We recommend using Azure Native.
azure.machinelearning.InferenceCluster
Manages a Machine Learning Inference Cluster.
Note: The Machine Learning Inference Cluster resource is used to attach an existing AKS cluster to the Machine Learning Workspace, it doesn’t create the AKS cluster itself. Therefore it can only be created and deleted, not updated. Any change to the configuration will recreate the resource.
Example Usage
import * as pulumi from "@pulumi/pulumi";
import * as azure from "@pulumi/azure";
const current = azure.core.getClientConfig({});
const example = new azure.core.ResourceGroup("example", {
    name: "example-rg",
    location: "west europe",
    tags: {
        stage: "example",
    },
});
const exampleInsights = new azure.appinsights.Insights("example", {
    name: "example-ai",
    location: example.location,
    resourceGroupName: example.name,
    applicationType: "web",
});
const exampleKeyVault = new azure.keyvault.KeyVault("example", {
    name: "example-kv",
    location: example.location,
    resourceGroupName: example.name,
    tenantId: current.then(current => current.tenantId),
    skuName: "standard",
    purgeProtectionEnabled: true,
});
const exampleAccount = new azure.storage.Account("example", {
    name: "examplesa",
    location: example.location,
    resourceGroupName: example.name,
    accountTier: "Standard",
    accountReplicationType: "LRS",
});
const exampleWorkspace = new azure.machinelearning.Workspace("example", {
    name: "example-mlw",
    location: example.location,
    resourceGroupName: example.name,
    applicationInsightsId: exampleInsights.id,
    keyVaultId: exampleKeyVault.id,
    storageAccountId: exampleAccount.id,
    identity: {
        type: "SystemAssigned",
    },
});
const exampleVirtualNetwork = new azure.network.VirtualNetwork("example", {
    name: "example-vnet",
    addressSpaces: ["10.1.0.0/16"],
    location: example.location,
    resourceGroupName: example.name,
});
const exampleSubnet = new azure.network.Subnet("example", {
    name: "example-subnet",
    resourceGroupName: example.name,
    virtualNetworkName: exampleVirtualNetwork.name,
    addressPrefixes: ["10.1.0.0/24"],
});
const exampleKubernetesCluster = new azure.containerservice.KubernetesCluster("example", {
    name: "example-aks",
    location: example.location,
    resourceGroupName: example.name,
    dnsPrefixPrivateCluster: "prefix",
    defaultNodePool: {
        name: "default",
        nodeCount: 3,
        vmSize: "Standard_D3_v2",
        vnetSubnetId: exampleSubnet.id,
    },
    identity: {
        type: "SystemAssigned",
    },
});
const exampleInferenceCluster = new azure.machinelearning.InferenceCluster("example", {
    name: "example",
    location: example.location,
    clusterPurpose: "FastProd",
    kubernetesClusterId: exampleKubernetesCluster.id,
    description: "This is an example cluster used with Terraform",
    machineLearningWorkspaceId: exampleWorkspace.id,
    tags: {
        stage: "example",
    },
});
import pulumi
import pulumi_azure as azure
current = azure.core.get_client_config()
example = azure.core.ResourceGroup("example",
    name="example-rg",
    location="west europe",
    tags={
        "stage": "example",
    })
example_insights = azure.appinsights.Insights("example",
    name="example-ai",
    location=example.location,
    resource_group_name=example.name,
    application_type="web")
example_key_vault = azure.keyvault.KeyVault("example",
    name="example-kv",
    location=example.location,
    resource_group_name=example.name,
    tenant_id=current.tenant_id,
    sku_name="standard",
    purge_protection_enabled=True)
example_account = azure.storage.Account("example",
    name="examplesa",
    location=example.location,
    resource_group_name=example.name,
    account_tier="Standard",
    account_replication_type="LRS")
example_workspace = azure.machinelearning.Workspace("example",
    name="example-mlw",
    location=example.location,
    resource_group_name=example.name,
    application_insights_id=example_insights.id,
    key_vault_id=example_key_vault.id,
    storage_account_id=example_account.id,
    identity={
        "type": "SystemAssigned",
    })
example_virtual_network = azure.network.VirtualNetwork("example",
    name="example-vnet",
    address_spaces=["10.1.0.0/16"],
    location=example.location,
    resource_group_name=example.name)
example_subnet = azure.network.Subnet("example",
    name="example-subnet",
    resource_group_name=example.name,
    virtual_network_name=example_virtual_network.name,
    address_prefixes=["10.1.0.0/24"])
example_kubernetes_cluster = azure.containerservice.KubernetesCluster("example",
