com.amazonaws.services.sagemaker.model

Class TextGenerationJobConfig

java.lang.Object

com.amazonaws.services.sagemaker.model.TextGenerationJobConfig

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

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

The collection of settings used by an AutoML job V2 for the text generation problem type.

The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
TextGenerationJobConfig()

Method Summary

Modifier and Type	Method and Description
TextGenerationJobConfig	addTextGenerationHyperParametersEntry(String key, String value) Add a single TextGenerationHyperParameters entry
TextGenerationJobConfig	clearTextGenerationHyperParametersEntries() Removes all the entries added into TextGenerationHyperParameters.
TextGenerationJobConfig	clone()
boolean	equals(Object obj)
String	getBaseModelName() The name of the base model to fine-tune.
AutoMLJobCompletionCriteria	getCompletionCriteria() How long a fine-tuning job is allowed to run.
ModelAccessConfig	getModelAccessConfig()
Map<String,String>	getTextGenerationHyperParameters() The hyperparameters used to configure and optimize the learning process of the base model.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setBaseModelName(String baseModelName) The name of the base model to fine-tune.
void	setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) How long a fine-tuning job is allowed to run.
void	setModelAccessConfig(ModelAccessConfig modelAccessConfig)
void	setTextGenerationHyperParameters(Map<String,String> textGenerationHyperParameters) The hyperparameters used to configure and optimize the learning process of the base model.
String	toString() Returns a string representation of this object.
TextGenerationJobConfig	withBaseModelName(String baseModelName) The name of the base model to fine-tune.
TextGenerationJobConfig	withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) How long a fine-tuning job is allowed to run.
TextGenerationJobConfig	withModelAccessConfig(ModelAccessConfig modelAccessConfig)
TextGenerationJobConfig	withTextGenerationHyperParameters(Map<String,String> textGenerationHyperParameters) The hyperparameters used to configure and optimize the learning process of the base model.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

TextGenerationJobConfig

public TextGenerationJobConfig()

Method Detail

setCompletionCriteria

public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)

How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

Parameters:

completionCriteria - How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

getCompletionCriteria

public AutoMLJobCompletionCriteria getCompletionCriteria()

How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

Returns:

How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

withCompletionCriteria

public TextGenerationJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)

How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

Parameters:

completionCriteria - How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

Returns:

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

setBaseModelName

public void setBaseModelName(String baseModelName)

The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

Parameters:

baseModelName - The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

getBaseModelName

public String getBaseModelName()

The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

Returns:

The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

withBaseModelName

public TextGenerationJobConfig withBaseModelName(String baseModelName)

The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

Parameters:

baseModelName - The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

Returns:

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

getTextGenerationHyperParameters

public Map<String,String> getTextGenerationHyperParameters()

The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

Returns:

The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

setTextGenerationHyperParameters

public void setTextGenerationHyperParameters(Map<String,String> textGenerationHyperParameters)

The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

Parameters:

textGenerationHyperParameters - The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

withTextGenerationHyperParameters

public TextGenerationJobConfig withTextGenerationHyperParameters(Map<String,String> textGenerationHyperParameters)

The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

Parameters:

textGenerationHyperParameters - The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

Returns:

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

addTextGenerationHyperParametersEntry

public TextGenerationJobConfig addTextGenerationHyperParametersEntry(String key,
                                                                     String value)

Add a single TextGenerationHyperParameters entry

See Also:

withTextGenerationHyperParameters(java.util.Map<java.lang.String, java.lang.String>)

clearTextGenerationHyperParametersEntries

public TextGenerationJobConfig clearTextGenerationHyperParametersEntries()

Removes all the entries added into TextGenerationHyperParameters.

Returns:

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

setModelAccessConfig

public void setModelAccessConfig(ModelAccessConfig modelAccessConfig)

Parameters:

modelAccessConfig -

getModelAccessConfig

public ModelAccessConfig getModelAccessConfig()

Returns:

withModelAccessConfig

public TextGenerationJobConfig withModelAccessConfig(ModelAccessConfig modelAccessConfig)

Parameters:

modelAccessConfig -

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 TextGenerationJobConfig 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.