import * as pulumi from "@pulumi/pulumi";
import * as inputs from "../types/input";
import * as outputs from "../types/output";
/**
 * Manages an AWS SageMaker AI Algorithm.
 *
 * ## Example Usage
 *
 * ### Basic Usage
 *
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as aws from "@pulumi/aws";
 *
 * const example = new aws.sagemaker.Algorithm("example", {
 *     trainingSpecification: {
 *         trainingChannels: [{
 *             name: "train",
 *             supportedContentTypes: ["text/csv"],
 *             supportedInputModes: ["File"],
 *         }],
 *         supportedTrainingInstanceTypes: ["ml.m5.large"],
 *         trainingImage: "123456789012.dkr.ecr.us-west-2.amazonaws.com/example-training:latest",
 *     },
 *     algorithmName: "example-algorithm",
 *     tags: {
 *         Environment: "test",
 *     },
 * });
 * ```
 *
 * ### Training Specification
 *
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as aws from "@pulumi/aws";
 *
 * const example = aws.sagemaker.getPrebuiltEcrImage({
 *     repositoryName: "linear-learner",
 *     imageTag: "1",
 * });
 * const exampleAlgorithm = new aws.sagemaker.Algorithm("example", {
 *     trainingSpecification: {
 *         metricDefinitions: [{
 *             name: "train:loss",
 *             regex: "loss=(.*?);",
 *         }],
 *         supportedHyperParameters: [
 *             {
 *                 range: {
 *                     continuousParameterRangeSpecification: {
 *                         minValue: "0.1",
 *                         maxValue: "0.9",
 *                     },
 *                 },
 *                 defaultValue: "0.5",
 *                 description: "Continuous learning rate",
 *                 isRequired: true,
 *                 isTunable: true,
 *                 name: "eta",
 *                 type: "Continuous",
 *             },
 *             {
 *                 range: {
 *                     integerParameterRangeSpecification: {
 *                         minValue: "1",
 *                         maxValue: "10",
 *                     },
 *                 },
 *                 defaultValue: "5",
 *                 description: "Maximum tree depth",
 *                 isRequired: false,
 *                 isTunable: true,
 *                 name: "max_depth",
 *                 type: "Integer",
 *             },
 *             {
 *                 range: {
 *                     categoricalParameterRangeSpecification: {
 *                         values: [
 *                             "reg:squarederror",
 *                             "binary:logistic",
 *                         ],
 *                     },
 *                 },
 *                 defaultValue: "reg:squarederror",
 *                 description: "Objective function",
 *                 isRequired: false,
 *                 isTunable: false,
 *                 name: "objective",
 *                 type: "Categorical",
 *             },
 *         ],
 *         supportedTuningJobObjectiveMetrics: [{
 *             metricName: "train:loss",
 *             type: "Minimize",
 *         }],
 *         trainingChannels: [
 *             {
 *                 description: "Training data channel",
 *                 isRequired: true,
 *                 name: "train",
 *                 supportedCompressionTypes: [
 *                     "None",
 *                     "Gzip",
 *                 ],
 *                 supportedContentTypes: ["text/csv"],
 *                 supportedInputModes: ["File"],
 *             },
 *             {
 *                 name: "validation",
 *                 supportedContentTypes: ["application/json"],
 *                 supportedInputModes: ["Pipe"],
 *             },
 *         ],
 *         supportedTrainingInstanceTypes: [
 *             "ml.m5.large",
 *             "ml.c5.xlarge",
 *         ],
 *         supportsDistributedTraining: true,
 *         trainingImage: example.then(example => example.registryPath),
 *     },
 *     algorithmName: "example-training-algorithm",
 * });
 * ```
 *
 * ### Inference Specification
 *
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as aws from "@pulumi/aws";
 *
 * const example = aws.sagemaker.getPrebuiltEcrImage({
 *     repositoryName: "linear-learner",
 *     imageTag: "1",
 * });
 * const exampleAlgorithm = new aws.sagemaker.Algorithm("example", {
 *     trainingSpecification: {
 *         trainingChannels: [{
 *             name: "train",
 *             supportedContentTypes: ["text/csv"],
 *             supportedInputModes: ["File"],
