com.amazonaws.services.sagemaker.model

Class AlgorithmSpecification

java.lang.Object

com.amazonaws.services.sagemaker.model.AlgorithmSpecification

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AlgorithmSpecification
extends Object
implements Serializable, Cloneable, StructuredPojo

Specifies the training algorithm to use in a CreateTrainingJob request.

For more information about algorithms provided by SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
AlgorithmSpecification()

Method Summary

Modifier and Type	Method and Description
AlgorithmSpecification	clone()
boolean	equals(Object obj)
String	getAlgorithmName() The name of the algorithm resource to use for the training job.
List<String>	getContainerArguments() The arguments for a container used to run a training job.
List<String>	getContainerEntrypoint() The entrypoint script for a Docker container used to run a training job.
Boolean	getEnableSageMakerMetricsTimeSeries() To generate and save time-series metrics during training, set to true.
List<MetricDefinition>	getMetricDefinitions() A list of metric definition objects.
String	getTrainingImage() The registry path of the Docker image that contains the training algorithm.
TrainingImageConfig	getTrainingImageConfig() The configuration to use an image from a private Docker registry for a training job.
String	getTrainingInputMode()
int	hashCode()
Boolean	isEnableSageMakerMetricsTimeSeries() To generate and save time-series metrics during training, set to true.
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setAlgorithmName(String algorithmName) The name of the algorithm resource to use for the training job.
void	setContainerArguments(Collection<String> containerArguments) The arguments for a container used to run a training job.
void	setContainerEntrypoint(Collection<String> containerEntrypoint) The entrypoint script for a Docker container used to run a training job.
void	setEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries) To generate and save time-series metrics during training, set to true.
void	setMetricDefinitions(Collection<MetricDefinition> metricDefinitions) A list of metric definition objects.
void	setTrainingImage(String trainingImage) The registry path of the Docker image that contains the training algorithm.
void	setTrainingImageConfig(TrainingImageConfig trainingImageConfig) The configuration to use an image from a private Docker registry for a training job.
void	setTrainingInputMode(String trainingInputMode)
String	toString() Returns a string representation of this object.
AlgorithmSpecification	withAlgorithmName(String algorithmName) The name of the algorithm resource to use for the training job.
AlgorithmSpecification	withContainerArguments(Collection<String> containerArguments) The arguments for a container used to run a training job.
AlgorithmSpecification	withContainerArguments(String... containerArguments) The arguments for a container used to run a training job.
AlgorithmSpecification	withContainerEntrypoint(Collection<String> containerEntrypoint) The entrypoint script for a Docker container used to run a training job.
AlgorithmSpecification	withContainerEntrypoint(String... containerEntrypoint) The entrypoint script for a Docker container used to run a training job.
AlgorithmSpecification	withEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries) To generate and save time-series metrics during training, set to true.
AlgorithmSpecification	withMetricDefinitions(Collection<MetricDefinition> metricDefinitions) A list of metric definition objects.
AlgorithmSpecification	withMetricDefinitions(MetricDefinition... metricDefinitions) A list of metric definition objects.
AlgorithmSpecification	withTrainingImage(String trainingImage) The registry path of the Docker image that contains the training algorithm.
AlgorithmSpecification	withTrainingImageConfig(TrainingImageConfig trainingImageConfig) The configuration to use an image from a private Docker registry for a training job.
AlgorithmSpecification	withTrainingInputMode(String trainingInputMode)
AlgorithmSpecification	withTrainingInputMode(TrainingInputMode trainingInputMode)

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AlgorithmSpecification

public AlgorithmSpecification()

Method Detail

setTrainingImage

public void setTrainingImage(String trainingImage)

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

Parameters:

trainingImage - The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

getTrainingImage

public String getTrainingImage()

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

Returns:

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

withTrainingImage

public AlgorithmSpecification withTrainingImage(String trainingImage)

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

Parameters:

trainingImage - The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

Returns:

Returns a reference to this object so that method calls can be chained together.

setAlgorithmName

public void setAlgorithmName(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 Amazon Web Services Marketplace.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

Parameters:

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 Amazon Web Services Marketplace.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

getAlgorithmName

public String getAlgorithmName()

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 Amazon Web Services Marketplace.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

Returns:

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 Amazon Web Services Marketplace.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

withAlgorithmName

public AlgorithmSpecification withAlgorithmName(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 Amazon Web Services Marketplace.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

Parameters:

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 Amazon Web Services Marketplace.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

Returns:

Returns a reference to this object so that method calls can be chained together.

setTrainingInputMode

public void setTrainingInputMode(String trainingInputMode)

Parameters:

trainingInputMode -

See Also:

TrainingInputMode

getTrainingInputMode

public String getTrainingInputMode()

Returns:

See Also:

