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Google Cloud Classic v6.66.0 published on Monday, Sep 18, 2023 by Pulumi

gcp.vertex.AiEndpoint

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Google Cloud Classic v6.66.0 published on Monday, Sep 18, 2023 by Pulumi

    Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

    To get more information about Endpoint, see:

    Example Usage

    Vertex Ai Endpoint Network

    using System.Collections.Generic;
    using System.Linq;
    using Pulumi;
    using Gcp = Pulumi.Gcp;
    
    return await Deployment.RunAsync(() => 
    {
        var vertexNetwork = Gcp.Compute.GetNetwork.Invoke(new()
        {
            Name = "network-name",
        });
    
        var vertexRange = new Gcp.Compute.GlobalAddress("vertexRange", new()
        {
            Purpose = "VPC_PEERING",
            AddressType = "INTERNAL",
            PrefixLength = 24,
            Network = vertexNetwork.Apply(getNetworkResult => getNetworkResult.Id),
        });
    
        var vertexVpcConnection = new Gcp.ServiceNetworking.Connection("vertexVpcConnection", new()
        {
            Network = vertexNetwork.Apply(getNetworkResult => getNetworkResult.Id),
            Service = "servicenetworking.googleapis.com",
            ReservedPeeringRanges = new[]
            {
                vertexRange.Name,
            },
        });
    
        var project = Gcp.Organizations.GetProject.Invoke();
    
        var endpoint = new Gcp.Vertex.AiEndpoint("endpoint", new()
        {
            DisplayName = "sample-endpoint",
            Description = "A sample vertex endpoint",
            Location = "us-central1",
            Region = "us-central1",
            Labels = 
            {
                { "label-one", "value-one" },
            },
            Network = Output.Tuple(project, vertexNetwork).Apply(values =>
            {
                var project = values.Item1;
                var vertexNetwork = values.Item2;
                return $"projects/{project.Apply(getProjectResult => getProjectResult.Number)}/global/networks/{vertexNetwork.Apply(getNetworkResult => getNetworkResult.Name)}";
            }),
            EncryptionSpec = new Gcp.Vertex.Inputs.AiEndpointEncryptionSpecArgs
            {
                KmsKeyName = "kms-name",
            },
        }, new CustomResourceOptions
        {
            DependsOn = new[]
            {
                vertexVpcConnection,
            },
        });
    
        var cryptoKey = new Gcp.Kms.CryptoKeyIAMMember("cryptoKey", new()
        {
            CryptoKeyId = "kms-name",
            Role = "roles/cloudkms.cryptoKeyEncrypterDecrypter",
            Member = $"serviceAccount:service-{project.Apply(getProjectResult => getProjectResult.Number)}@gcp-sa-aiplatform.iam.gserviceaccount.com",
        });
    
    });
    
    package main
    
    import (
    	"fmt"
    
    	"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/compute"
    	"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/kms"
    	"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/organizations"
    	"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/servicenetworking"
    	"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/vertex"
    	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
    )
    
    func main() {
    	pulumi.Run(func(ctx *pulumi.Context) error {
    		vertexNetwork, err := compute.LookupNetwork(ctx, &compute.LookupNetworkArgs{
    			Name: "network-name",
    		}, nil)
    		if err != nil {
    			return err
    		}
    		vertexRange, err := compute.NewGlobalAddress(ctx, "vertexRange", &compute.GlobalAddressArgs{
    			Purpose:      pulumi.String("VPC_PEERING"),
    			AddressType:  pulumi.String("INTERNAL"),
    			PrefixLength: pulumi.Int(24),
    			Network:      *pulumi.String(vertexNetwork.Id),
    		})
    		if err != nil {
    			return err
    		}
    		vertexVpcConnection, err := servicenetworking.NewConnection(ctx, "vertexVpcConnection", &servicenetworking.ConnectionArgs{
    			Network: *pulumi.String(vertexNetwork.Id),
    			Service: pulumi.String("servicenetworking.googleapis.com"),
    			ReservedPeeringRanges: pulumi.StringArray{
    				vertexRange.Name,
    			},
    		})
    		if err != nil {
    			return err
    		}
    		project, err := organizations.LookupProject(ctx, nil, nil)
    		if err != nil {
    			return err
    		}
    		_, err = vertex.NewAiEndpoint(ctx, "endpoint", &vertex.AiEndpointArgs{
    			DisplayName: pulumi.String("sample-endpoint"),
    			Description: pulumi.String("A sample vertex endpoint"),
    			Location:    pulumi.String("us-central1"),
    			Region:      pulumi.String("us-central1"),
    			Labels: pulumi.StringMap{
    				"label-one": pulumi.String("value-one"),
    			},
    			Network: pulumi.String(fmt.Sprintf("projects/%v/global/networks/%v", project.Number, vertexNetwork.Name)),
    			EncryptionSpec: &vertex.AiEndpointEncryptionSpecArgs{
    				KmsKeyName: pulumi.String("kms-name"),
    			},
    		}, pulumi.DependsOn([]pulumi.Resource{
    			vertexVpcConnection,
    		}))
    		if err != nil {
    			return err
    		}
    		_, err = kms.NewCryptoKeyIAMMember(ctx, "cryptoKey", &kms.CryptoKeyIAMMemberArgs{
    			CryptoKeyId: pulumi.String("kms-name"),
    			Role:        pulumi.String("roles/cloudkms.cryptoKeyEncrypterDecrypter"),
    			Member:      pulumi.String(fmt.Sprintf("serviceAccount:service-%v@gcp-sa-aiplatform.iam.gserviceaccount.com", project.Number)),
    		})
    		if err != nil {
    			return err
    		}
    		return nil
    	})
    }
    