 *         }],
 *         supportedTrainingInstanceTypes: ["ml.m5.large"],
 *         trainingImage: example.then(example => example.registryPath),
 *     },
 *     inferenceSpecification: {
 *         containers: [{
 *             baseModel: {
 *                 hubContentName: "basemodel",
 *                 hubContentVersion: "1.0.0",
 *                 recipeName: "recipe",
 *             },
 *             modelInput: {
 *                 dataInputConfig: "{}",
 *             },
 *             containerHostname: "test-host",
 *             environment: {
 *                 TEST: "value",
 *             },
 *             framework: "XGBOOST",
 *             frameworkVersion: "1.5-1",
 *             image: example.then(example => example.registryPath),
 *             isCheckpoint: true,
 *             nearestModelName: "nearest-model",
 *         }],
 *         supportedContentTypes: ["text/csv"],
 *         supportedRealtimeInferenceInstanceTypes: ["ml.m5.large"],
 *         supportedResponseMimeTypes: ["text/csv"],
 *         supportedTransformInstanceTypes: ["ml.m5.large"],
 *     },
 *     algorithmName: "example-inference-algorithm",
 * });
 * ```
 *
 * ### Validation Specification
 *
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as aws from "@pulumi/aws";
 *
 * const current = aws.getPartition({});
 * const example = aws.sagemaker.getPrebuiltEcrImage({
 *     repositoryName: "linear-learner",
 *     imageTag: "1",
 * });
 * const assumeRole = current.then(current => aws.iam.getPolicyDocument({
 *     statements: [{
 *         principals: [{
 *             type: "Service",
 *             identifiers: [`sagemaker.${current.dnsSuffix}`],
 *         }],
 *         actions: ["sts:AssumeRole"],
 *     }],
 * }));
 * const exampleRole = new aws.iam.Role("example", {
 *     name: "example-sagemaker-algorithm-role",
 *     assumeRolePolicy: assumeRole.then(assumeRole => assumeRole.json),
 * });
 * const exampleRolePolicyAttachment = new aws.iam.RolePolicyAttachment("example", {
 *     role: exampleRole.name,
 *     policyArn: current.then(current => `arn:${current.partition}:iam::aws:policy/AmazonSageMakerFullAccess`),
 * });
 * const exampleBucket = new aws.s3.Bucket("example", {
 *     bucket: "example-sagemaker-algorithm-validation-bucket",
 *     forceDestroy: true,
 * });
 * const s3Access = aws.iam.getPolicyDocumentOutput({
 *     statements: [{
 *         effect: "Allow",
 *         actions: [
 *             "s3:GetBucketLocation",
 *             "s3:ListBucket",
 *             "s3:GetObject",
 *             "s3:PutObject",
 *         ],
 *         resources: [
 *             exampleBucket.arn,
 *             pulumi.interpolate`${exampleBucket.arn}/*`,
 *         ],
 *     }],
 * });
 * const exampleRolePolicy = new aws.iam.RolePolicy("example", {
 *     role: exampleRole.name,
 *     policy: s3Access.json,
 * });
 * const training = new aws.s3.BucketObjectv2("training", {
 *     bucket: exampleBucket.bucket,
 *     key: "algorithm/training/data.csv",
 *     content: `1,1.0,0.0
 * 0,0.0,1.0
 * 1,1.0,1.0
 * 0,0.0,0.0
 * `,
 * });
 * const transform = new aws.s3.BucketObjectv2("transform", {
 *     bucket: exampleBucket.bucket,
 *     key: "algorithm/transform/input.csv",
 *     content: `1.0,0.0
 * 0.0,1.0
 * `,
 * });
 * const exampleAlgorithm = new aws.sagemaker.Algorithm("example", {
 *     trainingSpecification: {
 *         supportedHyperParameters: [
 *             {
 *                 range: {
 *                     integerParameterRangeSpecification: {
 *                         minValue: "2",
 *                         maxValue: "2",
 *                     },
 *                 },
 *                 defaultValue: "2",
 *                 description: "Feature dimension",
 *                 isRequired: true,
 *                 isTunable: false,
 *                 name: "feature_dim",
 *                 type: "Integer",
 *             },
 *             {
 *                 range: {
 *                     integerParameterRangeSpecification: {
 *                         minValue: "4",
 *                         maxValue: "4",
 *                     },
 *                 },
 *                 defaultValue: "4",
 *                 description: "Mini batch size",
 *                 isRequired: true,
 *                 isTunable: false,
 *                 name: "mini_batch_size",
 *                 type: "Integer",
 *             },
 *             {
 *                 range: {
 *                     categoricalParameterRangeSpecification: {
