import { Command as $Command } from "@smithy/smithy-client"; import { MetadataBearer as __MetadataBearer } from "@smithy/types"; import { CreateMlflowTrackingServerRequest, CreateMlflowTrackingServerResponse } from "../models/models_1"; import { SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes } from "../SageMakerClient"; /** * @public */ export type { __MetadataBearer }; export { $Command }; /** * @public * * The input for {@link CreateMlflowTrackingServerCommand}. */ export interface CreateMlflowTrackingServerCommandInput extends CreateMlflowTrackingServerRequest { } /** * @public * * The output of {@link CreateMlflowTrackingServerCommand}. */ export interface CreateMlflowTrackingServerCommandOutput extends CreateMlflowTrackingServerResponse, __MetadataBearer { } declare const CreateMlflowTrackingServerCommand_base: { new (input: CreateMlflowTrackingServerCommandInput): import("@smithy/smithy-client").CommandImpl; new (input: CreateMlflowTrackingServerCommandInput): import("@smithy/smithy-client").CommandImpl; getEndpointParameterInstructions(): import("@smithy/middleware-endpoint").EndpointParameterInstructions; }; /** *

Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.

* @example * Use a bare-bones client and the command you need to make an API call. * ```javascript * import { SageMakerClient, CreateMlflowTrackingServerCommand } from "@aws-sdk/client-sagemaker"; // ES Modules import * // const { SageMakerClient, CreateMlflowTrackingServerCommand } = require("@aws-sdk/client-sagemaker"); // CommonJS import * const client = new SageMakerClient(config); * const input = { // CreateMlflowTrackingServerRequest * TrackingServerName: "STRING_VALUE", // required * ArtifactStoreUri: "STRING_VALUE", // required * TrackingServerSize: "Small" || "Medium" || "Large", * MlflowVersion: "STRING_VALUE", * RoleArn: "STRING_VALUE", // required * AutomaticModelRegistration: true || false, * WeeklyMaintenanceWindowStart: "STRING_VALUE", * Tags: [ // TagList * { // Tag * Key: "STRING_VALUE", // required * Value: "STRING_VALUE", // required * }, * ], * }; * const command = new CreateMlflowTrackingServerCommand(input); * const response = await client.send(command); * // { // CreateMlflowTrackingServerResponse * // TrackingServerArn: "STRING_VALUE", * // }; * * ``` * * @param CreateMlflowTrackingServerCommandInput - {@link CreateMlflowTrackingServerCommandInput} * @returns {@link CreateMlflowTrackingServerCommandOutput} * @see {@link CreateMlflowTrackingServerCommandInput} for command's `input` shape. * @see {@link CreateMlflowTrackingServerCommandOutput} for command's `response` shape. * @see {@link SageMakerClientResolvedConfig | config} for SageMakerClient's `config` shape. * * @throws {@link ResourceLimitExceeded} (client fault) *

You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

* * @throws {@link SageMakerServiceException} *

Base exception class for all service exceptions from SageMaker service.

* * * @public */ export declare class CreateMlflowTrackingServerCommand extends CreateMlflowTrackingServerCommand_base { /** @internal type navigation helper, not in runtime. */ protected static __types: { api: { input: CreateMlflowTrackingServerRequest; output: CreateMlflowTrackingServerResponse; }; sdk: { input: CreateMlflowTrackingServerCommandInput; output: CreateMlflowTrackingServerCommandOutput; }; }; }