Class: AWS.SageMaker

var params = {
  ResourceArn: 'STRING_VALUE', /* required */
  Tags: [ /* required */
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ]
};
sagemaker.addTags(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • ResourceArn — (String)

    The Amazon Resource Name (ARN) of the resource that you want to tag.

  • Tags — (Array<map>)

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • Tags — (Array<map>)

      A list of tags associated with the SageMaker resource.

      • Key — required — (String)

        The tag key. Tag keys must be unique per resource.

      • Value — required — (String)

        The tag value.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  TrialComponentName: 'STRING_VALUE', /* required */
  TrialName: 'STRING_VALUE' /* required */
};
sagemaker.associateTrialComponent(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • TrialComponentName — (String)

    The name of the component to associated with the trial.

  • TrialName — (String)

    The name of the trial to associate with.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • TrialComponentArn — (String)

      The Amazon Resource Name (ARN) of the trial component.

    • TrialArn — (String)

      The Amazon Resource Name (ARN) of the trial.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  ModelPackageArnList: [ /* required */
    'STRING_VALUE',
    /* more items */
  ]
};
sagemaker.batchDescribeModelPackage(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • ModelPackageArnList — (Array<String>)

    The list of Amazon Resource Name (ARN) of the model package groups.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • ModelPackageSummaries — (map<map>)

      The summaries for the model package versions

      • ModelPackageGroupName — required — (String)

        The group name for the model package

      • ModelPackageVersion — (Integer)

        The version number of a versioned model.

      • ModelPackageArn — required — (String)

        The Amazon Resource Name (ARN) of the model package.

      • ModelPackageDescription — (String)

        The description of the model package.

      • CreationTime — required — (Date)

        The creation time of the mortgage package summary.

      • InferenceSpecification — required — (map)

        Defines how to perform inference generation after a training job is run.

        • Containers — required — (Array<map>)

          The Amazon ECR registry path of the Docker image that contains the inference code.

          • ContainerHostname — (String)

            The DNS host name for the Docker container.

          • Image — required — (String)

            The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

            If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

          • ImageDigest — (String)

            An MD5 hash of the training algorithm that identifies the Docker image used for training.

          • ModelDataUrl — (String)

            The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

            Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.
          • ModelDataSource — (map)

            Specifies the location of ML model data to deploy during endpoint creation.

            • S3DataSource — (map)

              Specifies the S3 location of ML model data to deploy.

              • S3Uri — required — (String)

                Specifies the S3 path of ML model data to deploy.

              • S3DataType — required — (String)

                Specifies the type of ML model data to deploy.

                If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

                If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

                Possible values include:
                • "S3Prefix"
                • "S3Object"
              • CompressionType — required — (String)

                Specifies how the ML model data is prepared.

                If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

                If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

                If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

                If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

                • If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

                • If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

                • Do not use any of the following as file names or directory names:

                  • An empty or blank string

                  • A string which contains null bytes

                  • A string longer than 255 bytes

                  • A single dot (.)

                  • A double dot (..)

                • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

                • Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

                Possible values include:
                • "None"
                • "Gzip"
              • ModelAccessConfig — (map)

                Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

                • AcceptEula — required — (Boolean)

                  Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

              • HubAccessConfig — (map)

                Configuration information for hub access.

                • HubContentArn — required — (String)

                  The ARN of the hub content for which deployment access is allowed.

          • ProductId — (String)

            The Amazon Web Services Marketplace product ID of the model package.

          • Environment — (map<String>)

            The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

          • ModelInput — (map)

            A structure with Model Input details.

            • DataInputConfig — required — (String)

              The input configuration object for the model.

          • Framework — (String)

            The machine learning framework of the model package container image.

          • FrameworkVersion — (String)

            The framework version of the Model Package Container Image.

          • NearestModelName — (String)

            The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

          • AdditionalS3DataSource — (map)

            The additional data source that is used during inference in the Docker container for your model package.

            • S3DataType — required — (String)

              The data type of the additional data source that you specify for use in inference or training.

              Possible values include:
              • "S3Object"
              • "S3Prefix"
            • S3Uri — required — (String)

              The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

            • CompressionType — (String)

              The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

              Possible values include:
              • "None"
              • "Gzip"
        • SupportedTransformInstanceTypes — (Array<String>)

          A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

          This parameter is required for unversioned models, and optional for versioned models.

        • SupportedRealtimeInferenceInstanceTypes — (Array<String>)

          A list of the instance types that are used to generate inferences in real-time.

          This parameter is required for unversioned models, and optional for versioned models.

        • SupportedContentTypes — (Array<String>)

          The supported MIME types for the input data.

        • SupportedResponseMIMETypes — (Array<String>)

          The supported MIME types for the output data.

      • ModelPackageStatus — required — (String)

        The status of the mortgage package.

        Possible values include:
        • "Pending"
        • "InProgress"
        • "Completed"
        • "Failed"
        • "Deleting"
      • ModelApprovalStatus — (String)

        The approval status of the model.

        Possible values include:
        • "Approved"
        • "Rejected"
        • "PendingManualApproval"
    • BatchDescribeModelPackageErrorMap — (map<map>)

      A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.

      • ErrorCode — required — (String)

      • ErrorResponse — required — (String)

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  ActionName: 'STRING_VALUE', /* required */
  ActionType: 'STRING_VALUE', /* required */
  Source: { /* required */
    SourceUri: 'STRING_VALUE', /* required */
    SourceId: 'STRING_VALUE',
    SourceType: 'STRING_VALUE'
  },
  Description: 'STRING_VALUE',
  MetadataProperties: {
    CommitId: 'STRING_VALUE',
    GeneratedBy: 'STRING_VALUE',
    ProjectId: 'STRING_VALUE',
    Repository: 'STRING_VALUE'
  },
  Properties: {
    '<StringParameterValue>': 'STRING_VALUE',
    /* '<StringParameterValue>': ... */
  },
  Status: Unknown | InProgress | Completed | Failed | Stopping | Stopped,
  Tags: [
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ]
};
sagemaker.createAction(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • ActionName — (String)

    The name of the action. Must be unique to your account in an Amazon Web Services Region.

  • Source — (map)

    The source type, ID, and URI.

    • SourceUri — required — (String)

      The URI of the source.

    • SourceType — (String)

      The type of the source.

    • SourceId — (String)

      The ID of the source.

  • ActionType — (String)

    The action type.

  • Description — (String)

    The description of the action.

  • Status — (String)

    The status of the action.

    Possible values include:
    • "Unknown"
    • "InProgress"
    • "Completed"
    • "Failed"
    • "Stopping"
    • "Stopped"
  • Properties — (map<String>)

    A list of properties to add to the action.

  • MetadataProperties — (map)

    Metadata properties of the tracking entity, trial, or trial component.

    • CommitId — (String)

      The commit ID.

    • Repository — (String)

      The repository.

    • GeneratedBy — (String)

      The entity this entity was generated by.

    • ProjectId — (String)

      The project ID.

  • Tags — (Array<map>)

    A list of tags to apply to the action.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • ActionArn — (String)