    name="example-aks",
    location=example.location,
    resource_group_name=example.name,
    dns_prefix_private_cluster="prefix",
    default_node_pool={
        "name": "default",
        "node_count": 3,
        "vm_size": "Standard_D3_v2",
        "vnet_subnet_id": example_subnet.id,
    },
    identity={
        "type": "SystemAssigned",
    })
example_inference_cluster = azure.machinelearning.InferenceCluster("example",
    name="example",
    location=example.location,
    cluster_purpose="FastProd",
    kubernetes_cluster_id=example_kubernetes_cluster.id,
    description="This is an example cluster used with Terraform",
    machine_learning_workspace_id=example_workspace.id,
    tags={
        "stage": "example",
    })
package main
import (
	"github.com/pulumi/pulumi-azure/sdk/v6/go/azure/appinsights"
	"github.com/pulumi/pulumi-azure/sdk/v6/go/azure/containerservice"
	"github.com/pulumi/pulumi-azure/sdk/v6/go/azure/core"
	"github.com/pulumi/pulumi-azure/sdk/v6/go/azure/keyvault"
	"github.com/pulumi/pulumi-azure/sdk/v6/go/azure/machinelearning"
	"github.com/pulumi/pulumi-azure/sdk/v6/go/azure/network"
	"github.com/pulumi/pulumi-azure/sdk/v6/go/azure/storage"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		current, err := core.GetClientConfig(ctx, map[string]interface{}{}, nil)
		if err != nil {
			return err
		}
		example, err := core.NewResourceGroup(ctx, "example", &core.ResourceGroupArgs{
			Name:     pulumi.String("example-rg"),
			Location: pulumi.String("west europe"),
			Tags: pulumi.StringMap{
				"stage": pulumi.String("example"),
			},
		})
		if err != nil {
			return err
		}
		exampleInsights, err := appinsights.NewInsights(ctx, "example", &appinsights.InsightsArgs{
			Name:              pulumi.String("example-ai"),
			Location:          example.Location,
			ResourceGroupName: example.Name,
			ApplicationType:   pulumi.String("web"),
		})
		if err != nil {
			return err
		}
		exampleKeyVault, err := keyvault.NewKeyVault(ctx, "example", &keyvault.KeyVaultArgs{
			Name:                   pulumi.String("example-kv"),
			Location:               example.Location,
			ResourceGroupName:      example.Name,
			TenantId:               pulumi.String(current.TenantId),
			SkuName:                pulumi.String("standard"),
			PurgeProtectionEnabled: pulumi.Bool(true),
		})
		if err != nil {
			return err
		}
		exampleAccount, err := storage.NewAccount(ctx, "example", &storage.AccountArgs{
			Name:                   pulumi.String("examplesa"),
			Location:               example.Location,
			ResourceGroupName:      example.Name,
			AccountTier:            pulumi.String("Standard"),
			AccountReplicationType: pulumi.String("LRS"),
		})
		if err != nil {
			return err
		}
		exampleWorkspace, err := machinelearning.NewWorkspace(ctx, "example", &machinelearning.WorkspaceArgs{
			Name:                  pulumi.String("example-mlw"),
			Location:              example.Location,
			ResourceGroupName:     example.Name,
			ApplicationInsightsId: exampleInsights.ID(),
			KeyVaultId:            exampleKeyVault.ID(),
			StorageAccountId:      exampleAccount.ID(),
			Identity: &machinelearning.WorkspaceIdentityArgs{
				Type: pulumi.String("SystemAssigned"),
			},
		})
		if err != nil {
			return err
		}
		exampleVirtualNetwork, err := network.NewVirtualNetwork(ctx, "example", &network.VirtualNetworkArgs{
			Name: pulumi.String("example-vnet"),
			AddressSpaces: pulumi.StringArray{
				pulumi.String("10.1.0.0/16"),
			},
			Location:          example.Location,
			ResourceGroupName: example.Name,
		})
		if err != nil {
			return err
		}
		exampleSubnet, err := network.NewSubnet(ctx, "example", &network.SubnetArgs{
			Name:               pulumi.String("example-subnet"),
			ResourceGroupName:  example.Name,
			VirtualNetworkName: exampleVirtualNetwork.Name,
			AddressPrefixes: pulumi.StringArray{
				pulumi.String("10.1.0.0/24"),
			},
		})
		if err != nil {
			return err
		}
		exampleKubernetesCluster, err := containerservice.NewKubernetesCluster(ctx, "example", &containerservice.KubernetesClusterArgs{
			Name:                    pulumi.String("example-aks"),
			Location:                example.Location,
			ResourceGroupName:       example.Name,
			DnsPrefixPrivateCluster: pulumi.String("prefix"),
			DefaultNodePool: &containerservice.KubernetesClusterDefaultNodePoolArgs{
				Name:         pulumi.String("default"),
				NodeCount:    pulumi.Int(3),
				VmSize:       pulumi.String("Standard_D3_v2"),
				VnetSubnetId: exampleSubnet.ID(),
			},
			Identity: &containerservice.KubernetesClusterIdentityArgs{
				Type: pulumi.String("SystemAssigned"),
			},
		})
		if err != nil {
			return err
		}
