@pulumi/gcp
Version:
A Pulumi package for creating and managing Google Cloud Platform resources.
163 lines • 6.45 kB
JavaScript
;
// *** WARNING: this file was generated by pulumi-language-nodejs. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***
Object.defineProperty(exports, "__esModule", { value: true });
exports.AiTensorboard = void 0;
const pulumi = require("@pulumi/pulumi");
const utilities = require("../utilities");
/**
* 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](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.tensorboards)
* * How-to Guides
* * [Official Documentation](https://cloud.google.com/vertex-ai/docs)
*
* ## Example Usage
*
* ### Vertex Ai Tensorboard
*
* ```typescript
* 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",
* });
* ```
* ### Vertex Ai Tensorboard Full
*
* ```typescript
* 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],
* });
* ```
*
* ## 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:
*
* ```sh
* $ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default projects/{{project}}/locations/{{region}}/tensorboards/{{name}}
* ```
*
* ```sh
* $ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{project}}/{{region}}/{{name}}
* ```
*
* ```sh
* $ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{region}}/{{name}}
* ```
*
* ```sh
* $ pulumi import gcp:vertex/aiTensorboard:AiTensorboard default {{name}}
* ```
*/
class AiTensorboard extends pulumi.CustomResource {
/**
* Get an existing AiTensorboard resource's state with the given name, ID, and optional extra
* properties used to qualify the lookup.
*
* @param name The _unique_ name of the resulting resource.
* @param id The _unique_ provider ID of the resource to lookup.
* @param state Any extra arguments used during the lookup.
* @param opts Optional settings to control the behavior of the CustomResource.
*/
static get(name, id, state, opts) {
return new AiTensorboard(name, state, { ...opts, id: id });
}
/**
* Returns true if the given object is an instance of AiTensorboard. This is designed to work even
* when multiple copies of the Pulumi SDK have been loaded into the same process.
*/
static isInstance(obj) {
if (obj === undefined || obj === null) {
return false;
}
return obj['__pulumiType'] === AiTensorboard.__pulumiType;
}
constructor(name, argsOrState, opts) {
let resourceInputs = {};
opts = opts || {};
if (opts.id) {
const state = argsOrState;
resourceInputs["blobStoragePathPrefix"] = state?.blobStoragePathPrefix;
resourceInputs["createTime"] = state?.createTime;
resourceInputs["description"] = state?.description;
resourceInputs["displayName"] = state?.displayName;
resourceInputs["effectiveLabels"] = state?.effectiveLabels;
resourceInputs["encryptionSpec"] = state?.encryptionSpec;
resourceInputs["labels"] = state?.labels;
resourceInputs["name"] = state?.name;
resourceInputs["project"] = state?.project;
resourceInputs["pulumiLabels"] = state?.pulumiLabels;
resourceInputs["region"] = state?.region;
resourceInputs["runCount"] = state?.runCount;
resourceInputs["updateTime"] = state?.updateTime;
}
else {
const args = argsOrState;
if (args?.displayName === undefined && !opts.urn) {
throw new Error("Missing required property 'displayName'");
}
resourceInputs["description"] = args?.description;
resourceInputs["displayName"] = args?.displayName;
resourceInputs["encryptionSpec"] = args?.encryptionSpec;
resourceInputs["labels"] = args?.labels;
resourceInputs["project"] = args?.project;
resourceInputs["region"] = args?.region;
resourceInputs["blobStoragePathPrefix"] = undefined /*out*/;
resourceInputs["createTime"] = undefined /*out*/;
resourceInputs["effectiveLabels"] = undefined /*out*/;
resourceInputs["name"] = undefined /*out*/;
resourceInputs["pulumiLabels"] = undefined /*out*/;
resourceInputs["runCount"] = undefined /*out*/;
resourceInputs["updateTime"] = undefined /*out*/;
}
opts = pulumi.mergeOptions(utilities.resourceOptsDefaults(), opts);
const secretOpts = { additionalSecretOutputs: ["effectiveLabels", "pulumiLabels"] };
opts = pulumi.mergeOptions(opts, secretOpts);
super(AiTensorboard.__pulumiType, name, resourceInputs, opts);
}
}
exports.AiTensorboard = AiTensorboard;
/** @internal */
AiTensorboard.__pulumiType = 'gcp:vertex/aiTensorboard:AiTensorboard';
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