pulumi-gcp-kotlin/com.pulumi.gcp.vertex.kotlin/AiTensorboardArgs

AiTensorboardArgs

data class AiTensorboardArgs(val description: Output<String>? = null, val displayName: Output<String>? = null, val encryptionSpec: Output<AiTensorboardEncryptionSpecArgs>? = null, val labels: Output<Map<String, String>>? = null, val project: Output<String>? = null, val region: Output<String>? = null) : ConvertibleToJava<AiTensorboardArgs>

Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a GCP project. If needed users can also create extra Tensorboards in their projects. To get more information about Tensorboard, see:

Example Usage

Vertex Ai Tensorboard

import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
const tensorboard = new gcp.vertex.AiTensorboard("tensorboard", {
    displayName: "terraform",
    description: "sample description",
    labels: {
        key1: "value1",
        key2: "value2",
    },
    region: "us-central1",
});
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import pulumi
import pulumi_gcp as gcp
tensorboard = gcp.vertex.AiTensorboard("tensorboard",
    display_name="terraform",
    description="sample description",
    labels={
        "key1": "value1",
        "key2": "value2",
    },
    region="us-central1")
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using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Gcp = Pulumi.Gcp;
return await Deployment.RunAsync(() =>
{
    var tensorboard = new Gcp.Vertex.AiTensorboard("tensorboard", new()
    {
        DisplayName = "terraform",
        Description = "sample description",
        Labels =
        {
            { "key1", "value1" },
            { "key2", "value2" },
        },
        Region = "us-central1",
    });
});
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package main
import (
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/vertex"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := vertex.NewAiTensorboard(ctx, "tensorboard", &vertex.AiTensorboardArgs{
			DisplayName: pulumi.String("terraform"),
			Description: pulumi.String("sample description"),
			Labels: pulumi.StringMap{
				"key1": pulumi.String("value1"),
				"key2": pulumi.String("value2"),
			},
			Region: pulumi.String("us-central1"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiTensorboard;
import com.pulumi.gcp.vertex.AiTensorboardArgs;
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) {
        var tensorboard = new AiTensorboard("tensorboard", AiTensorboardArgs.builder()
            .displayName("terraform")
            .description("sample description")
            .labels(Map.ofEntries(
                Map.entry("key1", "value1"),
                Map.entry("key2", "value2")
            ))
            .region("us-central1")
            .build());
    }
}
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resources:
  tensorboard:
    type: gcp:vertex:AiTensorboard
    properties:
      displayName: terraform
      description: sample description
      labels:
        key1: value1
        key2: value2
      region: us-central1
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Vertex Ai Tensorboard Full