 *                         values: ["binary_classifier"],
 *                     },
 *                 },
 *                 defaultValue: "binary_classifier",
 *                 description: "Predictor type",
 *                 isRequired: true,
 *                 isTunable: false,
 *                 name: "predictor_type",
 *                 type: "Categorical",
 *             },
 *         ],
 *         trainingChannels: [{
 *             name: "train",
 *             supportedContentTypes: ["text/csv"],
 *             supportedInputModes: ["File"],
 *         }],
 *         trainingImage: example.then(example => example.registryPath),
 *         supportedTrainingInstanceTypes: ["ml.m5.large"],
 *     },
 *     inferenceSpecification: {
 *         containers: [{
 *             image: example.then(example => example.registryPath),
 *         }],
 *         supportedContentTypes: ["text/csv"],
 *         supportedResponseMimeTypes: ["text/csv"],
 *         supportedTransformInstanceTypes: ["ml.m5.large"],
 *     },
 *     validationSpecification: {
 *         validationProfiles: {
 *             trainingJobDefinition: {
 *                 outputDataConfig: {
 *                     compressionType: "GZIP",
 *                     s3OutputPath: pulumi.interpolate`s3://${exampleBucket.bucket}/algorithm/output`,
 *                 },
 *                 resourceConfig: {
 *                     instanceCount: 1,
 *                     instanceType: "ml.m5.large",
 *                     keepAlivePeriodInSeconds: 60,
 *                     volumeSizeInGb: 30,
 *                 },
 *                 stoppingCondition: {
 *                     maxPendingTimeInSeconds: 7200,
 *                     maxRuntimeInSeconds: 1800,
 *                     maxWaitTimeInSeconds: 3600,
 *                 },
 *                 inputDataConfigs: [{
 *                     shuffleConfig: {
 *                         seed: 1,
 *                     },
 *                     dataSource: {
 *                         s3DataSource: {
 *                             attributeNames: ["label"],
 *                             s3DataDistributionType: "ShardedByS3Key",
 *                             s3DataType: "S3Prefix",
 *                             s3Uri: pulumi.interpolate`s3://${exampleBucket.bucket}/algorithm/training/`,
 *                         },
 *                     },
 *                     channelName: "train",
 *                     compressionType: "None",
 *                     contentType: "text/csv",
 *                     inputMode: "File",
 *                     recordWrapperType: "None",
 *                 }],
 *                 hyperParameters: {
 *                     feature_dim: "2",
 *                     mini_batch_size: "4",
 *                     predictor_type: "binary_classifier",
 *                 },
 *                 trainingInputMode: "File",
 *             },
 *             transformJobDefinition: {
 *                 transformInput: {
 *                     dataSource: {
 *                         s3DataSource: {
 *                             s3DataType: "S3Prefix",
 *                             s3Uri: pulumi.interpolate`s3://${exampleBucket.bucket}/algorithm/transform/`,
 *                         },
 *                     },
 *                     compressionType: "None",
 *                     contentType: "text/csv",
 *                     splitType: "Line",
 *                 },
 *                 transformOutput: {
 *                     accept: "text/csv",
 *                     assembleWith: "Line",
 *                     s3OutputPath: pulumi.interpolate`s3://${exampleBucket.bucket}/algorithm/transform-output`,
 *                 },
 *                 transformResources: {
 *                     instanceCount: 1,
 *                     instanceType: "ml.m5.large",
 *                 },
 *                 batchStrategy: "MultiRecord",
 *                 environment: {
 *                     Te: "enabled",
 *                 },
 *                 maxConcurrentTransforms: 1,
 *                 maxPayloadInMb: 6,
 *             },
 *             profileName: "validation-profile",
 *         },
 *         validationRole: exampleRole.arn,
 *     },
 *     algorithmName: "example-validation-algorithm",
 * }, {
 *     dependsOn: [
 *         exampleRolePolicyAttachment,
 *         exampleRolePolicy,
 *         training,
 *         transform,
 *     ],
 * });
 * ```
 *
 * ## Import
 *
 * ### Identity Schema
 *
 * #### Required
 *
 * * `algorithmName` - (String) Name of the algorithm.