TrainingInputMode

withTrainingInputMode

public AlgorithmSpecification withTrainingInputMode(String trainingInputMode)

Parameters:

trainingInputMode -

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

TrainingInputMode

withTrainingInputMode

public AlgorithmSpecification withTrainingInputMode(TrainingInputMode trainingInputMode)

Parameters:

trainingInputMode -

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

TrainingInputMode

getMetricDefinitions

public List<MetricDefinition> getMetricDefinitions()

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

Returns:

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

setMetricDefinitions

public void setMetricDefinitions(Collection<MetricDefinition> metricDefinitions)

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

Parameters:

metricDefinitions - A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

withMetricDefinitions

public AlgorithmSpecification withMetricDefinitions(MetricDefinition... metricDefinitions)

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

NOTE: This method appends the values to the existing list (if any). Use setMetricDefinitions(java.util.Collection) or withMetricDefinitions(java.util.Collection) if you want to override the existing values.

Parameters:

metricDefinitions - A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

Returns:

Returns a reference to this object so that method calls can be chained together.

withMetricDefinitions

public AlgorithmSpecification withMetricDefinitions(Collection<MetricDefinition> metricDefinitions)

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

Parameters:

metricDefinitions - A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

Returns:

Returns a reference to this object so that method calls can be chained together.

setEnableSageMakerMetricsTimeSeries

public void setEnableSageMakerMetricsTimeSeries(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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

Parameters:

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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

getEnableSageMakerMetricsTimeSeries

public Boolean getEnableSageMakerMetricsTimeSeries()

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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

Returns:

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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

withEnableSageMakerMetricsTimeSeries

public AlgorithmSpecification withEnableSageMakerMetricsTimeSeries(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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

Parameters:

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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

Returns:

Returns a reference to this object so that method calls can be chained together.

isEnableSageMakerMetricsTimeSeries

public Boolean isEnableSageMakerMetricsTimeSeries()

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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

Returns:

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 SageMaker built-in algorithms

• You use one of the following Prebuilt SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

getContainerEntrypoint

public List<String> getContainerEntrypoint()

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

Returns:

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

setContainerEntrypoint

public void setContainerEntrypoint(Collection<String> containerEntrypoint)

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

Parameters:

containerEntrypoint - The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

withContainerEntrypoint

public AlgorithmSpecification withContainerEntrypoint(String... containerEntrypoint)

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

NOTE: This method appends the values to the existing list (if any). Use setContainerEntrypoint(java.util.Collection) or withContainerEntrypoint(java.util.Collection) if you want to override the existing values.

Parameters:

containerEntrypoint - The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

Returns:

Returns a reference to this object so that method calls can be chained together.

withContainerEntrypoint

public AlgorithmSpecification withContainerEntrypoint(Collection<String> containerEntrypoint)

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

Parameters:

containerEntrypoint - The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

Returns:

Returns a reference to this object so that method calls can be chained together.

getContainerArguments

public List<String> getContainerArguments()

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

Returns:

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

setContainerArguments

public void setContainerArguments(Collection<String> containerArguments)

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

Parameters:

containerArguments - The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

withContainerArguments

public AlgorithmSpecification withContainerArguments(String... containerArguments)

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

NOTE: This method appends the values to the existing list (if any). Use setContainerArguments(java.util.Collection) or withContainerArguments(java.util.Collection) if you want to override the existing values.

Parameters:

containerArguments - The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

Returns:

Returns a reference to this object so that method calls can be chained together.

withContainerArguments

public AlgorithmSpecification withContainerArguments(Collection<String> containerArguments)

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

Parameters:

containerArguments - The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

Returns:

Returns a reference to this object so that method calls can be chained together.

setTrainingImageConfig

public void setTrainingImageConfig(TrainingImageConfig trainingImageConfig)

The configuration to use an image from a private Docker registry for a training job.

Parameters:

trainingImageConfig - The configuration to use an image from a private Docker registry for a training job.

getTrainingImageConfig

public TrainingImageConfig getTrainingImageConfig()

The configuration to use an image from a private Docker registry for a training job.

Returns:

The configuration to use an image from a private Docker registry for a training job.

withTrainingImageConfig

public AlgorithmSpecification withTrainingImageConfig(TrainingImageConfig trainingImageConfig)

The configuration to use an image from a private Docker registry for a training job.

Parameters:

trainingImageConfig - The configuration to use an image from a private Docker registry for a training job.

Returns:

Returns a reference to this object so that method calls can be chained together.

toString

public 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

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public AlgorithmSpecification clone()

Overrides:

clone in class Object

marshall

public void marshall(ProtocolMarshaller protocolMarshaller)

Description copied from interface: StructuredPojo

Marshalls this structured data using the given ProtocolMarshaller.

Specified by:

marshall in interface StructuredPojo

Parameters:

protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.