    package generated_program;
    
    import com.pulumi.Context;
    import com.pulumi.Pulumi;
    import com.pulumi.core.Output;
    import com.pulumi.gcp.compute.ComputeFunctions;
    import com.pulumi.gcp.compute.inputs.GetNetworkArgs;
    import com.pulumi.gcp.compute.GlobalAddress;
    import com.pulumi.gcp.compute.GlobalAddressArgs;
    import com.pulumi.gcp.servicenetworking.Connection;
    import com.pulumi.gcp.servicenetworking.ConnectionArgs;
    import com.pulumi.gcp.organizations.OrganizationsFunctions;
    import com.pulumi.gcp.organizations.inputs.GetProjectArgs;
    import com.pulumi.gcp.vertex.AiEndpoint;
    import com.pulumi.gcp.vertex.AiEndpointArgs;
    import com.pulumi.gcp.vertex.inputs.AiEndpointEncryptionSpecArgs;
    import com.pulumi.gcp.kms.CryptoKeyIAMMember;
    import com.pulumi.gcp.kms.CryptoKeyIAMMemberArgs;
    import com.pulumi.resources.CustomResourceOptions;
    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 vertexNetwork = ComputeFunctions.getNetwork(GetNetworkArgs.builder()
                .name("network-name")
                .build());
    
            var vertexRange = new GlobalAddress("vertexRange", GlobalAddressArgs.builder()        
                .purpose("VPC_PEERING")
                .addressType("INTERNAL")
                .prefixLength(24)
                .network(vertexNetwork.applyValue(getNetworkResult -> getNetworkResult.id()))
                .build());
    
            var vertexVpcConnection = new Connection("vertexVpcConnection", ConnectionArgs.builder()        
                .network(vertexNetwork.applyValue(getNetworkResult -> getNetworkResult.id()))
                .service("servicenetworking.googleapis.com")
                .reservedPeeringRanges(vertexRange.name())
                .build());
    
            final var project = OrganizationsFunctions.getProject();
    
            var endpoint = new AiEndpoint("endpoint", AiEndpointArgs.builder()        
                .displayName("sample-endpoint")
                .description("A sample vertex endpoint")
                .location("us-central1")
                .region("us-central1")
                .labels(Map.of("label-one", "value-one"))
                .network(String.format("projects/%s/global/networks/%s", project.applyValue(getProjectResult -> getProjectResult.number()),vertexNetwork.applyValue(getNetworkResult -> getNetworkResult.name())))
                .encryptionSpec(AiEndpointEncryptionSpecArgs.builder()
                    .kmsKeyName("kms-name")
                    .build())
                .build(), CustomResourceOptions.builder()
                    .dependsOn(vertexVpcConnection)
                    .build());
    
            var cryptoKey = new CryptoKeyIAMMember("cryptoKey", CryptoKeyIAMMemberArgs.builder()        
                .cryptoKeyId("kms-name")
                .role("roles/cloudkms.cryptoKeyEncrypterDecrypter")
                .member(String.format("serviceAccount:service-%s@gcp-sa-aiplatform.iam.gserviceaccount.com", project.applyValue(getProjectResult -> getProjectResult.number())))
                .build());
    
