker_bridge_cidr = "172.17.0.1/16"Īks_target = ComputeTarget.create(workspace=ws,

#Gehc learning factory for inferencing code

The following code creates a new AKS instance in the default subnet of a virtual network named mynetwork:ĪPPLIES TO: Python SDK azureml v1 from import ComputeTarget, AksCompute If you already have an AKS cluster in a virtual network, attach it to the workspace as described in How to deploy to AKS. You can also use the Azure Machine Learning SDK to add Azure Kubernetes Service in a virtual network. While the same IP is shared by all deployments to one AKS cluster, each AKS cluster will have a different IP address. The IP address shown in the image for the scoring endpoint will be different for your deployments. For information on viewing the scoring URI, see Consume a model deployed as a web service.

To find the IP address of the scoring endpoint, look at the scoring URI for the deployed service. Make sure that the network security group (NSG) that controls the virtual network has an inbound security rule enabled for the IP address of the scoring endpoint if you want to call it from outside the virtual network. When you deploy a model as a web service to AKS, a scoring endpoint is created to handle inferencing requests. It must not be in any subnet IP ranges, or the Kubernetes service address range (for example, 172.18.0.1/16). This IP address is assigned to Docker Bridge. In the Docker bridge address field, enter the Docker bridge address. It must be within the Kubernetes service address range (for example, 10.0.0.10). This IP address is assigned to the Kubernetes DNS service. In the Kubernetes DNS service IP address field, enter the Kubernetes DNS service IP address. It must not overlap with any subnet IP ranges (for example, 10.0.0.0/16). This address range uses a Classless Inter-Domain Routing (CIDR) notation IP range to define the IP addresses that are available for the cluster.

In the Kubernetes Service address range field, enter the Kubernetes service address range. If your workspace uses a private endpoint to connect to the virtual network, the Virtual network selection field is greyed out.

Using a public fully qualified domain name (FQDN) with a private AKS cluster is not supported with Azure Machine learning.

If your workspace has a private endpoint, the Azure Kubernetes Service cluster must be in the same Azure region as the workspace.

Please read tutorial create a secure workspace to add those private endpoints or service endpoints to your VNET.

If your AKS cluster is behind of a VNET, your workspace and its associated resources (storage, key vault, Azure Container Registry) must have private endpoints or service endpoints in the same VNET as AKS cluster's VNET.

Instead, consider using a Managed online endpoint with network isolation. When your Azure Machine Learning workspace is configured with a private endpoint, deploying to Azure Container Instances in a VNet is not supported. For more information on the v2 extension, see Azure ML CLI extension and Python SDK v2. We recommend that you transition to the ml, or v2, extension before September 30, 2025.

#Gehc learning factory for inferencing install

You will be able to install and use the v1 extension until that date. Support for the v1 extension will end on September 30, 2025. Some of the Azure CLI commands in this article use the azure-cli-ml, or v1, extension for Azure Machine Learning.