      The Amazon Resource Name (ARN) of the action.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  AlgorithmName: 'STRING_VALUE', /* required */
  TrainingSpecification: { /* required */
    SupportedTrainingInstanceTypes: [ /* required */
      ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge,
      /* more items */
    ],
    TrainingChannels: [ /* required */
      {
        Name: 'STRING_VALUE', /* required */
        SupportedContentTypes: [ /* required */
          'STRING_VALUE',
          /* more items */
        ],
        SupportedInputModes: [ /* required */
          Pipe | File | FastFile,
          /* more items */
        ],
        Description: 'STRING_VALUE',
        IsRequired: true || false,
        SupportedCompressionTypes: [
          None | Gzip,
          /* more items */
        ]
      },
      /* more items */
    ],
    TrainingImage: 'STRING_VALUE', /* required */
    AdditionalS3DataSource: {
      S3DataType: S3Object | S3Prefix, /* required */
      S3Uri: 'STRING_VALUE', /* required */
      CompressionType: None | Gzip
    },
    MetricDefinitions: [
      {
        Name: 'STRING_VALUE', /* required */
        Regex: 'STRING_VALUE' /* required */
      },
      /* more items */
    ],
    SupportedHyperParameters: [
      {
        Name: 'STRING_VALUE', /* required */
        Type: Integer | Continuous | Categorical | FreeText, /* required */
        DefaultValue: 'STRING_VALUE',
        Description: 'STRING_VALUE',
        IsRequired: true || false,
        IsTunable: true || false,
        Range: {
          CategoricalParameterRangeSpecification: {
            Values: [ /* required */
              'STRING_VALUE',
              /* more items */
            ]
          },
          ContinuousParameterRangeSpecification: {
            MaxValue: 'STRING_VALUE', /* required */
            MinValue: 'STRING_VALUE' /* required */
          },
          IntegerParameterRangeSpecification: {
            MaxValue: 'STRING_VALUE', /* required */
            MinValue: 'STRING_VALUE' /* required */
          }
        }
      },
      /* more items */
    ],
    SupportedTuningJobObjectiveMetrics: [
      {
        MetricName: 'STRING_VALUE', /* required */
        Type: Maximize | Minimize /* required */
      },
      /* more items */
    ],
    SupportsDistributedTraining: true || false,
    TrainingImageDigest: 'STRING_VALUE'
  },
  AlgorithmDescription: 'STRING_VALUE',
  CertifyForMarketplace: true || false,
  InferenceSpecification: {
    Containers: [ /* required */
      {
        Image: 'STRING_VALUE', /* required */
        AdditionalS3DataSource: {
          S3DataType: S3Object | S3Prefix, /* required */
          S3Uri: 'STRING_VALUE', /* required */
          CompressionType: None | Gzip
        },
        ContainerHostname: 'STRING_VALUE',
        Environment: {
          '<EnvironmentKey>': 'STRING_VALUE',
          /* '<EnvironmentKey>': ... */
        },
        Framework: 'STRING_VALUE',
        FrameworkVersion: 'STRING_VALUE',
        ImageDigest: 'STRING_VALUE',
        ModelDataSource: {
          S3DataSource: {
            CompressionType: None | Gzip, /* required */
            S3DataType: S3Prefix | S3Object, /* required */
            S3Uri: 'STRING_VALUE', /* required */
            HubAccessConfig: {
              HubContentArn: 'STRING_VALUE' /* required */
            },
            ModelAccessConfig: {
              AcceptEula: true || false /* required */
            }
          }
        },
        ModelDataUrl: 'STRING_VALUE',
        ModelInput: {
          DataInputConfig: 'STRING_VALUE' /* required */
        },
        NearestModelName: 'STRING_VALUE',
        ProductId: 'STRING_VALUE'
      },
      /* more items */
    ],
    SupportedContentTypes: [
      'STRING_VALUE',
      /* more items */
    ],
    SupportedRealtimeInferenceInstanceTypes: [
      ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge,
      /* more items */
    ],
    SupportedResponseMIMETypes: [
      'STRING_VALUE',
      /* more items */
    ],
    SupportedTransformInstanceTypes: [
      ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge,
      /* more items */
    ]
  },
  Tags: [
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ],
  ValidationSpecification: {
    ValidationProfiles: [ /* required */
      {
        ProfileName: 'STRING_VALUE', /* required */
        TrainingJobDefinition: { /* required */
          InputDataConfig: [ /* required */
            {
              ChannelName: 'STRING_VALUE', /* required */
              DataSource: { /* required */
                FileSystemDataSource: {
                  DirectoryPath: 'STRING_VALUE', /* required */
                  FileSystemAccessMode: rw | ro, /* required */
                  FileSystemId: 'STRING_VALUE', /* required */
                  FileSystemType: EFS | FSxLustre /* required */
                },
                S3DataSource: {
                  S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */
                  S3Uri: 'STRING_VALUE', /* required */
                  AttributeNames: [
                    'STRING_VALUE',
                    /* more items */
                  ],
                  InstanceGroupNames: [
                    'STRING_VALUE',
                    /* more items */
                  ],
                  S3DataDistributionType: FullyReplicated | ShardedByS3Key
                }
              },
              CompressionType: None | Gzip,
              ContentType: 'STRING_VALUE',
              InputMode: Pipe | File | FastFile,
              RecordWrapperType: None | RecordIO,
              ShuffleConfig: {
                Seed: 'NUMBER_VALUE' /* required */
              }
            },
            /* more items */
          ],
          OutputDataConfig: { /* required */
            S3OutputPath: 'STRING_VALUE', /* required */
            CompressionType: GZIP | NONE,
            KmsKeyId: 'STRING_VALUE'
          },
          ResourceConfig: { /* required */
            VolumeSizeInGB: 'NUMBER_VALUE', /* required */
            InstanceCount: 'NUMBER_VALUE',
            InstanceGroups: [
              {
                InstanceCount: 'NUMBER_VALUE', /* required */
                InstanceGroupName: 'STRING_VALUE', /* required */
                InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge /* required */
              },
              /* more items */
            ],
            InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge,
            KeepAlivePeriodInSeconds: 'NUMBER_VALUE',
            VolumeKmsKeyId: 'STRING_VALUE'
          },
          StoppingCondition: { /* required */
            MaxPendingTimeInSeconds: 'NUMBER_VALUE',
            MaxRuntimeInSeconds: 'NUMBER_VALUE',
            MaxWaitTimeInSeconds: 'NUMBER_VALUE'
          },
          TrainingInputMode: Pipe | File | FastFile, /* required */
          HyperParameters: {
            '<HyperParameterKey>': 'STRING_VALUE',
            /* '<HyperParameterKey>': ... */
          }
        },
        TransformJobDefinition: {
          TransformInput: { /* required */
            DataSource: { /* required */
              S3DataSource: { /* required */
                S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */
                S3Uri: 'STRING_VALUE' /* required */
              }
            },
            CompressionType: None | Gzip,
            ContentType: 'STRING_VALUE',
            SplitType: None | Line | RecordIO | TFRecord
          },
          TransformOutput: { /* required */
            S3OutputPath: 'STRING_VALUE', /* required */
            Accept: 'STRING_VALUE',
            AssembleWith: None | Line,
            KmsKeyId: 'STRING_VALUE'
          },
          TransformResources: { /* required */
            InstanceCount: 'NUMBER_VALUE', /* required */
            InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* required */
            VolumeKmsKeyId: 'STRING_VALUE'
          },
          BatchStrategy: MultiRecord | SingleRecord,
          Environment: {
            '<TransformEnvironmentKey>': 'STRING_VALUE',
            /* '<TransformEnvironmentKey>': ... */
          },
          MaxConcurrentTransforms: 'NUMBER_VALUE',
          MaxPayloadInMB: 'NUMBER_VALUE'
        }
      },
      /* more items */
    ],
    ValidationRole: 'STRING_VALUE' /* required */
  }
};
sagemaker.createAlgorithm(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • AlgorithmName — (String)

    The name of the algorithm.

  • AlgorithmDescription — (String)

    A description of the algorithm.

  • TrainingSpecification — (map)

    Specifies details about training jobs run by this algorithm, including the following:

    • The Amazon ECR path of the container and the version digest of the algorithm.

    • The hyperparameters that the algorithm supports.

    • The instance types that the algorithm supports for training.

    • Whether the algorithm supports distributed training.

    • The metrics that the algorithm emits to Amazon CloudWatch.

    • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

    • The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

    • TrainingImage — required — (String)

      The Amazon ECR registry path of the Docker image that contains the training algorithm.

    • TrainingImageDigest — (String)

      An MD5 hash of the training algorithm that identifies the Docker image used for training.

    • SupportedHyperParameters — (Array<map>)

      A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

      • Name — required — (String)

        The name of this hyperparameter. The name must be unique.

      • Description — (String)

        A brief description of the hyperparameter.

      • Type — required — (String)

        The type of this hyperparameter. The valid types are Integer, Continuous, Categorical, and FreeText.

        Possible values include:
        • "Integer"
        • "Continuous"
        • "Categorical"
        • "FreeText"
      • Range — (map)

        The allowed range for this hyperparameter.

        • IntegerParameterRangeSpecification — (map)

          A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

          • MinValue — required — (String)

            The minimum integer value allowed.

          • MaxValue — required — (String)

            The maximum integer value allowed.

        • ContinuousParameterRangeSpecification — (map)

          A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

          • MinValue — required — (String)

            The minimum floating-point value allowed.

          • MaxValue — required — (String)

            The maximum floating-point value allowed.

        • CategoricalParameterRangeSpecification — (map)

          A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

          • Values — required — (Array<String>)

            The allowed categories for the hyperparameter.

      • IsTunable — (Boolean)

        Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

      • IsRequired — (Boolean)

        Indicates whether this hyperparameter is required.

      • DefaultValue — (String)

        The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

    • SupportedTrainingInstanceTypes — required — (Array<String>)

      A list of the instance types that this algorithm can use for training.

    • SupportsDistributedTraining — (Boolean)

      Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

    • MetricDefinitions — (Array<map>)

      A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

      • Name — required — (String)

        The name of the metric.

      • Regex — required — (String)

        A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.

    • TrainingChannels — required — (Array<map>)

      A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

      • Name — required — (String)

        The name of the channel.

      • Description — (String)

        A brief description of the channel.

      • IsRequired — (Boolean)

        Indicates whether the channel is required by the algorithm.

      • SupportedContentTypes — required — (Array<String>)

        The supported MIME types for the data.

      • SupportedCompressionTypes — (Array<String>)

        The allowed compression types, if data compression is used.

      • SupportedInputModes — required — (Array<String>)

        The allowed input mode, either FILE or PIPE.

        In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.

        In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

    • SupportedTuningJobObjectiveMetrics — (Array<map>)

      A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

      • Type — required — (String)

        Whether to minimize or maximize the objective metric.

        Possible values include:
        • "Maximize"
        • "Minimize"
      • MetricName — required — (String)

        The name of the metric to use for the objective metric.

    • AdditionalS3DataSource — (map)

      The additional data source used during the training job.

      • S3DataType — required — (String)

        The data type of the additional data source that you specify for use in inference or training.

        Possible values include:
        • "S3Object"
        • "S3Prefix"
      • S3Uri — required — (String)

        The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

      • CompressionType — (String)

        The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

        Possible values include:
        • "None"
        • "Gzip"
  • InferenceSpecification — (map)

    Specifies details about inference jobs that the algorithm runs, including the following:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the algorithm supports for inference.

    • Containers — required — (Array<map>)

      The Amazon ECR registry path of the Docker image that contains the inference code.

      • ContainerHostname — (String)

        The DNS host name for the Docker container.

      • Image — required — (String)

        The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

        If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

      • ImageDigest — (String)

        An MD5 hash of the training algorithm that identifies the Docker image used for training.

      • ModelDataUrl — (String)

        The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

        Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.
      • ModelDataSource — (map)

        Specifies the location of ML model data to deploy during endpoint creation.

        • S3DataSource — (map)

          Specifies the S3 location of ML model data to deploy.

          • S3Uri — required — (String)

            Specifies the S3 path of ML model data to deploy.

          • S3DataType — required — (String)

            Specifies the type of ML model data to deploy.

            If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

            If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

            Possible values include:
            • "S3Prefix"
            • "S3Object"
          • CompressionType — required — (String)

            Specifies how the ML model data is prepared.

            If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

            If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

            If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

            If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

            • If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

            • If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

            • Do not use any of the following as file names or directory names:

              • An empty or blank string

              • A string which contains null bytes

              • A string longer than 255 bytes

              • A single dot (.)

              • A double dot (..)

            • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

            • Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

            Possible values include:
            • "None"
            • "Gzip"
          • ModelAccessConfig — (map)

            Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

            • AcceptEula — required — (Boolean)

              Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

          • HubAccessConfig — (map)

            Configuration information for hub access.

            • HubContentArn — required — (String)

              The ARN of the hub content for which deployment access is allowed.

      • ProductId — (String)

        The Amazon Web Services Marketplace product ID of the model package.

      • Environment — (map<String>)

        The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

      • ModelInput — (map)

        A structure with Model Input details.

        • DataInputConfig — required — (String)

          The input configuration object for the model.

      • Framework — (String)

        The machine learning framework of the model package container image.

