Serialized Form (AWS Java SDK for Amazon SageMaker 1.11.616 API)

Package com.amazonaws.services.sagemaker.model
- Class com.amazonaws.services.sagemaker.model.AddTagsRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - resourceArn
```
String resourceArn
```
      The Amazon Resource Name (ARN) of the resource that you want to tag.
    - tags
```
List<E> tags
```
      An array of Tag objects. Each tag is a key-value pair. Only the key parameter is required. If you don't specify a value, Amazon SageMaker sets the value to an empty string.
- Class com.amazonaws.services.sagemaker.model.AddTagsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - tags
```
List<E> tags
```
      A list of tags associated with the Amazon SageMaker resource.
- Class com.amazonaws.services.sagemaker.model.AlgorithmSpecification extends Object implements Serializable
  - Serialized Fields
    - trainingImage
```
String trainingImage
```
      The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
    - algorithmName
```
String algorithmName
```
      The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.
    - trainingInputMode
```
String trainingInputMode
```
      The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms. If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
      
      In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
      
      For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon 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 where one host in a training cluster is overloaded, thus becoming bottleneck in training.
    - metricDefinitions
```
List<E> metricDefinitions
```
      A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
- Class com.amazonaws.services.sagemaker.model.AlgorithmStatusDetails extends Object implements Serializable
  - Serialized Fields
    - validationStatuses
```
List<E> validationStatuses
```
      The status of algorithm validation.
    - imageScanStatuses
```
List<E> imageScanStatuses
```
      The status of the scan of the algorithm's Docker image container.
- Class com.amazonaws.services.sagemaker.model.AlgorithmStatusItem extends Object implements Serializable
  - Serialized Fields
    - name
```
String name
```
      The name of the algorithm for which the overall status is being reported.
    - status
```
String status
```
      The current status.
    - failureReason
```
String failureReason
```
      if the overall status is Failed, the reason for the failure.
- Class com.amazonaws.services.sagemaker.model.AlgorithmSummary extends Object implements Serializable
  - Serialized Fields
    - algorithmName
```
String algorithmName
```
      The name of the algorithm that is described by the summary.
    - algorithmArn
```
String algorithmArn
```
      The Amazon Resource Name (ARN) of the algorithm.
    - algorithmDescription
```
String algorithmDescription
```
      A brief description of the algorithm.
    - creationTime
```
Date creationTime
```
      A timestamp that shows when the algorithm was created.
    - algorithmStatus
```
String algorithmStatus
```
      The overall status of the algorithm.
- Class com.amazonaws.services.sagemaker.model.AlgorithmValidationProfile extends Object implements Serializable
  - Serialized Fields
    - profileName
```
String profileName
```
      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
```
TrainingJobDefinition trainingJobDefinition
```
      The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to validate your algorithm.
    - transformJobDefinition
```
TransformJobDefinition transformJobDefinition
```
      The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to validate your algorithm.
- Class com.amazonaws.services.sagemaker.model.AlgorithmValidationSpecification extends Object implements Serializable
  - Serialized Fields
    - validationRole
```
String validationRole
```
      The IAM roles that Amazon SageMaker uses to run the training jobs.
    - validationProfiles
```
List<E> validationProfiles
```
      An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that Amazon SageMaker runs to validate your algorithm.
- Class com.amazonaws.services.sagemaker.model.AmazonSageMakerException extends AmazonServiceException implements Serializable
  
  serialVersionUID:
  
  1L
- Class com.amazonaws.services.sagemaker.model.AnnotationConsolidationConfig extends Object implements Serializable
  - Serialized Fields
    - annotationConsolidationLambdaArn
```
String annotationConsolidationLambdaArn
```
      The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation.
      
      For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:
      
      Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
      
      arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox
      
      arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox
      
      arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox
      
      arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox
      
      arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox
      
      arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
      
      arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox
      
      arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox
      
      arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox
      
      arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox
      
      arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox
      
      arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
      
      Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
      
      arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass
      
      arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass
      
      arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass
      
      arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass
      
      arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass
      
      arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
      
      arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass
      
      arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass
      
      arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass
      
      arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass
      
      arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass
      
      arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
      
      Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
      
      arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation
      
      arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
      
      Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
      
      arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass
      
      arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass
      
      arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass
      
      arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass
      
      arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass
      
      arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
      
      arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass
      
      arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass
      
      arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass
      
      arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass
      
      arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass
      
      arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
      
      Named entity eecognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
      
      arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
      
      Named entity eecognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
      
      arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
      
      arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
      
      For more information, see Annotation Consolidation.
- Class com.amazonaws.services.sagemaker.model.CategoricalParameterRange extends Object implements Serializable
  - Serialized Fields
    - name
```
String name
```
      The name of the categorical hyperparameter to tune.
    - values
```
List<E> values
```
      A list of the categories for the hyperparameter.
- Class com.amazonaws.services.sagemaker.model.CategoricalParameterRangeSpecification extends Object implements Serializable
  - Serialized Fields
    - values
```
List<E> values
```
      The allowed categories for the hyperparameter.
- Class com.amazonaws.services.sagemaker.model.Channel extends Object implements Serializable
  - Serialized Fields
    - channelName
```
String channelName
```
      The name of the channel.
    - dataSource
```
DataSource dataSource
```
      The location of the channel data.
    - contentType
```
String contentType
```
      The MIME type of the data.
    - compressionType
```
String compressionType
```
      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.
    - recordWrapperType
```
String recordWrapperType
```
      Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon 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.
    - inputMode
```
String inputMode
```
      (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, Amazon 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.
    - shuffleConfig
```
ShuffleConfig shuffleConfig
```
      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.
- Class com.amazonaws.services.sagemaker.model.ChannelSpecification extends Object implements Serializable
  - Serialized Fields
    - name
```
String name
```
      The name of the channel.
    - description
```
String description
```
      A brief description of the channel.
    - isRequired
```
Boolean isRequired
```
      Indicates whether the channel is required by the algorithm.
    - supportedContentTypes
```
List<E> supportedContentTypes
```
      The supported MIME types for the data.
    - supportedCompressionTypes
```
List<E> supportedCompressionTypes
```
      The allowed compression types, if data compression is used.
    - supportedInputModes
```
List<E> supportedInputModes
```
      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.
- Class com.amazonaws.services.sagemaker.model.CheckpointConfig extends Object implements Serializable
  - Serialized Fields
    - s3Uri
```
String s3Uri
```
      Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
    - localPath
```
String localPath
```
      (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.
- Class com.amazonaws.services.sagemaker.model.CodeRepositorySummary extends Object implements Serializable
  - Serialized Fields
    - codeRepositoryName
```
String codeRepositoryName
```
      The name of the Git repository.
    - codeRepositoryArn
```
String codeRepositoryArn
```
      The Amazon Resource Name (ARN) of the Git repository.
    - creationTime
```
Date creationTime
```
      The date and time that the Git repository was created.
    - lastModifiedTime
```
Date lastModifiedTime
```
      The date and time that the Git repository was last modified.
    - gitConfig
```
GitConfig gitConfig
```
      Configuration details for the Git repository, including the URL where it is located and the ARN of the AWS Secrets Manager secret that contains the credentials used to access the repository.
- Class com.amazonaws.services.sagemaker.model.CognitoMemberDefinition extends Object implements Serializable
  - Serialized Fields
    - userPool
```
String userPool
```
      An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
    - userGroup
```
String userGroup
```
      An identifier for a user group.
    - clientId
```
String clientId
```
      An identifier for an application client. You must create the app client ID using Amazon Cognito.
- Class com.amazonaws.services.sagemaker.model.CompilationJobSummary extends Object implements Serializable
  - Serialized Fields
    - compilationJobName
```
String compilationJobName
```
      The name of the model compilation job that you want a summary for.
    - compilationJobArn
```
String compilationJobArn
```
      The Amazon Resource Name (ARN) of the model compilation job.
    - creationTime
```
Date creationTime
```
      The time when the model compilation job was created.
    - compilationStartTime
```
Date compilationStartTime
```
      The time when the model compilation job started.
    - compilationEndTime
```
Date compilationEndTime
```
      The time when the model compilation job completed.
    - compilationTargetDevice
```
String compilationTargetDevice
```
      The type of device that the model will run on after compilation has completed.
    - lastModifiedTime
```
Date lastModifiedTime
```
      The time when the model compilation job was last modified.
    - compilationJobStatus
```
String compilationJobStatus
```
      The status of the model compilation job.
- Class com.amazonaws.services.sagemaker.model.ContainerDefinition extends Object implements Serializable
  - Serialized Fields
    - containerHostname
```
String containerHostname
```
      This parameter is ignored for models that contain only a PrimaryContainer.
      
