@pulumi/databricks
Version:
A Pulumi package for creating and managing databricks cloud resources.
109 lines • 4.71 kB
JavaScript
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// *** 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.MlflowModel = void 0;
const pulumi = require("@pulumi/pulumi");
const utilities = require("./utilities");
/**
* This resource allows you to create [MLflow models](https://docs.databricks.com/applications/mlflow/models.html) in Databricks.
*
* > This documentation covers the Workspace Model Registry. Databricks recommends using Models in Unity Catalog. Models in Unity Catalog provides centralized model governance, cross-workspace access, lineage, and deployment.
*
* ## Example Usage
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as databricks from "@pulumi/databricks";
*
* const test = new databricks.MlflowModel("test", {
* name: "My MLflow Model",
* description: "My MLflow model description",
* tags: [
* {
* key: "key1",
* value: "value1",
* },
* {
* key: "key2",
* value: "value2",
* },
* ],
* });
* ```
*
* ## Access Control
*
* * databricks.Permissions can control which groups or individual users can *Read*, *Edit*, *Manage Staging Versions*, *Manage Production Versions*, and *Manage* individual models.
*
* ## Related Resources
*
* The following resources are often used in the same context:
*
* * 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.ModelServing to serve this model on a Databricks serving endpoint.
* * databricks.Directory to manage directories in [Databricks Workspace](https://docs.databricks.com/workspace/workspace-objects.html).
* * databricks.MlflowExperiment to manage [MLflow experiments](https://docs.databricks.com/data/data-sources/mlflow-experiment.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 resource can be imported using the name
*
* bash
*
* ```sh
* $ pulumi import databricks:index/mlflowModel:MlflowModel this <name>
* ```
*/
class MlflowModel extends pulumi.CustomResource {
/**
* Get an existing MlflowModel 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 MlflowModel(name, state, Object.assign(Object.assign({}, opts), { id: id }));
}
/**
* Returns true if the given object is an instance of MlflowModel. 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'] === MlflowModel.__pulumiType;
}
constructor(name, argsOrState, opts) {
let resourceInputs = {};
opts = opts || {};
if (opts.id) {
const state = argsOrState;
resourceInputs["description"] = state ? state.description : undefined;
resourceInputs["name"] = state ? state.name : undefined;
resourceInputs["registeredModelId"] = state ? state.registeredModelId : undefined;
resourceInputs["tags"] = state ? state.tags : undefined;
}
else {
const args = argsOrState;
resourceInputs["description"] = args ? args.description : undefined;
resourceInputs["name"] = args ? args.name : undefined;
resourceInputs["tags"] = args ? args.tags : undefined;
resourceInputs["registeredModelId"] = undefined /*out*/;
}
opts = pulumi.mergeOptions(utilities.resourceOptsDefaults(), opts);
super(MlflowModel.__pulumiType, name, resourceInputs, opts);
}
}
exports.MlflowModel = MlflowModel;
/** @internal */
MlflowModel.__pulumiType = 'databricks:index/mlflowModel:MlflowModel';
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