		_, err = machinelearning.NewInferenceCluster(ctx, "example", &machinelearning.InferenceClusterArgs{
			Name:                       pulumi.String("example"),
			Location:                   example.Location,
			ClusterPurpose:             pulumi.String("FastProd"),
			KubernetesClusterId:        exampleKubernetesCluster.ID(),
			Description:                pulumi.String("This is an example cluster used with Terraform"),
			MachineLearningWorkspaceId: exampleWorkspace.ID(),
			Tags: pulumi.StringMap{
				"stage": pulumi.String("example"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Azure = Pulumi.Azure;
return await Deployment.RunAsync(() => 
{
    var current = Azure.Core.GetClientConfig.Invoke();
    var example = new Azure.Core.ResourceGroup("example", new()
    {
        Name = "example-rg",
        Location = "west europe",
        Tags = 
        {
            { "stage", "example" },
        },
    });
    var exampleInsights = new Azure.AppInsights.Insights("example", new()
    {
        Name = "example-ai",
        Location = example.Location,
        ResourceGroupName = example.Name,
        ApplicationType = "web",
    });
    var exampleKeyVault = new Azure.KeyVault.KeyVault("example", new()
    {
        Name = "example-kv",
        Location = example.Location,
        ResourceGroupName = example.Name,
        TenantId = current.Apply(getClientConfigResult => getClientConfigResult.TenantId),
        SkuName = "standard",
        PurgeProtectionEnabled = true,
    });
    var exampleAccount = new Azure.Storage.Account("example", new()
    {
        Name = "examplesa",
        Location = example.Location,
        ResourceGroupName = example.Name,
        AccountTier = "Standard",
        AccountReplicationType = "LRS",
    });
    var exampleWorkspace = new Azure.MachineLearning.Workspace("example", new()
    {
        Name = "example-mlw",
        Location = example.Location,
        ResourceGroupName = example.Name,
        ApplicationInsightsId = exampleInsights.Id,
        KeyVaultId = exampleKeyVault.Id,
        StorageAccountId = exampleAccount.Id,
        Identity = new Azure.MachineLearning.Inputs.WorkspaceIdentityArgs
        {
            Type = "SystemAssigned",
        },
    });
    var exampleVirtualNetwork = new Azure.Network.VirtualNetwork("example", new()
    {
        Name = "example-vnet",
        AddressSpaces = new[]
        {
            "10.1.0.0/16",
        },
        Location = example.Location,
        ResourceGroupName = example.Name,
    });
    var exampleSubnet = new Azure.Network.Subnet("example", new()
    {
        Name = "example-subnet",
        ResourceGroupName = example.Name,
        VirtualNetworkName = exampleVirtualNetwork.Name,
        AddressPrefixes = new[]
        {
            "10.1.0.0/24",
        },
    });
    var exampleKubernetesCluster = new Azure.ContainerService.KubernetesCluster("example", new()
    {
        Name = "example-aks",
        Location = example.Location,
        ResourceGroupName = example.Name,
        DnsPrefixPrivateCluster = "prefix",
        DefaultNodePool = new Azure.ContainerService.Inputs.KubernetesClusterDefaultNodePoolArgs
        {
            Name = "default",
            NodeCount = 3,
            VmSize = "Standard_D3_v2",
            VnetSubnetId = exampleSubnet.Id,
        },
        Identity = new Azure.ContainerService.Inputs.KubernetesClusterIdentityArgs
        {
            Type = "SystemAssigned",
        },
    });
    var exampleInferenceCluster = new Azure.MachineLearning.InferenceCluster("example", new()
    {
        Name = "example",
        Location = example.Location,
        ClusterPurpose = "FastProd",
        KubernetesClusterId = exampleKubernetesCluster.Id,
        Description = "This is an example cluster used with Terraform",
        MachineLearningWorkspaceId = exampleWorkspace.Id,
        Tags = 
        {
            { "stage", "example" },
        },
    });
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azure.core.CoreFunctions;
import com.pulumi.azure.core.ResourceGroup;
import com.pulumi.azure.core.ResourceGroupArgs;
import com.pulumi.azure.appinsights.Insights;
import com.pulumi.azure.appinsights.InsightsArgs;
import com.pulumi.azure.keyvault.KeyVault;
import com.pulumi.azure.keyvault.KeyVaultArgs;
import com.pulumi.azure.storage.Account;
import com.pulumi.azure.storage.AccountArgs;
import com.pulumi.azure.machinelearning.Workspace;
import com.pulumi.azure.machinelearning.WorkspaceArgs;
import com.pulumi.azure.machinelearning.inputs.WorkspaceIdentityArgs;
import com.pulumi.azure.network.VirtualNetwork;
import com.pulumi.azure.network.VirtualNetworkArgs;
import com.pulumi.azure.network.Subnet;
