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

AiTensorboard

class AiTensorboard : KotlinCustomResource

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:

API documentation
How-to Guides

Official Documentation

Example Usage

Vertex Ai Tensorboard

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());
    }
}

Vertex Ai Tensorboard Full

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());
    }
}

Import

Tensorboard can be imported using any of these accepted formats

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

$ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{project}}/{{region}}/{{name}}

$ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{region}}/{{name}}

$ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{name}}

Properties

blobStoragePathPrefix

val blobStoragePathPrefix: Output<String>

Consumer project Cloud Storage path prefix used to store blob data, which can either be a bucket or directory. Does not end with a '/'.

createTime

val createTime: Output<String>

The timestamp of when the Tensorboard was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

description

val description: Output<String>?

Description of this Tensorboard.

displayName

val displayName: Output<String>

User provided name of this Tensorboard.

encryptionSpec

val encryptionSpec: Output<AiTensorboardEncryptionSpec>?

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.