import type { MetadataBearer as __MetadataBearer } from "@smithy/types"; import type { GetBatchPredictionInput, GetBatchPredictionOutput } from "../models/models_0"; /** * @public */ export type { __MetadataBearer }; /** * @public * * The input for {@link GetBatchPredictionCommand}. */ export interface GetBatchPredictionCommandInput extends GetBatchPredictionInput { } /** * @public * * The output of {@link GetBatchPredictionCommand}. */ export interface GetBatchPredictionCommandOutput extends GetBatchPredictionOutput, __MetadataBearer { } declare const GetBatchPredictionCommand_base: { new (input: GetBatchPredictionCommandInput): import("@smithy/core/client").CommandImpl; new (input: GetBatchPredictionCommandInput): import("@smithy/core/client").CommandImpl; getEndpointParameterInstructions(): import("@smithy/types").EndpointParameterInstructions; }; /** *

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a * Batch Prediction request.

* @example * Use a bare-bones client and the command you need to make an API call. * ```javascript * import { MachineLearningClient, GetBatchPredictionCommand } from "@aws-sdk/client-machine-learning"; // ES Modules import * // const { MachineLearningClient, GetBatchPredictionCommand } = require("@aws-sdk/client-machine-learning"); // CommonJS import * // import type { MachineLearningClientConfig } from "@aws-sdk/client-machine-learning"; * const config = {}; // type is MachineLearningClientConfig * const client = new MachineLearningClient(config); * const input = { // GetBatchPredictionInput * BatchPredictionId: "STRING_VALUE", // required * }; * const command = new GetBatchPredictionCommand(input); * const response = await client.send(command); * // { // GetBatchPredictionOutput * // BatchPredictionId: "STRING_VALUE", * // MLModelId: "STRING_VALUE", * // BatchPredictionDataSourceId: "STRING_VALUE", * // InputDataLocationS3: "STRING_VALUE", * // CreatedByIamUser: "STRING_VALUE", * // CreatedAt: new Date("TIMESTAMP"), * // LastUpdatedAt: new Date("TIMESTAMP"), * // Name: "STRING_VALUE", * // Status: "PENDING" || "INPROGRESS" || "FAILED" || "COMPLETED" || "DELETED", * // OutputUri: "STRING_VALUE", * // LogUri: "STRING_VALUE", * // Message: "STRING_VALUE", * // ComputeTime: Number("long"), * // FinishedAt: new Date("TIMESTAMP"), * // StartedAt: new Date("TIMESTAMP"), * // TotalRecordCount: Number("long"), * // InvalidRecordCount: Number("long"), * // }; * * ``` * * @param GetBatchPredictionCommandInput - {@link GetBatchPredictionCommandInput} * @returns {@link GetBatchPredictionCommandOutput} * @see {@link GetBatchPredictionCommandInput} for command's `input` shape. * @see {@link GetBatchPredictionCommandOutput} for command's `response` shape. * @see {@link MachineLearningClientResolvedConfig | config} for MachineLearningClient's `config` shape. * * @throws {@link InternalServerException} (server fault) *

An error on the server occurred when trying to process a request.

* * @throws {@link InvalidInputException} (client fault) *

An error on the client occurred. Typically, the cause is an invalid input value.

* * @throws {@link ResourceNotFoundException} (client fault) *

A specified resource cannot be located.

* * @throws {@link MachineLearningServiceException} *

Base exception class for all service exceptions from MachineLearning service.

* * * @public */ export declare class GetBatchPredictionCommand extends GetBatchPredictionCommand_base { /** @internal type navigation helper, not in runtime. */ protected static __types: { api: { input: GetBatchPredictionInput; output: GetBatchPredictionOutput; }; sdk: { input: GetBatchPredictionCommandInput; output: GetBatchPredictionCommandOutput; }; }; }