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.
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.
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.
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.
Boolean enableSageMakerMetricsTimeSeries
To generate and save time-series metrics during training, set to true. The default is
false and time-series metrics aren't generated except in the following cases:
You use one of the Amazon SageMaker built-in algorithms
You use one of the following Prebuilt Amazon SageMaker Docker Images:
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
String algorithmName
The name of the algorithm that is described by the summary.
String algorithmArn
The Amazon Resource Name (ARN) of the algorithm.
String algorithmDescription
A brief description of the algorithm.
Date creationTime
A timestamp that shows when the algorithm was created.
String algorithmStatus
The overall status of the algorithm.
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
The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to
validate your algorithm.
TransformJobDefinition transformJobDefinition
The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to
validate your algorithm.
String validationRole
The IAM roles that Amazon SageMaker uses to run the training jobs.
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.
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 recognition - 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
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
For more information, see Annotation Consolidation.
String candidateName
The candidate name.
FinalAutoMLJobObjectiveMetric finalAutoMLJobObjectiveMetric
String objectiveStatus
The objective status.
List<E> candidateSteps
The candidate's steps.
String candidateStatus
The candidate's status.
List<E> inferenceContainers
The inference containers.
Date creationTime
The creation time.
Date endTime
The end time.
Date lastModifiedTime
The last modified time.
String failureReason
The failure reason.
AutoMLDataSource dataSource
The data source.
String compressionType
You can use Gzip or None. The default value is None.
String targetAttributeName
The name of the target variable in supervised learning, a.k.a. 'y'.
String image
The ECR path of the container. Refer to ContainerDefinition for more details.
String modelDataUrl
The location of the model artifacts. Refer to ContainerDefinition for more details.
Map<K,V> environment
Environment variables to set in the container. Refer to ContainerDefinition for more details.
AutoMLS3DataSource s3DataSource
The Amazon S3 location of the data.
Integer maxCandidates
The maximum number of times a training job is allowed to run.
Integer maxRuntimePerTrainingJobInSeconds
The maximum time, in seconds, a job is allowed to run.
Integer maxAutoMLJobRuntimeInSeconds
The maximum time, in seconds, an AutoML job is allowed to wait for a trial to complete. It must be equal to or greater than MaxRuntimePerTrainingJobInSeconds.
AutoMLJobCompletionCriteria completionCriteria
How long a job is allowed to run, or how many candidates a job is allowed to generate.
AutoMLSecurityConfig securityConfig
Security configuration for traffic encryption or Amazon VPC settings.
String metricName
The name of the metric.
String autoMLJobName
The name of the object you are requesting.
String autoMLJobArn
The ARN of the job.
String autoMLJobStatus
The job's status.
String autoMLJobSecondaryStatus
The job's secondary status.
Date creationTime
When the job was created.
Date endTime
The end time.
Date lastModifiedTime
When the job was last modified.
String failureReason
The failure reason.
String captureMode
String channelName
The name of the channel.
DataSource dataSource
The location of the channel data.
String contentType
The MIME type of the data.
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.
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.
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
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.
String name
The name of the channel.
String description
A brief description of the channel.
Boolean isRequired
Indicates whether the channel is required by the algorithm.
List<E> supportedContentTypes
The supported MIME types for the data.
List<E> supportedCompressionTypes
The allowed compression types, if data compression is used.
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.
String codeRepositoryName
The name of the Git repository.
String codeRepositoryArn
The Amazon Resource Name (ARN) of the Git repository.
Date creationTime
The date and time that the Git repository was created.
Date lastModifiedTime
The date and time that the Git repository was last modified.
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.
String userPool
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
String userGroup
An identifier for a user group.
String clientId
An identifier for an application client. You must create the app client ID using Amazon Cognito.
String collectionName
The name of the tensor collection. The name must be unique relative to other rule configuration names.
Map<K,V> collectionParameters
Parameter values for the tensor collection. The allowed parameters are "name",
"include_regex", "reduction_config", "save_config",
"tensor_names", and "save_histogram".
String compilationJobName
The name of the model compilation job that you want a summary for.
String compilationJobArn
The Amazon Resource Name (ARN) of the model compilation job.
Date creationTime
The time when the model compilation job was created.
Date compilationStartTime
The time when the model compilation job started.
Date compilationEndTime
The time when the model compilation job completed.
String compilationTargetDevice
The type of device that the model will run on after compilation has completed.
Date lastModifiedTime
The time when the model compilation job was last modified.
String compilationJobStatus
The status of the model compilation job.
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 the 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.
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
String mode
Whether the container hosts a single model or multiple models.
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.
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.
String modelPackageName
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
String name
The name of the continuous hyperparameter to tune.
String minValue
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValuefor tuning.
String maxValue
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue
value and this value for tuning.
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:
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
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.
Hyperparameter 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.
String algorithmName
The name of the algorithm.
String algorithmDescription
A description of the algorithm.
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
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.
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.
Boolean certifyForMarketplace
Whether to certify the algorithm so that it can be listed in AWS Marketplace.
String algorithmArn
The Amazon Resource Name (ARN) of the new algorithm.
String domainId
The domain ID.
String userProfileName
The user profile name.
String appType
The type of app.
String appName
The name of the app.
List<E> tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
ResourceSpec resourceSpec
The instance type and quantity.
String appArn
The app's Amazon Resource Name (ARN).
String autoMLJobName
Identifies an AutoPilot job. Must be unique to your account and is case-insensitive.
List<E> inputDataConfig
Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV.
AutoMLOutputDataConfig outputDataConfig
Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.
String problemType
Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.
AutoMLJobObjective autoMLJobObjective
Defines the job's objective. You provide a MetricName and AutoML will infer minimize or maximize. If this is not provided, the most commonly used ObjectiveMetric for problem type will be selected.
AutoMLJobConfig autoMLJobConfig
Contains CompletionCriteria and SecurityConfig.
String roleArn
The ARN of the role that will be used to access the data.
Boolean generateCandidateDefinitionsOnly
This will generate possible candidates without training a model. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
List<E> tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
String autoMLJobArn
When a job is created, it is assigned a unique ARN.
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
Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
String codeRepositoryArn
The Amazon Resource Name (ARN) of the new repository.
String compilationJobName
A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.
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
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
Provides information about the output location for the compiled model and the target device the model runs on.
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.
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.
String domainName
A name for the domain.
String authMode
The mode of authentication that member use to access the domain.
UserSettings defaultUserSettings
The default user settings.
List<E> subnetIds
Security setting to limit to a set of subnets.
String vpcId
Security setting to limit the domain's communication to a Amazon Virtual Private Cloud.
List<E> tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
String homeEfsFileSystemKmsKeyId
The AWS Key Management Service encryption key ID.
String endpointConfigName
The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
List<E> productionVariants
An list of ProductionVariant objects, one for each model that you want to host at this endpoint.
DataCaptureConfig dataCaptureConfig
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.
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.
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
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint,
UpdateEndpoint requests. For more information, refer to the AWS Key Management Service section Using Key Policies in AWS KMS
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 KmsKeyId when using an
instance type with local storage. If any of the models that you specify in the ProductionVariants
parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId
parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local
storage, the call to CreateEndpointConfig fails.
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.
String endpointConfigArn
The Amazon Resource Name (ARN) of the endpoint configuration.
String endpointName
The name of the endpoint. The name must be unique within an AWS Region in your AWS account.
String endpointConfigName
The name of an endpoint configuration. For more information, see CreateEndpointConfig.
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.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
String experimentName
The name of the experiment. The name must be unique in your AWS account and is not case-sensitive.
String displayName
The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify
DisplayName, the value in ExperimentName is displayed.
String description
The description of the experiment.
List<E> tags
A list of tags to associate with the experiment. You can use Search API to search on the tags.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String flowDefinitionName
The name of your flow definition.
HumanLoopActivationConfig humanLoopActivationConfig
An object containing information about the events that trigger a human workflow.
HumanLoopConfig humanLoopConfig
An object containing information about the tasks the human reviewers will perform.
FlowDefinitionOutputConfig outputConfig
An object containing information about where the human review results will be uploaded.
String roleArn
The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example,
arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.
List<E> tags
An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
String flowDefinitionArn
The Amazon Resource Name (ARN) of the flow definition you create.
String humanTaskUiName
The name of the user interface you are creating.
UiTemplate uiTemplate
List<E> tags
An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.
String humanTaskUiArn
The Amazon Resource Name (ARN) of the human review workflow user interface you create.
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
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
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.
List<E> trainingJobDefinitions
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.
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.
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.
String labelingJobName
The name of the labeling job. This name is used to identify the job in a list of labeling jobs.
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".
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.
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.
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.
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"
}
]
}
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
Configures the information required to perform automated data labeling.
HumanTaskConfig humanTaskConfig
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
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.
String labelingJobArn
The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
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).
String modelPackageDescription
A description of the model package.
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.
ModelPackageValidationSpecification validationSpecification
Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
SourceAlgorithmSpecification sourceAlgorithmSpecification
Details about the algorithm that was used to create the model package.
Boolean certifyForMarketplace
Whether to certify the model package for listing on AWS Marketplace.
String modelPackageArn
The Amazon Resource Name (ARN) of the new model package.
String modelName
The name of the new model.
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.
List<E> containers
Specifies the containers in the inference pipeline.
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.
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
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.
Boolean enableNetworkIsolation
Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
String modelArn
The ARN of the model created in Amazon SageMaker.
String monitoringScheduleName
The name of the monitoring schedule. The name must be unique within an AWS Region within an AWS account.
MonitoringScheduleConfig monitoringScheduleConfig
The configuration object that specifies the monitoring schedule and defines the monitoring job.
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
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.
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.
String notebookInstanceLifecycleConfigArn
The Amazon Resource Name (ARN) of the lifecycle configuration.
String notebookInstanceName
The name of the new notebook instance.
String instanceType
The type of ML compute instance to launch for the notebook instance.
String subnetId
The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
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.
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) permissions 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.
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.
List<E> tags
A list of tags to associate with the notebook instance. You can add tags later by using the
CreateTags API.
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.
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.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
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.
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.
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.
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.
String notebookInstanceArn
The Amazon Resource Name (ARN) of the notebook instance.
String authorizedUrl
The presigned URL.
String authorizedUrl
A JSON object that contains the URL string.
List<E> processingInputs
For each input, data is downloaded from S3 into the processing container before the processing job begins running
if "S3InputMode" is set to File.
ProcessingOutputConfig processingOutputConfig
Output configuration for the processing job.
String processingJobName
The name of the processing job. The name must be unique within an AWS Region in the AWS account.
ProcessingResources processingResources
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ProcessingStoppingCondition stoppingCondition
The time limit for how long the processing job is allowed to run.
AppSpecification appSpecification
Configures the processing job to run a specified Docker container image.
Map<K,V> environment
Sets the environment variables in the Docker container.
NetworkConfig networkConfig
Networking options for a processing job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
ExperimentConfig experimentConfig
String processingJobArn
The Amazon Resource Name (ARN) of the processing job.
String trainingJobName
The name of the training job. The name must be unique within an AWS Region in an AWS account.
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
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.
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.
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
Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
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
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
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.
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.
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.
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.
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
Contains information about the output location for managed spot training checkpoint data.
DebugHookConfig debugHookConfig
List<E> debugRuleConfigurations
Configuration information for debugging rules.
TensorBoardOutputConfig tensorBoardOutputConfig
ExperimentConfig experimentConfig
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String transformJobName
The name of the transform job. The name must be unique within an AWS Region in an AWS account.
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.
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 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.
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.
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 the SplitType property of the DataProcessing
object 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.
Map<K,V> environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput transformInput
Describes the input source and the way the transform job consumes it.
TransformOutput transformOutput
Describes the results of the transform job.
