software.amazon.awscdk.services.stepfunctions.tasks

Interface SagemakerTransformProps

All Superinterfaces:

software.amazon.jsii.JsiiSerializable

All Known Implementing Classes:

SagemakerTransformProps.Jsii$Proxy

@Generated(value="jsii-pacmak/0.13.3 (build 3624e0f)",
           date="2019-07-04T19:44:32.573Z")
 @Stability(value=Experimental)
public interface SagemakerTransformProps
extends software.amazon.jsii.JsiiSerializable

EXPERIMENTAL

Nested Class Summary

Nested Classes

Modifier and Type	Interface and Description
static class	SagemakerTransformProps.Builder A builder for SagemakerTransformProps
static class	SagemakerTransformProps.Jsii$Proxy A proxy class which represents a concrete javascript instance of this type.

Method Summary

Modifier and Type	Method and Description
static SagemakerTransformProps.Builder	builder()
BatchStrategy	getBatchStrategy() Number of records to include in a mini-batch for an HTTP inference request.
Map<String,Object>	getEnvironment() Environment variables to set in the Docker container.
Number	getMaxConcurrentTransforms() Maximum number of parallel requests that can be sent to each instance in a transform job.
Number	getMaxPayloadInMb() Maximum allowed size of the payload, in MB.
String	getModelName() Name of the model that you want to use for the transform job.
software.amazon.awscdk.services.iam.IRole	getRole() Role for thte Training Job.
Boolean	getSynchronous() Specify if the task is synchronous or asychronous.
Map<String,Object>	getTags() Tags to be applied to the train job.
TransformInput	getTransformInput() Dataset to be transformed and the Amazon S3 location where it is stored.
String	getTransformJobName() Training Job Name.
TransformOutput	getTransformOutput() S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources	getTransformResources() ML compute instances for the transform job.

Methods inherited from interface software.amazon.jsii.JsiiSerializable

$jsii$toJson

Method Detail

getModelName

@Stability(value=Experimental)
String getModelName()

Name of the model that you want to use for the transform job. EXPERIMENTAL

getTransformInput

@Stability(value=Experimental)
TransformInput getTransformInput()

Dataset to be transformed and the Amazon S3 location where it is stored. EXPERIMENTAL

getTransformJobName

@Stability(value=Experimental)
String getTransformJobName()

Training Job Name. EXPERIMENTAL

getTransformOutput

@Stability(value=Experimental)
TransformOutput getTransformOutput()

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

getBatchStrategy

@Stability(value=Experimental)
BatchStrategy getBatchStrategy()

Number of records to include in a mini-batch for an HTTP inference request. EXPERIMENTAL

getEnvironment

@Stability(value=Experimental)
Map<String,Object> getEnvironment()

Environment variables to set in the Docker container. EXPERIMENTAL

getMaxConcurrentTransforms

@Stability(value=Experimental)
Number getMaxConcurrentTransforms()

Maximum number of parallel requests that can be sent to each instance in a transform job. EXPERIMENTAL

getMaxPayloadInMb

@Stability(value=Experimental)
Number getMaxPayloadInMb()

Maximum allowed size of the payload, in MB. EXPERIMENTAL

getRole

@Stability(value=Experimental)
software.amazon.awscdk.services.iam.IRole getRole()

Role for thte Training Job. EXPERIMENTAL

getSynchronous

@Stability(value=Experimental)
Boolean getSynchronous()

Specify if the task is synchronous or asychronous. EXPERIMENTAL

getTags

@Stability(value=Experimental)
Map<String,Object> getTags()

Tags to be applied to the train job. EXPERIMENTAL

getTransformResources

@Stability(value=Experimental)
TransformResources getTransformResources()

ML compute instances for the transform job. EXPERIMENTAL

builder

@Stability(value=Experimental)
static SagemakerTransformProps.Builder builder()

Returns:

a SagemakerTransformProps.Builder of SagemakerTransformProps