      • FrameworkVersion — (String)

        The framework version of the Model Package Container Image.

      • NearestModelName — (String)

        The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

      • AdditionalS3DataSource — (map)

        The additional data source that is used during inference in the Docker container for your model package.

        • S3DataType — required — (String)

          The data type of the additional data source that you specify for use in inference or training.

          Possible values include:
          • "S3Object"
          • "S3Prefix"
        • S3Uri — required — (String)

          The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

        • CompressionType — (String)

          The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

          Possible values include:
          • "None"
          • "Gzip"
    • SupportedTransformInstanceTypes — (Array<String>)

      A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

      This parameter is required for unversioned models, and optional for versioned models.

    • SupportedRealtimeInferenceInstanceTypes — (Array<String>)

      A list of the instance types that are used to generate inferences in real-time.

      This parameter is required for unversioned models, and optional for versioned models.

    • SupportedContentTypes — (Array<String>)

      The supported MIME types for the input data.

    • SupportedResponseMIMETypes — (Array<String>)

      The supported MIME types for the output data.

  • ValidationSpecification — (map)

    Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.

    • ValidationRole — required — (String)

      The IAM roles that SageMaker uses to run the training jobs.

    • ValidationProfiles — required — (Array<map>)

      An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.

      • ProfileName — required — (String)

        The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

      • TrainingJobDefinition — required — (map)

        The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.

        • TrainingInputMode — required — (String)

          The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

          Pipe mode

          If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

          File mode

          If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

          You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

          For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

          FastFile mode

          If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

          FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

          Possible values include:
          • "Pipe"
          • "File"
          • "FastFile"
        • HyperParameters — (map<String>)

          The hyperparameters used for the training job.

        • InputDataConfig — required — (Array<map>)

          An array of Channel objects, each of which specifies an input source.

          • ChannelName — required — (String)

            The name of the channel.

          • DataSource — required — (map)

            The location of the channel data.

            • S3DataSource — (map)

              The S3 location of the data source that is associated with a channel.

              • S3DataType — required — (String)

                If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

                If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

                If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe.

                Possible values include:
                • "ManifestFile"
                • "S3Prefix"
                • "AugmentedManifestFile"
              • S3Uri — required — (String)

                Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

                • A key name prefix might look like this: s3://bucketname/exampleprefix/

                • A manifest might look like this: s3://bucketname/example.manifest

                  A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.

                  The following code example shows a valid manifest format:

                  [ {"prefix": "s3://customer_bucket/some/prefix/"},

                  "relative/path/to/custdata-1",

                  "relative/path/custdata-2",

                  ...

                  "relative/path/custdata-N"

                  ]

                  This JSON is equivalent to the following S3Uri list:

                  s3://customer_bucket/some/prefix/relative/path/to/custdata-1

                  s3://customer_bucket/some/prefix/relative/path/custdata-2

                  ...

                  s3://customer_bucket/some/prefix/relative/path/custdata-N

                  The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.

                Your input bucket must be located in same Amazon Web Services region as your training job.

              • S3DataDistributionType — (String)

                If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.

                If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

                Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

                In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.

                Possible values include:
                • "FullyReplicated"
                • "ShardedByS3Key"
              • AttributeNames — (Array<String>)

                A list of one or more attribute names to use that are found in a specified augmented manifest file.

              • InstanceGroupNames — (Array<String>)

                A list of names of instance groups that get data from the S3 data source.

            • FileSystemDataSource — (map)

              The file system that is associated with a channel.

              • FileSystemId — required — (String)

                The file system id.

              • FileSystemAccessMode — required — (String)

                The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

                Possible values include:
                • "rw"
                • "ro"
              • FileSystemType — required — (String)

                The file system type.

                Possible values include:
                • "EFS"
                • "FSxLustre"
              • DirectoryPath — required — (String)

                The full path to the directory to associate with the channel.

          • ContentType — (String)

            The MIME type of the data.

          • CompressionType — (String)

            If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

            Possible values include:
            • "None"
            • "Gzip"
          • RecordWrapperType — (String)

            Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.

            In File mode, leave this field unset or set it to None.

            Possible values include:
            • "None"
            • "RecordIO"
          • InputMode — (String)

            (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

            To use a model for incremental training, choose File input model.

            Possible values include:
            • "Pipe"
            • "File"
            • "FastFile"
          • ShuffleConfig — (map)

            A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

            For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

            • Seed — required — (Integer)

              Determines the shuffling order in ShuffleConfig value.

        • OutputDataConfig — required — (map)

          the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

          • KmsKeyId — (String)

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

            • // KMS Key ID

              "1234abcd-12ab-34cd-56ef-1234567890ab"

            • // Amazon Resource Name (ARN) of a KMS Key

              "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

            • // KMS Key Alias

              "alias/ExampleAlias"

            • // Amazon Resource Name (ARN) of a KMS Key Alias

              "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

            If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

            The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

          • S3OutputPath — required — (String)

            Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

          • CompressionType — (String)

            The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

            Possible values include:
            • "GZIP"
            • "NONE"
        • ResourceConfig — required — (map)

          The resources, including the ML compute instances and ML storage volumes, to use for model training.

          • InstanceType — (String)

            The ML compute instance type.

            Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

            • US East (N. Virginia) (us-east-1)
            • US West (Oregon) (us-west-2)

            To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
            Possible values include:
            • "ml.m4.xlarge"
            • "ml.m4.2xlarge"
            • "ml.m4.4xlarge"
            • "ml.m4.10xlarge"
            • "ml.m4.16xlarge"
            • "ml.g4dn.xlarge"
            • "ml.g4dn.2xlarge"
            • "ml.g4dn.4xlarge"
            • "ml.g4dn.8xlarge"
            • "ml.g4dn.12xlarge"
            • "ml.g4dn.16xlarge"
            • "ml.m5.large"
            • "ml.m5.xlarge"
            • "ml.m5.2xlarge"
            • "ml.m5.4xlarge"
            • "ml.m5.12xlarge"
            • "ml.m5.24xlarge"
            • "ml.c4.xlarge"
            • "ml.c4.2xlarge"
            • "ml.c4.4xlarge"
            • "ml.c4.8xlarge"
            • "ml.p2.xlarge"
            • "ml.p2.8xlarge"
            • "ml.p2.16xlarge"
            • "ml.p3.2xlarge"
            • "ml.p3.8xlarge"
            • "ml.p3.16xlarge"
            • "ml.p3dn.24xlarge"
            • "ml.p4d.24xlarge"
            • "ml.p4de.24xlarge"
            • "ml.p5.48xlarge"
            • "ml.c5.xlarge"
            • "ml.c5.2xlarge"
            • "ml.c5.4xlarge"
            • "ml.c5.9xlarge"
            • "ml.c5.18xlarge"
            • "ml.c5n.xlarge"
            • "ml.c5n.2xlarge"
            • "ml.c5n.4xlarge"
            • "ml.c5n.9xlarge"
            • "ml.c5n.18xlarge"
            • "ml.g5.xlarge"
            • "ml.g5.2xlarge"
            • "ml.g5.4xlarge"
            • "ml.g5.8xlarge"
            • "ml.g5.16xlarge"
            • "ml.g5.12xlarge"
            • "ml.g5.24xlarge"
            • "ml.g5.48xlarge"
            • "ml.trn1.2xlarge"
            • "ml.trn1.32xlarge"
            • "ml.trn1n.32xlarge"
            • "ml.m6i.large"
            • "ml.m6i.xlarge"
            • "ml.m6i.2xlarge"
            • "ml.m6i.4xlarge"
            • "ml.m6i.8xlarge"
            • "ml.m6i.12xlarge"
            • "ml.m6i.16xlarge"
            • "ml.m6i.24xlarge"
            • "ml.m6i.32xlarge"
            • "ml.c6i.xlarge"
            • "ml.c6i.2xlarge"
            • "ml.c6i.8xlarge"
            • "ml.c6i.4xlarge"
            • "ml.c6i.12xlarge"
            • "ml.c6i.16xlarge"
            • "ml.c6i.24xlarge"
            • "ml.c6i.32xlarge"
            • "ml.r5d.large"
            • "ml.r5d.xlarge"
            • "ml.r5d.2xlarge"
            • "ml.r5d.4xlarge"
            • "ml.r5d.8xlarge"
            • "ml.r5d.12xlarge"
            • "ml.r5d.16xlarge"
            • "ml.r5d.24xlarge"
            • "ml.t3.medium"
            • "ml.t3.large"
            • "ml.t3.xlarge"
            • "ml.t3.2xlarge"
            • "ml.r5.large"
            • "ml.r5.xlarge"
            • "ml.r5.2xlarge"
            • "ml.r5.4xlarge"
            • "ml.r5.8xlarge"
            • "ml.r5.12xlarge"
            • "ml.r5.16xlarge"
            • "ml.r5.24xlarge"
          • InstanceCount — (Integer)

            The number of ML compute instances to use. For distributed training, provide a value greater than 1.

          • VolumeSizeInGB — required — (Integer)

            The size of the ML storage volume that you want to provision.

            ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

            When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d, ml.g4dn, and ml.g5.

            When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2.

            To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.

            To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

          • VolumeKmsKeyId — (String)

            The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

            Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.

            The VolumeKmsKeyId can be in any of the following formats:

            • // KMS Key ID

              "1234abcd-12ab-34cd-56ef-1234567890ab"

            • // Amazon Resource Name (ARN) of a KMS Key

              "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

          • KeepAlivePeriodInSeconds — (Integer)

            The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

          • InstanceGroups — (Array<map>)

            The configuration of a heterogeneous cluster in JSON format.

            • InstanceType — required — (String)

              Specifies the instance type of the instance group.