        }
    }
    
    import pulumi
    import pulumi_gcp as gcp
    
    vertex_network = gcp.compute.get_network(name="network-name")
    vertex_range = gcp.compute.GlobalAddress("vertexRange",
        purpose="VPC_PEERING",
        address_type="INTERNAL",
        prefix_length=24,
        network=vertex_network.id)
    vertex_vpc_connection = gcp.servicenetworking.Connection("vertexVpcConnection",
        network=vertex_network.id,
        service="servicenetworking.googleapis.com",
        reserved_peering_ranges=[vertex_range.name])
    project = gcp.organizations.get_project()
    endpoint = gcp.vertex.AiEndpoint("endpoint",
        display_name="sample-endpoint",
        description="A sample vertex endpoint",
        location="us-central1",
        region="us-central1",
        labels={
            "label-one": "value-one",
        },
        network=f"projects/{project.number}/global/networks/{vertex_network.name}",
        encryption_spec=gcp.vertex.AiEndpointEncryptionSpecArgs(
            kms_key_name="kms-name",
        ),
        opts=pulumi.ResourceOptions(depends_on=[vertex_vpc_connection]))
    crypto_key = gcp.kms.CryptoKeyIAMMember("cryptoKey",
        crypto_key_id="kms-name",
        role="roles/cloudkms.cryptoKeyEncrypterDecrypter",
        member=f"serviceAccount:service-{project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com")
    
    import * as pulumi from "@pulumi/pulumi";
    import * as gcp from "@pulumi/gcp";
    
    const vertexNetwork = gcp.compute.getNetwork({
        name: "network-name",
    });
    const vertexRange = new gcp.compute.GlobalAddress("vertexRange", {
        purpose: "VPC_PEERING",
        addressType: "INTERNAL",
        prefixLength: 24,
        network: vertexNetwork.then(vertexNetwork => vertexNetwork.id),
    });
    const vertexVpcConnection = new gcp.servicenetworking.Connection("vertexVpcConnection", {
        network: vertexNetwork.then(vertexNetwork => vertexNetwork.id),
        service: "servicenetworking.googleapis.com",
        reservedPeeringRanges: [vertexRange.name],
    });
    const project = gcp.organizations.getProject({});
    const endpoint = new gcp.vertex.AiEndpoint("endpoint", {
        displayName: "sample-endpoint",
        description: "A sample vertex endpoint",
        location: "us-central1",
        region: "us-central1",
        labels: {
            "label-one": "value-one",
        },
        network: Promise.all([project, vertexNetwork]).then(([project, vertexNetwork]) => `projects/${project.number}/global/networks/${vertexNetwork.name}`),
        encryptionSpec: {
            kmsKeyName: "kms-name",
        },
    }, {
        dependsOn: [vertexVpcConnection],
    });
    const cryptoKey = new gcp.kms.CryptoKeyIAMMember("cryptoKey", {
        cryptoKeyId: "kms-name",
        role: "roles/cloudkms.cryptoKeyEncrypterDecrypter",
        member: project.then(project => `serviceAccount:service-${project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com`),
    });
    
    resources:
      endpoint:
        type: gcp:vertex:AiEndpoint
        properties:
          displayName: sample-endpoint
          description: A sample vertex endpoint
          location: us-central1
          region: us-central1
          labels:
            label-one: value-one
          network: projects/${project.number}/global/networks/${vertexNetwork.name}
          encryptionSpec:
            kmsKeyName: kms-name
        options:
          dependson:
            - ${vertexVpcConnection}
      vertexVpcConnection:
        type: gcp:servicenetworking:Connection
        properties:
          network: ${vertexNetwork.id}
          service: servicenetworking.googleapis.com
          reservedPeeringRanges:
            - ${vertexRange.name}
      vertexRange:
        type: gcp:compute:GlobalAddress
        properties:
          purpose: VPC_PEERING
          addressType: INTERNAL
          prefixLength: 24
          network: ${vertexNetwork.id}
      cryptoKey:
        type: gcp:kms:CryptoKeyIAMMember
        properties:
          cryptoKeyId: kms-name
          role: roles/cloudkms.cryptoKeyEncrypterDecrypter
          member: serviceAccount:service-${project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com
    variables:
      vertexNetwork:
        fn::invoke:
          Function: gcp:compute:getNetwork
          Arguments:
            name: network-name
      project:
        fn::invoke:
          Function: gcp:organizations:getProject
          Arguments: {}
    