      When a ContainerDefinition is part of an inference pipeline, the value of ths parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
    - image
```
String image
```
      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 Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker
    - modelDataUrl
```
String modelDataUrl
```
      The 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). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
      
      If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
      
      If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
    - environment
```
Map<K,V> environment
```
      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.
    - modelPackageName
```
String modelPackageName
```
      The name or Amazon Resource Name (ARN) of the model package to use to create the model.
- Class com.amazonaws.services.sagemaker.model.ContinuousParameterRange extends Object implements Serializable
  - Serialized Fields
    - name
```
String name
```
      The name of the continuous hyperparameter to tune.
    - minValue
```
String minValue
```
      The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.
    - maxValue
```
String maxValue
```
      The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
    - scalingType
```
String scalingType
```
      The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
      
      Auto
      
      Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
      
      Linear
      
      Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
      
      Logarithmic
      
      Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
      
      Logarithmic scaling works only for ranges that have only values greater than 0.
      
      ReverseLogarithmic
      
      Hyperparemeter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
      
      Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
- Class com.amazonaws.services.sagemaker.model.ContinuousParameterRangeSpecification extends Object implements Serializable
  - Serialized Fields
    - minValue
```
String minValue
```
      The minimum floating-point value allowed.
    - maxValue
```
String maxValue
```
      The maximum floating-point value allowed.
- Class com.amazonaws.services.sagemaker.model.CreateAlgorithmRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - algorithmName
```
String algorithmName
```
      The name of the algorithm.
    - algorithmDescription
```
String algorithmDescription
```
      A description of the algorithm.
    - trainingSpecification
```
TrainingSpecification trainingSpecification
```
      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.
    - inferenceSpecification
```
InferenceSpecification inferenceSpecification
```
      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.
    - validationSpecification
```
AlgorithmValidationSpecification validationSpecification
```
      Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm's inference code.
    - certifyForMarketplace
```
Boolean certifyForMarketplace
```
      Whether to certify the algorithm so that it can be listed in AWS Marketplace.
- Class com.amazonaws.services.sagemaker.model.CreateAlgorithmResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - algorithmArn
```
String algorithmArn
```
      The Amazon Resource Name (ARN) of the new algorithm.
- Class com.amazonaws.services.sagemaker.model.CreateCodeRepositoryRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - codeRepositoryName
```
String codeRepositoryName
```
      The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
    - gitConfig
```
GitConfig gitConfig
```
      Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
- Class com.amazonaws.services.sagemaker.model.CreateCodeRepositoryResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - codeRepositoryArn
```
String codeRepositoryArn
```
      The Amazon Resource Name (ARN) of the new repository.
- Class com.amazonaws.services.sagemaker.model.CreateCompilationJobRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - compilationJobName
```
String compilationJobName
```
      A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.
    - roleArn
```
String roleArn
```
      The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
      
      During model compilation, Amazon SageMaker needs your permission to:
      
      Read input data from an S3 bucket
      
      Write model artifacts to an S3 bucket
      
      Write logs to Amazon CloudWatch Logs
      
      Publish metrics to Amazon CloudWatch
      
      You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.
    - inputConfig
```
InputConfig inputConfig
```
      Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
    - outputConfig
```
OutputConfig outputConfig
```
      Provides information about the output location for the compiled model and the target device the model runs on.
    - stoppingCondition
```
StoppingCondition stoppingCondition
```
      Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
- Class com.amazonaws.services.sagemaker.model.CreateCompilationJobResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - compilationJobArn
```
String compilationJobArn
```
      If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
      
      CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.
- Class com.amazonaws.services.sagemaker.model.CreateEndpointConfigRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - endpointConfigName
```
String endpointConfigName
```
      The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
    - productionVariants
```
List<E> productionVariants
```
      An list of ProductionVariant objects, one for each model that you want to host at this endpoint.
    - tags
```
List<E> tags
```
      A list of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
    - kmsKeyId
```
String kmsKeyId
```
      The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
- Class com.amazonaws.services.sagemaker.model.CreateEndpointConfigResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - endpointConfigArn
```
String endpointConfigArn
```
      The Amazon Resource Name (ARN) of the endpoint configuration.
- Class com.amazonaws.services.sagemaker.model.CreateEndpointRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - endpointName
```
String endpointName
```
      The name of the endpoint. The name must be unique within an AWS Region in your AWS account.
    - endpointConfigName
```
String endpointConfigName
```
      The name of an endpoint configuration. For more information, see CreateEndpointConfig.
    - tags
```
List<E> tags
```
      An array of key-value pairs. For more information, see Using Cost Allocation Tagsin the AWS Billing and Cost Management User Guide.
- Class com.amazonaws.services.sagemaker.model.CreateEndpointResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - endpointArn
```
String endpointArn
```
      The Amazon Resource Name (ARN) of the endpoint.
- Class com.amazonaws.services.sagemaker.model.CreateHyperParameterTuningJobRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - hyperParameterTuningJobName
```
String hyperParameterTuningJobName
```
      The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. The name must have { } to { } characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
    - hyperParameterTuningJobConfig
```
HyperParameterTuningJobConfig hyperParameterTuningJobConfig
```
      The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see automatic-model-tuning
    - trainingJobDefinition
```
HyperParameterTrainingJobDefinition trainingJobDefinition
```
      The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
    - warmStartConfig
```
HyperParameterTuningJobWarmStartConfig warmStartConfig
```
      Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
      
      All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
      
      All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
    - tags
```
List<E> tags
```
      An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see AWS Tagging Strategies.
      
      Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
- Class com.amazonaws.services.sagemaker.model.CreateHyperParameterTuningJobResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - hyperParameterTuningJobArn
```
String hyperParameterTuningJobArn
```
      The Amazon Resource Name (ARN) of the tuning job. Amazon SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
- Class com.amazonaws.services.sagemaker.model.CreateLabelingJobRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - labelingJobName
```
String labelingJobName
```
      The name of the labeling job. This name is used to identify the job in a list of labeling jobs.
    - labelAttributeName
```
String labelAttributeName
```
      The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The name can't end with "-metadata". If you are running a semantic segmentation labeling job, the attribute name must end with "-ref". If you are running any other kind of labeling job, the attribute name must not end with "-ref".
    - inputConfig
```
LabelingJobInputConfig inputConfig
```
      Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
    - outputConfig
```
LabelingJobOutputConfig outputConfig
```
      The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
    - roleArn
```
String roleArn
```
      The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
    - labelCategoryConfigS3Uri
```
String labelCategoryConfigS3Uri
```
      The S3 URL of the file that defines the categories used to label the data objects.
      
      The file is a JSON structure in the following format:
      
      {
      
      "document-version": "2018-11-28"
      
      "labels": [
      
      {
      
      "label": "label 1"
      
      },
      
      {
      
      "label": "label 2"
      
      },
      
      ...
      
      {
      
      "label": "label n"
      
      }
      
      ]
      
      }
    - stoppingConditions
```
LabelingJobStoppingConditions stoppingConditions
```
      A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
    - labelingJobAlgorithmsConfig
```
LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig
```
      Configures the information required to perform automated data labeling.
    - humanTaskConfig
```
HumanTaskConfig humanTaskConfig
```
      Configures the information required for human workers to complete a labeling task.
    - tags
```
List<E> tags
```
      An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
- Class com.amazonaws.services.sagemaker.model.CreateLabelingJobResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - labelingJobArn
```
String labelingJobArn
```
      The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
- Class com.amazonaws.services.sagemaker.model.CreateModelPackageRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - modelPackageName
```
String modelPackageName
```
      The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
    - modelPackageDescription
```
String modelPackageDescription
```
      A description of the model package.
    - inferenceSpecification
```
InferenceSpecification inferenceSpecification
```
      Specifies details about inference jobs that can be run with models based on this model package, including the following:
      
      The Amazon ECR paths of containers that contain the inference code and model artifacts.
      
      The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
      
      The input and output content formats that the model package supports for inference.
    - validationSpecification
```
ModelPackageValidationSpecification validationSpecification
```
      Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
    - sourceAlgorithmSpecification
```
SourceAlgorithmSpecification sourceAlgorithmSpecification
```
      Details about the algorithm that was used to create the model package.
    - certifyForMarketplace
```
Boolean certifyForMarketplace
```
      Whether to certify the model package for listing on AWS Marketplace.
- Class com.amazonaws.services.sagemaker.model.CreateModelPackageResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - modelPackageArn
```
String modelPackageArn
```
      The Amazon Resource Name (ARN) of the new model package.
- Class com.amazonaws.services.sagemaker.model.CreateModelRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - modelName
```
String modelName
```
      The name of the new model.
    - primaryContainer
```
ContainerDefinition primaryContainer
```
      The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
    - containers
```
List<E> containers
```
      Specifies the containers in the inference pipeline.
    - executionRoleArn
```
String executionRoleArn
```
      The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.
      