import com.pulumi.azure.network.SubnetArgs;
import com.pulumi.azure.containerservice.KubernetesCluster;
import com.pulumi.azure.containerservice.KubernetesClusterArgs;
import com.pulumi.azure.containerservice.inputs.KubernetesClusterDefaultNodePoolArgs;
import com.pulumi.azure.containerservice.inputs.KubernetesClusterIdentityArgs;
import com.pulumi.azure.machinelearning.InferenceCluster;
import com.pulumi.azure.machinelearning.InferenceClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        final var current = CoreFunctions.getClientConfig(%!v(PANIC=Format method: runtime error: invalid memory address or nil pointer dereference);
        var example = new ResourceGroup("example", ResourceGroupArgs.builder()
            .name("example-rg")
            .location("west europe")
            .tags(Map.of("stage", "example"))
            .build());
        var exampleInsights = new Insights("exampleInsights", InsightsArgs.builder()
            .name("example-ai")
            .location(example.location())
            .resourceGroupName(example.name())
            .applicationType("web")
            .build());
        var exampleKeyVault = new KeyVault("exampleKeyVault", KeyVaultArgs.builder()
            .name("example-kv")
            .location(example.location())
            .resourceGroupName(example.name())
            .tenantId(current.tenantId())
            .skuName("standard")
            .purgeProtectionEnabled(true)
            .build());
        var exampleAccount = new Account("exampleAccount", AccountArgs.builder()
            .name("examplesa")
            .location(example.location())
            .resourceGroupName(example.name())
            .accountTier("Standard")
            .accountReplicationType("LRS")
            .build());
        var exampleWorkspace = new Workspace("exampleWorkspace", WorkspaceArgs.builder()
            .name("example-mlw")
            .location(example.location())
            .resourceGroupName(example.name())
            .applicationInsightsId(exampleInsights.id())
            .keyVaultId(exampleKeyVault.id())
            .storageAccountId(exampleAccount.id())
            .identity(WorkspaceIdentityArgs.builder()
                .type("SystemAssigned")
                .build())
            .build());
        var exampleVirtualNetwork = new VirtualNetwork("exampleVirtualNetwork", VirtualNetworkArgs.builder()
            .name("example-vnet")
            .addressSpaces("10.1.0.0/16")
            .location(example.location())
            .resourceGroupName(example.name())
            .build());
        var exampleSubnet = new Subnet("exampleSubnet", SubnetArgs.builder()
            .name("example-subnet")
            .resourceGroupName(example.name())
            .virtualNetworkName(exampleVirtualNetwork.name())
            .addressPrefixes("10.1.0.0/24")
            .build());
        var exampleKubernetesCluster = new KubernetesCluster("exampleKubernetesCluster", KubernetesClusterArgs.builder()
            .name("example-aks")
            .location(example.location())
            .resourceGroupName(example.name())
            .dnsPrefixPrivateCluster("prefix")
            .defaultNodePool(KubernetesClusterDefaultNodePoolArgs.builder()
                .name("default")
                .nodeCount(3)
                .vmSize("Standard_D3_v2")
                .vnetSubnetId(exampleSubnet.id())
                .build())
            .identity(KubernetesClusterIdentityArgs.builder()
                .type("SystemAssigned")
                .build())
            .build());
        var exampleInferenceCluster = new InferenceCluster("exampleInferenceCluster", InferenceClusterArgs.builder()
            .name("example")
            .location(example.location())
            .clusterPurpose("FastProd")
            .kubernetesClusterId(exampleKubernetesCluster.id())
            .description("This is an example cluster used with Terraform")
            .machineLearningWorkspaceId(exampleWorkspace.id())
            .tags(Map.of("stage", "example"))
            .build());
    }
}
resources:
  example:
    type: azure:core:ResourceGroup
    properties:
      name: example-rg
      location: west europe
      tags:
        stage: example
  exampleInsights:
    type: azure:appinsights:Insights
    name: example
    properties:
      name: example-ai
      location: ${example.location}
      resourceGroupName: ${example.name}
      applicationType: web
  exampleKeyVault:
    type: azure:keyvault:KeyVault
    name: example
    properties:
      name: example-kv
      location: ${example.location}
      resourceGroupName: ${example.name}
      tenantId: ${current.tenantId}
      skuName: standard
      purgeProtectionEnabled: true