TransformResources transformResources
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
DataProcessing dataProcessing
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
ExperimentConfig experimentConfig
String transformJobArn
The Amazon Resource Name (ARN) of the transform job.
String trialComponentName
The name of the component. The name must be unique in your AWS account and is not case-sensitive.
String displayName
The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, TrialComponentName is displayed.
TrialComponentStatus status
The status of the component. States include:
InProgress
Completed
Failed
Date startTime
When the component started.
Date endTime
When the component ended.
Map<K,V> parameters
The hyperparameters for the component.
Map<K,V> inputArtifacts
The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
Map<K,V> outputArtifacts
The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
List<E> tags
A list of tags to associate with the component. You can use Search API to search on the tags.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
String trialName
The name of the trial. The name must be unique in your AWS account and is not case-sensitive.
String displayName
The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, TrialName is displayed.
String experimentName
The name of the experiment to associate the trial with.
List<E> tags
A list of tags to associate with the trial. You can use Search API to search on the tags.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String domainId
The ID of the associated Domain.
String userProfileName
A name for the UserProfile.
String singleSignOnUserIdentifier
A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is SSO, this field is required. If the Domain's AuthMode is not SSO, this field cannot be specified.
String singleSignOnUserValue
The username of the associated AWS Single Sign-On User for this UserProfile. If the Domain's AuthMode is SSO, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not SSO, this field cannot be specified.
List<E> tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
UserSettings userSettings
A collection of settings.
String userProfileArn
The user profile Amazon Resource Name (ARN).
String workteamName
The name of the work team. Use this name to identify the work team.
List<E> memberDefinitions
A list of MemberDefinition objects that contains objects that identify the Amazon Cognito user pool
that makes up the work team. For more information, see Amazon Cognito
User Pools.
All of the CognitoMemberDefinition objects that make up the member definition must have the same
ClientId and UserPool values.
String description
A description of the work team.
NotificationConfiguration notificationConfiguration
Configures notification of workers regarding available or expiring work items.
List<E> tags
An array of key-value pairs.
For more information, see Resource Tag and Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
String workteamArn
The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
String inputFilter
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon
SageMaker to pass the entire input dataset to the algorithm, accept the default value $.
Examples: "$", "$[1:]", "$.features"
String outputFilter
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch
transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the
default value, $. If you specify indexes that aren't within the dimension size of the joined
dataset, you get an error.
Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
String joinSource
Specifies the source of the data to join with the transformed data. The valid values are None and
Input. The default value is None, which specifies not to join the input with the
transformed data. If you want the batch transform job to join the original input data with the transformed data,
set JoinSource to Input.
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON
object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair
object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON
file, and the input data is stored under the SageMakerInput key and the results are stored in
SageMakerOutput.
For CSV files, Amazon SageMaker combines the transformed data with the input data at the end of the input data and stores it in the output file. The joined data has the joined input data followed by the transformed data and the output is a CSV file.
S3DataSource s3DataSource
The S3 location of the data source that is associated with a channel.
FileSystemDataSource fileSystemDataSource
The file system that is associated with a channel.
String localPath
Path to local storage location for tensors. Defaults to /opt/ml/output/tensors/.
String s3OutputPath
Path to Amazon S3 storage location for tensors.
Map<K,V> hookParameters
Configuration information for the debug hook parameters.
List<E> collectionConfigurations
Configuration information for tensor collections.
String ruleConfigurationName
The name of the rule configuration. It must be unique relative to other rule configuration names.
String localPath
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
String s3OutputPath
Path to Amazon S3 storage location for rules.
String ruleEvaluatorImage
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
String instanceType
The instance type to deploy for a training job.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume attached to the processing instance.
Map<K,V> ruleParameters
Runtime configuration for rule container.
String ruleConfigurationName
The name of the rule configuration
String ruleEvaluationJobArn
The Amazon Resource Name (ARN) of the rule evaluation job.
String ruleEvaluationStatus
Status of the rule evaluation.
String statusDetails
Details from the rule evaluation.
Date lastModifiedTime
Timestamp when the rule evaluation status was last modified.
String algorithmName
The name of the algorithm to delete.
String codeRepositoryName
The name of the Git repository to delete.
String domainId
The domain ID.
RetentionPolicy retentionPolicy
The retention policy for this domain, which specifies which resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).
String endpointConfigName
The name of the endpoint configuration that you want to delete.
String endpointName
The name of the endpoint that you want to delete.
String experimentName
The name of the experiment to delete.
String experimentArn
The Amazon Resource Name (ARN) of the experiment that is being deleted.
String flowDefinitionName
The name of the flow definition you are deleting.
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).
String modelName
The name of the model to delete.
String monitoringScheduleName
The name of the monitoring schedule to delete.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration to delete.
String notebookInstanceName
The name of the Amazon SageMaker notebook instance to delete.
String trialComponentName
The name of the component to delete.
String trialComponentArn
The Amazon Resource Name (ARN) of the component is being deleted.
String trialName
The name of the trial to delete.
String trialArn
The Amazon Resource Name (ARN) of the trial that is being deleted.
String workteamName
The name of the work team to delete.
Boolean success
Returns true if the work team was successfully deleted; otherwise, returns false.
String specifiedImage
The image path you specified when you created the model.
String resolvedImage
The specific digest path of the image hosted in this ProductionVariant.
Date resolutionTime
The date and time when the image path for the model resolved to the ResolvedImage
String algorithmName
The name of the algorithm to describe.
String algorithmName
The name of the algorithm being described.
String algorithmArn
The Amazon Resource Name (ARN) of the algorithm.
String algorithmDescription
A brief summary about the algorithm.
Date creationTime
A timestamp specifying when the algorithm was created.
TrainingSpecification trainingSpecification
Details about training jobs run by this algorithm.
InferenceSpecification inferenceSpecification
Details about inference jobs that the algorithm runs.
AlgorithmValidationSpecification validationSpecification
Details about configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.
String algorithmStatus
The current status of the algorithm.
AlgorithmStatusDetails algorithmStatusDetails
Details about the current status of the algorithm.
String productId
The product identifier of the algorithm.
Boolean certifyForMarketplace
Whether the algorithm is certified to be listed in AWS Marketplace.
String appArn
The app's Amazon Resource Name (ARN).
String appType
The type of app.
String appName
The name of the app.
String domainId
The domain ID.
String userProfileName
The user profile name.
String status
The status.
Date lastHealthCheckTimestamp
The timestamp of the last health check.
Date lastUserActivityTimestamp
The timestamp of the last user's activity.
Date creationTime
The creation time.
String failureReason
The failure reason.
ResourceSpec resourceSpec
The instance type and quantity.
String autoMLJobName
Request information about a job using that job's unique name.
String autoMLJobName
Returns the name of a job.
String autoMLJobArn
Returns the job's ARN.
List<E> inputDataConfig
Returns the job's input data config.
AutoMLOutputDataConfig outputDataConfig
Returns the job's output data config.
String roleArn
The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.
AutoMLJobObjective autoMLJobObjective
Returns the job's objective.
String problemType
Returns the job's problem type.
AutoMLJobConfig autoMLJobConfig
Returns the job's config.
Date creationTime
Returns the job's creation time.
Date endTime
Returns the job's end time.
Date lastModifiedTime
Returns the job's last modified time.
String failureReason
Returns the job's FailureReason.
AutoMLCandidate bestCandidate
Returns the job's BestCandidate.
String autoMLJobStatus
Returns the job's AutoMLJobStatus.
String autoMLJobSecondaryStatus
Returns the job's AutoMLJobSecondaryStatus.
Boolean generateCandidateDefinitionsOnly
Returns the job's output from GenerateCandidateDefinitionsOnly.
AutoMLJobArtifacts autoMLJobArtifacts
Returns information on the job's artifacts found in AutoMLJobArtifacts.
ResolvedAttributes resolvedAttributes
This contains ProblemType, AutoMLJobObjective and CompletionCriteria. They're auto-inferred values, if not provided by you. If you do provide them, then they'll be the same as provided.
String codeRepositoryName
The name of the Git repository to describe.
String codeRepositoryName
The name of the Git repository.
String codeRepositoryArn
The Amazon Resource Name (ARN) of the Git repository.
Date creationTime
The date and time that the repository was created.
Date lastModifiedTime
The date and time that the repository was last changed.
GitConfig gitConfig
Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the repository.
String compilationJobName
The name of the model compilation job that you want information about.
String compilationJobName
The name of the model compilation job.
String compilationJobArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
String compilationJobStatus
The status of the model compilation job.
Date compilationStartTime
The time when the model compilation job started the CompilationJob instances.
You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.
Date compilationEndTime
The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
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.
Date creationTime
The time that the model compilation job was created.
Date lastModifiedTime
The time that the status of the model compilation job was last modified.
String failureReason
If a model compilation job failed, the reason it failed.
ModelArtifacts modelArtifacts
Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
String roleArn
The Amazon Resource Name (ARN) of the model compilation job.
InputConfig inputConfig
Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
OutputConfig outputConfig
Information about the output location for the compiled model and the target device that the model runs on.
String domainId
The domain ID.
String domainArn
The domain's Amazon Resource Name (ARN).
String domainId
The domain ID.
String domainName
The domain name.
String homeEfsFileSystemId
The ID of the Amazon Elastic File System (EFS) managed by this Domain.
String singleSignOnManagedApplicationInstanceId
The SSO managed application instance ID.
String status
The status.
Date creationTime
The creation time.
Date lastModifiedTime
The last modified time.
String failureReason
The failure reason.
String authMode
The domain's authentication mode.
UserSettings defaultUserSettings
Settings which are applied to all UserProfile in this domain, if settings are not explicitly specified in a given UserProfile.
String homeEfsFileSystemKmsKeyId
The AWS Key Management Service encryption key ID.
List<E> subnetIds
Security setting to limit to a set of subnets.
String url
The domain's URL.
String vpcId
The ID of the Amazon Virtual Private Cloud.
String endpointConfigName
The name of the endpoint configuration.
String endpointConfigName
Name of the Amazon SageMaker endpoint configuration.
String endpointConfigArn
The Amazon Resource Name (ARN) of the endpoint configuration.
List<E> productionVariants
An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
DataCaptureConfig dataCaptureConfig
String kmsKeyId
AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
Date creationTime
A timestamp that shows when the endpoint configuration was created.
String endpointName
The name of the endpoint.
String endpointName
Name of the endpoint.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
String endpointConfigName
The name of the endpoint configuration associated with this endpoint.
List<E> productionVariants
An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
DataCaptureConfigSummary dataCaptureConfig
String endpointStatus
The status of the endpoint.
OutOfService: Endpoint is not available to take incoming requests.
Creating: CreateEndpoint is executing.
Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled
until it has completed. This maintenance operation does not change any customer-specified values such as VPC
config, KMS encryption, model, instance type, or instance count.
RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process
of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an
InService status. This transitional status only applies to an endpoint that has autoscaling enabled
and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call
or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.
InService: Endpoint is available to process incoming requests.
Deleting: DeleteEndpoint is executing.
Failed: Endpoint could not be created, updated, or re-scaled. Use
DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only
operation that can be performed on a failed endpoint.
String failureReason
If the status of the endpoint is Failed, the reason why it failed.
Date creationTime
A timestamp that shows when the endpoint was created.
Date lastModifiedTime
A timestamp that shows when the endpoint was last modified.
String experimentName
The name of the experiment to describe.
String experimentName
The name of the experiment.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String displayName
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName
is displayed.
ExperimentSource source
The ARN of the source and, optionally, the type.
String description
The description of the experiment.
Date creationTime
When the experiment was created.
UserContext createdBy
Who created the experiment.
Date lastModifiedTime
When the experiment was last modified.
UserContext lastModifiedBy
Who last modified the experiment.
String flowDefinitionName
The name of the flow definition.
String flowDefinitionArn
The Amazon Resource Name (ARN) of the flow defintion.
String flowDefinitionName
The Amazon Resource Name (ARN) of the flow definition.
String flowDefinitionStatus
The status of the flow definition. Valid values are listed below.
Date creationTime
The timestamp when the flow definition was created.
HumanLoopActivationConfig humanLoopActivationConfig
An object containing information about what triggers a human review workflow.
HumanLoopConfig humanLoopConfig
An object containing information about who works on the task, the workforce task price, and other task details.
FlowDefinitionOutputConfig outputConfig
An object containing information about the output file.
String roleArn
The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) execution role for the flow definition.
String failureReason
String humanTaskUiName
The name of the human task user interface you want information about.
String humanTaskUiArn
The Amazon Resource Name (ARN) of the human task user interface.
String humanTaskUiName
The name of the human task user interface.
Date creationTime
The timestamp when the human task user interface was created.
UiTemplateInfo uiTemplate
String hyperParameterTuningJobName
The name of the tuning job to describe.
String hyperParameterTuningJobName
The name of the tuning job.
String hyperParameterTuningJobArn
The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobConfig hyperParameterTuningJobConfig
The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
HyperParameterTrainingJobDefinition trainingJobDefinition
The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
List<E> trainingJobDefinitions
String hyperParameterTuningJobStatus
The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.
Date creationTime
The date and time that the tuning job started.
Date hyperParameterTuningEndTime
The date and time that the tuning job ended.
Date lastModifiedTime
The date and time that the status of the tuning job was modified.
TrainingJobStatusCounters trainingJobStatusCounters
The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
ObjectiveStatusCounters objectiveStatusCounters
The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
HyperParameterTrainingJobSummary bestTrainingJob
A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.
HyperParameterTrainingJobSummary overallBestTrainingJob
If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of
IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the
best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for
the warm start tuning job.
HyperParameterTuningJobWarmStartConfig warmStartConfig
The configuration for starting the hyperparameter parameter 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.
String failureReason
If the tuning job failed, the reason it failed.
String labelingJobName
The name of the labeling job to return information for.
String labelingJobStatus
The processing status of the labeling job.
LabelCounters labelCounters
Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn't be labeled, and the total number of objects labeled.
String failureReason
If the job failed, the reason that it failed.
Date creationTime
The date and time that the labeling job was created.
Date lastModifiedTime
The date and time that the labeling job was last updated.
String jobReferenceCode
A unique identifier for work done as part of a labeling job.
String labelingJobName
The name assigned to the labeling job when it was created.
String labelingJobArn
The Amazon Resource Name (ARN) of the labeling job.
String labelAttributeName
The attribute used as the label in the output manifest file.
LabelingJobInputConfig inputConfig
Input configuration information 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.
LabelingJobOutputConfig outputConfig
The location of the job's output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
String roleArn
The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.
String labelCategoryConfigS3Uri
The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:
Semantic segmentation labeling jobs using automated labeling: 20 labels
Box bounding labeling jobs (all): 10 labels
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"
}
]
}
LabelingJobStoppingConditions stoppingConditions
A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.
LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig
Configuration information for automated data labeling.
HumanTaskConfig humanTaskConfig
Configuration information required for human workers to complete a labeling task.
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.
LabelingJobOutput labelingJobOutput
The location of the output produced by the labeling job.
String modelPackageName
The name of the model package to describe.
String modelPackageName
The name of the model package being described.
String modelPackageArn
The Amazon Resource Name (ARN) of the model package.
String modelPackageDescription
A brief summary of the model package.
Date creationTime
A timestamp specifying when the model package was created.
InferenceSpecification inferenceSpecification
Details about inference jobs that can be run with models based on this model package.
SourceAlgorithmSpecification sourceAlgorithmSpecification
Details about the algorithm that was used to create the model package.
ModelPackageValidationSpecification validationSpecification
Configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
String modelPackageStatus
The current status of the model package.
ModelPackageStatusDetails modelPackageStatusDetails
Details about the current status of the model package.
Boolean certifyForMarketplace
Whether the model package is certified for listing on AWS Marketplace.
String modelName
The name of the model.
String modelName
Name of the Amazon SageMaker model.
ContainerDefinition primaryContainer
The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
List<E> containers
The containers in the inference pipeline.
String executionRoleArn
The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud
Date creationTime
A timestamp that shows when the model was created.
String modelArn
The Amazon Resource Name (ARN) of the model.
Boolean enableNetworkIsolation
If True, no inbound or outbound network calls can be made to or from the model container.
String monitoringScheduleName
Name of a previously created monitoring schedule.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
String monitoringScheduleName
Name of the monitoring schedule.
String monitoringScheduleStatus
The status of an monitoring job.
String failureReason
A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.
Date creationTime
The time at which the monitoring job was created.
Date lastModifiedTime
The time at which the monitoring job was last modified.
MonitoringScheduleConfig monitoringScheduleConfig
The configuration object that specifies the monitoring schedule and defines the monitoring job.
String endpointName
The name of the endpoint for the monitoring job.
MonitoringExecutionSummary lastMonitoringExecutionSummary
Describes metadata on the last execution to run, if there was one.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration to describe.
String notebookInstanceLifecycleConfigArn
The Amazon Resource Name (ARN) of the lifecycle configuration.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
List<E> onCreate
The shell script that runs only once, when you create a notebook instance.
List<E> onStart
The shell script that runs every time you start a notebook instance, including when you create the notebook instance.
Date lastModifiedTime
A timestamp that tells when the lifecycle configuration was last modified.
Date creationTime
A timestamp that tells when the lifecycle configuration was created.
String notebookInstanceName
The name of the notebook instance that you want information about.
String notebookInstanceArn
The Amazon Resource Name (ARN) of the notebook instance.
String notebookInstanceName
The name of the Amazon SageMaker notebook instance.
String notebookInstanceStatus
The status of the notebook instance.
String failureReason
If status is Failed, the reason it failed.
String url
The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
String instanceType
The type of ML compute instance running on the notebook instance.
String subnetId
The ID of the VPC subnet.
List<E> securityGroups
The IDs of the VPC security groups.
String roleArn
The Amazon Resource Name (ARN) of the IAM role associated with the instance.
String kmsKeyId
The AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
String networkInterfaceId
The network interface IDs that Amazon SageMaker created at the time of creating the instance.
Date lastModifiedTime
A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
Date creationTime
A timestamp. Use this parameter to return the time when the notebook instance was created
String notebookInstanceLifecycleConfigName
Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
String directInternetAccess
Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume attached to the notebook instance.
List<E> acceleratorTypes
A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
String defaultCodeRepository
The Git repository associated 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.
List<E> additionalCodeRepositories
An array of up to three Git repositories associated 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.
String rootAccess
Whether root access is enabled or disabled for users of the notebook instance.
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.
String processingJobName
The name of the processing job. The name must be unique within an AWS Region in the AWS account.
List<E> processingInputs
The inputs for a processing job.
ProcessingOutputConfig processingOutputConfig
Output configuration for the processing job.
String processingJobName
The name of the processing job. The name must be unique within an AWS Region in the AWS account.
ProcessingResources processingResources
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ProcessingStoppingCondition stoppingCondition
The time limit for how long the processing job is allowed to run.
AppSpecification appSpecification
Configures the processing job to run a specified container image.
Map<K,V> environment
The environment variables set in the Docker container.
NetworkConfig networkConfig
Networking options for a processing job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
ExperimentConfig experimentConfig
The configuration information used to create an experiment.
String processingJobArn
The Amazon Resource Name (ARN) of the processing job.
String processingJobStatus
Provides the status of a processing job.
String exitMessage
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
String failureReason
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
Date processingEndTime
The time at which the processing job completed.
Date processingStartTime
The time at which the processing job started.
Date lastModifiedTime
The time at which the processing job was last modified.
Date creationTime
The time at which the processing job was created.
String monitoringScheduleArn
The ARN of a monitoring schedule for an endpoint associated with this processing job.
String autoMLJobArn
The ARN of an AutoML job associated with this processing job.
String trainingJobArn
The ARN of a training job associated with this processing job.
String workteamArn
The Amazon Resource Name (ARN) of the subscribed work team to describe.
SubscribedWorkteam subscribedWorkteam
A Workteam instance that contains information about the work team.
String trainingJobName
The name of the training job.
String trainingJobName
Name of the model training job.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String tuningJobArn
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
String labelingJobArn
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
String autoMLJobArn
ModelArtifacts modelArtifacts
Information about the Amazon S3 location that is configured for storing model artifacts.
String trainingJobStatus
The status of the training job.
Amazon SageMaker provides the following training job statuses:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the
FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
String secondaryStatus
Provides detailed information about the state of the training job. For detailed information on the secondary
status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode.
It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Interrupted - The job stopped because the managed spot training instances were interrupted.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTmeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
String failureReason
If the training job failed, the reason it failed.
Map<K,V> hyperParameters
Algorithm-specific parameters.
AlgorithmSpecification algorithmSpecification
Information about the algorithm used for training, and algorithm metadata.
String roleArn
The AWS Identity and Access Management (IAM) role configured for the training job.
List<E> inputDataConfig
An array of Channel objects that describes each data input channel.
OutputDataConfig outputDataConfig
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
ResourceConfig resourceConfig
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
StoppingCondition stoppingCondition
Specifies a limit to how long a model training job can run. It also specifies the maximum time to wait for a spot instance. 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.
Date creationTime
A timestamp that indicates when the training job was created.
Date trainingStartTime
Indicates the time when the training job starts on training instances. You are billed for the time interval
between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later
than this time. The difference is due to the time it takes to download the training data and to the size of the
training container.
Date trainingEndTime
Indicates the time when the training job ends on training instances. You are billed for the time interval between
the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time
after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job
failure.
Date lastModifiedTime
A timestamp that indicates when the status of the training job was last modified.
List<E> secondaryStatusTransitions
A history of all of the secondary statuses that the training job has transitioned through.
List<E> finalMetricDataList
A collection of MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch.
Boolean enableNetworkIsolation
If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster
for distributed training, choose True. 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.
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
algorithms in distributed training.
Boolean enableManagedSpotTraining
A Boolean indicating whether managed spot training is enabled (True) or not (False).
CheckpointConfig checkpointConfig
Integer trainingTimeInSeconds
The training time in seconds.
Integer billableTimeInSeconds
The billable time in seconds.
You can calculate the savings from using managed spot training using the formula
(1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if
BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.
DebugHookConfig debugHookConfig
ExperimentConfig experimentConfig
List<E> debugRuleConfigurations
Configuration information for debugging rules.
TensorBoardOutputConfig tensorBoardOutputConfig
List<E> debugRuleEvaluationStatuses
Status about the debug rule evaluation.
String transformJobName
The name of the transform job that you want to view details of.
String transformJobName
The name of the transform job.
String transformJobArn
The Amazon Resource Name (ARN) of the transform job.
String transformJobStatus
The status of the transform job. If the transform job failed, the reason is returned in the
FailureReason field.
String failureReason
If the transform job failed, FailureReason describes why it failed. A transform job creates a log
file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with
Amazon CloudWatch.
String modelName
The name of the model used in the transform job.
Integer maxConcurrentTransforms
The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
Integer maxPayloadInMB
The maximum payload size, in MB, used in the transform job.
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.
Map<K,V> environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput transformInput
Describes the dataset to be transformed and the Amazon S3 location where it is stored.
TransformOutput transformOutput
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources transformResources
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
Date creationTime
A timestamp that shows when the transform Job was created.
Date transformStartTime
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time
and the value of TransformEndTime.
Date transformEndTime
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time
interval between this time and the value of TransformStartTime.
String labelingJobArn
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
String autoMLJobArn
DataProcessing dataProcessing
ExperimentConfig experimentConfig
String trialComponentName
The name of the trial component to describe.
String trialComponentName
The name of the trial component.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
String displayName
The name of the component as displayed. If DisplayName isn't specified,
TrialComponentName is displayed.
TrialComponentSource source
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
TrialComponentStatus status
The status of the component. States include:
InProgress
Completed
Failed
Date startTime
When the component started.
Date endTime
When the component ended.
Date creationTime
When the component was created.
UserContext createdBy
Who created the component.
Date lastModifiedTime
When the component was last modified.
UserContext lastModifiedBy
Who last modified the component.
Map<K,V> parameters
The hyperparameters of the component.
Map<K,V> inputArtifacts
The input artifacts of the component.
Map<K,V> outputArtifacts
The output artifacts of the component.
List<E> metrics
The metrics for the component.
String trialName
The name of the trial to describe.
String trialName
The name of the trial.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String displayName
The name of the trial as displayed. If DisplayName isn't specified, TrialName is
displayed.
String experimentName
The name of the experiment the trial is part of.
TrialSource source
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
Date creationTime
When the trial was created.
UserContext createdBy
Who created the trial.
Date lastModifiedTime
When the trial was last modified.
UserContext lastModifiedBy
Who last modified the trial.
String domainId
The domain ID.
String userProfileArn
The user profile Amazon Resource Name (ARN).
String userProfileName
The user profile name.
String homeEfsFileSystemUid
The homa Amazon Elastic File System (EFS) Uid.
String status
The status.
Date lastModifiedTime
The last modified time.
Date creationTime
The creation time.
String failureReason
The failure reason.
String singleSignOnUserIdentifier
The SSO user identifier.
String singleSignOnUserValue
The SSO user value.
UserSettings userSettings
A collection of settings.
String workforceName
The name of the private workforce whose access you want to restrict. WorkforceName is automatically
set to default when a workforce is created and cannot be modified.
Workforce workforce
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each AWS Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
String workteamName
The name of the work team to return a description of.
Workteam workteam
A Workteam instance that contains information about the work team.
String domainArn
The domain's Amazon Resource Name (ARN).
String domainId
The domain ID.
String domainName
The domain name.
String status
The status.
Date creationTime
The creation time.
Date lastModifiedTime
The last modified time.
String url
The domain's URL.
String endpointName
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
String localPath
Path to the filesystem where the endpoint data is available to the container.
String s3InputMode
Whether the Pipe or File is used as the input mode for transfering data for the
monitoring job. Pipe mode is recommended for large datasets. File mode is useful for
small files that fit in memory. Defaults to File.
String s3DataDistributionType
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defauts to
FullyReplicated
String endpointName
The name of the endpoint.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
Date creationTime
A timestamp that shows when the endpoint was created.
Date lastModifiedTime
A timestamp that shows when the endpoint was last modified.
String endpointStatus
The status of the endpoint.
OutOfService: Endpoint is not available to take incoming requests.
Creating: CreateEndpoint is executing.
Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled
until it has completed. This maintenance operation does not change any customer-specified values such as VPC
config, KMS encryption, model, instance type, or instance count.
RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process
of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an
InService status. This transitional status only applies to an endpoint that has autoscaling enabled
and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call
or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.
InService: Endpoint is available to process incoming requests.
Deleting: DeleteEndpoint is executing.
Failed: Endpoint could not be created, updated, or re-scaled. Use
DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only
operation that can be performed on a failed endpoint.
To get a list of endpoints with a specified status, use the ListEndpointsInput$StatusEquals filter.
String experimentName
The name of the experiment.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String displayName
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName
is displayed.
ExperimentSource source
String description
The description of the experiment.
Date creationTime
When the experiment was created.
UserContext createdBy
Date lastModifiedTime
When the experiment was last modified.
UserContext lastModifiedBy
List<E> tags
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String experimentName
The name of the experiment.
String displayName
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName
is displayed.
ExperimentSource experimentSource
Date creationTime
When the experiment was created.
Date lastModifiedTime
When the experiment was last modified.
String fileSystemId
The file system id.
String fileSystemAccessMode
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.
String fileSystemType
The file system type.
String directoryPath
The full path to the directory to associate with the channel.
String name
A property name. For example, TrainingJobName. For the list of valid property names returned in a
search result for each supported resource, see TrainingJob properties. You must specify a valid property
name for the resource.
String operator
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
The specified resource in Name equals the specified Value.
The specified resource in Name does not equal the specified Value.
The specified resource in Name is greater than the specified Value. Not supported for
text-based properties.
The specified resource in Name is greater than or equal to the specified Value. Not
supported for text-based properties.
The specified resource in Name is less than the specified Value. Not supported for
text-based properties.
The specified resource in Name is less than or equal to the specified Value. Not
supported for text-based properties.
Only supported for text-based properties. The word-list of the property contains the specified Value
. A SearchExpression can include only one Contains operator.
If you have specified a filter Value, the default is Equals.
String value
A value used with Resource and Operator to determine if objects satisfy the filter's
condition. For numerical properties, Value must be an integer or floating-point decimal. For
timestamp properties, Value must be an ISO 8601 date-time string of the following format:
YYYY-mm-dd'T'HH:MM:SS.
String flowDefinitionName
The name of the flow definition.
String flowDefinitionArn
The Amazon Resource Name (ARN) of the flow definition.
String flowDefinitionStatus
The status of the flow definition. Valid values:
Date creationTime
The timestamp when SageMaker created the flow definition.
String failureReason
The reason why the flow definition creation failed. A failure reason is returned only when the flow definition
status is Failed.
String resource
The name of the Amazon SageMaker resource to Search for.
SuggestionQuery suggestionQuery
Limits the property names that are included in the response.
String repositoryUrl
The URL where the Git repository is located.
String branch
The default branch for the Git repository.
String secretArn
The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the
git repository. The secret must have a staging label of AWSCURRENT and must be in the following
format:
{"username": UserName, "password": Password}
String secretArn
The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the
git repository. The secret must have a staging label of AWSCURRENT and must be in the following
format:
{"username": UserName, "password": Password}
String humanLoopActivationConditions
JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract.
HumanLoopRequestSource humanLoopRequestSource
Container for configuring the source of human task requests.
HumanLoopActivationConditionsConfig humanLoopActivationConditionsConfig
Container structure for defining under what conditions SageMaker creates a human loop.
String workteamArn
Amazon Resource Name (ARN) of a team of workers.
String humanTaskUiArn
The Amazon Resource Name (ARN) of the human task user interface.
String taskTitle
A title for the human worker task.
String taskDescription
A description for the human worker task.
Integer taskCount
The number of distinct workers who will perform the same task on each object. For example, if
TaskCount is set to 3 for an image classification labeling job, three workers will
classify each input image. Increasing TaskCount can improve label accuracy.
Integer taskAvailabilityLifetimeInSeconds
The length of time that a task remains available for labeling by human workers.
Integer taskTimeLimitInSeconds
The amount of time that a worker has to complete a task.
List<E> taskKeywords
Keywords used to describe the task so that workers can discover the task.
PublicWorkforceTaskPrice publicWorkforceTaskPrice
String awsManagedHumanLoopRequestSource
Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
String workteamArn
The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
UiConfig uiConfig
Information about the user interface that workers use to complete the labeling task.
String preHumanTaskLambdaArn
The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:
US East (Northern Virginia) (us-east-1):
arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
US East (Ohio) (us-east-2):
arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
US West (Oregon) (us-west-2):
arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
Canada (Central) (ca-central-1):
arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
EU (Ireland) (eu-west-1):
arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
EU (London) (eu-west-2):
arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
EU Frankfurt (eu-central-1):
arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
Asia Pacific (Tokyo) (ap-northeast-1):
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
Asia Pacific (Seoul) (ap-northeast-2):
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
Asia Pacific (Mumbai) (ap-south-1):
arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
Asia Pacific (Singapore) (ap-southeast-1):
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
Asia Pacific (Sydney) (ap-southeast-2):
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
List<E> taskKeywords
Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
String taskTitle
A title for the task for your human workers.
String taskDescription
A description of the task for your human workers.
Integer numberOfHumanWorkersPerDataObject
The number of human workers that will label an object.
Integer taskTimeLimitInSeconds
The amount of time that a worker has to complete a task.
Integer taskAvailabilityLifetimeInSeconds
The length of time that a task remains available for labeling by human workers. If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43200). The default value is 864000 seconds (1 day). For private and vendor workforces, the maximum is as listed.
Integer maxConcurrentTaskCount
Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects.
AnnotationConsolidationConfig annotationConsolidationConfig
Configures how labels are consolidated across human workers.
PublicWorkforceTaskPrice publicWorkforceTaskPrice
The price that you pay for each task performed by an Amazon Mechanical Turk worker.
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.
String trainingInputMode
The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.
For more information about input modes, see Algorithms.
String algorithmName
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this
parameter, do not specify a value for TrainingImage.
List<E> metricDefinitions
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
String name
The name of this hyperparameter. The name must be unique.
String description
A brief description of the hyperparameter.
String type
The type of this hyperparameter. The valid types are Integer, Continuous,
Categorical, and FreeText.
ParameterRange range
The allowed range for this hyperparameter.
Boolean isTunable
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
Boolean isRequired
Indicates whether this hyperparameter is required.
String defaultValue
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
String definitionName
The job definition name.
HyperParameterTuningJobObjective tuningObjective
ParameterRanges hyperParameterRanges
Map<K,V> staticHyperParameters
Specifies the values of hyperparameters that do not change for the tuning job.
HyperParameterAlgorithmSpecification algorithmSpecification
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
String roleArn
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
List<E> inputDataConfig
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
VpcConfig vpcConfig
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches 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.
OutputDataConfig outputDataConfig
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
ResourceConfig resourceConfig
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
for scratch space. If you want Amazon SageMaker to use the 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.
StoppingCondition stoppingCondition
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you are willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
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 network isolation is used 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.
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.
Boolean enableManagedSpotTraining
A Boolean indicating whether managed spot training is enabled (True) or not (False).
CheckpointConfig checkpointConfig
String trainingJobDefinitionName
The training job definition name.
String trainingJobName
The name of the training job.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String tuningJobName
The HyperParameter tuning job that launched the training job.
Date creationTime
The date and time that the training job was created.
Date trainingStartTime
The date and time that the training job started.
Date trainingEndTime
Specifies the time when the training job ends on training instances. You are billed for the time interval between
the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time
after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job
failure.
String trainingJobStatus
The status of the training job.
Map<K,V> tunedHyperParameters
A list of the hyperparameters for which you specified ranges to search.
String failureReason
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric finalHyperParameterTuningJobObjectiveMetric
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
String objectiveStatus
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
String strategy
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job
it launches. To use the Bayesian search strategy, set this to Bayesian. To randomly search, set it
to Random. For information about search strategies, see How
Hyperparameter Tuning Works.
HyperParameterTuningJobObjective hyperParameterTuningJobObjective
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
ResourceLimits resourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
ParameterRanges parameterRanges
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
String trainingJobEarlyStoppingType
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be
one of the following values (the default value is OFF):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
TuningJobCompletionCriteria tuningJobCompletionCriteria
The tuning job's completion criteria.
String hyperParameterTuningJobName
The name of the tuning job.
String hyperParameterTuningJobArn
The Amazon Resource Name (ARN) of the tuning job.
String hyperParameterTuningJobStatus
The status of the tuning job.
String strategy
Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.
Date creationTime
The date and time that the tuning job was created.
Date hyperParameterTuningEndTime
The date and time that the tuning job ended.
Date lastModifiedTime
The date and time that the tuning job was modified.
TrainingJobStatusCounters trainingJobStatusCounters
The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
ObjectiveStatusCounters objectiveStatusCounters
The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
ResourceLimits resourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
List<E> parentHyperParameterTuningJobs
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
String warmStartType
Specifies one of the following:
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
List<E> containers
The Amazon ECR registry path of the Docker image that contains the inference code.
List<E> supportedTransformInstanceTypes
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
List<E> supportedRealtimeInferenceInstanceTypes
A list of the instance types that are used to generate inferences in real-time.
List<E> supportedContentTypes
The supported MIME types for the input data.
List<E> supportedResponseMIMETypes
The supported MIME types for the output data.
String s3Uri
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).
String dataInputConfig
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a
dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary
format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last)
format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats
required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order
using a dictionary format for your trained model. The dictionary formats required for the console and CLI are
different.
Examples for one input:
If using the console, {"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
Examples for two inputs:
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order
using a dictionary format for your trained model or you can specify the shape only using a list format. The
dictionary formats required for the console and CLI are different. The list formats for the console and CLI are
the same.
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
Examples for two inputs in dictionary format:
If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST: input data name and shape are not needed.
String framework
Identifies the framework in which the model was trained. For example: TENSORFLOW.
String name
The name of the hyperparameter to search.
String minValue
The minimum value of the hyperparameter to search.
String maxValue
The maximum value of the hyperparameter to search.
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:
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
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.
ResourceSpec defaultResourceSpec
The instance type and quantity.
ResourceSpec defaultResourceSpec
The instance type and quantity.
Integer totalLabeled
The total number of objects labeled.
Integer humanLabeled
The total number of objects labeled by a human worker.
Integer machineLabeled
The total number of objects labeled by automated data labeling.
Integer failedNonRetryableError
The total number of objects that could not be labeled due to an error.
Integer unlabeled
The total number of objects not yet labeled.
String labelingJobAlgorithmSpecificationArn
Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
Image classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
Text classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
Object detection
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
Semantic Segmentation
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation
String initialActiveLearningModelArn
At the end of an auto-label job Amazon SageMaker Ground Truth sends the Amazon Resource Nam (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
LabelingJobResourceConfig labelingJobResourceConfig
Provides configuration information for a labeling job.
LabelingJobS3DataSource s3DataSource
The Amazon S3 location of the input data objects.
String labelingJobName
The name of the labeling job that the work team is assigned to.
String jobReferenceCode
A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
String workRequesterAccountId
Date creationTime
The date and time that the labeling job was created.
LabelCountersForWorkteam labelCounters
Provides information about the progress of a labeling job.
Integer numberOfHumanWorkersPerDataObject
The configured number of workers per data object.
LabelingJobDataSource dataSource
The location of the input data.
LabelingJobDataAttributes dataAttributes
Attributes of the data specified by the customer.
String s3OutputPath
The Amazon S3 location to write output data.
String kmsKeyId
The AWS Key Management Service ID of the key used to encrypt the output data, if any.
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include
permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the
default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with
KMS-managed keys for LabelingJobOutputConfig. If you use a bucket policy with an
s3:PutObject permission that only allows objects with server-side encryption, set the condition key
of s3:x-amz-server-side-encryption to "aws:kms". 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 CreateLabelingJob
request. For more information, see Using Key Policies in AWS KMS
in the AWS Key Management Service Developer Guide.
String volumeKmsKeyId
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume
attached to the ML compute instance(s) that run the training job. The VolumeKmsKeyId 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"
String manifestS3Uri
The Amazon S3 location of the manifest file that describes the input data objects.
String labelingJobName
The name of the labeling job.
String labelingJobArn
The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
Date creationTime
The date and time that the job was created (timestamp).
Date lastModifiedTime
The date and time that the job was last modified (timestamp).
String labelingJobStatus
The current status of the labeling job.
LabelCounters labelCounters
Counts showing the progress of the labeling job.
String workteamArn
The Amazon Resource Name (ARN) of the work team assigned to the job.
String preHumanTaskLambdaArn
The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
String annotationConsolidationLambdaArn
The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.
String failureReason
If the LabelingJobStatus field is Failed, this field contains a description of the
error.
LabelingJobOutput labelingJobOutput
The location of the output produced by the labeling job.
LabelingJobInputConfig inputConfig
Input configuration for the labeling job.
Date creationTimeAfter
A filter that returns only algorithms created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only algorithms created before the specified time (timestamp).
Integer maxResults
The maximum number of algorithms to return in the response.
String nameContains
A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.
String nextToken
If the response to a previous ListAlgorithms request was truncated, the response includes a
NextToken. To retrieve the next set of algorithms, use the token in the next request.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String sortOrder
The sort order for the results. The default is Ascending.
String nextToken
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Integer maxResults
Returns a list up to a specified limit.
String sortOrder
The sort order for the results. The default is Ascending.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String domainIdEquals
A parameter to search for the domain ID.
String userProfileNameEquals
A parameter to search by user profile name.
Date creationTimeAfter
Request a list of jobs, using a filter for time.
Date creationTimeBefore
Request a list of jobs, using a filter for time.
Date lastModifiedTimeAfter
Request a list of jobs, using a filter for time.
Date lastModifiedTimeBefore
Request a list of jobs, using a filter for time.
String nameContains
Request a list of jobs, using a search filter for name.
String statusEquals
Request a list of jobs, using a filter for status.
String sortOrder
The sort order for the results. The default is Descending.
String sortBy
The parameter by which to sort the results. The default is AutoMLJobName.
Integer maxResults
Request a list of jobs up to a specified limit.
String nextToken
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
String autoMLJobName
List the Candidates created for the job by providing the job's name.
String statusEquals
List the Candidates for the job and filter by status.
String candidateNameEquals
List the Candidates for the job and filter by candidate name.
String sortOrder
The sort order for the results. The default is Ascending.
String sortBy
The parameter by which to sort the results. The default is Descending.
Integer maxResults
List the job's Candidates up to a specified limit.
String nextToken
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Date creationTimeAfter
A filter that returns only Git repositories that were created after the specified time.
Date creationTimeBefore
A filter that returns only Git repositories that were created before the specified time.
Date lastModifiedTimeAfter
A filter that returns only Git repositories that were last modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only Git repositories that were last modified before the specified time.
Integer maxResults
The maximum number of Git repositories to return in the response.
String nameContains
A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.
String nextToken
If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a
NextToken. To get the next set of Git repositories, use the token in the next request.
String sortBy
The field to sort results by. The default is Name.
String sortOrder
The sort order for results. The default is Ascending.
List<E> codeRepositorySummaryList
Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:
Name
Amazon Resource Name (ARN)
Creation time
Last modified time
Configuration information, including the URL location of the repository and the ARN of the AWS Secrets Manager secret that contains the credentials used to access the repository.
String nextToken
If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a
NextToken. To get the next set of Git repositories, use the token in the next request.
String nextToken
If the result of the previous ListCompilationJobs request was truncated, the response includes a
NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
Integer maxResults
The maximum number of model compilation jobs to return in the response.
Date creationTimeAfter
A filter that returns the model compilation jobs that were created after a specified time.
Date creationTimeBefore
A filter that returns the model compilation jobs that were created before a specified time.
Date lastModifiedTimeAfter
A filter that returns the model compilation jobs that were modified after a specified time.
Date lastModifiedTimeBefore
A filter that returns the model compilation jobs that were modified before a specified time.
String nameContains
A filter that returns the model compilation jobs whose name contains a specified string.
String statusEquals
A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.
String sortBy
The field by which to sort results. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
List<E> compilationJobSummaries
An array of CompilationJobSummary objects, each describing a model compilation job.
String nextToken
If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve the next set of
model compilation jobs, use this token in the next request.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of the previous ListEndpointConfig request was truncated, the response includes a
NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.
Integer maxResults
The maximum number of training jobs to return in the response.
String nameContains
A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.
Date creationTimeBefore
A filter that returns only endpoint configurations created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).
String sortBy
Sorts the list of results. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of a ListEndpoints request was truncated, the response includes a
NextToken. To retrieve the next set of endpoints, use the token in the next request.
Integer maxResults
The maximum number of endpoints to return in the response.
String nameContains
A string in endpoint names. This filter returns only endpoints whose name contains the specified string.
Date creationTimeBefore
A filter that returns only endpoints that were created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only endpoints that were modified before the specified timestamp.
Date lastModifiedTimeAfter
A filter that returns only endpoints that were modified after the specified timestamp.
String statusEquals
A filter that returns only endpoints with the specified status.
Date createdAfter
A filter that returns only experiments created after the specified time.
Date createdBefore
A filter that returns only experiments created before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nextToken
If the previous call to ListExperiments didn't return the full set of experiments, the call returns
a token for getting the next set of experiments.
Integer maxResults
The maximum number of experiments to return in the response. The default value is 10.
Date creationTimeAfter
A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
Date creationTimeBefore
A filter that returns only flow definitions that were created before the specified timestamp.
String sortOrder
An optional value that specifies whether you want the results sorted in Ascending or
Descending order.
String nextToken
A token to resume pagination.
Integer maxResults
The total number of items to return. If the total number of available items is more than the value specified in
MaxResults, then a NextToken will be provided in the output that you can use to resume
pagination.
Date creationTimeAfter
A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
Date creationTimeBefore
A filter that returns only human task user interfaces that were created before the specified timestamp.
String sortOrder
An optional value that specifies whether you want the results sorted in Ascending or
Descending order.
String nextToken
A token to resume pagination.
Integer maxResults
The total number of items to return. If the total number of available items is more than the value specified in
MaxResults, then a NextToken will be provided in the output that you can use to resume
pagination.
String nextToken
If the result of the previous ListHyperParameterTuningJobs request was truncated, the response
includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
Integer maxResults
The maximum number of tuning jobs to return. The default value is 10.
String sortBy
The field to sort results by. The default is Name.
String sortOrder
The sort order for results. The default is Ascending.
String nameContains
A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
Date creationTimeAfter
A filter that returns only tuning jobs that were created after the specified time.
Date creationTimeBefore
A filter that returns only tuning jobs that were created before the specified time.
Date lastModifiedTimeAfter
A filter that returns only tuning jobs that were modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only tuning jobs that were modified before the specified time.
String statusEquals
A filter that returns only tuning jobs with the specified status.
List<E> hyperParameterTuningJobSummaries
A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the
ListHyperParameterTuningJobs request returned.
String nextToken
If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a
NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
String workteamArn
The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
Integer maxResults
The maximum number of labeling jobs to return in each page of the response.
String nextToken
If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response
includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
Date creationTimeAfter
A filter that returns only labeling jobs created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only labeling jobs created before the specified time (timestamp).
String jobReferenceCodeContains
A filter the limits jobs to only the ones whose job reference code contains the specified string.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
Date creationTimeAfter
A filter that returns only labeling jobs created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only labeling jobs created before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only labeling jobs modified after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only labeling jobs modified before the specified time (timestamp).
Integer maxResults
The maximum number of labeling jobs to return in each page of the response.
String nextToken
If the result of the previous ListLabelingJobs request was truncated, the response includes a
NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
String nameContains
A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
String statusEquals
A filter that retrieves only labeling jobs with a specific status.
Date creationTimeAfter
A filter that returns only model packages created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only model packages created before the specified time (timestamp).
Integer maxResults
The maximum number of model packages to return in the response.
String nameContains
A string in the model package name. This filter returns only model packages whose name contains the specified string.
String nextToken
If the response to a previous ListModelPackages request was truncated, the response includes a
NextToken. To retrieve the next set of model packages, use the token in the next request.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String sortOrder
The sort order for the results. The default is Ascending.
List<E> modelPackageSummaryList
An array of ModelPackageSummary objects, each of which lists a model package.
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.
String sortBy
Sorts the list of results. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the response to a previous ListModels request was truncated, the response includes a
NextToken. To retrieve the next set of models, use the token in the next request.
Integer maxResults
The maximum number of models to return in the response.
String nameContains
A string in the training job name. This filter returns only models in the training job whose name contains the specified string.
Date creationTimeBefore
A filter that returns only models created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).
String monitoringScheduleName
Name of a specific schedule to fetch jobs for.
String endpointName
Name of a specific endpoint to fetch jobs for.
String sortBy
Whether to sort results by Status, CreationTime, ScheduledTime field. The
default is CreationTime.
String sortOrder
Whether to sort the results in Ascending or Descending order. The default is
Descending.
String nextToken
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
Integer maxResults
The maximum number of jobs to return in the response. The default value is 10.
Date scheduledTimeBefore
Filter for jobs scheduled before a specified time.
Date scheduledTimeAfter
Filter for jobs scheduled after a specified time.
Date creationTimeBefore
A filter that returns only jobs created before a specified time.
Date creationTimeAfter
A filter that returns only jobs created after a specified time.
Date lastModifiedTimeBefore
A filter that returns only jobs modified after a specified time.
Date lastModifiedTimeAfter
A filter that returns only jobs modified before a specified time.
String statusEquals
A filter that retrieves only jobs with a specific status.
List<E> monitoringExecutionSummaries
A JSON array in which each element is a summary for a monitoring execution.
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent reques
String endpointName
Name of a specific endpoint to fetch schedules for.
String sortBy
Whether to sort results by Status, CreationTime, ScheduledTime field. The
default is CreationTime.
String sortOrder
Whether to sort the results in Ascending or Descending order. The default is
Descending.
String nextToken
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
Integer maxResults
The maximum number of jobs to return in the response. The default value is 10.
String nameContains
Filter for monitoring schedules whose name contains a specified string.
Date creationTimeBefore
A filter that returns only monitoring schedules created before a specified time.
Date creationTimeAfter
A filter that returns only monitoring schedules created after a specified time.
Date lastModifiedTimeBefore
A filter that returns only monitoring schedules modified before a specified time.
Date lastModifiedTimeAfter
A filter that returns only monitoring schedules modified after a specified time.
String statusEquals
A filter that returns only monitoring schedules modified before a specified time.
String nextToken
If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes
a NextToken. To get the next set of lifecycle configurations, use the token in the next request.
Integer maxResults
The maximum number of lifecycle configurations to return in the response.
String sortBy
Sorts the list of results. The default is CreationTime.
String sortOrder
The sort order for results.
String nameContains
A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.
Date creationTimeBefore
A filter that returns only lifecycle configurations that were created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only lifecycle configurations that were created after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.
List<E> notebookInstanceLifecycleConfigs
An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.
String nextToken
If the previous call to the ListNotebookInstances is truncated, the response includes a
NextToken. You can use this token in your subsequent ListNotebookInstances request to
fetch the next set of notebook instances.
You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.
Integer maxResults
The maximum number of notebook instances to return.
String sortBy
The field to sort results by. The default is Name.
String sortOrder
The sort order for results.
String nameContains
A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.
Date creationTimeBefore
A filter that returns only notebook instances that were created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only notebook instances that were created after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only notebook instances that were modified before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only notebook instances that were modified after the specified time (timestamp).
String statusEquals
A filter that returns only notebook instances with the specified status.
String notebookInstanceLifecycleConfigNameContains
A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.
String defaultCodeRepositoryContains
A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.
String additionalCodeRepositoryEquals
A filter that returns only notebook instances with associated with the specified git repository.
String nextToken
If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker
returns this token. To retrieve the next set of notebook instances, use the token in the next request.
List<E> notebookInstances
An array of NotebookInstanceSummary objects, one for each notebook instance.
Date creationTimeAfter
A filter that returns only processing jobs created after the specified time.
Date creationTimeBefore
A filter that returns only processing jobs created after the specified time.
Date lastModifiedTimeAfter
A filter that returns only processing jobs modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only processing jobs modified before the specified time.
String nameContains
A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.
String statusEquals
A filter that retrieves only processing jobs with a specific status.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
String nextToken
If the result of the previous ListProcessingJobs request was truncated, the response includes a
NextToken. To retrieve the next set of processing jobs, use the token in the next request.
Integer maxResults
The maximum number of processing jobs to return in the response.
String nameContains
A string in the work team name. This filter returns only work teams whose name contains the specified string.
String nextToken
If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a
NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
Integer maxResults
The maximum number of work teams to return in each page of the response.
String resourceArn
The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.
String nextToken
If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token.
To retrieve the next set of tags, use it in the subsequent request.
Integer maxResults
Maximum number of tags to return.
String hyperParameterTuningJobName
The name of the tuning job whose training jobs you want to list.
String nextToken
If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the
response includes a NextToken. To retrieve the next set of training jobs, use the token in the next
request.
Integer maxResults
The maximum number of training jobs to return. The default value is 10.
String statusEquals
A filter that returns only training jobs with the specified status.
String sortBy
The field to sort results by. The default is Name.
If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an
objective metric are not listed.
String sortOrder
The sort order for results. The default is Ascending.
List<E> trainingJobSummaries
A list of TrainingJobSummary objects that describe the training jobs that the
ListTrainingJobsForHyperParameterTuningJob request returned.
String nextToken
If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response
includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
String nextToken
If the result of the previous ListTrainingJobs request was truncated, the response includes a
NextToken. To retrieve the next set of training jobs, use the token in the next request.
Integer maxResults
The maximum number of training jobs to return in the response.
Date creationTimeAfter
A filter that returns only training jobs created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only training jobs created before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only training jobs modified after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only training jobs modified before the specified time (timestamp).
String nameContains
A string in the training job name. This filter returns only training jobs whose name contains the specified string.
String statusEquals
A filter that retrieves only training jobs with a specific status.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
Date creationTimeAfter
A filter that returns only transform jobs created after the specified time.
Date creationTimeBefore
A filter that returns only transform jobs created before the specified time.
Date lastModifiedTimeAfter
A filter that returns only transform jobs modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only transform jobs modified before the specified time.
String nameContains
A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.
String statusEquals
A filter that retrieves only transform jobs with a specific status.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of the previous ListTransformJobs request was truncated, the response includes a
NextToken. To retrieve the next set of transform jobs, use the token in the next request.
Integer maxResults
The maximum number of transform jobs to return in the response. The default value is 10.
String experimentName
A filter that returns only components that are part of the specified experiment. If you specify
ExperimentName, you can't filter by SourceArn or TrialName.
String trialName
A filter that returns only components that are part of the specified trial. If you specify TrialName
, you can't filter by ExperimentName or SourceArn.
String sourceArn
A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify
SourceArn, you can't filter by ExperimentName or TrialName.
Date createdAfter
A filter that returns only components created after the specified time.
Date createdBefore
A filter that returns only components created before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
Integer maxResults
The maximum number of components to return in the response. The default value is 10.
String nextToken
If the previous call to ListTrialComponents didn't return the full set of components, the call
returns a token for getting the next set of components.
String experimentName
A filter that returns only trials that are part of the specified experiment.
String trialComponentName
A filter that returns only trials that are associated with the specified trial component.
Date createdAfter
A filter that returns only trials created after the specified time.
Date createdBefore
A filter that returns only trials created before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
Integer maxResults
The maximum number of trials to return in the response. The default value is 10.
String nextToken
If the previous call to ListTrials didn't return the full set of trials, the call returns a token
for getting the next set of trials.
String nextToken
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Integer maxResults
Returns a list up to a specified limit.
String sortOrder
The sort order for the results. The default is Ascending.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String domainIdEquals
A parameter by which to filter the results.
String userProfileNameContains
A parameter by which to filter the results.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
String nameContains
A string in the work team's name. This filter returns only work teams whose name contains the specified string.
String nextToken
If the result of the previous ListWorkteams request was truncated, the response includes a
NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
Integer maxResults
The maximum number of work teams to return in each page of the response.
CognitoMemberDefinition cognitoMemberDefinition
The Amazon Cognito user group that is part of the work team.
String name
The name of the metric.
String regex
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 Objective Metrics.
String s3ModelArtifacts
The path of the S3 object that contains the model artifacts. For example,
s3://bucket-name/keynameprefix/model.tar.gz.
String containerHostname
The DNS host name for the Docker container.
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.
String imageDigest
An MD5 hash of the training algorithm that identifies the Docker image used for training.
String modelDataUrl
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).
String productId
The AWS Marketplace product ID of the model package.
String modelPackageName
The name of the model package.
String modelPackageArn
The Amazon Resource Name (ARN) of the model package.
String modelPackageDescription
A brief description of the model package.
Date creationTime
A timestamp that shows when the model package was created.
String modelPackageStatus
The overall status of the model package.
String profileName
The name of the profile for the model package.
TransformJobDefinition transformJobDefinition
The TransformJobDefinition object that describes the transform job used for the validation of the
model package.
String validationRole
The IAM roles to be used for the validation of the model package.
List<E> validationProfiles
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job
that Amazon SageMaker runs to validate your model package.
String imageUri
The container image to be run by the monitoring job.
List<E> containerEntrypoint
Specifies the entrypoint for a container used to run the monitoring job.
List<E> containerArguments
An array of arguments for the container used to run the monitoring job.
String recordPreprocessorSourceUri
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
String postAnalyticsProcessorSourceUri
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
MonitoringConstraintsResource constraintsResource
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
MonitoringStatisticsResource statisticsResource
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
Integer instanceCount
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
String instanceType
The ML compute instance type for the processing job.
Integer volumeSizeInGB
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
String volumeKmsKeyId
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
String s3Uri
The Amazon S3 URI for the constraints resource.
String monitoringScheduleName
The name of the monitoring schedule.
Date scheduledTime
The time the monitoring job was scheduled.
Date creationTime
The time at which the monitoring job was created.
Date lastModifiedTime
A timestamp that indicates the last time the monitoring job was modified.
String monitoringExecutionStatus
The status of the monitoring job.
String processingJobArn
The Amazon Resource Name (ARN) of the monitoring job.
String endpointName
The name of teh endpoint used to run the monitoring job.
String failureReason
Contains the reason a monitoring job failed, if it failed.
EndpointInput endpointInput
The endpoint for a monitoring job.
MonitoringBaselineConfig baselineConfig
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
List<E> monitoringInputs
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
MonitoringOutputConfig monitoringOutputConfig
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringResources monitoringResources
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
MonitoringAppSpecification monitoringAppSpecification
Configures the monitoring job to run a specified Docker container image.
MonitoringStoppingCondition stoppingCondition
Specifies a time limit for how long the monitoring job is allowed to run.
Map<K,V> environment
Sets the environment variables in the Docker container.
NetworkConfig networkConfig
Specifies networking options for an monitoring job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringS3Output s3Output
The Amazon S3 storage location where the results of a monitoring job are saved.
List<E> monitoringOutputs
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
String kmsKeyId
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringClusterConfig clusterConfig
The configuration for the cluster resources used to run the processing job.
String s3Uri
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
String localPath
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
String s3UploadMode
Whether to upload the results of the monitoring job continuously or after the job completes.
ScheduleConfig scheduleConfig
Configures the monitoring schedule.
MonitoringJobDefinition monitoringJobDefinition
Defines the monitoring job.
String monitoringScheduleName
The name of the monitoring schedule.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
Date creationTime
The creation time of the monitoring schedule.
Date lastModifiedTime
The last time the monitoring schedule was modified.
String monitoringScheduleStatus
The status of the monitoring schedule.
String endpointName
The name of the endpoint using the monitoring schedule.
String s3Uri
The Amazon S3 URI for the statistics resource.
Integer maxRuntimeInSeconds
The maximum runtime allowed in seconds.
String nestedPropertyName
The name of the property to use in the nested filters. The value must match a listed property name, such as
InputDataConfig.
List<E> filters
A list of filters. Each filter acts on a property. Filters must contain at least one Filters value.
For example, a NestedFilters call might include a filter on the PropertyName parameter
of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
String notebookInstanceLifecycleConfigArn
The Amazon Resource Name (ARN) of the lifecycle configuration.
Date creationTime
A timestamp that tells when the lifecycle configuration was created.
Date lastModifiedTime
A timestamp that tells when the lifecycle configuration was last modified.
String content
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
String notebookInstanceName
The name of the notebook instance that you want a summary for.
String notebookInstanceArn
The Amazon Resource Name (ARN) of the notebook instance.
String notebookInstanceStatus
The status of the notebook instance.
String url
The URL that you use to connect to the Jupyter instance running in your notebook instance.
String instanceType
The type of ML compute instance that the notebook instance is running on.
Date creationTime
A timestamp that shows when the notebook instance was created.
Date lastModifiedTime
A timestamp that shows when the notebook instance was last modified.
String notebookInstanceLifecycleConfigName
The name of a notebook instance lifecycle configuration associated with this notebook instance.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
String defaultCodeRepository
The Git repository associated 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.
List<E> additionalCodeRepositories
An array of up to three Git repositories associated 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.
String notificationTopicArn
The ARN for the SNS topic to which notifications should be published.
Integer succeeded
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Integer pending
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Integer failed
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
String s3OutputLocation
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
String targetDevice
Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.
String kmsKeyId
The AWS Key Management Service (AWS 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:
// 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 master key, the Amazon SageMaker execution role must include
permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the
default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with
KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject
permission that only allows objects with server-side encryption, set the condition key of
s3:x-amz-server-side-encryption to "aws:kms". 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 CreateTrainingJob,
CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information,
see Using Key Policies in AWS
KMS in the AWS Key Management Service Developer Guide.
String s3OutputPath
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.
IntegerParameterRangeSpecification integerParameterRangeSpecification
A IntegerParameterRangeSpecification object that defines the possible values for an integer
hyperparameter.
ContinuousParameterRangeSpecification continuousParameterRangeSpecification
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous
hyperparameter.
CategoricalParameterRangeSpecification categoricalParameterRangeSpecification
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical
hyperparameter.
List<E> integerParameterRanges
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
List<E> continuousParameterRanges
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
List<E> categoricalParameterRanges
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
String hyperParameterTuningJobName
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
Integer instanceCount
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
String instanceType
The ML compute instance type for the processing job.
Integer volumeSizeInGB
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
String volumeKmsKeyId
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
String inputName
The name of the inputs for the processing job.
ProcessingS3Input s3Input
The S3 inputs for the processing job.
String processingJobName
The name of the processing job.
String processingJobArn
The Amazon Resource Name (ARN) of the processing job..
Date creationTime
The time at which the processing job was created.
Date processingEndTime
The time at which the processing job completed.
Date lastModifiedTime
A timestamp that indicates the last time the processing job was modified.
String processingJobStatus
The status of the processing job.
String failureReason
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
String exitMessage
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
String outputName
The name for the processing job output.
ProcessingS3Output s3Output
Configuration for processing job outputs in Amazon S3.
List<E> outputs
Output configuration information for a processing job.
String kmsKeyId
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output.
KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The
KmsKeyId is applied to all outputs.
ProcessingClusterConfig clusterConfig
The configuration for the resources in a cluster used to run the processing job.
String s3Uri
The URI for the Amazon S3 storage where you want Amazon SageMaker to download the artifacts needed to run a processing job.
String localPath
The local path to the Amazon S3 bucket where you want Amazon SageMaker to download the inputs to run a processing
job. LocalPath is an absolute path to the input data.
String s3DataType
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose
S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with
the specified key name prefix for the processing job. 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 the processing job.
String s3InputMode
Wether to use File or Pipe input mode. 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.
String s3DataDistributionType
Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key.
String s3CompressionType
Whether to use Gzip compresion for Amazon S3 storage.
String s3Uri
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
String localPath
The local path to the Amazon S3 bucket where you want Amazon SageMaker to save the results of an processing job.
LocalPath is an absolute path to the input data.
String s3UploadMode
Whether to upload the results of the processing job continuously or after the job completes.
Integer maxRuntimeInSeconds
Specifies the maximum runtime in seconds.
String variantName
The name of the production variant.
String modelName
The name of the model that you want to host. This is the name that you specified when creating the model.
Integer initialInstanceCount
Number of instances to launch initially.
String instanceType
The ML compute instance type.
Float initialVariantWeight
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration.
The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of
all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
String acceleratorType
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
String variantName
The name of the variant.
List<E> deployedImages
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the
inference images deployed on instances of this ProductionVariant.
Float currentWeight
The weight associated with the variant.
Float desiredWeight
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
Integer currentInstanceCount
The number of instances associated with the variant.
Integer desiredInstanceCount
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
String propertyNameHint
Text that begins a property's name.
String propertyName
A suggested property name based on what you entered in the search textbox in the Amazon SageMaker console.
USD amountInUsd
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
String input
A JSON object that contains values for the variables defined in the template. It is made available to the
template under the substitution variable task.input. For example, if you define a variable
task.input.text in your template, you can supply the variable in the JSON object as
"text": "sample text".
UiTemplate uiTemplate
A Template object containing the worker UI template to render.
RenderableTask task
A RenderableTask object containing a representative task to render.
String roleArn
The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
AutoMLJobObjective autoMLJobObjective
String problemType
The problem type.
AutoMLJobCompletionCriteria completionCriteria
String instanceType
The ML compute instance type.
Integer instanceCount
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
Integer volumeSizeInGB
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.
You must specify sufficient ML storage for your scenario.
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When
using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2
storage. You can't request a VolumeSizeInGB greater than the total size of the local instance
storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.
String volumeKmsKeyId
The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
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"
String homeEfsFileSystem
The home Amazon Elastic File System (EFS).
String s3DataType
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon 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 Amazon 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.
String s3Uri
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:
The preceding JSON matches the following s3Uris:
[ {"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 is 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.
String s3DataDistributionType
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify FullyReplicated.
If you want Amazon 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.
List<E> attributeNames
A list of one or more attribute names to use that are found in a specified augmented manifest file.
String scheduleExpression
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
If you want to set the job to start every hour, please use the following:
Hourly: cron(0 * ? * * *)
If you want to start the job daily:
cron(0 [00-23] ? * * *)
For example, the following are valid cron expressions:
Daily at noon UTC: cron(0 12 ? * * *)
Daily at midnight UTC: cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
Every two hours starting at midnight: cron(0 0/2 ? * * *)
Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
List<E> filters
A list of filter objects.
List<E> nestedFilters
A list of nested filter objects.
List<E> subExpressions
A list of search expression objects.
String operator
A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists
to be satisfied for the entire search expression to be true, specify And. If only a single
conditional statement needs to be true for the entire search expression to be true, specify Or. The
default value is And.
TrainingJob trainingJob
A TrainingJob object that is returned as part of a Search request.
Experiment experiment
A summary of the properties of an experiment.
Trial trial
A summary of the properties of a trial.
TrialComponent trialComponent
A summary of the properties of a trial component.
String resource
The name of the Amazon SageMaker resource to search for.
SearchExpression searchExpression
A Boolean conditional statement. Resource objects must satisfy this condition to be included in search results.
You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive
SubExpressions, NestedFilters, and Filters that can be included in a
SearchExpression object is 50.
String sortBy
The name of the resource property used to sort the SearchResults. The default is
LastModifiedTime.
String sortOrder
How SearchResults are ordered. Valid values are Ascending or Descending.
The default is Descending.
String nextToken
If more than MaxResults resource objects match the specified SearchExpression, the
SearchResponse includes a NextToken. The NextToken can be passed to the
next SearchRequest to continue retrieving results for the specified SearchExpression
and Sort parameters.
Integer maxResults
The maximum number of results to return in a SearchResponse.
String status
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode.
It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
Date startTime
A timestamp that shows when the training job transitioned to the current secondary status state.
Date endTime
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
String statusMessage
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Downloading the training image.
Training image download completed. Training in progress.
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and
SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example,
at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
Long seed
Determines the shuffling order in ShuffleConfig value.
String modelDataUrl
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).
String algorithmName
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
List<E> cidrs
A list of one to four Classless Inter-Domain Routing (CIDR) values.
Maximum: Four CIDR values
The following Length Constraints apply to individual CIDR values in the CIDR value list.
String monitoringScheduleName
The name of the schedule to start.
String notebookInstanceName
The name of the notebook instance to start.
String autoMLJobName
The name of the object you are requesting.
String compilationJobName
The name of the model compilation job to stop.
String hyperParameterTuningJobName
The name of the tuning job to stop.
String labelingJobName
The name of the labeling job to stop.
String monitoringScheduleName
The name of the schedule to stop.
String notebookInstanceName
The name of the notebook instance to terminate.
Integer maxRuntimeInSeconds
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
Integer maxWaitTimeInSeconds
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to
complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs.
It must be equal to or greater than MaxRuntimeInSeconds.
String processingJobName
The name of the processing job to stop.
String trainingJobName
The name of the training job to stop.
String transformJobName
The name of the transform job to stop.
String workteamArn
The Amazon Resource Name (ARN) of the vendor that you have subscribed.
String marketplaceTitle
The title of the service provided by the vendor in the Amazon Marketplace.
String sellerName
The name of the vendor in the Amazon Marketplace.
String marketplaceDescription
The description of the vendor from the Amazon Marketplace.
String listingId
PropertyNameQuery propertyNameQuery
Defines a property name hint. Only property names that begin with the specified hint are included in the response.
ResourceSpec defaultResourceSpec
The instance type and quantity.
String trainingJobName
The name of the training job.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String tuningJobArn
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
String labelingJobArn
The Amazon Resource Name (ARN) of the labeling job.
String autoMLJobArn
The Amazon Resource Name (ARN) of the job.
ModelArtifacts modelArtifacts
Information about the Amazon S3 location that is configured for storing model artifacts.
String trainingJobStatus
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the
FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
String secondaryStatus
Provides detailed information about the state of the training job. For detailed information about the secondary
status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode.
It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
String failureReason
If the training job failed, the reason it failed.
Map<K,V> hyperParameters
Algorithm-specific parameters.
AlgorithmSpecification algorithmSpecification
Information about the algorithm used for training, and algorithm metadata.
String roleArn
The AWS Identity and Access Management (IAM) role configured for the training job.
List<E> inputDataConfig
An array of Channel objects that describes each data input channel.
OutputDataConfig outputDataConfig
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
ResourceConfig resourceConfig
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
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.
Date creationTime
A timestamp that indicates when the training job was created.
Date trainingStartTime
Indicates the time when the training job starts on training instances. You are billed for the time interval
between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later
than this time. The difference is due to the time it takes to download the training data and to the size of the
training container.
Date trainingEndTime
Indicates the time when the training job ends on training instances. You are billed for the time interval between
the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time
after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job
failure.
Date lastModifiedTime
A timestamp that indicates when the status of the training job was last modified.
List<E> secondaryStatusTransitions
A history of all of the secondary statuses that the training job has transitioned through.
List<E> finalMetricDataList
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
Boolean enableNetworkIsolation
If the TrainingJob was created with network isolation, the value is set to true. If
network isolation is enabled, nodes can't communicate beyond the VPC they run in.
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.
Boolean enableManagedSpotTraining
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see model-managed-spot-training.
CheckpointConfig checkpointConfig
Integer trainingTimeInSeconds
The training time in seconds.
Integer billableTimeInSeconds
The billable time in seconds.
DebugHookConfig debugHookConfig
ExperimentConfig experimentConfig
List<E> debugRuleConfigurations
Information about the debug rule configuration.
TensorBoardOutputConfig tensorBoardOutputConfig
List<E> debugRuleEvaluationStatuses
Information about the evaluation status of the rules for the training job.
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.
String trainingInputMode
The input mode used by the algorithm for the training job. 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.
Map<K,V> hyperParameters
The hyperparameters used for the training job.
List<E> inputDataConfig
An array of Channel objects, each of which specifies an input source.
OutputDataConfig outputDataConfig
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
ResourceConfig resourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
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.
Integer completed
The number of completed training jobs launched by the hyperparameter tuning job.
Integer inProgress
The number of in-progress training jobs launched by a hyperparameter tuning job.
Integer retryableError
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
Integer nonRetryableError
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Integer stopped
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
String trainingJobName
The name of the training job that you want a summary for.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
Date creationTime
A timestamp that shows when the training job was created.
Date trainingEndTime
A timestamp that shows when the training job ended. This field is set only if the training job has one of the
terminal statuses (Completed, Failed, or Stopped).
Date lastModifiedTime
Timestamp when the training job was last modified.
String trainingJobStatus
The status of the training job.
String trainingImage
The Amazon ECR registry path of the Docker image that contains the training algorithm.
String trainingImageDigest
An MD5 hash of the training algorithm that identifies the Docker image used for training.
List<E> supportedHyperParameters
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This
is required if the algorithm supports automatic model tuning.>
List<E> supportedTrainingInstanceTypes
A list of the instance types that this algorithm can use for training.
Boolean supportsDistributedTraining
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
List<E> metricDefinitions
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
List<E> trainingChannels
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
List<E> supportedTuningJobObjectiveMetrics
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
TransformS3DataSource s3DataSource
The S3 location of the data source that is associated with a channel.
TransformDataSource dataSource
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
String contentType
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.
String compressionType
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.
String splitType
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.
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.
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.
Integer maxConcurrentTransforms
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
Integer maxPayloadInMB
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
String batchStrategy
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.
Map<K,V> environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput transformInput
A description of the input source and the way the transform job consumes it.
TransformOutput transformOutput
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources transformResources
Identifies the ML compute instances for the transform job.
String transformJobName
The name of the transform job.
String transformJobArn
The Amazon Resource Name (ARN) of the transform job.
Date creationTime
A timestamp that shows when the transform Job was created.
Date transformEndTime
Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.
Date lastModifiedTime
Indicates when the transform job was last modified.
String transformJobStatus
The status of the transform job.
String failureReason
If the transform job failed, the reason it failed.
String s3OutputPath
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.
String accept
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.
String assembleWith
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.
String kmsKeyId
The AWS Key Management Service (AWS 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 AWS KMS in the AWS Key Management Service Developer Guide.
String instanceType
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.large instance types.
Integer instanceCount
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value
greater than 1. The default value is 1.
String volumeKmsKeyId
The AWS Key Management Service (AWS 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. 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
String s3DataType
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
String s3Uri
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.
String trialName
The name of the trial.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String displayName
The name of the trial as displayed. If DisplayName isn't specified, TrialName is
displayed.
String experimentName
The name of the experiment the trial is part of.
TrialSource source
Date creationTime
When the trial was created.
UserContext createdBy
Date lastModifiedTime
Who last modified the trial.
UserContext lastModifiedBy
List<E> tags
The list of tags that are associated with the trial. You can use Search API to search on the tags.
List<E> trialComponentSummaries
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
String trialComponentName
The name of the trial component.
String displayName
The name of the component as displayed. If DisplayName isn't specified,
TrialComponentName is displayed.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource source
TrialComponentStatus status
Date startTime
When the component started.
Date endTime
When the component ended.
Date creationTime
When the component was created.
UserContext createdBy
Date lastModifiedTime
When the component was last modified.
UserContext lastModifiedBy
Map<K,V> parameters
The hyperparameters of the component.
Map<K,V> inputArtifacts
The input artifacts of the component.
Map<K,V> outputArtifacts
The output artifacts of the component.
List<E> metrics
The metrics for the component.
TrialComponentSourceDetail sourceDetail
The source of the trial component.>
List<E> tags
The list of tags that are associated with the component. You can use Search API to search on the tags.
List<E> parents
An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
String mediaType
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
String value
The location of the artifact.
String metricName
The name of the metric.
String sourceArn
The Amazon Resource Name (ARN) of the source.
Date timeStamp
When the metric was last updated.
Double max
The maximum value of the metric.
Double min
The minimum value of the metric.
Double last
The most recent value of the metric.
Integer count
The number of samples used to generate the metric.
Double avg
The average value of the metric.
Double stdDev
The standard deviation of the metric.
String stringValue
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify
the NumberValue parameter.
Double numberValue
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the
StringValue parameter.
String trialComponentName
The name of the trial component.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource trialComponentSource
Date creationTime
When the component was created.
UserContext createdBy
String sourceArn
The Amazon Resource Name (ARN) of the source.
TrainingJob trainingJob
String trialComponentName
The name of the trial component.
String trialComponentArn
The ARN of the trial component.
String displayName
The name of the component as displayed. If DisplayName isn't specified,
TrialComponentName is displayed.
TrialComponentSource trialComponentSource
TrialComponentStatus status
The status of the component. States include:
InProgress
Completed
Failed
Date startTime
When the component started.
Date endTime
When the component ended.
Date creationTime
When the component was created.
UserContext createdBy
Who created the component.
Date lastModifiedTime
When the component was last modified.
UserContext lastModifiedBy
Who last modified the component.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String trialName
The name of the trial.
String displayName
The name of the trial as displayed. If DisplayName isn't specified, TrialName is
displayed.
TrialSource trialSource
Date creationTime
When the trial was created.
Date lastModifiedTime
When the trial was last modified.
Float targetObjectiveMetricValue
The objective metric's value.
String uiTemplateS3Uri
The Amazon S3 bucket location of the UI template. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.
String content
The content of the Liquid template for the worker user interface.
String codeRepositoryName
The name of the Git repository to update.
GitConfigForUpdate gitConfig
The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the AWS Secrets
Manager secret that contains the credentials used to access the repository. The secret must have a staging label
of AWSCURRENT and must be in the following format:
{"username": UserName, "password": Password}
String codeRepositoryArn
The ARN of the Git repository.
String domainId
The domain ID.
UserSettings defaultUserSettings
A collection of settings.
String domainArn
The domain Amazon Resource Name (ARN).
String endpointName
The name of the endpoint whose configuration you want to update.
String endpointConfigName
The name of the new endpoint configuration.
Boolean retainAllVariantProperties
When updating endpoint resources, enables or disables the retention of variant properties, such as the instance
count or the variant weight. To retain the variant properties of an endpoint when updating it, set
RetainAllVariantProperties to true. To use the variant properties specified in a new
EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to
false.
List<E> excludeRetainedVariantProperties
When you are updating endpoint resources with RetainAllVariantProperties, whose value is set to true,
ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override
with the values provided by EndpointConfig. If you don't specify a value for
ExcludeAllVariantProperties, no variant properties are overridden.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
String endpointArn
The Amazon Resource Name (ARN) of the updated endpoint.
String experimentName
The name of the experiment to update.
String displayName
The name of the experiment as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, ExperimentName is displayed.
String description
The description of the experiment.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String monitoringScheduleName
The name of the monitoring schedule. The name must be unique within an AWS Region within an AWS account.
MonitoringScheduleConfig monitoringScheduleConfig
The configuration object that specifies the monitoring schedule and defines the monitoring job.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
List<E> onCreate
The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
List<E> onStart
The 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.
String notebookInstanceName
The name of the notebook instance to update.
String instanceType
The Amazon ML compute instance type.
String roleArn
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. 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.
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.
Boolean disassociateLifecycleConfig
Set to true to remove the notebook instance lifecycle configuration currently associated with the
notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated
with the notebook instance when you call this method, it does not throw an error.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.
String defaultCodeRepository
The 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.
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.
List<E> acceleratorTypes
A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
Boolean disassociateAcceleratorTypes
A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.
Boolean disassociateDefaultCodeRepository
The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
Boolean disassociateAdditionalCodeRepositories
A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
String rootAccess
Whether root access is enabled or disabled for users of the notebook instance. The default value is
Enabled.
If you set this to Disabled, users don't have root access on the notebook instance, but lifecycle
configuration scripts still run with root permissions.
String trialComponentName
The name of the component to update.
String displayName
The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, TrialComponentName is displayed.
TrialComponentStatus status
The new status of the component.
Date startTime
When the component started.
Date endTime
When the component ended.
Map<K,V> parameters
Replaces all of the component's hyperparameters with the specified hyperparameters.
List<E> parametersToRemove
The hyperparameters to remove from the component.
Map<K,V> inputArtifacts
Replaces all of the component's input artifacts with the specified artifacts.
List<E> inputArtifactsToRemove
The input artifacts to remove from the component.
Map<K,V> outputArtifacts
Replaces all of the component's output artifacts with the specified artifacts.
List<E> outputArtifactsToRemove
The output artifacts to remove from the component.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String domainId
The domain ID.
String userProfileName
The user profile name.
UserSettings userSettings
A collection of settings.
String userProfileArn
The user profile Amazon Resource Name (ARN).
String workforceName
The name of the private workforce whose access you want to restrict. WorkforceName is automatically
set to default when a workforce is created and cannot be modified.
SourceIpConfig sourceIpConfig
A list of one to four worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce.
Maximum: Four CIDR values
Workforce workforce
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each AWS Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
String workteamName
The name of the work team to update.
List<E> memberDefinitions
A list of MemberDefinition objects that contain the updated work team members.
String description
An updated description for the work team.
NotificationConfiguration notificationConfiguration
Configures SNS topic notifications for available or expiring work items
Workteam workteam
A Workteam object that describes the updated work team.
String executionRole
The execution role for the user.
List<E> securityGroups
The security groups.
SharingSettings sharingSettings
The sharing settings.
JupyterServerAppSettings jupyterServerAppSettings
The Jupyter server's app settings.
KernelGatewayAppSettings kernelGatewayAppSettings
The kernel gateway app settings.
TensorBoardAppSettings tensorBoardAppSettings
The TensorBoard app settings.
String variantPropertyType
The type of variant property. The supported values are:
DesiredInstanceCount: Overrides the existing variant instance counts using the InitialInstanceCount values in the ProductionVariants.
DesiredWeight: Overrides the existing variant weights using the InitialVariantWeight values in the ProductionVariants.
DataCaptureConfig: (Not currently supported.)
List<E> securityGroupIds
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in
the Subnets field.
List<E> subnets
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.
String workforceName
The name of the private workforce whose access you want to restrict. WorkforceName is automatically
set to default when a workforce is created and cannot be modified.
String workforceArn
The Amazon Resource Name (ARN) of the private workforce.
Date lastUpdatedDate
The most recent date that was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.
SourceIpConfig sourceIpConfig
A list of one to four IP address ranges (CIDRs) to be added to the workforce allow list.
String workteamName
The name of the work team.
List<E> memberDefinitions
The Amazon Cognito user groups that make up the work team.
String workteamArn
The Amazon Resource Name (ARN) that identifies the work team.
List<E> productListingIds
The Amazon Marketplace identifier for a vendor's work team.
String description
A description of the work team.
String subDomain
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
Date createDate
The date and time that the work team was created (timestamp).
Date lastUpdatedDate
The date and time that the work team was last updated (timestamp).
NotificationConfiguration notificationConfiguration
Configures SNS notifications of available or expiring work items for work teams.
Copyright © 2020. All rights reserved.