              Possible values include:
              • "ml.m4.xlarge"
              • "ml.m4.2xlarge"
              • "ml.m4.4xlarge"
              • "ml.m4.10xlarge"
              • "ml.m4.16xlarge"
              • "ml.g4dn.xlarge"
              • "ml.g4dn.2xlarge"
              • "ml.g4dn.4xlarge"
              • "ml.g4dn.8xlarge"
              • "ml.g4dn.12xlarge"
              • "ml.g4dn.16xlarge"
              • "ml.m5.large"
              • "ml.m5.xlarge"
              • "ml.m5.2xlarge"
              • "ml.m5.4xlarge"
              • "ml.m5.12xlarge"
              • "ml.m5.24xlarge"
              • "ml.c4.xlarge"
              • "ml.c4.2xlarge"
              • "ml.c4.4xlarge"
              • "ml.c4.8xlarge"
              • "ml.p2.xlarge"
              • "ml.p2.8xlarge"
              • "ml.p2.16xlarge"
              • "ml.p3.2xlarge"
              • "ml.p3.8xlarge"
              • "ml.p3.16xlarge"
              • "ml.p3dn.24xlarge"
              • "ml.p4d.24xlarge"
              • "ml.p4de.24xlarge"
              • "ml.p5.48xlarge"
              • "ml.c5.xlarge"
              • "ml.c5.2xlarge"
              • "ml.c5.4xlarge"
              • "ml.c5.9xlarge"
              • "ml.c5.18xlarge"
              • "ml.c5n.xlarge"
              • "ml.c5n.2xlarge"
              • "ml.c5n.4xlarge"
              • "ml.c5n.9xlarge"
              • "ml.c5n.18xlarge"
              • "ml.g5.xlarge"
              • "ml.g5.2xlarge"
              • "ml.g5.4xlarge"
              • "ml.g5.8xlarge"
              • "ml.g5.16xlarge"
              • "ml.g5.12xlarge"
              • "ml.g5.24xlarge"
              • "ml.g5.48xlarge"
              • "ml.trn1.2xlarge"
              • "ml.trn1.32xlarge"
              • "ml.trn1n.32xlarge"
              • "ml.m6i.large"
              • "ml.m6i.xlarge"
              • "ml.m6i.2xlarge"
              • "ml.m6i.4xlarge"
              • "ml.m6i.8xlarge"
              • "ml.m6i.12xlarge"
              • "ml.m6i.16xlarge"
              • "ml.m6i.24xlarge"
              • "ml.m6i.32xlarge"
              • "ml.c6i.xlarge"
              • "ml.c6i.2xlarge"
              • "ml.c6i.8xlarge"
              • "ml.c6i.4xlarge"
              • "ml.c6i.12xlarge"
              • "ml.c6i.16xlarge"
              • "ml.c6i.24xlarge"
              • "ml.c6i.32xlarge"
              • "ml.r5d.large"
              • "ml.r5d.xlarge"
              • "ml.r5d.2xlarge"
              • "ml.r5d.4xlarge"
              • "ml.r5d.8xlarge"
              • "ml.r5d.12xlarge"
              • "ml.r5d.16xlarge"
              • "ml.r5d.24xlarge"
              • "ml.t3.medium"
              • "ml.t3.large"
              • "ml.t3.xlarge"
              • "ml.t3.2xlarge"
              • "ml.r5.large"
              • "ml.r5.xlarge"
              • "ml.r5.2xlarge"
              • "ml.r5.4xlarge"
              • "ml.r5.8xlarge"
              • "ml.r5.12xlarge"
              • "ml.r5.16xlarge"
              • "ml.r5.24xlarge"
            • InstanceCount — required — (Integer)

              Specifies the number of instances of the instance group.

            • InstanceGroupName — required — (String)

              Specifies the name of the instance group.

        • StoppingCondition — required — (map)

          Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

          To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

          • MaxRuntimeInSeconds — (Integer)

            The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

            For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

            For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

            The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

          • MaxWaitTimeInSeconds — (Integer)

            The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds. If the job does not complete during this time, SageMaker ends the job.

            When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

          • MaxPendingTimeInSeconds — (Integer)

            The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

      • TransformJobDefinition — (map)

        The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.

        • MaxConcurrentTransforms — (Integer)

          The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

        • MaxPayloadInMB — (Integer)

          The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

        • BatchStrategy — (String)

          A string that determines the number of records included in a single mini-batch.

          SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.

          Possible values include:
          • "MultiRecord"
          • "SingleRecord"
        • Environment — (map<String>)

          The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

        • TransformInput — required — (map)

          A description of the input source and the way the transform job consumes it.

          • DataSource — required — (map)

            Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

            • S3DataSource — required — (map)

              The S3 location of the data source that is associated with a channel.

              • S3DataType — required — (String)

                If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

                If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

                The following values are compatible: ManifestFile, S3Prefix

                The following value is not compatible: AugmentedManifestFile

                Possible values include:
                • "ManifestFile"
                • "S3Prefix"
                • "AugmentedManifestFile"
              • S3Uri — required — (String)

                Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

                • A key name prefix might look like this: s3://bucketname/exampleprefix/.

                • A manifest might look like this: s3://bucketname/example.manifest

                  The manifest is an S3 object which is a JSON file with the following format:

                  [ {"prefix": "s3://customer_bucket/some/prefix/"},

                  "relative/path/to/custdata-1",

                  "relative/path/custdata-2",

                  ...

                  "relative/path/custdata-N"

                  ]

                  The preceding JSON matches the following S3Uris:

                  s3://customer_bucket/some/prefix/relative/path/to/custdata-1

                  s3://customer_bucket/some/prefix/relative/path/custdata-2

                  ...

                  s3://customer_bucket/some/prefix/relative/path/custdata-N

                  The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

          • ContentType — (String)

            The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

          • CompressionType — (String)

            If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.

            Possible values include:
            • "None"
            • "Gzip"
          • SplitType — (String)

            The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:

            • RecordIO

            • TFRecord

            When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.

            Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord. For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
            Possible values include:
            • "None"
            • "Line"
            • "RecordIO"
            • "TFRecord"
        • TransformOutput — required — (map)

          Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

          • S3OutputPath — required — (String)

            The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.

            For every S3 object used as input for the transform job, batch transform stores the transformed data with an .out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.

          • Accept — (String)

            The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

          • AssembleWith — (String)

            Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.

            Possible values include:
            • "None"
            • "Line"
          • KmsKeyId — (String)

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

            • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

            • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

            • Alias name: alias/ExampleAlias

            • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

            If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

            The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

        • TransformResources — required — (map)

          Identifies the ML compute instances for the transform job.

          • InstanceType — required — (String)

            The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.largeinstance types.

            Possible values include:
            • "ml.m4.xlarge"
            • "ml.m4.2xlarge"
            • "ml.m4.4xlarge"
            • "ml.m4.10xlarge"
            • "ml.m4.16xlarge"
            • "ml.c4.xlarge"
            • "ml.c4.2xlarge"
            • "ml.c4.4xlarge"
            • "ml.c4.8xlarge"
            • "ml.p2.xlarge"
            • "ml.p2.8xlarge"
            • "ml.p2.16xlarge"
            • "ml.p3.2xlarge"
            • "ml.p3.8xlarge"
            • "ml.p3.16xlarge"
            • "ml.c5.xlarge"
            • "ml.c5.2xlarge"
            • "ml.c5.4xlarge"
            • "ml.c5.9xlarge"
            • "ml.c5.18xlarge"
            • "ml.m5.large"
            • "ml.m5.xlarge"
            • "ml.m5.2xlarge"
            • "ml.m5.4xlarge"
            • "ml.m5.12xlarge"
            • "ml.m5.24xlarge"
            • "ml.m6i.large"
            • "ml.m6i.xlarge"
            • "ml.m6i.2xlarge"
            • "ml.m6i.4xlarge"
            • "ml.m6i.8xlarge"
            • "ml.m6i.12xlarge"
            • "ml.m6i.16xlarge"
            • "ml.m6i.24xlarge"
            • "ml.m6i.32xlarge"
            • "ml.c6i.large"
            • "ml.c6i.xlarge"
            • "ml.c6i.2xlarge"
            • "ml.c6i.4xlarge"
            • "ml.c6i.8xlarge"
            • "ml.c6i.12xlarge"
            • "ml.c6i.16xlarge"
            • "ml.c6i.24xlarge"
            • "ml.c6i.32xlarge"
            • "ml.r6i.large"
            • "ml.r6i.xlarge"
            • "ml.r6i.2xlarge"
            • "ml.r6i.4xlarge"
            • "ml.r6i.8xlarge"
            • "ml.r6i.12xlarge"
            • "ml.r6i.16xlarge"
            • "ml.r6i.24xlarge"
            • "ml.r6i.32xlarge"
            • "ml.m7i.large"
            • "ml.m7i.xlarge"
            • "ml.m7i.2xlarge"
            • "ml.m7i.4xlarge"
            • "ml.m7i.8xlarge"
            • "ml.m7i.12xlarge"
            • "ml.m7i.16xlarge"
            • "ml.m7i.24xlarge"
            • "ml.m7i.48xlarge"
            • "ml.c7i.large"
            • "ml.c7i.xlarge"
            • "ml.c7i.2xlarge"
            • "ml.c7i.4xlarge"
            • "ml.c7i.8xlarge"
            • "ml.c7i.12xlarge"
            • "ml.c7i.16xlarge"
            • "ml.c7i.24xlarge"
            • "ml.c7i.48xlarge"
            • "ml.r7i.large"
            • "ml.r7i.xlarge"
            • "ml.r7i.2xlarge"
            • "ml.r7i.4xlarge"
            • "ml.r7i.8xlarge"
            • "ml.r7i.12xlarge"
            • "ml.r7i.16xlarge"
            • "ml.r7i.24xlarge"
            • "ml.r7i.48xlarge"
            • "ml.g4dn.xlarge"
            • "ml.g4dn.2xlarge"
            • "ml.g4dn.4xlarge"
            • "ml.g4dn.8xlarge"
            • "ml.g4dn.12xlarge"
            • "ml.g4dn.16xlarge"
            • "ml.g5.xlarge"
            • "ml.g5.2xlarge"
            • "ml.g5.4xlarge"
            • "ml.g5.8xlarge"
            • "ml.g5.12xlarge"
            • "ml.g5.16xlarge"
            • "ml.g5.24xlarge"
            • "ml.g5.48xlarge"
          • InstanceCount — required — (Integer)

            The number of ML compute instances to use in the transform job. The default value is 1, and the maximum is 100. For distributed transform jobs, specify a value greater than 1.

          • VolumeKmsKeyId — (String)

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.

            Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.