  exampleAccount:
    type: azure:storage:Account
    name: example
    properties:
      name: examplesa
      location: ${example.location}
      resourceGroupName: ${example.name}
      accountTier: Standard
      accountReplicationType: LRS
  exampleWorkspace:
    type: azure:machinelearning:Workspace
    name: example
    properties:
      name: example-mlw
      location: ${example.location}
      resourceGroupName: ${example.name}
      applicationInsightsId: ${exampleInsights.id}
      keyVaultId: ${exampleKeyVault.id}
      storageAccountId: ${exampleAccount.id}
      identity:
        type: SystemAssigned
  exampleVirtualNetwork:
    type: azure:network:VirtualNetwork
    name: example
    properties:
      name: example-vnet
      addressSpaces:
        - 10.1.0.0/16
      location: ${example.location}
      resourceGroupName: ${example.name}
  exampleSubnet:
    type: azure:network:Subnet
    name: example
    properties:
      name: example-subnet
      resourceGroupName: ${example.name}
      virtualNetworkName: ${exampleVirtualNetwork.name}
      addressPrefixes:
        - 10.1.0.0/24
  exampleKubernetesCluster:
    type: azure:containerservice:KubernetesCluster
    name: example
    properties:
      name: example-aks
      location: ${example.location}
      resourceGroupName: ${example.name}
      dnsPrefixPrivateCluster: prefix
      defaultNodePool:
        name: default
        nodeCount: 3
        vmSize: Standard_D3_v2
        vnetSubnetId: ${exampleSubnet.id}
      identity:
        type: SystemAssigned
  exampleInferenceCluster:
    type: azure:machinelearning:InferenceCluster
    name: example
    properties:
      name: example
      location: ${example.location}
      clusterPurpose: FastProd
      kubernetesClusterId: ${exampleKubernetesCluster.id}
      description: This is an example cluster used with Terraform
      machineLearningWorkspaceId: ${exampleWorkspace.id}
      tags:
        stage: example
variables:
  current:
    fn::invoke:
      function: azure:core:getClientConfig
      arguments: {}
API Providers
This resource uses the following Azure API Providers:
- Microsoft.ContainerService- 2025-05-01
- Microsoft.MachineLearningServices- 2025-06-01
Create InferenceCluster Resource
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new InferenceCluster(name: string, args: InferenceClusterArgs, opts?: CustomResourceOptions);@overload
def InferenceCluster(resource_name: str,
                     args: InferenceClusterArgs,
                     opts: Optional[ResourceOptions] = None)
@overload
def InferenceCluster(resource_name: str,
                     opts: Optional[ResourceOptions] = None,
                     kubernetes_cluster_id: Optional[str] = None,
                     machine_learning_workspace_id: Optional[str] = None,
                     cluster_purpose: Optional[str] = None,
                     description: Optional[str] = None,
                     identity: Optional[InferenceClusterIdentityArgs] = None,
                     location: Optional[str] = None,
                     name: Optional[str] = None,
                     ssl: Optional[InferenceClusterSslArgs] = None,
                     tags: Optional[Mapping[str, str]] = None)func NewInferenceCluster(ctx *Context, name string, args InferenceClusterArgs, opts ...ResourceOption) (*InferenceCluster, error)public InferenceCluster(string name, InferenceClusterArgs args, CustomResourceOptions? opts = null)
public InferenceCluster(String name, InferenceClusterArgs args)
public InferenceCluster(String name, InferenceClusterArgs args, CustomResourceOptions options)
type: azure:machinelearning:InferenceCluster
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.
Parameters
- name string
- The unique name of the resource.
- args InferenceClusterArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- resource_name str
- The unique name of the resource.
- args InferenceClusterArgs
- The arguments to resource properties.
- opts ResourceOptions
- Bag of options to control resource's behavior.
- ctx Context
- Context object for the current deployment.
- name string
- The unique name of the resource.
- args InferenceClusterArgs
- The arguments to resource properties.
- opts ResourceOption
- Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args InferenceClusterArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- name String
- The unique name of the resource.
- args InferenceClusterArgs
- The arguments to resource properties.
- options CustomResourceOptions
- Bag of options to control resource's behavior.
Constructor example
The following reference example uses placeholder values for all input properties.