    Create AiEndpoint Resource

    new AiEndpoint(name: string, args: AiEndpointArgs, opts?: CustomResourceOptions);
    @overload
    def AiEndpoint(resource_name: str,
                   opts: Optional[ResourceOptions] = None,
                   description: Optional[str] = None,
                   display_name: Optional[str] = None,
                   encryption_spec: Optional[AiEndpointEncryptionSpecArgs] = None,
                   labels: Optional[Mapping[str, str]] = None,
                   location: Optional[str] = None,
                   name: Optional[str] = None,
                   network: Optional[str] = None,
                   project: Optional[str] = None,
                   region: Optional[str] = None)
    @overload
    def AiEndpoint(resource_name: str,
                   args: AiEndpointArgs,
                   opts: Optional[ResourceOptions] = None)
    func NewAiEndpoint(ctx *Context, name string, args AiEndpointArgs, opts ...ResourceOption) (*AiEndpoint, error)
    public AiEndpoint(string name, AiEndpointArgs args, CustomResourceOptions? opts = null)
    public AiEndpoint(String name, AiEndpointArgs args)
    public AiEndpoint(String name, AiEndpointArgs args, CustomResourceOptions options)
    
    type: gcp:vertex:AiEndpoint
    properties: # The arguments to resource properties.
    options: # Bag of options to control resource's behavior.
    
    
    name string
    The unique name of the resource.
    args AiEndpointArgs
    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 AiEndpointArgs
    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 AiEndpointArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args AiEndpointArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args AiEndpointArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    AiEndpoint Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    The AiEndpoint resource accepts the following input properties:

    DisplayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    Location string

    The location for the resource


    Description string

    The description of the Endpoint.

    EncryptionSpec AiEndpointEncryptionSpec

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    Labels Dictionary<string, string>

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    Name string

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    Network string

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    Project string

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    Region string

    The region for the resource

    DisplayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    Location string

    The location for the resource


    Description string

    The description of the Endpoint.

    EncryptionSpec AiEndpointEncryptionSpecArgs

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    Labels map[string]string

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    Name string

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    Network string

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    Project string

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    Region string

    The region for the resource

    displayName String

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    location String

    The location for the resource


    description String

    The description of the Endpoint.

    encryptionSpec AiEndpointEncryptionSpec

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    labels Map<String,String>

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    name String

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network String

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project String

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region String

    The region for the resource

    displayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    location string

    The location for the resource


    description string

    The description of the Endpoint.

    encryptionSpec AiEndpointEncryptionSpec

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    labels {[key: string]: string}

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    name string

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network string

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project string

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region string

    The region for the resource

    display_name str

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    location str

    The location for the resource


    description str

    The description of the Endpoint.

    encryption_spec AiEndpointEncryptionSpecArgs

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    labels Mapping[str, str]

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    name str

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network str

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project str

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region str

    The region for the resource

    displayName String

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    location String

    The location for the resource


    description String

    The description of the Endpoint.

    encryptionSpec Property Map

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    labels Map<String>

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    name String

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network String

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project String

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region String

    The region for the resource

    Outputs

    All input properties are implicitly available as output properties. Additionally, the AiEndpoint resource produces the following output properties:

    CreateTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    DeployedModels List<AiEndpointDeployedModel>

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    Etag string

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    Id string

    The provider-assigned unique ID for this managed resource.

    ModelDeploymentMonitoringJob string

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    UpdateTime string

    Output only. Timestamp when this Endpoint was last updated.

    CreateTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    DeployedModels []AiEndpointDeployedModel

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    Etag string

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    Id string

    The provider-assigned unique ID for this managed resource.

    ModelDeploymentMonitoringJob string

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    UpdateTime string

    Output only. Timestamp when this Endpoint was last updated.

    createTime String

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployedModels List<AiEndpointDeployedModel>

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    etag String

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    id String

    The provider-assigned unique ID for this managed resource.

    modelDeploymentMonitoringJob String

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    updateTime String

    Output only. Timestamp when this Endpoint was last updated.

    createTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployedModels AiEndpointDeployedModel[]

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    etag string

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    id string

    The provider-assigned unique ID for this managed resource.

    modelDeploymentMonitoringJob string

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    updateTime string

    Output only. Timestamp when this Endpoint was last updated.

    create_time str

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployed_models Sequence[AiEndpointDeployedModel]

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    etag str

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    id str

    The provider-assigned unique ID for this managed resource.

    model_deployment_monitoring_job str

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    update_time str

    Output only. Timestamp when this Endpoint was last updated.

    createTime String

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployedModels List<Property Map>

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    etag String

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    id String

    The provider-assigned unique ID for this managed resource.

    modelDeploymentMonitoringJob String

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    updateTime String

    Output only. Timestamp when this Endpoint was last updated.

