com.amazonaws.services.sagemaker.model.TextGenerationJobConfig Maven / Gradle / Ivy
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/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* 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 AWS
* API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class TextGenerationJobConfig implements Serializable, Cloneable, StructuredPojo {
/**
*
* How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the
* MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to
* 72h (259200s).
*
*/
private AutoMLJobCompletionCriteria completionCriteria;
/**
*
* 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.
*
*/
private String baseModelName;
/**
*
* 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" }
*
*/
private java.util.Map textGenerationHyperParameters;
private ModelAccessConfig modelAccessConfig;
/**
*
* How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the
* MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to
* 72h (259200s).
*
*
* @param completionCriteria
* How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the
* MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria
* defaults to 72h (259200s).
*/
public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) {
this.completionCriteria = completionCriteria;
}
/**
*
* How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the
* MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to
* 72h (259200s).
*
*
* @return How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the
* MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria
* defaults to 72h (259200s).
*/
public AutoMLJobCompletionCriteria getCompletionCriteria() {
return this.completionCriteria;
}
/**
*
* How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the
* MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to
* 72h (259200s).
*
*
* @param completionCriteria
* How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the
* MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria
* defaults to 72h (259200s).
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TextGenerationJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) {
setCompletionCriteria(completionCriteria);
return this;
}
/**
*
* 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.
*
*
* @param 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.
*/
public void setBaseModelName(String baseModelName) {
this.baseModelName = 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.
*
*
* @return 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.
*/
public String getBaseModelName() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TextGenerationJobConfig withBaseModelName(String baseModelName) {
setBaseModelName(baseModelName);
return this;
}
/**
*
* 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" }
*
*
* @return 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" }
*/
public java.util.Map getTextGenerationHyperParameters() {
return 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" }
*
*
* @param 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" }
*/
public void setTextGenerationHyperParameters(java.util.Map textGenerationHyperParameters) {
this.textGenerationHyperParameters = 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" }
*
*
* @param 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" }
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TextGenerationJobConfig withTextGenerationHyperParameters(java.util.Map textGenerationHyperParameters) {
setTextGenerationHyperParameters(textGenerationHyperParameters);
return this;
}
/**
* Add a single TextGenerationHyperParameters entry
*
* @see TextGenerationJobConfig#withTextGenerationHyperParameters
* @returns a reference to this object so that method calls can be chained together.
*/
public TextGenerationJobConfig addTextGenerationHyperParametersEntry(String key, String value) {
if (null == this.textGenerationHyperParameters) {
this.textGenerationHyperParameters = new java.util.HashMap();
}
if (this.textGenerationHyperParameters.containsKey(key))
throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
this.textGenerationHyperParameters.put(key, value);
return this;
}
/**
* Removes all the entries added into TextGenerationHyperParameters.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TextGenerationJobConfig clearTextGenerationHyperParametersEntries() {
this.textGenerationHyperParameters = null;
return this;
}
/**
* @param modelAccessConfig
*/
public void setModelAccessConfig(ModelAccessConfig modelAccessConfig) {
this.modelAccessConfig = modelAccessConfig;
}
/**
* @return
*/
public ModelAccessConfig getModelAccessConfig() {
return this.modelAccessConfig;
}
/**
* @param modelAccessConfig
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TextGenerationJobConfig withModelAccessConfig(ModelAccessConfig modelAccessConfig) {
setModelAccessConfig(modelAccessConfig);
return this;
}
/**
* 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.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getCompletionCriteria() != null)
sb.append("CompletionCriteria: ").append(getCompletionCriteria()).append(",");
if (getBaseModelName() != null)
sb.append("BaseModelName: ").append(getBaseModelName()).append(",");
if (getTextGenerationHyperParameters() != null)
sb.append("TextGenerationHyperParameters: ").append(getTextGenerationHyperParameters()).append(",");
if (getModelAccessConfig() != null)
sb.append("ModelAccessConfig: ").append(getModelAccessConfig());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof TextGenerationJobConfig == false)
return false;
TextGenerationJobConfig other = (TextGenerationJobConfig) obj;
if (other.getCompletionCriteria() == null ^ this.getCompletionCriteria() == null)
return false;
if (other.getCompletionCriteria() != null && other.getCompletionCriteria().equals(this.getCompletionCriteria()) == false)
return false;
if (other.getBaseModelName() == null ^ this.getBaseModelName() == null)
return false;
if (other.getBaseModelName() != null && other.getBaseModelName().equals(this.getBaseModelName()) == false)
return false;
if (other.getTextGenerationHyperParameters() == null ^ this.getTextGenerationHyperParameters() == null)
return false;
if (other.getTextGenerationHyperParameters() != null
&& other.getTextGenerationHyperParameters().equals(this.getTextGenerationHyperParameters()) == false)
return false;
if (other.getModelAccessConfig() == null ^ this.getModelAccessConfig() == null)
return false;
if (other.getModelAccessConfig() != null && other.getModelAccessConfig().equals(this.getModelAccessConfig()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getCompletionCriteria() == null) ? 0 : getCompletionCriteria().hashCode());
hashCode = prime * hashCode + ((getBaseModelName() == null) ? 0 : getBaseModelName().hashCode());
hashCode = prime * hashCode + ((getTextGenerationHyperParameters() == null) ? 0 : getTextGenerationHyperParameters().hashCode());
hashCode = prime * hashCode + ((getModelAccessConfig() == null) ? 0 : getModelAccessConfig().hashCode());
return hashCode;
}
@Override
public TextGenerationJobConfig clone() {
try {
return (TextGenerationJobConfig) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.TextGenerationJobConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}