 *
 * #### Optional
 *
 * * `accountId` - (String) AWS account where this resource is managed.
 * * `region` - (String) Region where this resource is managed.
 *
 * Using `pulumi import`, import SageMaker AI Algorithms using `algorithmName`. For example:
 *
 * ```sh
 * $ pulumi import aws:sagemaker/algorithm:Algorithm example example-algorithm
 * ```
 */
export declare class Algorithm extends pulumi.CustomResource {
    /**
     * Get an existing Algorithm 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: string, id: pulumi.Input<pulumi.ID>, state?: AlgorithmState, opts?: pulumi.CustomResourceOptions): Algorithm;
    /**
     * Returns true if the given object is an instance of Algorithm.  This is designed to work even
     * when multiple copies of the Pulumi SDK have been loaded into the same process.
     */
    static isInstance(obj: any): obj is Algorithm;
    /**
     * Description of the algorithm.
     */
    readonly algorithmDescription: pulumi.Output<string | undefined>;
    /**
     * Name of the algorithm.
     */
    readonly algorithmName: pulumi.Output<string>;
    /**
     * Status of the algorithm.
     */
    readonly algorithmStatus: pulumi.Output<string>;
    /**
     * ARN of the algorithm.
     */
    readonly arn: pulumi.Output<string>;
    /**
     * Whether to certify the algorithm for AWS Marketplace.
     */
    readonly certifyForMarketplace: pulumi.Output<boolean>;
    /**
     * Time when the algorithm was created, in RFC3339 format.
     */
    readonly creationTime: pulumi.Output<string>;
    /**
     * Configuration for inference jobs that use this algorithm. See Inference Specification.
     */
    readonly inferenceSpecification: pulumi.Output<outputs.sagemaker.AlgorithmInferenceSpecification | undefined>;
    /**
     * AWS Marketplace product ID associated with the algorithm.
     */
    readonly productId: pulumi.Output<string>;
    /**
     * Region where this resource is managed. Defaults to the Region set in the provider configuration.
     */
    readonly region: pulumi.Output<string>;
    /**
     * Map of tags to assign to the resource.
     */
    readonly tags: pulumi.Output<{
        [key: string]: string;
    } | undefined>;
    /**
     * Map of tags assigned to the resource, including tags inherited from the provider `defaultTags` configuration block.
     */
    readonly tagsAll: pulumi.Output<{
        [key: string]: string;
    }>;
    readonly timeouts: pulumi.Output<outputs.sagemaker.AlgorithmTimeouts | undefined>;
    /**
     * Configuration for training jobs that use this algorithm. See Training Specification.
     */
    readonly trainingSpecification: pulumi.Output<outputs.sagemaker.AlgorithmTrainingSpecification>;
    /**
     * Configuration used to validate the algorithm. See Validation Specification.
     */
    readonly validationSpecification: pulumi.Output<outputs.sagemaker.AlgorithmValidationSpecification | undefined>;
    /**
     * Create a Algorithm resource with the given unique name, arguments, and options.