    Look up Existing AiEndpoint Resource

    Get an existing AiEndpoint 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?: AiEndpointState, opts?: CustomResourceOptions): AiEndpoint
    @staticmethod
    def get(resource_name: str,
            id: str,
            opts: Optional[ResourceOptions] = None,
            create_time: Optional[str] = None,
            deployed_models: Optional[Sequence[AiEndpointDeployedModelArgs]] = None,
            description: Optional[str] = None,
            display_name: Optional[str] = None,
            encryption_spec: Optional[AiEndpointEncryptionSpecArgs] = None,
            etag: Optional[str] = None,
            labels: Optional[Mapping[str, str]] = None,
            location: Optional[str] = None,
            model_deployment_monitoring_job: Optional[str] = None,
            name: Optional[str] = None,
            network: Optional[str] = None,
            project: Optional[str] = None,
            region: Optional[str] = None,
            update_time: Optional[str] = None) -> AiEndpoint
    func GetAiEndpoint(ctx *Context, name string, id IDInput, state *AiEndpointState, opts ...ResourceOption) (*AiEndpoint, error)
    public static AiEndpoint Get(string name, Input<string> id, AiEndpointState? state, CustomResourceOptions? opts = null)
    public static AiEndpoint get(String name, Output<String> id, AiEndpointState state, CustomResourceOptions options)
    Resource lookup is not supported in YAML
    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.
    The following state arguments are supported:
    CreateTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    DeployedModels List<AiEndpointDeployedModel>

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    Description string

    The description of the Endpoint.

    DisplayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    EncryptionSpec AiEndpointEncryptionSpec

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    Etag string

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    Labels Dictionary<string, string>

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    Location string

    The location for the resource


    ModelDeploymentMonitoringJob string

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    Name string

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    Network string

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    Project string

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    Region string

    The region for the resource

    UpdateTime string

    Output only. Timestamp when this Endpoint was last updated.

    CreateTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    DeployedModels []AiEndpointDeployedModelArgs

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    Description string

    The description of the Endpoint.

    DisplayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    EncryptionSpec AiEndpointEncryptionSpecArgs

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    Etag string

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    Labels map[string]string

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    Location string

    The location for the resource


    ModelDeploymentMonitoringJob string

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    Name string

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    Network string

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    Project string

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    Region string

    The region for the resource

    UpdateTime string

    Output only. Timestamp when this Endpoint was last updated.

    createTime String

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployedModels List<AiEndpointDeployedModel>

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    description String

    The description of the Endpoint.

    displayName String

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    encryptionSpec AiEndpointEncryptionSpec

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    etag String

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    labels Map<String,String>

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    location String

    The location for the resource


    modelDeploymentMonitoringJob String

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    name String

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network String

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project String

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region String

    The region for the resource

    updateTime String

    Output only. Timestamp when this Endpoint was last updated.

    createTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployedModels AiEndpointDeployedModel[]

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    description string

    The description of the Endpoint.

    displayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    encryptionSpec AiEndpointEncryptionSpec

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    etag string

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    labels {[key: string]: string}

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    location string

    The location for the resource


    modelDeploymentMonitoringJob string

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    name string

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network string

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project string

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region string

    The region for the resource

    updateTime string

    Output only. Timestamp when this Endpoint was last updated.

    create_time str

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployed_models Sequence[AiEndpointDeployedModelArgs]

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    description str

    The description of the Endpoint.

    display_name str

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    encryption_spec AiEndpointEncryptionSpecArgs

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    etag str

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    labels Mapping[str, str]

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    location str

    The location for the resource


    model_deployment_monitoring_job str

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    name str

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network str

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project str

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region str

    The region for the resource

    update_time str

    Output only. Timestamp when this Endpoint was last updated.

    createTime String

    (Output) Output only. Timestamp when the DeployedModel was created.

    deployedModels List<Property Map>

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. Models can also be deployed and undeployed using the Cloud Console. Structure is documented below.

    description String

    The description of the Endpoint.

    displayName String

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    encryptionSpec Property Map

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Structure is documented below.

    etag String

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    labels Map<String>

    The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    location String

    The location for the resource


    modelDeploymentMonitoringJob String

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

    name String

    The resource name of the Endpoint. The name must be numeric with no leading zeros and can be at most 10 digits.

    network String

    The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.

    project String

    The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

    region String

    The region for the resource

    updateTime String

    Output only. Timestamp when this Endpoint was last updated.

    Supporting Types

    AiEndpointDeployedModel, AiEndpointDeployedModelArgs

    AutomaticResources List<AiEndpointDeployedModelAutomaticResource>

    (Output) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Structure is documented below.

    CreateTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    DedicatedResources List<AiEndpointDeployedModelDedicatedResource>

    (Output) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. Structure is documented below.

    DisplayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    EnableAccessLogging bool

    (Output) These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

    EnableContainerLogging bool

    (Output) If true, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging. Only supported for custom-trained Models and AutoML Tabular Models.

    Id string

    (Output) The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.

    Model string

    (Output) The name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

    ModelVersionId string

    (Output) Output only. The version ID of the model that is deployed.

    PrivateEndpoints List<AiEndpointDeployedModelPrivateEndpoint>

    (Output) Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured. Structure is documented below.

    ServiceAccount string

    (Output) The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

    SharedResources string

    (Output) The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

    AutomaticResources []AiEndpointDeployedModelAutomaticResource

    (Output) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Structure is documented below.

    CreateTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    DedicatedResources []AiEndpointDeployedModelDedicatedResource

    (Output) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. Structure is documented below.

    DisplayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    EnableAccessLogging bool

    (Output) These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

    EnableContainerLogging bool

    (Output) If true, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging. Only supported for custom-trained Models and AutoML Tabular Models.

    Id string

    (Output) The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.

    Model string

    (Output) The name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

    ModelVersionId string

    (Output) Output only. The version ID of the model that is deployed.

    PrivateEndpoints []AiEndpointDeployedModelPrivateEndpoint

    (Output) Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured. Structure is documented below.

    ServiceAccount string

    (Output) The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

    SharedResources string

    (Output) The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

    automaticResources List<AiEndpointDeployedModelAutomaticResource>

    (Output) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Structure is documented below.

    createTime String

    (Output) Output only. Timestamp when the DeployedModel was created.

    dedicatedResources List<AiEndpointDeployedModelDedicatedResource>

    (Output) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. Structure is documented below.

    displayName String

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    enableAccessLogging Boolean

    (Output) These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

    enableContainerLogging Boolean

    (Output) If true, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging. Only supported for custom-trained Models and AutoML Tabular Models.

    id String

    (Output) The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.

    model String

    (Output) The name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

    modelVersionId String

    (Output) Output only. The version ID of the model that is deployed.

    privateEndpoints List<AiEndpointDeployedModelPrivateEndpoint>

    (Output) Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured. Structure is documented below.

    serviceAccount String

    (Output) The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

    sharedResources String

    (Output) The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

    automaticResources AiEndpointDeployedModelAutomaticResource[]

    (Output) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Structure is documented below.

    createTime string

    (Output) Output only. Timestamp when the DeployedModel was created.

    dedicatedResources AiEndpointDeployedModelDedicatedResource[]

    (Output) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. Structure is documented below.

    displayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    enableAccessLogging boolean

    (Output) These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

    enableContainerLogging boolean

    (Output) If true, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging. Only supported for custom-trained Models and AutoML Tabular Models.

    id string

    (Output) The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.

    model string

    (Output) The name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

    modelVersionId string

    (Output) Output only. The version ID of the model that is deployed.

    privateEndpoints AiEndpointDeployedModelPrivateEndpoint[]

    (Output) Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured. Structure is documented below.

    serviceAccount string

    (Output) The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

    sharedResources string

    (Output) The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

    automatic_resources Sequence[AiEndpointDeployedModelAutomaticResource]

    (Output) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Structure is documented below.

    create_time str

    (Output) Output only. Timestamp when the DeployedModel was created.

    dedicated_resources Sequence[AiEndpointDeployedModelDedicatedResource]

    (Output) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. Structure is documented below.

    display_name str

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    enable_access_logging bool

    (Output) These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

    enable_container_logging bool

    (Output) If true, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging. Only supported for custom-trained Models and AutoML Tabular Models.

    id str

    (Output) The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.

    model str

    (Output) The name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

    model_version_id str

    (Output) Output only. The version ID of the model that is deployed.

    private_endpoints Sequence[AiEndpointDeployedModelPrivateEndpoint]

    (Output) Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured. Structure is documented below.

    service_account str

    (Output) The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

    shared_resources str

    (Output) The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

    automaticResources List<Property Map>

    (Output) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Structure is documented below.

    createTime String

    (Output) Output only. Timestamp when the DeployedModel was created.

    dedicatedResources List<Property Map>

    (Output) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. Structure is documented below.

    displayName String

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    enableAccessLogging Boolean

    (Output) These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

    enableContainerLogging Boolean

    (Output) If true, the container of the DeployedModel instances will send stderr and stdout streams to Stackdriver Logging. Only supported for custom-trained Models and AutoML Tabular Models.

