@pulumi/databricks/modelServingProvisionedThroughput.js

A Pulumi package for creating and managing databricks cloud resources.

"use strict";
// *** 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.ModelServingProvisionedThroughput = void 0;
const pulumi = require("@pulumi/pulumi");
const utilities = require("./utilities");
/**
 * This resource allows you to manage [Foundation Model provisioned throughput](https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/deploy-prov-throughput-foundation-model-apis) endpoints in Databricks.
 *
 * > This resource is currently in private preview, and only available for enrolled customers.
 *
 * > This resource can only be used with a workspace-level provider!
 *
 * ## Example Usage
 *
 * Creating a Foundation Model provisioned throughput endpoint
 *
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as databricks from "@pulumi/databricks";
 *
 * const llama = new databricks.ModelServingProvisionedThroughput("llama", {
 *     aiGateway: {
 *         usageTrackingConfig: {
 *             enabled: true,
 *         },
 *     },
 *     config: {
 *         servedEntities: [{
 *             entityName: "system.ai.llama-4-maverick",
 *             entityVersion: "1",
 *             provisionedModelUnits: 100,
 *         }],
 *     },
 * });
 * ```
 *
 * ## Access Control
 *
 * * databricks.Permissions can control which groups or individual users can *Manage*, *Query* or *View* individual serving endpoints.
 *
 * ## Related Resources
 *
 * The following resources are often used in the same context:
 *
 * * databricks.ModelServing to create custom and external serving endpoints in Databricks.
 * * databricks.RegisteredModel to create [Models in Unity Catalog](https://docs.databricks.com/en/mlflow/models-in-uc.html) in Databricks.
 * * End to end workspace management guide.
 * * databricks.Directory to manage directories in [Databricks Workspace](https://docs.databricks.com/workspace/workspace-objects.html).
 * * databricks.MlflowModel to create models in the [workspace model registry](https://docs.databricks.com/en/mlflow/model-registry.html) in Databricks.
 * * databricks.Notebook to manage [Databricks Notebooks](https://docs.databricks.com/notebooks/index.html).
 * * databricks.Notebook data to export a notebook from Databricks Workspace.
 * * databricks.Repo to manage [Databricks Repos](https://docs.databricks.com/repos.html).
 *
 * ## Import
 *
 * The model serving provisioned throughput resource can be imported using the name of the endpoint:
 *
 * hcl
 *
 * import {
 *
 *   to = databricks_model_serving_provisioned_throughput.this
 *
 *   id = "<model-serving-endpoint-name>"
 *
 * }
 *
 * ```sh
 * $ pulumi import databricks:index/modelServingProvisionedThroughput:ModelServingProvisionedThroughput Alternatively, when using Pulumi version 1.4 or earlier, import using the command:
 * ```
 *
 * bash
 *
 * ```sh
 * $ pulumi import databricks:index/modelServingProvisionedThroughput:ModelServingProvisionedThroughput this <model-serving-endpoint-name>
 * ```
 */
class ModelServingProvisionedThroughput extends pulumi.CustomResource {
    /**
     * Get an existing ModelServingProvisionedThroughput 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 ModelServingProvisionedThroughput(name, state, { ...opts, id: id });
    }
    /**
     * Returns true if the given object is an instance of ModelServingProvisionedThroughput.  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'] === ModelServingProvisionedThroughput.__pulumiType;
    }
    constructor(name, argsOrState, opts) {
        let resourceInputs = {};
        opts = opts || {};
        if (opts.id) {
            const state = argsOrState;
            resourceInputs["aiGateway"] = state?.aiGateway;
            resourceInputs["budgetPolicyId"] = state?.budgetPolicyId;
            resourceInputs["config"] = state?.config;
            resourceInputs["emailNotifications"] = state?.emailNotifications;
            resourceInputs["name"] = state?.name;
            resourceInputs["servingEndpointId"] = state?.servingEndpointId;
            resourceInputs["tags"] = state?.tags;
        }
        else {
            const args = argsOrState;
            if (args?.config === undefined && !opts.urn) {
                throw new Error("Missing required property 'config'");
            }
            resourceInputs["aiGateway"] = args?.aiGateway;
            resourceInputs["budgetPolicyId"] = args?.budgetPolicyId;
            resourceInputs["config"] = args?.config;
            resourceInputs["emailNotifications"] = args?.emailNotifications;
            resourceInputs["name"] = args?.name;
            resourceInputs["tags"] = args?.tags;
            resourceInputs["servingEndpointId"] = undefined /*out*/;
        }
        opts = pulumi.mergeOptions(utilities.resourceOptsDefaults(), opts);
        super(ModelServingProvisionedThroughput.__pulumiType, name, resourceInputs, opts);
    }
}
exports.ModelServingProvisionedThroughput = ModelServingProvisionedThroughput;
/** @internal */
ModelServingProvisionedThroughput.__pulumiType = 'databricks:index/modelServingProvisionedThroughput:ModelServingProvisionedThroughput';
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