      To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
    - tags
```
List<E> tags
```
      An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
    - vpcConfig
```
VpcConfig vpcConfig
```
      A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.
    - enableNetworkIsolation
```
Boolean enableNetworkIsolation
```
      Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
      
      The Semantic Segmentation built-in algorithm does not support network isolation.
- Class com.amazonaws.services.sagemaker.model.CreateModelResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - modelArn
```
String modelArn
```
      The ARN of the model created in Amazon SageMaker.
- Class com.amazonaws.services.sagemaker.model.CreateNotebookInstanceLifecycleConfigRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - notebookInstanceLifecycleConfigName
```
String notebookInstanceLifecycleConfigName
```
      The name of the lifecycle configuration.
    - onCreate
```
List<E> onCreate
```
      A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
    - onStart
```
List<E> onStart
```
      A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
- Class com.amazonaws.services.sagemaker.model.CreateNotebookInstanceLifecycleConfigResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - notebookInstanceLifecycleConfigArn
```
String notebookInstanceLifecycleConfigArn
```
      The Amazon Resource Name (ARN) of the lifecycle configuration.
- Class com.amazonaws.services.sagemaker.model.CreateNotebookInstanceRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - notebookInstanceName
```
String notebookInstanceName
```
      The name of the new notebook instance.
    - instanceType
```
String instanceType
```
      The type of ML compute instance to launch for the notebook instance.
    - subnetId
```
String subnetId
```
      The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
    - securityGroupIds
```
List<E> securityGroupIds
```
      The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
    - roleArn
```
String roleArn
```
      When you send any requests to AWS resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissionsto to assume this role. For more information, see Amazon SageMaker Roles.
      
      To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
    - kmsKeyId
```
String kmsKeyId
```
      The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the AWS Key Management Service Developer Guide.
    - tags
```
List<E> tags
```
      A list of tags to associate with the notebook instance. You can add tags later by using the CreateTags API.
    - lifecycleConfigName
```
String lifecycleConfigName
```
      The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
    - directInternetAccess
```
String directInternetAccess
```
      Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance will be able to access resources only in your VPC, and will not be able to connect to Amazon SageMaker training and endpoint services unless your configure a NAT Gateway in your VPC.
      
      For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.
    - volumeSizeInGB
```
Integer volumeSizeInGB
```
      The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
    - acceleratorTypes
```
List<E> acceleratorTypes
```
      A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
    - defaultCodeRepository
```
String defaultCodeRepository
```
      A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
    - additionalCodeRepositories
```
List<E> additionalCodeRepositories
```
      An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
    - rootAccess
```
String rootAccess
```
      Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.
      
      Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
- Class com.amazonaws.services.sagemaker.model.CreateNotebookInstanceResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - notebookInstanceArn
```
String notebookInstanceArn
```
      The Amazon Resource Name (ARN) of the notebook instance.
- Class com.amazonaws.services.sagemaker.model.CreatePresignedNotebookInstanceUrlRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - notebookInstanceName
```
String notebookInstanceName
```
      The name of the notebook instance.
    - sessionExpirationDurationInSeconds
```
Integer sessionExpirationDurationInSeconds
```
      The duration of the session, in seconds. The default is 12 hours.
- Class com.amazonaws.services.sagemaker.model.CreatePresignedNotebookInstanceUrlResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - authorizedUrl
```
String authorizedUrl
```
      A JSON object that contains the URL string.
- Class com.amazonaws.services.sagemaker.model.CreateTrainingJobRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - trainingJobName
```
String trainingJobName
```
      The name of the training job. The name must be unique within an AWS Region in an AWS account.
    - hyperParameters
```
Map<K,V> hyperParameters
```
      Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
      
      You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.
    - algorithmSpecification
```
AlgorithmSpecification algorithmSpecification
```
      The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
    - roleArn
```
String roleArn
```
      The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
      
      During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.
      
      To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
    - inputDataConfig
```
List<E> inputDataConfig
```
      An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.
      
      Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.
      
      Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
    - outputDataConfig
```
OutputDataConfig outputDataConfig
```
      Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
    - resourceConfig
```
ResourceConfig resourceConfig
```
      The resources, including the ML compute instances and ML storage volumes, to use for model training.
      
      ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
    - vpcConfig
```
VpcConfig vpcConfig
```
      A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
    - stoppingCondition
```
StoppingCondition stoppingCondition
```
      Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
      
      To stop a job, Amazon 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, so the results of training are not lost.
    - tags
```
List<E> tags
```
      An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
    - enableNetworkIsolation
```
Boolean enableNetworkIsolation
```
      Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
      
      The Semantic Segmentation built-in algorithm does not support network isolation.
    - enableInterContainerTrafficEncryption
```
Boolean enableInterContainerTrafficEncryption
```
      To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
    - enableManagedSpotTraining
```
Boolean enableManagedSpotTraining
```
      To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
      
      The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
    - checkpointConfig
```
CheckpointConfig checkpointConfig
```
      Contains information about the output location for managed spot training checkpoint data.
- Class com.amazonaws.services.sagemaker.model.CreateTrainingJobResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable
  - Serialized Fields
    - trainingJobArn
```
String trainingJobArn
```
      The Amazon Resource Name (ARN) of the training job.
- Class com.amazonaws.services.sagemaker.model.CreateTransformJobRequest extends AmazonWebServiceRequest implements Serializable
  - Serialized Fields
    - transformJobName
```
String transformJobName
```
      The name of the transform job. The name must be unique within an AWS Region in an AWS account.
    - modelName
```
String modelName
```
      The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.
    - maxConcurrentTransforms
```
Integer maxConcurrentTransforms
```
      The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the optimal settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.
    - maxPayloadInMB
```
Integer maxPayloadInMB
```
      The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.
      
      For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.
    - batchStrategy
```
String batchStrategy
```
      Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
      
      To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.
      
      To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.
      
      To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

Serialized Form

Package com.amazonaws.services.sagemaker.model

Class com.amazonaws.services.sagemaker.model.AddTagsRequest extends AmazonWebServiceRequest implements Serializable

Serialized Fields

resourceArn

tags

Class com.amazonaws.services.sagemaker.model.AddTagsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

Serialized Fields

tags

Class com.amazonaws.services.sagemaker.model.AlgorithmSpecification extends Object implements Serializable

Serialized Fields

trainingImage

algorithmName

trainingInputMode

metricDefinitions

Class com.amazonaws.services.sagemaker.model.AlgorithmStatusDetails extends Object implements Serializable

Serialized Fields

validationStatuses

imageScanStatuses

Class com.amazonaws.services.sagemaker.model.AlgorithmStatusItem extends Object implements Serializable

Serialized Fields

name

status

failureReason

Class com.amazonaws.services.sagemaker.model.AlgorithmSummary extends Object implements Serializable

Serialized Fields

algorithmName

algorithmArn

algorithmDescription

creationTime

algorithmStatus

Class com.amazonaws.services.sagemaker.model.AlgorithmValidationProfile extends Object implements Serializable

Serialized Fields

profileName

trainingJobDefinition

transformJobDefinition

Class com.amazonaws.services.sagemaker.model.AlgorithmValidationSpecification extends Object implements Serializable

Serialized Fields

validationRole

validationProfiles

Class com.amazonaws.services.sagemaker.model.AmazonSageMakerException extends AmazonServiceException implements Serializable

Class com.amazonaws.services.sagemaker.model.AnnotationConsolidationConfig extends Object implements Serializable

Serialized Fields

annotationConsolidationLambdaArn

Class com.amazonaws.services.sagemaker.model.CategoricalParameterRange extends Object implements Serializable

Serialized Fields

name

values

Class com.amazonaws.services.sagemaker.model.CategoricalParameterRangeSpecification extends Object implements Serializable

Serialized Fields

values

Class com.amazonaws.services.sagemaker.model.Channel extends Object implements Serializable

Serialized Fields

channelName

dataSource

contentType

compressionType

recordWrapperType

inputMode

shuffleConfig

Class com.amazonaws.services.sagemaker.model.ChannelSpecification extends Object implements Serializable

Serialized Fields

name

description

isRequired

supportedContentTypes

supportedCompressionTypes

supportedInputModes

Class com.amazonaws.services.sagemaker.model.CheckpointConfig extends Object implements Serializable

Serialized Fields

s3Uri

localPath

Class com.amazonaws.services.sagemaker.model.CodeRepositorySummary extends Object implements Serializable

Serialized Fields

codeRepositoryName

codeRepositoryArn

creationTime

lastModifiedTime

gitConfig

Class com.amazonaws.services.sagemaker.model.CognitoMemberDefinition extends Object implements Serializable