var inferenceClusterResource = new Azure.MachineLearning.InferenceCluster("inferenceClusterResource", new()
{
    KubernetesClusterId = "string",
    MachineLearningWorkspaceId = "string",
    ClusterPurpose = "string",
    Description = "string",
    Identity = new Azure.MachineLearning.Inputs.InferenceClusterIdentityArgs
    {
        Type = "string",
        IdentityIds = new[]
        {
            "string",
        },
        PrincipalId = "string",
        TenantId = "string",
    },
    Location = "string",
    Name = "string",
    Ssl = new Azure.MachineLearning.Inputs.InferenceClusterSslArgs
    {
        Cert = "string",
        Cname = "string",
        Key = "string",
        LeafDomainLabel = "string",
        OverwriteExistingDomain = false,
    },
    Tags = 
    {
        { "string", "string" },
    },
});
example, err := machinelearning.NewInferenceCluster(ctx, "inferenceClusterResource", &machinelearning.InferenceClusterArgs{
	KubernetesClusterId:        pulumi.String("string"),
	MachineLearningWorkspaceId: pulumi.String("string"),
	ClusterPurpose:             pulumi.String("string"),
	Description:                pulumi.String("string"),
	Identity: &machinelearning.InferenceClusterIdentityArgs{
		Type: pulumi.String("string"),
		IdentityIds: pulumi.StringArray{
			pulumi.String("string"),
		},
		PrincipalId: pulumi.String("string"),
		TenantId:    pulumi.String("string"),
	},
	Location: pulumi.String("string"),
	Name:     pulumi.String("string"),
	Ssl: &machinelearning.InferenceClusterSslArgs{
		Cert:                    pulumi.String("string"),
		Cname:                   pulumi.String("string"),
		Key:                     pulumi.String("string"),
		LeafDomainLabel:         pulumi.String("string"),
		OverwriteExistingDomain: pulumi.Bool(false),
	},
	Tags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
})
var inferenceClusterResource = new InferenceCluster("inferenceClusterResource", InferenceClusterArgs.builder()
    .kubernetesClusterId("string")
    .machineLearningWorkspaceId("string")
    .clusterPurpose("string")
    .description("string")
    .identity(InferenceClusterIdentityArgs.builder()
        .type("string")
        .identityIds("string")
        .principalId("string")
        .tenantId("string")
        .build())
    .location("string")
    .name("string")
    .ssl(InferenceClusterSslArgs.builder()
        .cert("string")
        .cname("string")
        .key("string")
        .leafDomainLabel("string")
        .overwriteExistingDomain(false)
        .build())
    .tags(Map.of("string", "string"))
    .build());
inference_cluster_resource = azure.machinelearning.InferenceCluster("inferenceClusterResource",
    kubernetes_cluster_id="string",
    machine_learning_workspace_id="string",
    cluster_purpose="string",
    description="string",
    identity={
        "type": "string",
        "identity_ids": ["string"],
        "principal_id": "string",
        "tenant_id": "string",
    },
    location="string",
    name="string",
    ssl={
        "cert": "string",
        "cname": "string",
        "key": "string",
        "leaf_domain_label": "string",
        "overwrite_existing_domain": False,
    },
    tags={
        "string": "string",
    })
const inferenceClusterResource = new azure.machinelearning.InferenceCluster("inferenceClusterResource", {
    kubernetesClusterId: "string",
    machineLearningWorkspaceId: "string",
    clusterPurpose: "string",
    description: "string",
    identity: {
        type: "string",
        identityIds: ["string"],
        principalId: "string",
        tenantId: "string",
    },
    location: "string",
    name: "string",
    ssl: {
        cert: "string",
        cname: "string",
        key: "string",
        leafDomainLabel: "string",
        overwriteExistingDomain: false,
    },
    tags: {
        string: "string",
    },
});
type: azure:machinelearning:InferenceCluster
properties:
    clusterPurpose: string
    description: string
    identity:
        identityIds:
            - string
        principalId: string
        tenantId: string
        type: string
    kubernetesClusterId: string
    location: string
    machineLearningWorkspaceId: string
    name: string
    ssl:
        cert: string
        cname: string
        key: string
        leafDomainLabel: string
        overwriteExistingDomain: false
    tags:
        string: string
InferenceCluster Resource Properties
To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.
Inputs
In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.
The InferenceCluster resource accepts the following input properties:
- KubernetesCluster stringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- MachineLearning stringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- ClusterPurpose string
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- Description string
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Identity
InferenceCluster Identity 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Location string
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- Name string
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Ssl
InferenceCluster Ssl 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Dictionary<string, string>
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- KubernetesCluster stringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- MachineLearning stringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- ClusterPurpose string
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- Description string
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Identity
InferenceCluster Identity Args 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Location string
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- Name string
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Ssl
InferenceCluster Ssl Args 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- map[string]string
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetesCluster StringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- machineLearning StringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- clusterPurpose String
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description String
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity
InferenceCluster Identity 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- location String
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- name String
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl
InferenceCluster Ssl 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Map<String,String>
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetesCluster stringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- machineLearning stringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- clusterPurpose string
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description string
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity
InferenceCluster Identity 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- location string
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- name string
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl
InferenceCluster Ssl 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- {[key: string]: string}
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetes_cluster_ strid 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- machine_learning_ strworkspace_ id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- cluster_purpose str
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description str
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity
InferenceCluster Identity Args 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- location str
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- name str
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl
InferenceCluster Ssl Args 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Mapping[str, str]
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetesCluster StringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- machineLearning StringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- clusterPurpose String
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description String
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity Property Map
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- location String
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- name String
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl Property Map
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Map<String>
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
Outputs
All input properties are implicitly available as output properties. Additionally, the InferenceCluster resource produces the following output properties:
- Id string
- The provider-assigned unique ID for this managed resource.
- Id string
- The provider-assigned unique ID for this managed resource.
- id String
- The provider-assigned unique ID for this managed resource.
- id string
- The provider-assigned unique ID for this managed resource.
- id str
- The provider-assigned unique ID for this managed resource.
- id String
- The provider-assigned unique ID for this managed resource.