import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
const project = gcp.organizations.getProject({});
const cryptoKey = new gcp.kms.CryptoKeyIAMMember("crypto_key", {
    cryptoKeyId: "kms-name",
    role: "roles/cloudkms.cryptoKeyEncrypterDecrypter",
    member: project.then(project => `serviceAccount:service-${project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com`),
});
const tensorboard = new gcp.vertex.AiTensorboard("tensorboard", {
    displayName: "terraform",
    description: "sample description",
    labels: {
        key1: "value1",
        key2: "value2",
    },
    region: "us-central1",
    encryptionSpec: {
        kmsKeyName: "kms-name",
    },
}, {
    dependsOn: [cryptoKey],
});
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import pulumi
import pulumi_gcp as gcp
project = gcp.organizations.get_project()
crypto_key = gcp.kms.CryptoKeyIAMMember("crypto_key",
    crypto_key_id="kms-name",
    role="roles/cloudkms.cryptoKeyEncrypterDecrypter",
    member=f"serviceAccount:service-{project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com")
tensorboard = gcp.vertex.AiTensorboard("tensorboard",
    display_name="terraform",
    description="sample description",
    labels={
        "key1": "value1",
        "key2": "value2",
    },
    region="us-central1",
    encryption_spec={
        "kms_key_name": "kms-name",
    },
    opts = pulumi.ResourceOptions(depends_on=[crypto_key]))
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using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Gcp = Pulumi.Gcp;
return await Deployment.RunAsync(() =>
{
    var project = Gcp.Organizations.GetProject.Invoke();
    var cryptoKey = new Gcp.Kms.CryptoKeyIAMMember("crypto_key", new()
    {
        CryptoKeyId = "kms-name",
        Role = "roles/cloudkms.cryptoKeyEncrypterDecrypter",
        Member = $"serviceAccount:service-{project.Apply(getProjectResult => getProjectResult.Number)}@gcp-sa-aiplatform.iam.gserviceaccount.com",
    });
    var tensorboard = new Gcp.Vertex.AiTensorboard("tensorboard", new()
    {
        DisplayName = "terraform",
        Description = "sample description",
        Labels =
        {
            { "key1", "value1" },
            { "key2", "value2" },
        },
        Region = "us-central1",
        EncryptionSpec = new Gcp.Vertex.Inputs.AiTensorboardEncryptionSpecArgs
        {
            KmsKeyName = "kms-name",
        },
    }, new CustomResourceOptions
    {
        DependsOn =
        {
            cryptoKey,
        },
    });
});
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package main
import (
	"fmt"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/kms"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/organizations"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/vertex"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		project, err := organizations.LookupProject(ctx, nil, nil)
		if err != nil {
			return err
		}
		cryptoKey, err := kms.NewCryptoKeyIAMMember(ctx, "crypto_key", &kms.CryptoKeyIAMMemberArgs{
			CryptoKeyId: pulumi.String("kms-name"),
			Role:        pulumi.String("roles/cloudkms.cryptoKeyEncrypterDecrypter"),
			Member:      pulumi.Sprintf("serviceAccount:service-%v@gcp-sa-aiplatform.iam.gserviceaccount.com", project.Number),
		})
		if err != nil {
			return err
		}
		_, err = vertex.NewAiTensorboard(ctx, "tensorboard", &vertex.AiTensorboardArgs{
			DisplayName: pulumi.String("terraform"),
			Description: pulumi.String("sample description"),
			Labels: pulumi.StringMap{
				"key1": pulumi.String("value1"),
				"key2": pulumi.String("value2"),
			},
			Region: pulumi.String("us-central1"),
			EncryptionSpec: &vertex.AiTensorboardEncryptionSpecArgs{
				KmsKeyName: pulumi.String("kms-name"),
			},
		}, pulumi.DependsOn([]pulumi.Resource{
			cryptoKey,
		}))
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.organizations.OrganizationsFunctions;
import com.pulumi.gcp.organizations.inputs.GetProjectArgs;
import com.pulumi.gcp.kms.CryptoKeyIAMMember;
import com.pulumi.gcp.kms.CryptoKeyIAMMemberArgs;
import com.pulumi.gcp.vertex.AiTensorboard;
import com.pulumi.gcp.vertex.AiTensorboardArgs;
import com.pulumi.gcp.vertex.inputs.AiTensorboardEncryptionSpecArgs;
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 project = OrganizationsFunctions.getProject();
        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());
        var tensorboard = new AiTensorboard("tensorboard", AiTensorboardArgs.builder()
            .displayName("terraform")
            .description("sample description")
            .labels(Map.ofEntries(
                Map.entry("key1", "value1"),
                Map.entry("key2", "value2")
            ))
            .region("us-central1")
            .encryptionSpec(AiTensorboardEncryptionSpecArgs.builder()
                .kmsKeyName("kms-name")
                .build())
            .build(), CustomResourceOptions.builder()
                .dependsOn(cryptoKey)
                .build());
    }
}
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resources:
  tensorboard:
    type: gcp:vertex:AiTensorboard
    properties:
      displayName: terraform
      description: sample description
      labels:
        key1: value1
        key2: value2
      region: us-central1
      encryptionSpec:
        kmsKeyName: kms-name
    options:
      dependson:
        - ${cryptoKey}
  cryptoKey:
    type: gcp:kms:CryptoKeyIAMMember
    name: crypto_key
    properties:
      cryptoKeyId: kms-name
      role: roles/cloudkms.cryptoKeyEncrypterDecrypter
      member: serviceAccount:service-${project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com
variables:
  project:
    fn::invoke:
      Function: gcp:organizations:getProject
      Arguments: {}
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Import

Tensorboard can be imported using any of these accepted formats:

  • projects/{{project}}/locations/{{region}}/tensorboards/{{name}}

  • {{project}}/{{region}}/{{name}}

  • {{region}}/{{name}}

  • {{name}} When using the pulumi import command, Tensorboard can be imported using one of the formats above. For example:

$ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default projects/{{project}}/locations/{{region}}/tensorboards/{{name}}
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$ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{project}}/{{region}}/{{name}}
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$ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{region}}/{{name}}
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$ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{name}}
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Constructors

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constructor(description: Output<String>? = null, displayName: Output<String>? = null, encryptionSpec: Output<AiTensorboardEncryptionSpecArgs>? = null, labels: Output<Map<String, String>>? = null, project: Output<String>? = null, region: Output<String>? = null)

Properties

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val description: Output<String>? = null

Description of this Tensorboard.

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val displayName: Output<String>? = null

User provided name of this Tensorboard.

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val encryptionSpec: Output<AiTensorboardEncryptionSpecArgs>? = null

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

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val labels: Output<Map<String, String>>? = null

The labels with user-defined metadata to organize your Tensorboards. Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

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val project: Output<String>? = null

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

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val region: Output<String>? = null

The region of the tensorboard. eg us-central1

Functions

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open override fun toJava(): AiTensorboardArgs