            The VolumeKmsKeyId can be any of the following formats:

            • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

            • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

            • Alias name: alias/ExampleAlias

            • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

  • CertifyForMarketplace — (Boolean)

    Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

  • Tags — (Array<map>)

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • AlgorithmArn — (String)

      The Amazon Resource Name (ARN) of the new algorithm.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  AppName: 'STRING_VALUE', /* required */
  AppType: JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* required */
  DomainId: 'STRING_VALUE', /* required */
  ResourceSpec: {
    InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge,
    LifecycleConfigArn: 'STRING_VALUE',
    SageMakerImageArn: 'STRING_VALUE',
    SageMakerImageVersionAlias: 'STRING_VALUE',
    SageMakerImageVersionArn: 'STRING_VALUE'
  },
  SpaceName: 'STRING_VALUE',
  Tags: [
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ],
  UserProfileName: 'STRING_VALUE'
};
sagemaker.createApp(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • DomainId — (String)

    The domain ID.

  • UserProfileName — (String)

    The user profile name. If this value is not set, then SpaceName must be set.

  • SpaceName — (String)

    The name of the space. If this value is not set, then UserProfileName must be set.

  • AppType — (String)

    The type of app.

    Possible values include:
    • "JupyterServer"
    • "KernelGateway"
    • "DetailedProfiler"
    • "TensorBoard"
    • "CodeEditor"
    • "JupyterLab"
    • "RStudioServerPro"
    • "RSessionGateway"
    • "Canvas"
  • AppName — (String)

    The name of the app.

  • Tags — (Array<map>)

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

  • ResourceSpec — (map)

    The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

    Note: The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.
    • SageMakerImageArn — (String)

      The ARN of the SageMaker image that the image version belongs to.

    • SageMakerImageVersionArn — (String)

      The ARN of the image version created on the instance.

    • SageMakerImageVersionAlias — (String)

      The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

    • InstanceType — (String)

      The instance type that the image version runs on.

      Note: JupyterServer apps only support the system value. For KernelGateway apps, the system value is translated to ml.t3.medium. KernelGateway apps also support all other values for available instance types.
      Possible values include:
      • "system"
      • "ml.t3.micro"
      • "ml.t3.small"
      • "ml.t3.medium"
      • "ml.t3.large"
      • "ml.t3.xlarge"
      • "ml.t3.2xlarge"
      • "ml.m5.large"
      • "ml.m5.xlarge"
      • "ml.m5.2xlarge"
      • "ml.m5.4xlarge"
      • "ml.m5.8xlarge"
      • "ml.m5.12xlarge"
      • "ml.m5.16xlarge"
      • "ml.m5.24xlarge"
      • "ml.m5d.large"
      • "ml.m5d.xlarge"
      • "ml.m5d.2xlarge"
      • "ml.m5d.4xlarge"
      • "ml.m5d.8xlarge"
      • "ml.m5d.12xlarge"
      • "ml.m5d.16xlarge"
      • "ml.m5d.24xlarge"
      • "ml.c5.large"
      • "ml.c5.xlarge"
      • "ml.c5.2xlarge"
      • "ml.c5.4xlarge"
      • "ml.c5.9xlarge"
      • "ml.c5.12xlarge"
      • "ml.c5.18xlarge"
      • "ml.c5.24xlarge"
      • "ml.p3.2xlarge"
      • "ml.p3.8xlarge"
      • "ml.p3.16xlarge"
      • "ml.p3dn.24xlarge"
      • "ml.g4dn.xlarge"
      • "ml.g4dn.2xlarge"
      • "ml.g4dn.4xlarge"
      • "ml.g4dn.8xlarge"
      • "ml.g4dn.12xlarge"
      • "ml.g4dn.16xlarge"
      • "ml.r5.large"
      • "ml.r5.xlarge"
      • "ml.r5.2xlarge"
      • "ml.r5.4xlarge"
      • "ml.r5.8xlarge"
      • "ml.r5.12xlarge"
      • "ml.r5.16xlarge"
      • "ml.r5.24xlarge"
      • "ml.g5.xlarge"
      • "ml.g5.2xlarge"
      • "ml.g5.4xlarge"
      • "ml.g5.8xlarge"
      • "ml.g5.16xlarge"
      • "ml.g5.12xlarge"
      • "ml.g5.24xlarge"
      • "ml.g5.48xlarge"
      • "ml.g6.xlarge"
      • "ml.g6.2xlarge"
      • "ml.g6.4xlarge"
      • "ml.g6.8xlarge"
      • "ml.g6.12xlarge"
      • "ml.g6.16xlarge"
      • "ml.g6.24xlarge"
      • "ml.g6.48xlarge"
      • "ml.geospatial.interactive"
      • "ml.p4d.24xlarge"
      • "ml.p4de.24xlarge"
      • "ml.trn1.2xlarge"
      • "ml.trn1.32xlarge"
      • "ml.trn1n.32xlarge"
      • "ml.p5.48xlarge"
      • "ml.m6i.large"
      • "ml.m6i.xlarge"
      • "ml.m6i.2xlarge"
      • "ml.m6i.4xlarge"
      • "ml.m6i.8xlarge"
      • "ml.m6i.12xlarge"
      • "ml.m6i.16xlarge"
      • "ml.m6i.24xlarge"
      • "ml.m6i.32xlarge"
      • "ml.m7i.large"
      • "ml.m7i.xlarge"
      • "ml.m7i.2xlarge"
      • "ml.m7i.4xlarge"
      • "ml.m7i.8xlarge"
      • "ml.m7i.12xlarge"
      • "ml.m7i.16xlarge"
      • "ml.m7i.24xlarge"
      • "ml.m7i.48xlarge"
      • "ml.c6i.large"
      • "ml.c6i.xlarge"
      • "ml.c6i.2xlarge"
      • "ml.c6i.4xlarge"
      • "ml.c6i.8xlarge"
      • "ml.c6i.12xlarge"
      • "ml.c6i.16xlarge"
      • "ml.c6i.24xlarge"
      • "ml.c6i.32xlarge"
      • "ml.c7i.large"
      • "ml.c7i.xlarge"
      • "ml.c7i.2xlarge"
      • "ml.c7i.4xlarge"
      • "ml.c7i.8xlarge"
      • "ml.c7i.12xlarge"
      • "ml.c7i.16xlarge"
      • "ml.c7i.24xlarge"
      • "ml.c7i.48xlarge"
      • "ml.r6i.large"
      • "ml.r6i.xlarge"
      • "ml.r6i.2xlarge"
      • "ml.r6i.4xlarge"
      • "ml.r6i.8xlarge"
      • "ml.r6i.12xlarge"
      • "ml.r6i.16xlarge"
      • "ml.r6i.24xlarge"
      • "ml.r6i.32xlarge"
      • "ml.r7i.large"
      • "ml.r7i.xlarge"
      • "ml.r7i.2xlarge"
      • "ml.r7i.4xlarge"
      • "ml.r7i.8xlarge"
      • "ml.r7i.12xlarge"
      • "ml.r7i.16xlarge"
      • "ml.r7i.24xlarge"
      • "ml.r7i.48xlarge"
      • "ml.m6id.large"
      • "ml.m6id.xlarge"
      • "ml.m6id.2xlarge"
      • "ml.m6id.4xlarge"
      • "ml.m6id.8xlarge"
      • "ml.m6id.12xlarge"
      • "ml.m6id.16xlarge"
      • "ml.m6id.24xlarge"
      • "ml.m6id.32xlarge"
      • "ml.c6id.large"
      • "ml.c6id.xlarge"
      • "ml.c6id.2xlarge"
      • "ml.c6id.4xlarge"
      • "ml.c6id.8xlarge"
      • "ml.c6id.12xlarge"
      • "ml.c6id.16xlarge"
      • "ml.c6id.24xlarge"
      • "ml.c6id.32xlarge"
      • "ml.r6id.large"
      • "ml.r6id.xlarge"
      • "ml.r6id.2xlarge"
      • "ml.r6id.4xlarge"
      • "ml.r6id.8xlarge"
      • "ml.r6id.12xlarge"
      • "ml.r6id.16xlarge"
      • "ml.r6id.24xlarge"
      • "ml.r6id.32xlarge"
    • LifecycleConfigArn — (String)

      The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • AppArn — (String)

      The Amazon Resource Name (ARN) of the app.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  AppImageConfigName: 'STRING_VALUE', /* required */
  CodeEditorAppImageConfig: {
    ContainerConfig: {
      ContainerArguments: [
        'STRING_VALUE',
        /* more items */
      ],
      ContainerEntrypoint: [
        'STRING_VALUE',
        /* more items */
      ],
      ContainerEnvironmentVariables: {
        '<NonEmptyString256>': 'STRING_VALUE',
        /* '<NonEmptyString256>': ... */
      }
    },
    FileSystemConfig: {
      DefaultGid: 'NUMBER_VALUE',
      DefaultUid: 'NUMBER_VALUE',
      MountPath: 'STRING_VALUE'
    }
  },
  JupyterLabAppImageConfig: {
    ContainerConfig: {
      ContainerArguments: [
        'STRING_VALUE',
        /* more items */
      ],
      ContainerEntrypoint: [
        'STRING_VALUE',
        /* more items */
      ],
      ContainerEnvironmentVariables: {
        '<NonEmptyString256>': 'STRING_VALUE',
        /* '<NonEmptyString256>': ... */
      }
    },
    FileSystemConfig: {
      DefaultGid: 'NUMBER_VALUE',
      DefaultUid: 'NUMBER_VALUE',
      MountPath: 'STRING_VALUE'
    }
  },
  KernelGatewayImageConfig: {
    KernelSpecs: [ /* required */
      {
        Name: 'STRING_VALUE', /* required */
        DisplayName: 'STRING_VALUE'
      },
      /* more items */
    ],
    FileSystemConfig: {
      DefaultGid: 'NUMBER_VALUE',
      DefaultUid: 'NUMBER_VALUE',
      MountPath: 'STRING_VALUE'
    }
  },
  Tags: [
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ]
};
sagemaker.createAppImageConfig(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • AppImageConfigName — (String)

    The name of the AppImageConfig. Must be unique to your account.

