@pulumi/gcp
Version:
A Pulumi package for creating and managing Google Cloud Platform resources.
137 lines • 5.58 kB
JavaScript
;
// *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***
Object.defineProperty(exports, "__esModule", { value: true });
exports.EngineModel = void 0;
const pulumi = require("@pulumi/pulumi");
const utilities = require("../utilities");
/**
* Represents a machine learning solution.
*
* A model can have multiple versions, each of which is a deployed, trained model
* ready to receive prediction requests. The model itself is just a container.
*
* To get more information about Model, see:
*
* * [API documentation](https://cloud.google.com/ai-platform/prediction/docs/reference/rest/v1/projects.models)
* * How-to Guides
* * [Official Documentation](https://cloud.google.com/ai-platform/prediction/docs/deploying-models)
*
* ## Example Usage
*
* ### Ml Model Basic
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as gcp from "@pulumi/gcp";
*
* const _default = new gcp.ml.EngineModel("default", {
* name: "default",
* description: "My model",
* regions: "us-central1",
* });
* ```
* ### Ml Model Full
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as gcp from "@pulumi/gcp";
*
* const _default = new gcp.ml.EngineModel("default", {
* name: "default",
* description: "My model",
* regions: "us-central1",
* labels: {
* my_model: "foo",
* },
* onlinePredictionLogging: true,
* onlinePredictionConsoleLogging: true,
* });
* ```
*
* ## Import
*
* Model can be imported using any of these accepted formats:
*
* * `projects/{{project}}/models/{{name}}`
*
* * `{{project}}/{{name}}`
*
* * `{{name}}`
*
* When using the `pulumi import` command, Model can be imported using one of the formats above. For example:
*
* ```sh
* $ pulumi import gcp:ml/engineModel:EngineModel default projects/{{project}}/models/{{name}}
* ```
*
* ```sh
* $ pulumi import gcp:ml/engineModel:EngineModel default {{project}}/{{name}}
* ```
*
* ```sh
* $ pulumi import gcp:ml/engineModel:EngineModel default {{name}}
* ```
*/
class EngineModel extends pulumi.CustomResource {
/**
* Get an existing EngineModel 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 EngineModel(name, state, Object.assign(Object.assign({}, opts), { id: id }));
}
/**
* Returns true if the given object is an instance of EngineModel. 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'] === EngineModel.__pulumiType;
}
constructor(name, argsOrState, opts) {
let resourceInputs = {};
opts = opts || {};
if (opts.id) {
const state = argsOrState;
resourceInputs["defaultVersion"] = state ? state.defaultVersion : undefined;
resourceInputs["description"] = state ? state.description : undefined;
resourceInputs["effectiveLabels"] = state ? state.effectiveLabels : undefined;
resourceInputs["labels"] = state ? state.labels : undefined;
resourceInputs["name"] = state ? state.name : undefined;
resourceInputs["onlinePredictionConsoleLogging"] = state ? state.onlinePredictionConsoleLogging : undefined;
resourceInputs["onlinePredictionLogging"] = state ? state.onlinePredictionLogging : undefined;
resourceInputs["project"] = state ? state.project : undefined;
resourceInputs["pulumiLabels"] = state ? state.pulumiLabels : undefined;
resourceInputs["regions"] = state ? state.regions : undefined;
}
else {
const args = argsOrState;
resourceInputs["defaultVersion"] = args ? args.defaultVersion : undefined;
resourceInputs["description"] = args ? args.description : undefined;
resourceInputs["labels"] = args ? args.labels : undefined;
resourceInputs["name"] = args ? args.name : undefined;
resourceInputs["onlinePredictionConsoleLogging"] = args ? args.onlinePredictionConsoleLogging : undefined;
resourceInputs["onlinePredictionLogging"] = args ? args.onlinePredictionLogging : undefined;
resourceInputs["project"] = args ? args.project : undefined;
resourceInputs["regions"] = args ? args.regions : undefined;
resourceInputs["effectiveLabels"] = undefined /*out*/;
resourceInputs["pulumiLabels"] = undefined /*out*/;
}
opts = pulumi.mergeOptions(utilities.resourceOptsDefaults(), opts);
const secretOpts = { additionalSecretOutputs: ["effectiveLabels", "pulumiLabels"] };
opts = pulumi.mergeOptions(opts, secretOpts);
super(EngineModel.__pulumiType, name, resourceInputs, opts);
}
}
exports.EngineModel = EngineModel;
/** @internal */
EngineModel.__pulumiType = 'gcp:ml/engineModel:EngineModel';
//# sourceMappingURL=engineModel.js.map