software.amazon.awssdk.services.sagemaker.model

Class TrainingJobDefinition

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.TrainingJobDefinition

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<TrainingJobDefinition.Builder,TrainingJobDefinition>

@Generated(value="software.amazon.awssdk:codegen")
public final class TrainingJobDefinition
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<TrainingJobDefinition.Builder,TrainingJobDefinition>

Defines the input needed to run a training job using the algorithm.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	TrainingJobDefinition.Builder

Method Summary

Modifier and Type	Method and Description
static TrainingJobDefinition.Builder	builder()
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
boolean	hasHyperParameters() For responses, this returns true if the service returned a value for the HyperParameters property.
boolean	hasInputDataConfig() For responses, this returns true if the service returned a value for the InputDataConfig property.
Map<String,String>	hyperParameters() The hyperparameters used for the training job.
List<Channel>	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.
ResourceConfig	resourceConfig() The resources, including the ML compute instances and ML storage volumes, to use for model training.
List<SdkField<?>>	sdkFields()
static Class<? extends TrainingJobDefinition.Builder>	serializableBuilderClass()
StoppingCondition	stoppingCondition() Specifies a limit to how long a model training job can run.
TrainingJobDefinition.Builder	toBuilder()
String	toString() Returns a string representation of this object.
TrainingInputMode	trainingInputMode() The input mode used by the algorithm for the training job.
String	trainingInputModeAsString() The input mode used by the algorithm for the training job.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

trainingInputMode

public final TrainingInputMode 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.

If the service returns an enum value that is not available in the current SDK version, trainingInputMode will return TrainingInputMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from trainingInputModeAsString().

Returns:

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.

See Also:

TrainingInputMode

trainingInputModeAsString

public final String trainingInputModeAsString()

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.

If the service returns an enum value that is not available in the current SDK version, trainingInputMode will return TrainingInputMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from trainingInputModeAsString().

Returns:

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.

See Also:

TrainingInputMode

hasHyperParameters

public final boolean hasHyperParameters()

For responses, this returns true if the service returned a value for the HyperParameters property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

hyperParameters

public final Map<String,String> hyperParameters()

The hyperparameters used for the training job.

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasHyperParameters() method.

Returns:

The hyperparameters used for the training job.

hasInputDataConfig

public final boolean hasInputDataConfig()

For responses, this returns true if the service returned a value for the InputDataConfig property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

inputDataConfig

public final List<Channel> inputDataConfig()

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

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasInputDataConfig() method.

Returns:

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

outputDataConfig

public final OutputDataConfig outputDataConfig()

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

Returns:

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

resourceConfig

public final ResourceConfig resourceConfig()

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

Returns:

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

stoppingCondition

public final StoppingCondition stoppingCondition()

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, 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.

Returns:

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, 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.

toBuilder

public TrainingJobDefinition.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<TrainingJobDefinition.Builder,TrainingJobDefinition>

builder

public static TrainingJobDefinition.Builder builder()

serializableBuilderClass

public static Class<? extends TrainingJobDefinition.Builder> serializableBuilderClass()

hashCode

public final int hashCode()

Overrides:

hashCode in class Object

equals

public final boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public final boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public final String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

getValueForField

public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)

sdkFields

public final List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo