@Stability(value=Experimental) public static final class SageMakerCreateTrainingJobProps.Builder extends Object implements software.amazon.jsii.Builder<SageMakerCreateTrainingJobProps>
SageMakerCreateTrainingJobProps| Constructor and Description |
|---|
Builder() |
@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder algorithmSpecification(AlgorithmSpecification algorithmSpecification)
SageMakerCreateTrainingJobProps.getAlgorithmSpecification()algorithmSpecification - Identifies the training algorithm to use. This parameter is required.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder inputDataConfig(List<? extends Channel> inputDataConfig)
SageMakerCreateTrainingJobProps.getInputDataConfig()inputDataConfig - Describes the various datasets (e.g. train, validation, test) and the Amazon S3 location where stored. This parameter is required.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder outputDataConfig(OutputDataConfig outputDataConfig)
SageMakerCreateTrainingJobProps.getOutputDataConfig()outputDataConfig - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training. This parameter is required.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder trainingJobName(String trainingJobName)
SageMakerCreateTrainingJobProps.getTrainingJobName()trainingJobName - Training Job Name. This parameter is required.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder hyperparameters(Map<String,? extends Object> hyperparameters)
SageMakerCreateTrainingJobProps.getHyperparameters()hyperparameters - Algorithm-specific parameters that influence the quality of the model.
Set hyperparameters before you start the learning process.
For a list of hyperparameters provided by Amazon SageMakerthis@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder resourceConfig(ResourceConfig resourceConfig)
SageMakerCreateTrainingJobProps.getResourceConfig()resourceConfig - Specifies the resources, ML compute instances, and ML storage volumes to deploy for model training.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder role(IRole role)
SageMakerCreateTrainingJobProps.getRole()role - Role for the Training Job.
The role must be granted all necessary permissions for the SageMaker training job to
be able to operate.
See https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms
this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder stoppingCondition(StoppingCondition stoppingCondition)
SageMakerCreateTrainingJobProps.getStoppingCondition()stoppingCondition - Sets a time limit for training.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder tags(Map<String,String> tags)
SageMakerCreateTrainingJobProps.getTags()tags - Tags to be applied to the train job.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder vpcConfig(VpcConfig vpcConfig)
SageMakerCreateTrainingJobProps.getVpcConfig()vpcConfig - Specifies the VPC that you want your training job to connect to.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder comment(String comment)
TaskStateBaseProps.getComment()comment - An optional description for this state.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder heartbeat(Duration heartbeat)
TaskStateBaseProps.getHeartbeat()heartbeat - Timeout for the heartbeat.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder inputPath(String inputPath)
TaskStateBaseProps.getInputPath()inputPath - JSONPath expression to select part of the state to be the input to this state.
May also be the special value JsonPath.DISCARD, which will cause the effective
input to be the empty object {}.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder integrationPattern(IntegrationPattern integrationPattern)
TaskStateBaseProps.getIntegrationPattern()integrationPattern - AWS Step Functions integrates with services directly in the Amazon States Language.
You can control these AWS services using service integration patternsthis@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder outputPath(String outputPath)
TaskStateBaseProps.getOutputPath()outputPath - JSONPath expression to select select a portion of the state output to pass to the next state.
May also be the special value JsonPath.DISCARD, which will cause the effective
output to be the empty object {}.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder resultPath(String resultPath)
TaskStateBaseProps.getResultPath()resultPath - JSONPath expression to indicate where to inject the state's output.
May also be the special value JsonPath.DISCARD, which will cause the state's
input to become its output.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder resultSelector(Map<String,? extends Object> resultSelector)
TaskStateBaseProps.getResultSelector()resultSelector - The JSON that will replace the state's raw result and become the effective result before ResultPath is applied.
You can use ResultSelector to create a payload with values that are static
or selected from the state's raw result.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps.Builder timeout(Duration timeout)
TaskStateBaseProps.getTimeout()timeout - Timeout for the state machine.this@Stability(value=Experimental) public SageMakerCreateTrainingJobProps build()
build in interface software.amazon.jsii.Builder<SageMakerCreateTrainingJobProps>SageMakerCreateTrainingJobPropsNullPointerException - if any required attribute was not providedCopyright © 2021. All rights reserved.