  • Tags — (Array<map>)

    A list of tags to apply to the AppImageConfig.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

  • KernelGatewayImageConfig — (map)

    The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.

    • KernelSpecs — required — (Array<map>)

      The specification of the Jupyter kernels in the image.

      • Name — required — (String)

        The name of the Jupyter kernel in the image. This value is case sensitive.

      • DisplayName — (String)

        The display name of the kernel.

    • FileSystemConfig — (map)

      The Amazon Elastic File System storage configuration for a SageMaker image.

      • MountPath — (String)

        The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.

      • DefaultUid — (Integer)

        The default POSIX user ID (UID). If not specified, defaults to 1000.

      • DefaultGid — (Integer)

        The default POSIX group ID (GID). If not specified, defaults to 100.

  • JupyterLabAppImageConfig — (map)

    The JupyterLabAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.

    • FileSystemConfig — (map)

      The Amazon Elastic File System storage configuration for a SageMaker image.

      • MountPath — (String)

        The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.

      • DefaultUid — (Integer)

        The default POSIX user ID (UID). If not specified, defaults to 1000.

      • DefaultGid — (Integer)

        The default POSIX group ID (GID). If not specified, defaults to 100.

    • ContainerConfig — (map)

      The configuration used to run the application image container.

      • ContainerArguments — (Array<String>)

        The arguments for the container when you're running the application.

      • ContainerEntrypoint — (Array<String>)

        The entrypoint used to run the application in the container.

      • ContainerEnvironmentVariables — (map<String>)

        The environment variables to set in the container

  • CodeEditorAppImageConfig — (map)

    The CodeEditorAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.

    • FileSystemConfig — (map)

      The Amazon Elastic File System storage configuration for a SageMaker image.

      • MountPath — (String)

        The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.

      • DefaultUid — (Integer)

        The default POSIX user ID (UID). If not specified, defaults to 1000.

      • DefaultGid — (Integer)

        The default POSIX group ID (GID). If not specified, defaults to 100.

    • ContainerConfig — (map)

      The configuration used to run the application image container.

      • ContainerArguments — (Array<String>)

        The arguments for the container when you're running the application.

      • ContainerEntrypoint — (Array<String>)

        The entrypoint used to run the application in the container.

      • ContainerEnvironmentVariables — (map<String>)

        The environment variables to set in the container

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • AppImageConfigArn — (String)

      The ARN of the AppImageConfig.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  ArtifactType: 'STRING_VALUE', /* required */
  Source: { /* required */
    SourceUri: 'STRING_VALUE', /* required */
    SourceTypes: [
      {
        SourceIdType: MD5Hash | S3ETag | S3Version | Custom, /* required */
        Value: 'STRING_VALUE' /* required */
      },
      /* more items */
    ]
  },
  ArtifactName: 'STRING_VALUE',
  MetadataProperties: {
    CommitId: 'STRING_VALUE',
    GeneratedBy: 'STRING_VALUE',
    ProjectId: 'STRING_VALUE',
    Repository: 'STRING_VALUE'
  },
  Properties: {
    '<StringParameterValue>': 'STRING_VALUE',
    /* '<StringParameterValue>': ... */
  },
  Tags: [
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ]
};
sagemaker.createArtifact(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • ArtifactName — (String)

    The name of the artifact. Must be unique to your account in an Amazon Web Services Region.

  • Source — (map)

    The ID, ID type, and URI of the source.

    • SourceUri — required — (String)

      The URI of the source.

    • SourceTypes — (Array<map>)

      A list of source types.

      • SourceIdType — required — (String)

        The type of ID.

        Possible values include:
        • "MD5Hash"
        • "S3ETag"
        • "S3Version"
        • "Custom"
      • Value — required — (String)

        The ID.

  • ArtifactType — (String)

    The artifact type.

  • Properties — (map<String>)

    A list of properties to add to the artifact.

  • MetadataProperties — (map)

    Metadata properties of the tracking entity, trial, or trial component.

    • CommitId — (String)

      The commit ID.

    • Repository — (String)

      The repository.

    • GeneratedBy — (String)

      The entity this entity was generated by.

    • ProjectId — (String)

      The project ID.

  • Tags — (Array<map>)

    A list of tags to apply to the artifact.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • ArtifactArn — (String)

      The Amazon Resource Name (ARN) of the artifact.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  AutoMLJobName: 'STRING_VALUE', /* required */
  InputDataConfig: [ /* required */
    {
      TargetAttributeName: 'STRING_VALUE', /* required */
      ChannelType: training | validation,
      CompressionType: None | Gzip,
      ContentType: 'STRING_VALUE',
      DataSource: {
        S3DataSource: { /* required */
          S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */
          S3Uri: 'STRING_VALUE' /* required */
        }
      },
      SampleWeightAttributeName: 'STRING_VALUE'
    },
    /* more items */
  ],
  OutputDataConfig: { /* required */
    S3OutputPath: 'STRING_VALUE', /* required */
    KmsKeyId: 'STRING_VALUE'
  },
  RoleArn: 'STRING_VALUE', /* required */
  AutoMLJobConfig: {
    CandidateGenerationConfig: {
      AlgorithmsConfig: [
        {
          AutoMLAlgorithms: [ /* required */
            xgboost | linear-learner | mlp | lightgbm | catboost | randomforest | extra-trees | nn-torch | fastai | cnn-qr | deepar | prophet | npts | arima | ets,
            /* more items */
          ]
        },
        /* more items */
      ],
      FeatureSpecificationS3Uri: 'STRING_VALUE'
    },
    CompletionCriteria: {
      MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE',
      MaxCandidates: 'NUMBER_VALUE',
      MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE'
    },
    DataSplitConfig: {
      ValidationFraction: 'NUMBER_VALUE'
    },
    Mode: AUTO | ENSEMBLING | HYPERPARAMETER_TUNING,
    SecurityConfig: {
      EnableInterContainerTrafficEncryption: true || false,
      VolumeKmsKeyId: 'STRING_VALUE',
      VpcConfig: {
        SecurityGroupIds: [ /* required */
          'STRING_VALUE',
          /* more items */
        ],
        Subnets: [ /* required */
          'STRING_VALUE',
          /* more items */
        ]
      }
    }
  },
  AutoMLJobObjective: {
    MetricName: Accuracy | MSE | F1 | F1macro | AUC | RMSE | BalancedAccuracy | R2 | Recall | RecallMacro | Precision | PrecisionMacro | MAE | MAPE | MASE | WAPE | AverageWeightedQuantileLoss /* required */
  },
  GenerateCandidateDefinitionsOnly: true || false,
  ModelDeployConfig: {
    AutoGenerateEndpointName: true || false,
    EndpointName: 'STRING_VALUE'
  },
  ProblemType: BinaryClassification | MulticlassClassification | Regression,
  Tags: [
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ]
};
sagemaker.createAutoMLJob(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • AutoMLJobName — (String)

    Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

  • InputDataConfig — (Array<map>)

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    • DataSource — (map)

      The data source for an AutoML channel.

      • S3DataSource — required — (map)

        The Amazon S3 location of the input data.

        • S3DataType — required — (String)

          The data type.

          • If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

            The S3Prefix should have the following format:

            s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

          • If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

            A ManifestFile should have the format shown below:

            [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},

            "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",

            "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",

            ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

          • If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2).

            Here is a minimal, single-record example of an AugmentedManifestFile:

            {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",

            "label-metadata": {"class-name": "cat" }

            For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.

          Possible values include:
          • "ManifestFile"
          • "S3Prefix"
          • "AugmentedManifestFile"
        • S3Uri — required — (String)

          The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

    • CompressionType — (String)

      You can use Gzip or None. The default value is None.

      Possible values include:
      • "None"
      • "Gzip"
    • TargetAttributeName — required — (String)

      The name of the target variable in supervised learning, usually represented by 'y'.

    • ContentType — (String)

      The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

    • ChannelType — (String)

      The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.

      Possible values include:
      • "training"
      • "validation"
    • SampleWeightAttributeName — (String)

      If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

      Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

      Support for sample weights is available in Ensembling mode only.

  • OutputDataConfig — (map)

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

    • KmsKeyId — (String)

      The Key Management Service encryption key ID.

    • S3OutputPath — required — (String)

      The Amazon S3 output path. Must be 512 characters or less.

  • ProblemType — (String)

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    Possible values include:
    • "BinaryClassification"
    • "MulticlassClassification"
    • "Regression"
  • AutoMLJobObjective — (map)

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

    • MetricName — required — (String)

      The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

      The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

      • For tabular problem types:

        • List of available metrics:

          • Regression: MAE, MSE, R2, RMSE

          • Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall

          • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro

          For a description of each metric, see Autopilot metrics for classification and regression.

        • Default objective metrics:

          • Regression: MSE.

          • Binary classification: F1.

          • Multiclass classification: Accuracy.

      • For image or text classification problem types:

        • List of available metrics: Accuracy

          For a description of each metric, see Autopilot metrics for text and image classification.

        • Default objective metrics: Accuracy

      • For time-series forecasting problem types:

        • List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE

          For a description of each metric, see Autopilot metrics for time-series forecasting.

        • Default objective metrics: AverageWeightedQuantileLoss

      • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

      Possible values include:
      • "Accuracy"
      • "MSE"
      • "F1"
      • "F1macro"
      • "AUC"
      • "RMSE"
      • "BalancedAccuracy"
      • "R2"
      • "Recall"
      • "RecallMacro"
      • "Precision"
      • "PrecisionMacro"
      • "MAE"
      • "MAPE"
      • "MASE"
      • "WAPE"
      • "AverageWeightedQuantileLoss"
  • AutoMLJobConfig — (map)

    A collection of settings used to configure an AutoML job.

    • CompletionCriteria — (map)

      How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates — (Integer)

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds — (Integer)

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds — (Integer)

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • SecurityConfig — (map)

      The security configuration for traffic encryption or Amazon VPC settings.

      • VolumeKmsKeyId — (String)

        The key used to encrypt stored data.

      • EnableInterContainerTrafficEncryption — (Boolean)

        Whether to use traffic encryption between the container layers.

      • VpcConfig — (map)

        The VPC configuration.