Look up Existing InferenceCluster Resource
Get an existing InferenceCluster resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.
public static get(name: string, id: Input<ID>, state?: InferenceClusterState, opts?: CustomResourceOptions): InferenceCluster@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        cluster_purpose: Optional[str] = None,
        description: Optional[str] = None,
        identity: Optional[InferenceClusterIdentityArgs] = None,
        kubernetes_cluster_id: Optional[str] = None,
        location: Optional[str] = None,
        machine_learning_workspace_id: Optional[str] = None,
        name: Optional[str] = None,
        ssl: Optional[InferenceClusterSslArgs] = None,
        tags: Optional[Mapping[str, str]] = None) -> InferenceClusterfunc GetInferenceCluster(ctx *Context, name string, id IDInput, state *InferenceClusterState, opts ...ResourceOption) (*InferenceCluster, error)public static InferenceCluster Get(string name, Input<string> id, InferenceClusterState? state, CustomResourceOptions? opts = null)public static InferenceCluster get(String name, Output<String> id, InferenceClusterState state, CustomResourceOptions options)resources:  _:    type: azure:machinelearning:InferenceCluster    get:      id: ${id}- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- resource_name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- ClusterPurpose string
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- Description string
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Identity
InferenceCluster Identity 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- KubernetesCluster stringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Location string
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- MachineLearning stringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- Name string
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Ssl
InferenceCluster Ssl 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Dictionary<string, string>
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ClusterPurpose string
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- Description string
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Identity
InferenceCluster Identity Args 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- KubernetesCluster stringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Location string
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- MachineLearning stringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- Name string
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- Ssl
InferenceCluster Ssl Args 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- map[string]string
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- clusterPurpose String
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description String
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity
InferenceCluster Identity 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetesCluster StringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- location String
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- machineLearning StringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- name String
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl
InferenceCluster Ssl 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Map<String,String>
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- clusterPurpose string
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description string
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity
InferenceCluster Identity 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetesCluster stringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- location string
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- machineLearning stringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- name string
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl
InferenceCluster Ssl 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- {[key: string]: string}
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- cluster_purpose str
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description str
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity
InferenceCluster Identity Args 
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetes_cluster_ strid 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- location str
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- machine_learning_ strworkspace_ id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- name str
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl
InferenceCluster Ssl Args 
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Mapping[str, str]
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- clusterPurpose String
- The purpose of the Inference Cluster. Options are - DevTest,- DenseProdand- FastProd. If used for Development or Testing, use- DevTesthere. Default purpose is- FastProd, which is recommended for production workloads. Changing this forces a new Machine Learning Inference Cluster to be created.- Note: When creating or attaching a cluster, if the cluster will be used for production ( - cluster_purpose = "FastProd"), then it must contain at least 12 virtual CPUs. The number of virtual CPUs can be calculated by multiplying the number of nodes in the cluster by the number of cores provided by the VM size selected. For example, if you use a VM size of "Standard_D3_v2", which has 4 virtual cores, then you should select 3 or greater as the number of nodes.
- description String
- The description of the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- identity Property Map
- An identityblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- kubernetesCluster StringId 
- The ID of the Kubernetes Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- location String
- The Azure Region where the Machine Learning Inference Cluster should exist. Changing this forces a new Machine Learning Inference Cluster to be created.
- machineLearning StringWorkspace Id 
- The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Inference Cluster to be created.
- name String
- The name which should be used for this Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
- ssl Property Map
- A sslblock as defined below. Changing this forces a new Machine Learning Inference Cluster to be created.
- Map<String>
- A mapping of tags which should be assigned to the Machine Learning Inference Cluster. Changing this forces a new Machine Learning Inference Cluster to be created.
Supporting Types
InferenceClusterIdentity, InferenceClusterIdentityArgs      
- Type string
- Specifies the type of Managed Service Identity that should be configured on this Machine Learning Inference Cluster. Possible values are SystemAssigned,UserAssigned,SystemAssigned, UserAssigned(to enable both). Changing this forces a new resource to be created.
- IdentityIds List<string>
- Specifies a list of User Assigned Managed Identity IDs to be assigned to this Machine Learning Inference Cluster. Changing this forces a new resource to be created. - Note: This is required when - typeis set to- UserAssignedor- SystemAssigned, UserAssigned.
- PrincipalId string
- The Principal ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- TenantId string
- The Tenant ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- Type string
- Specifies the type of Managed Service Identity that should be configured on this Machine Learning Inference Cluster. Possible values are SystemAssigned,UserAssigned,SystemAssigned, UserAssigned(to enable both). Changing this forces a new resource to be created.
- IdentityIds []string
- Specifies a list of User Assigned Managed Identity IDs to be assigned to this Machine Learning Inference Cluster. Changing this forces a new resource to be created. - Note: This is required when - typeis set to- UserAssignedor- SystemAssigned, UserAssigned.