     *
     * @param name The _unique_ name of the resource.
     * @param args The arguments to use to populate this resource's properties.
     * @param opts A bag of options that control this resource's behavior.
     */
    constructor(name: string, args: AlgorithmArgs, opts?: pulumi.CustomResourceOptions);
}
/**
 * Input properties used for looking up and filtering Algorithm resources.
 */
export interface AlgorithmState {
    /**
     * Description of the algorithm.
     */
    algorithmDescription?: pulumi.Input<string | undefined>;
    /**
     * Name of the algorithm.
     */
    algorithmName?: pulumi.Input<string | undefined>;
    /**
     * Status of the algorithm.
     */
    algorithmStatus?: pulumi.Input<string | undefined>;
    /**
     * ARN of the algorithm.
     */
    arn?: pulumi.Input<string | undefined>;
    /**
     * Whether to certify the algorithm for AWS Marketplace.
     */
    certifyForMarketplace?: pulumi.Input<boolean | undefined>;
    /**
     * Time when the algorithm was created, in RFC3339 format.
     */
    creationTime?: pulumi.Input<string | undefined>;
    /**
     * Configuration for inference jobs that use this algorithm. See Inference Specification.
     */
    inferenceSpecification?: pulumi.Input<inputs.sagemaker.AlgorithmInferenceSpecification | undefined>;
    /**
     * AWS Marketplace product ID associated with the algorithm.
     */
    productId?: pulumi.Input<string | undefined>;
    /**
     * Region where this resource is managed. Defaults to the Region set in the provider configuration.
     */
    region?: pulumi.Input<string | undefined>;
    /**
     * Map of tags to assign to the resource.
     */
    tags?: pulumi.Input<{
        [key: string]: pulumi.Input<string>;
    } | undefined>;
    /**
     * Map of tags assigned to the resource, including tags inherited from the provider `defaultTags` configuration block.
     */
    tagsAll?: pulumi.Input<{
        [key: string]: pulumi.Input<string>;
    } | undefined>;
    timeouts?: pulumi.Input<inputs.sagemaker.AlgorithmTimeouts | undefined>;
    /**
     * Configuration for training jobs that use this algorithm. See Training Specification.
     */
    trainingSpecification?: pulumi.Input<inputs.sagemaker.AlgorithmTrainingSpecification | undefined>;
    /**
     * Configuration used to validate the algorithm. See Validation Specification.
     */
    validationSpecification?: pulumi.Input<inputs.sagemaker.AlgorithmValidationSpecification | undefined>;
}
/**
 * The set of arguments for constructing a Algorithm resource.
 */
export interface AlgorithmArgs {
    /**
     * Description of the algorithm.
     */
    algorithmDescription?: pulumi.Input<string | undefined>;
    /**
     * Name of the algorithm.
     */
    algorithmName: pulumi.Input<string>;
    /**
     * Whether to certify the algorithm for AWS Marketplace.
     */
    certifyForMarketplace?: pulumi.Input<boolean | undefined>;
    /**
     * Configuration for inference jobs that use this algorithm. See Inference Specification.
     */
    inferenceSpecification?: pulumi.Input<inputs.sagemaker.AlgorithmInferenceSpecification | undefined>;
    /**
     * Region where this resource is managed. Defaults to the Region set in the provider configuration.
     */
    region?: pulumi.Input<string | undefined>;
    /**
     * Map of tags to assign to the resource.
     */
    tags?: pulumi.Input<{
        [key: string]: pulumi.Input<string>;
    } | undefined>;
    timeouts?: pulumi.Input<inputs.sagemaker.AlgorithmTimeouts | undefined>;
    /**
     * Configuration for training jobs that use this algorithm. See Training Specification.
     */
    trainingSpecification: pulumi.Input<inputs.sagemaker.AlgorithmTrainingSpecification>;
    /**
     * Configuration used to validate the algorithm. See Validation Specification.
     */
    validationSpecification?: pulumi.Input<inputs.sagemaker.AlgorithmValidationSpecification | undefined>;
}
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