        • SecurityGroupIds — required — (Array<String>)

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

        • Subnets — required — (Array<String>)

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

    • CandidateGenerationConfig — (map)

      The configuration for generating a candidate for an AutoML job (optional).

      • FeatureSpecificationS3Uri — (String)

        A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below:

        { "FeatureAttributeNames":["col1", "col2", ...] }.

        You can also specify the data type of the feature (optional) in the format shown below:

        { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

        Note: These column keys may not include the target column.

        In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

        If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

        If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

        The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

      • AlgorithmsConfig — (Array<map>)

        Stores the configuration information for the selection of algorithms trained on tabular data.

        The list of available algorithms to choose from depends on the training mode set in TabularJobConfig.Mode .

        • AlgorithmsConfig should not be set if the training mode is set on AUTO.

        • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

          If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

        • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

        For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig.

        For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

        • AutoMLAlgorithms — required — (Array<String>)

          The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

          • For the tabular problem type TabularJobConfig:

            Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

            • In ENSEMBLING mode:

              • "catboost"

              • "extra-trees"

              • "fastai"

              • "lightgbm"

              • "linear-learner"

              • "nn-torch"

              • "randomforest"

              • "xgboost"

            • In HYPERPARAMETER_TUNING mode:

              • "linear-learner"

              • "mlp"

              • "xgboost"

          • For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

            • Choose your algorithms from this list.

              • "cnn-qr"

              • "deepar"

              • "prophet"

              • "arima"

              • "npts"

              • "ets"

    • DataSplitConfig — (map)

      The configuration for splitting the input training dataset.

      Type: AutoMLDataSplitConfig

      • ValidationFraction — (Float)

        The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

    • Mode — (String)

      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

      Possible values include:
      • "AUTO"
      • "ENSEMBLING"
      • "HYPERPARAMETER_TUNING"
  • RoleArn — (String)

    The ARN of the role that is used to access the data.

  • GenerateCandidateDefinitionsOnly — (Boolean)

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

  • Tags — (Array<map>)

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

  • ModelDeployConfig — (map)

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

    • AutoGenerateEndpointName — (Boolean)

      Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False.

      Note: If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown.
    • EndpointName — (String)

      Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

      Note: Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • AutoMLJobArn — (String)

      The unique ARN assigned to the AutoML job when it is created.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  AutoMLJobInputDataConfig: [ /* required */
    {
      ChannelType: training | validation,
      CompressionType: None | Gzip,
      ContentType: 'STRING_VALUE',
      DataSource: {
        S3DataSource: { /* required */
          S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */
          S3Uri: 'STRING_VALUE' /* required */
        }
      }
    },
    /* more items */
  ],
  AutoMLJobName: 'STRING_VALUE', /* required */
  AutoMLProblemTypeConfig: { /* required */
    ImageClassificationJobConfig: {
      CompletionCriteria: {
        MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE',
        MaxCandidates: 'NUMBER_VALUE',
        MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE'
      }
    },
    TabularJobConfig: {
      TargetAttributeName: 'STRING_VALUE', /* required */
      CandidateGenerationConfig: {
        AlgorithmsConfig: [
          {
            AutoMLAlgorithms: [ /* required */
              xgboost | linear-learner | mlp | lightgbm | catboost | randomforest | extra-trees | nn-torch | fastai | cnn-qr | deepar | prophet | npts | arima | ets,
              /* more items */
            ]
          },
          /* more items */
        ]
      },
      CompletionCriteria: {
        MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE',
        MaxCandidates: 'NUMBER_VALUE',
        MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE'
      },
      FeatureSpecificationS3Uri: 'STRING_VALUE',
      GenerateCandidateDefinitionsOnly: true || false,
      Mode: AUTO | ENSEMBLING | HYPERPARAMETER_TUNING,
      ProblemType: BinaryClassification | MulticlassClassification | Regression,
      SampleWeightAttributeName: 'STRING_VALUE'
    },
    TextClassificationJobConfig: {
      ContentColumn: 'STRING_VALUE', /* required */
      TargetLabelColumn: 'STRING_VALUE', /* required */
      CompletionCriteria: {
        MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE',
        MaxCandidates: 'NUMBER_VALUE',
        MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE'
      }
    },
    TextGenerationJobConfig: {
      BaseModelName: 'STRING_VALUE',
      CompletionCriteria: {
        MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE',
        MaxCandidates: 'NUMBER_VALUE',
        MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE'
      },
      ModelAccessConfig: {
        AcceptEula: true || false /* required */
      },
      TextGenerationHyperParameters: {
        '<TextGenerationHyperParameterKey>': 'STRING_VALUE',
        /* '<TextGenerationHyperParameterKey>': ... */
      }
    },
    TimeSeriesForecastingJobConfig: {
      ForecastFrequency: 'STRING_VALUE', /* required */
      ForecastHorizon: 'NUMBER_VALUE', /* required */
      TimeSeriesConfig: { /* required */
        ItemIdentifierAttributeName: 'STRING_VALUE', /* required */
        TargetAttributeName: 'STRING_VALUE', /* required */
        TimestampAttributeName: 'STRING_VALUE', /* required */
        GroupingAttributeNames: [
          'STRING_VALUE',
          /* more items */
        ]
      },
      CandidateGenerationConfig: {
        AlgorithmsConfig: [
          {
            AutoMLAlgorithms: [ /* required */
              xgboost | linear-learner | mlp | lightgbm | catboost | randomforest | extra-trees | nn-torch | fastai | cnn-qr | deepar | prophet | npts | arima | ets,
              /* more items */
            ]
          },
          /* more items */
        ]
      },
      CompletionCriteria: {
        MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE',
        MaxCandidates: 'NUMBER_VALUE',
        MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE'
      },
      FeatureSpecificationS3Uri: 'STRING_VALUE',
      ForecastQuantiles: [
        'STRING_VALUE',
        /* more items */
      ],
      HolidayConfig: [
        {
          CountryCode: 'STRING_VALUE'
        },
        /* more items */
      ],
      Transformations: {
        Aggregation: {
          '<TransformationAttributeName>': sum | avg | first | min | max,
          /* '<TransformationAttributeName>': ... */
        },
        Filling: {
          '<TransformationAttributeName>': {
            '<FillingType>': 'STRING_VALUE',
            /* '<FillingType>': ... */
          },
          /* '<TransformationAttributeName>': ... */
        }
      }
    }
  },
  OutputDataConfig: { /* required */
    S3OutputPath: 'STRING_VALUE', /* required */
    KmsKeyId: 'STRING_VALUE'
  },
  RoleArn: 'STRING_VALUE', /* required */
  AutoMLComputeConfig: {
    EmrServerlessComputeConfig: {
      ExecutionRoleARN: 'STRING_VALUE' /* required */
    }
  },
  AutoMLJobObjective: {
    MetricName: Accuracy | MSE | F1 | F1macro | AUC | RMSE | BalancedAccuracy | R2 | Recall | RecallMacro | Precision | PrecisionMacro | MAE | MAPE | MASE | WAPE | AverageWeightedQuantileLoss /* required */
  },
  DataSplitConfig: {
    ValidationFraction: 'NUMBER_VALUE'
  },
  ModelDeployConfig: {
    AutoGenerateEndpointName: true || false,
    EndpointName: 'STRING_VALUE'
  },
  SecurityConfig: {
    EnableInterContainerTrafficEncryption: true || false,
    VolumeKmsKeyId: 'STRING_VALUE',
    VpcConfig: {
      SecurityGroupIds: [ /* required */
        'STRING_VALUE',
        /* more items */
      ],
      Subnets: [ /* required */
        'STRING_VALUE',
        /* more items */
      ]
    }
  },
  Tags: [
    {
      Key: 'STRING_VALUE', /* required */
      Value: 'STRING_VALUE' /* required */
    },
    /* more items */
  ]
};
sagemaker.createAutoMLJobV2(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • AutoMLJobName — (String)

    Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

  • AutoMLJobInputDataConfig — (Array<map>)

    An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

    • For tabular problem types: S3Prefix, ManifestFile.

    • For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

    • For text classification: S3Prefix.

    • For time-series forecasting: S3Prefix.

    • For text generation (LLMs fine-tuning): S3Prefix.

    • ChannelType — (String)

      The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

      Note: The type of channel defaults to training for the time-series forecasting problem type.
      Possible values include:
      • "training"
      • "validation"
    • ContentType — (String)

      The content type of the data from the input source. The following are the allowed content types for different problems:

      • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For image classification: image/png, image/jpeg, or image/*. The default value is image/*.

      • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

    • CompressionType — (String)

      The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

      Possible values include:
      • "None"
      • "Gzip"
    • DataSource — (map)

      The data source for an AutoML channel (Required).

      • S3DataSource — required — (map)

        The Amazon S3 location of the input data.

        • S3DataType — required — (String)

          The data type.

          • If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

            The S3Prefix should have the following format:

            s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

          • If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

            A ManifestFile should have the format shown below:

            [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},

            "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",

            "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",

            ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

          • If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2).

            Here is a minimal, single-record example of an AugmentedManifestFile:

            {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",

            "label-metadata": {"class-name": "cat" }

            For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.

          Possible values include:
          • "ManifestFile"
          • "S3Prefix"
          • "AugmentedManifestFile"
        • S3Uri — required — (String)

          The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

  • OutputDataConfig — (map)

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

    • KmsKeyId — (String)

      The Key Management Service encryption key ID.

    • S3OutputPath — required — (String)

      The Amazon S3 output path. Must be 512 characters or less.

  • AutoMLProblemTypeConfig — (map)

    Defines the configuration settings of one of the supported problem types.

    • ImageClassificationJobConfig — (map)

      Settings used to configure an AutoML job V2 for the image classification problem type.

      • CompletionCriteria — (map)

        How long a job is allowed to run, or how many candidates a job is allowed to generate.

        • MaxCandidates — (Integer)

          The maximum number of times a training job is allowed to run.

          For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • MaxRuntimePerTrainingJobInSeconds — (Integer)

          The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

          For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • MaxAutoMLJobRuntimeInSeconds — (Integer)

          The maximum runtime, in seconds, an AutoML job has to complete.

          If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • TextClassificationJobConfig — (map)

      Settings used to configure an AutoML job V2 for the text classification problem type.

