TextGenerationJobConfig (AWS SDK for Java

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AWS SDK for Java 1.x API Reference - 1.12.795

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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 ()

Constructors
Constructor and Description
`TextGenerationJobConfig ()`

Method Summary

All Methods
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.

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AWS SDK for Java 1.x API Reference - 1.12.795

Class TextGenerationJobConfig

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

TextGenerationJobConfig

Method Detail

setCompletionCriteria

getCompletionCriteria

withCompletionCriteria

setBaseModelName

getBaseModelName

withBaseModelName

getTextGenerationHyperParameters

setTextGenerationHyperParameters

withTextGenerationHyperParameters

addTextGenerationHyperParametersEntry

clearTextGenerationHyperParametersEntries

setModelAccessConfig

getModelAccessConfig

withModelAccessConfig

toString

equals

hashCode

clone

marshall