    id String

    (Output) The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.

    model String

    (Output) The name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

    modelVersionId String

    (Output) Output only. The version ID of the model that is deployed.

    privateEndpoints List<Property Map>

    (Output) Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured. Structure is documented below.

    serviceAccount String

    (Output) The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

    sharedResources String

    (Output) The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

    AiEndpointDeployedModelAutomaticResource, AiEndpointDeployedModelAutomaticResourceArgs

    MaxReplicaCount int

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    MinReplicaCount int

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    MaxReplicaCount int

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    MinReplicaCount int

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    maxReplicaCount Integer

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    minReplicaCount Integer

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    maxReplicaCount number

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    minReplicaCount number

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    max_replica_count int

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    min_replica_count int

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    maxReplicaCount Number

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    minReplicaCount Number

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    AiEndpointDeployedModelDedicatedResource, AiEndpointDeployedModelDedicatedResourceArgs

    AutoscalingMetricSpecs List<AiEndpointDeployedModelDedicatedResourceAutoscalingMetricSpec>

    (Output) The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80. Structure is documented below.

    MachineSpecs List<AiEndpointDeployedModelDedicatedResourceMachineSpec>

    (Output) The specification of a single machine used by the prediction. Structure is documented below.

    MaxReplicaCount int

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    MinReplicaCount int

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    AutoscalingMetricSpecs []AiEndpointDeployedModelDedicatedResourceAutoscalingMetricSpec

    (Output) The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80. Structure is documented below.

    MachineSpecs []AiEndpointDeployedModelDedicatedResourceMachineSpec

    (Output) The specification of a single machine used by the prediction. Structure is documented below.

    MaxReplicaCount int

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    MinReplicaCount int

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    autoscalingMetricSpecs List<AiEndpointDeployedModelDedicatedResourceAutoscalingMetricSpec>

    (Output) The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80. Structure is documented below.

    machineSpecs List<AiEndpointDeployedModelDedicatedResourceMachineSpec>

    (Output) The specification of a single machine used by the prediction. Structure is documented below.

    maxReplicaCount Integer

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    minReplicaCount Integer

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    autoscalingMetricSpecs AiEndpointDeployedModelDedicatedResourceAutoscalingMetricSpec[]

    (Output) The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80. Structure is documented below.

    machineSpecs AiEndpointDeployedModelDedicatedResourceMachineSpec[]

    (Output) The specification of a single machine used by the prediction. Structure is documented below.

    maxReplicaCount number

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    minReplicaCount number

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    autoscaling_metric_specs Sequence[AiEndpointDeployedModelDedicatedResourceAutoscalingMetricSpec]

    (Output) The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80. Structure is documented below.

    machine_specs Sequence[AiEndpointDeployedModelDedicatedResourceMachineSpec]

    (Output) The specification of a single machine used by the prediction. Structure is documented below.

    max_replica_count int

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    min_replica_count int

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    autoscalingMetricSpecs List<Property Map>

    (Output) The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80. Structure is documented below.

    machineSpecs List<Property Map>

    (Output) The specification of a single machine used by the prediction. Structure is documented below.

    maxReplicaCount Number

    (Output) The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

    minReplicaCount Number

    (Output) The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

    AiEndpointDeployedModelDedicatedResourceAutoscalingMetricSpec, AiEndpointDeployedModelDedicatedResourceAutoscalingMetricSpecArgs

    MetricName string

    (Output) The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization

    Target int

    (Output) The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    MetricName string

    (Output) The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization

    Target int

    (Output) The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    metricName String

    (Output) The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization

    target Integer

    (Output) The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    metricName string

    (Output) The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization

    target number

    (Output) The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    metric_name str

    (Output) The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization

    target int

    (Output) The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    metricName String

    (Output) The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization

    target Number

    (Output) The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

    AiEndpointDeployedModelDedicatedResourceMachineSpec, AiEndpointDeployedModelDedicatedResourceMachineSpecArgs

    AcceleratorCount int

    (Output) The number of accelerators to attach to the machine.

    AcceleratorType string

    (Output) The type of accelerator(s) that may be attached to the machine as per accelerator_count. See possible values here.

    MachineType string

    (Output) The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. TODO(rsurowka): Try to better unify the required vs optional.

    AcceleratorCount int

    (Output) The number of accelerators to attach to the machine.

    AcceleratorType string

    (Output) The type of accelerator(s) that may be attached to the machine as per accelerator_count. See possible values here.