      • CompletionCriteria — (map)

        How long a job is allowed to run, or how many candidates a job is allowed to generate.

        • MaxCandidates — (Integer)

          The maximum number of times a training job is allowed to run.

          For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • MaxRuntimePerTrainingJobInSeconds — (Integer)

          The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

          For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • MaxAutoMLJobRuntimeInSeconds — (Integer)

          The maximum runtime, in seconds, an AutoML job has to complete.

          If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • ContentColumn — required — (String)

        The name of the column used to provide the sentences to be classified. It should not be the same as the target column.

      • TargetLabelColumn — required — (String)

        The name of the column used to provide the class labels. It should not be same as the content column.

    • TimeSeriesForecastingJobConfig — (map)

      Settings used to configure an AutoML job V2 for the time-series forecasting problem type.

      • FeatureSpecificationS3Uri — (String)

        A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig.

        You can input FeatureAttributeNames (optional) in JSON format as shown below:

        { "FeatureAttributeNames":["col1", "col2", ...] }.

        You can also specify the data type of the feature (optional) in the format shown below:

        { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

        Autopilot supports the following data types: numeric, categorical, text, and datetime.

        Note: These column keys must not include any column set in TimeSeriesConfig.
      • CompletionCriteria — (map)

        How long a job is allowed to run, or how many candidates a job is allowed to generate.

        • MaxCandidates — (Integer)

          The maximum number of times a training job is allowed to run.

          For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • MaxRuntimePerTrainingJobInSeconds — (Integer)

          The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

          For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • MaxAutoMLJobRuntimeInSeconds — (Integer)

          The maximum runtime, in seconds, an AutoML job has to complete.

          If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • ForecastFrequency — required — (String)

        The frequency of predictions in a forecast.

        Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.

        The valid values for each frequency are the following:

        • Minute - 1-59

        • Hour - 1-23

        • Day - 1-6

        • Week - 1-4

        • Month - 1-11

        • Year - 1

      • ForecastHorizon — required — (Integer)

        The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.

      • ForecastQuantiles — (Array<String>)

        The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

      • Transformations — (map)

        The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

        • Filling — (map<map<String>>)

          A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.

          The supported filling methods and their corresponding options are:

          • frontfill: none (Supported only for target column)

          • middlefill: zero, value, median, mean, min, max

          • backfill: zero, value, median, mean, min, max

          • futurefill: zero, value, median, mean, min, max

          To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value"), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2, you must include two parameters: "backfill": "value" and "backfill_value":"2".

        • Aggregation — (map<String>)

          A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.

          The supported aggregation methods are sum (default), avg, first, min, max.

          Note: Aggregation is only supported for the target column.
      • TimeSeriesConfig — required — (map)

        The collection of components that defines the time-series.

        • TargetAttributeName — required — (String)

          The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.

        • TimestampAttributeName — required — (String)

          The name of the column indicating a point in time at which the target value of a given item is recorded.

        • ItemIdentifierAttributeName — required — (String)

          The name of the column that represents the set of item identifiers for which you want to predict the target value.

        • GroupingAttributeNames — (Array<String>)

          A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.

      • HolidayConfig — (Array<map>)

        The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

        • CountryCode — (String)

          The country code for the holiday calendar.

          For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.

      • CandidateGenerationConfig — (map)

        Stores the configuration information for how model candidates are generated using an AutoML job V2.

        • AlgorithmsConfig — (Array<map>)

          Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

          AlgorithmsConfig stores the customized selection of algorithms to train on your data.

          • For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

            • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

            • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

              If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            For the list of all algorithms per training mode, see AlgorithmConfig.

            For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

          • For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

            For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

            • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

              If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

            • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

          • AutoMLAlgorithms — required — (Array<String>)

            The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

            • For the tabular problem type TabularJobConfig:

              Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

              • In ENSEMBLING mode:

                • "catboost"

                • "extra-trees"

                • "fastai"

                • "lightgbm"

                • "linear-learner"

                • "nn-torch"

                • "randomforest"

                • "xgboost"

              • In HYPERPARAMETER_TUNING mode:

                • "linear-learner"

                • "mlp"

                • "xgboost"

            • For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

              • Choose your algorithms from this list.

                • "cnn-qr"

                • "deepar"

                • "prophet"

                • "arima"

                • "npts"

                • "ets"

    • TabularJobConfig — (map)

      Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).

      • CandidateGenerationConfig — (map)

        The configuration information of how model candidates are generated.

        • AlgorithmsConfig — (Array<map>)

          Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

          AlgorithmsConfig stores the customized selection of algorithms to train on your data.

          • For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

            • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

            • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

              If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            For the list of all algorithms per training mode, see AlgorithmConfig.

            For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

          • For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

            For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

            • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

              If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

            • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

          • AutoMLAlgorithms — required — (Array<String>)

            The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

            • For the tabular problem type TabularJobConfig:

              Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

              • In ENSEMBLING mode:

                • "catboost"

                • "extra-trees"

                • "fastai"

                • "lightgbm"

                • "linear-learner"

                • "nn-torch"

                • "randomforest"

                • "xgboost"

              • In HYPERPARAMETER_TUNING mode:

                • "linear-learner"

                • "mlp"

                • "xgboost"

            • For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

              • Choose your algorithms from this list.

                • "cnn-qr"

                • "deepar"

                • "prophet"

                • "arima"

                • "npts"

                • "ets"

      • CompletionCriteria — (map)

        How long a job is allowed to run, or how many candidates a job is allowed to generate.

        • MaxCandidates — (Integer)

          The maximum number of times a training job is allowed to run.

          For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • MaxRuntimePerTrainingJobInSeconds — (Integer)

          The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

          For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • MaxAutoMLJobRuntimeInSeconds — (Integer)

          The maximum runtime, in seconds, an AutoML job has to complete.

          If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • FeatureSpecificationS3Uri — (String)

        A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

        { "FeatureAttributeNames":["col1", "col2", ...] }.

        You can also specify the data type of the feature (optional) in the format shown below:

        { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

        Note: These column keys may not include the target column.

        In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

        If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

        If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

        The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

      • Mode — (String)

        The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

        The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

        The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

        Possible values include:
        • "AUTO"
        • "ENSEMBLING"
        • "HYPERPARAMETER_TUNING"
      • GenerateCandidateDefinitionsOnly — (Boolean)

        Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

      • ProblemType — (String)

        The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

        Note: You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
        Possible values include:
        • "BinaryClassification"
        • "MulticlassClassification"
        • "Regression"
      • TargetAttributeName — required — (String)

        The name of the target variable in supervised learning, usually represented by 'y'.

      • SampleWeightAttributeName — (String)

        If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

        Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

        Support for sample weights is available in Ensembling mode only.

    • TextGenerationJobConfig — (map)

      Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.

      Note: The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.
      • CompletionCriteria — (map)

        How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

        • MaxCandidates — (Integer)

          The maximum number of times a training job is allowed to run.

          For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • MaxRuntimePerTrainingJobInSeconds — (Integer)

          The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

          For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • MaxAutoMLJobRuntimeInSeconds — (Integer)

          The maximum runtime, in seconds, an AutoML job has to complete.

          If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • BaseModelName — (String)

        The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

      • TextGenerationHyperParameters — (map<String>)

        The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

        • "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

        • "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

        • "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

        • "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

        Here is an example where all four hyperparameters are configured.

        { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

      • ModelAccessConfig — (map)

        The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig.

        • If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.

        • If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

        • AcceptEula — required — (Boolean)

          Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

  • RoleArn — (String)

    The ARN of the role that is used to access the data.

  • Tags — (Array<map>)

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    • Key — required — (String)

      The tag key. Tag keys must be unique per resource.

    • Value — required — (String)

      The tag value.

  • SecurityConfig — (map)

    The security configuration for traffic encryption or Amazon VPC settings.

    • VolumeKmsKeyId — (String)

      The key used to encrypt stored data.

    • EnableInterContainerTrafficEncryption — (Boolean)

      Whether to use traffic encryption between the container layers.

    • VpcConfig — (map)

      The VPC configuration.

      • SecurityGroupIds — required — (Array<String>)

        The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

      • Subnets — required — (Array<String>)

        The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

  • AutoMLJobObjective — (map)

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

    Note:

    • For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.
    • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
    • MetricName — required — (String)

      The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

      The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

      • For tabular problem types:

        • List of available metrics:

          • Regression: MAE, MSE, R2, RMSE

          • Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall

          • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro

          For a description of each metric, see Autopilot metrics for classification and regression.

        • Default objective metrics:

          • Regression: MSE.

          • Binary classification: F1.

          • Multiclass classification: Accuracy.

      • For image or text classification problem types:

        • List of available metrics: Accuracy

          For a description of each metric, see Autopilot metrics for text and image classification.

        • Default objective metrics: Accuracy

      • For time-series forecasting problem types:

        • List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE

          For a description of each metric, see Autopilot metrics for time-series forecasting.

        • Default objective metrics: AverageWeightedQuantileLoss

      • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

      Possible values include:
      • "Accuracy"
      • "MSE"
      • "F1"
      • "F1macro"
      • "AUC"
      • "RMSE"
      • "BalancedAccuracy"
      • "R2"
      • "Recall"
      • "RecallMacro"
      • "Precision"
      • "PrecisionMacro"
      • "MAE"
      • "MAPE"
      • "MASE"
      • "WAPE"
      • "AverageWeightedQuantileLoss"
  • ModelDeployConfig — (map)

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

    • AutoGenerateEndpointName — (Boolean)

      Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False.

      Note: If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown.
    • EndpointName — (String)

      Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

      Note: Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown.
  • DataSplitConfig — (map)

    This structure specifies how to split the data into train and validation datasets.

    The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

    Note: This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.
    • ValidationFraction — (Float)

      The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

  • AutoMLComputeConfig — (map)

    Specifies the compute configuration for the AutoML job V2.

    • EmrServerlessComputeConfig — (map)

      The configuration for using EMR Serverless to run the AutoML job V2.

      To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request.

      By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.

      EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.

      • ExecutionRoleARN — required — (String)

        The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see "How to configure AutoML to initiate a remote job on EMR Serverless for large datasets" in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.