- PrincipalId string
- The Principal ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- TenantId string
- The Tenant ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- type String
- Specifies the type of Managed Service Identity that should be configured on this Machine Learning Inference Cluster. Possible values are SystemAssigned,UserAssigned,SystemAssigned, UserAssigned(to enable both). Changing this forces a new resource to be created.
- identityIds List<String>
- Specifies a list of User Assigned Managed Identity IDs to be assigned to this Machine Learning Inference Cluster. Changing this forces a new resource to be created. - Note: This is required when - typeis set to- UserAssignedor- SystemAssigned, UserAssigned.
- principalId String
- The Principal ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- tenantId String
- The Tenant ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- type string
- Specifies the type of Managed Service Identity that should be configured on this Machine Learning Inference Cluster. Possible values are SystemAssigned,UserAssigned,SystemAssigned, UserAssigned(to enable both). Changing this forces a new resource to be created.
- identityIds string[]
- Specifies a list of User Assigned Managed Identity IDs to be assigned to this Machine Learning Inference Cluster. Changing this forces a new resource to be created. - Note: This is required when - typeis set to- UserAssignedor- SystemAssigned, UserAssigned.
- principalId string
- The Principal ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- tenantId string
- The Tenant ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- type str
- Specifies the type of Managed Service Identity that should be configured on this Machine Learning Inference Cluster. Possible values are SystemAssigned,UserAssigned,SystemAssigned, UserAssigned(to enable both). Changing this forces a new resource to be created.
- identity_ids Sequence[str]
- Specifies a list of User Assigned Managed Identity IDs to be assigned to this Machine Learning Inference Cluster. Changing this forces a new resource to be created. - Note: This is required when - typeis set to- UserAssignedor- SystemAssigned, UserAssigned.
- principal_id str
- The Principal ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- tenant_id str
- The Tenant ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- type String
- Specifies the type of Managed Service Identity that should be configured on this Machine Learning Inference Cluster. Possible values are SystemAssigned,UserAssigned,SystemAssigned, UserAssigned(to enable both). Changing this forces a new resource to be created.
- identityIds List<String>
- Specifies a list of User Assigned Managed Identity IDs to be assigned to this Machine Learning Inference Cluster. Changing this forces a new resource to be created. - Note: This is required when - typeis set to- UserAssignedor- SystemAssigned, UserAssigned.
- principalId String
- The Principal ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
- tenantId String
- The Tenant ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Inference Cluster.
InferenceClusterSsl, InferenceClusterSslArgs      
- Cert string
- The certificate for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- Cname string
- The cname of the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- Key string
- The key content for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- LeafDomain stringLabel 
- The leaf domain label for the SSL configuration. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cname. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- OverwriteExisting boolDomain 
- Whether or not to overwrite existing leaf domain. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cnameChanging this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- Cert string
- The certificate for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- Cname string
- The cname of the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- Key string
- The key content for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- LeafDomain stringLabel 
- The leaf domain label for the SSL configuration. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cname. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- OverwriteExisting boolDomain 
- Whether or not to overwrite existing leaf domain. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cnameChanging this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cert String
- The certificate for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cname String
- The cname of the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- key String
- The key content for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- leafDomain StringLabel 
- The leaf domain label for the SSL configuration. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cname. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- overwriteExisting BooleanDomain 
- Whether or not to overwrite existing leaf domain. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cnameChanging this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cert string
- The certificate for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cname string
- The cname of the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- key string
- The key content for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- leafDomain stringLabel 
- The leaf domain label for the SSL configuration. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cname. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- overwriteExisting booleanDomain 
- Whether or not to overwrite existing leaf domain. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cnameChanging this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cert str
- The certificate for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cname str
- The cname of the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- key str
- The key content for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- leaf_domain_ strlabel 
- The leaf domain label for the SSL configuration. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cname. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- overwrite_existing_ booldomain 
- Whether or not to overwrite existing leaf domain. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cnameChanging this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cert String
- The certificate for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- cname String
- The cname of the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- key String
- The key content for the SSL configuration.Conflicts with ssl[0].leaf_domain_label,ssl[0].overwrite_existing_domain. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- leafDomain StringLabel 
- The leaf domain label for the SSL configuration. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cname. Changing this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
- overwriteExisting BooleanDomain 
- Whether or not to overwrite existing leaf domain. Conflicts with ssl[0].cert,ssl[0].key,ssl[0].cnameChanging this forces a new Machine Learning Inference Cluster to be created. Defaults to"".
Import
Machine Learning Inference Clusters can be imported using the resource id, e.g.
$ pulumi import azure:machinelearning/inferenceCluster:InferenceCluster example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/resGroup1/providers/Microsoft.MachineLearningServices/workspaces/workspace1/computes/cluster1
To learn more about importing existing cloud resources, see Importing resources.
Package Details
- Repository
- Azure Classic pulumi/pulumi-azure
- License
- Apache-2.0
- Notes
- This Pulumi package is based on the azurermTerraform Provider.