    MachineType string

    (Output) The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. TODO(rsurowka): Try to better unify the required vs optional.

    acceleratorCount Integer

    (Output) The number of accelerators to attach to the machine.

    acceleratorType String

    (Output) The type of accelerator(s) that may be attached to the machine as per accelerator_count. See possible values here.

    machineType String

    (Output) The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. TODO(rsurowka): Try to better unify the required vs optional.

    acceleratorCount number

    (Output) The number of accelerators to attach to the machine.

    acceleratorType string

    (Output) The type of accelerator(s) that may be attached to the machine as per accelerator_count. See possible values here.

    machineType string

    (Output) The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. TODO(rsurowka): Try to better unify the required vs optional.

    accelerator_count int

    (Output) The number of accelerators to attach to the machine.

    accelerator_type str

    (Output) The type of accelerator(s) that may be attached to the machine as per accelerator_count. See possible values here.

    machine_type str

    (Output) The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. TODO(rsurowka): Try to better unify the required vs optional.

    acceleratorCount Number

    (Output) The number of accelerators to attach to the machine.

    acceleratorType String

    (Output) The type of accelerator(s) that may be attached to the machine as per accelerator_count. See possible values here.

    machineType String

    (Output) The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. TODO(rsurowka): Try to better unify the required vs optional.

    AiEndpointDeployedModelPrivateEndpoint, AiEndpointDeployedModelPrivateEndpointArgs

    ExplainHttpUri string

    (Output) Output only. Http(s) path to send explain requests.

    HealthHttpUri string

    (Output) Output only. Http(s) path to send health check requests.

    PredictHttpUri string

    (Output) Output only. Http(s) path to send prediction requests.

    ServiceAttachment string

    (Output) Output only. The name of the service attachment resource. Populated if private service connect is enabled.

    ExplainHttpUri string

    (Output) Output only. Http(s) path to send explain requests.

    HealthHttpUri string

    (Output) Output only. Http(s) path to send health check requests.

    PredictHttpUri string

    (Output) Output only. Http(s) path to send prediction requests.

    ServiceAttachment string

    (Output) Output only. The name of the service attachment resource. Populated if private service connect is enabled.

    explainHttpUri String

    (Output) Output only. Http(s) path to send explain requests.

    healthHttpUri String

    (Output) Output only. Http(s) path to send health check requests.

    predictHttpUri String

    (Output) Output only. Http(s) path to send prediction requests.

    serviceAttachment String

    (Output) Output only. The name of the service attachment resource. Populated if private service connect is enabled.

    explainHttpUri string

    (Output) Output only. Http(s) path to send explain requests.

    healthHttpUri string

    (Output) Output only. Http(s) path to send health check requests.

    predictHttpUri string

    (Output) Output only. Http(s) path to send prediction requests.

    serviceAttachment string

    (Output) Output only. The name of the service attachment resource. Populated if private service connect is enabled.

    explain_http_uri str

    (Output) Output only. Http(s) path to send explain requests.

    health_http_uri str

    (Output) Output only. Http(s) path to send health check requests.

    predict_http_uri str

    (Output) Output only. Http(s) path to send prediction requests.

    service_attachment str

    (Output) Output only. The name of the service attachment resource. Populated if private service connect is enabled.

    explainHttpUri String

    (Output) Output only. Http(s) path to send explain requests.

    healthHttpUri String

    (Output) Output only. Http(s) path to send health check requests.

    predictHttpUri String

    (Output) Output only. Http(s) path to send prediction requests.

    serviceAttachment String

    (Output) Output only. The name of the service attachment resource. Populated if private service connect is enabled.

    AiEndpointEncryptionSpec, AiEndpointEncryptionSpecArgs

    KmsKeyName string

    Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    KmsKeyName string

    Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    kmsKeyName String

    Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    kmsKeyName string

    Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    kms_key_name str

    Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    kmsKeyName String

    Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    Import

    Endpoint can be imported using any of these accepted formats

     $ pulumi import gcp:vertex/aiEndpoint:AiEndpoint default projects/{{project}}/locations/{{location}}/endpoints/{{name}}
    
     $ pulumi import gcp:vertex/aiEndpoint:AiEndpoint default {{project}}/{{location}}/{{name}}
    
     $ pulumi import gcp:vertex/aiEndpoint:AiEndpoint default {{location}}/{{name}}
    

    Package Details

    Repository
    Google Cloud (GCP) Classic pulumi/pulumi-gcp
    License
    Apache-2.0
    Notes

    This Pulumi package is based on the google-beta Terraform Provider.

    gcp logo
    Google Cloud Classic v6.66.0 published on Monday, Sep 18, 2023 by Pulumi